Front Cover
 Title Page
 Table of Contents
 Experimental design and accumulation...
 Discussion (D.D. Caton)
 Analysis techniques in industry-wide...
 Discussion (William G. Brown)
 Decision theory and range livestock...
 Discussion (N. K. Roberts )
 Adoption of range improvement practices...
 Discussion (Harry G. Sitler )
 Market effects on ranch adjustments...
 Discussion (D. F. Jones)
 The national land reserve: adjustment...
 Roster of attendance at the 1961...

Group Title: Adjustments in the range livestock utility; report no. 3
Title: Economic research in the use and development of range resources
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Permanent Link: http://ufdc.ufl.edu/UF00054779/00001
 Material Information
Title: Economic research in the use and development of range resources
Series Title: Adjustments in the range livestock utility; report no. 3
Physical Description: Book
Language: English
Creator: Committee on the Economics of Range Use and Development of Western Agricultural Economics Research Council
Publisher: Committee on the Economics of Range Use and Development of Western Agricultural Economics Research Council
Publication Date: 1961
Subject: Farming   ( lcsh )
Agriculture   ( lcsh )
Farm life   ( lcsh )
Funding: Electronic resources created as part of a prototype UF Institutional Repository and Faculty Papers project by the University of Florida.
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Bibliographic ID: UF00054779
Volume ID: VID00001
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Resource Identifier: oclc - 32441397

Table of Contents
    Front Cover
        Front Cover
    Title Page
        Title Page
        Page i
        Page ii
    Table of Contents
        Page iii
    Experimental design and accumulation problems (Wilbur R. Maki)
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
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        Page 15
        Page 16
        Page 17
        Page 18
        Page 19
        Page 20
        Page 21
        Page 22
    Discussion (D.D. Caton)
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
    Analysis techniques in industry-wide adjustments (Don Bostwick)
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
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        Page 50
        Page 51
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
    Discussion (William G. Brown)
        Page 57
        Page 58
        Page 59
        Page 60
    Decision theory and range livestock operations (R. J. Mcconnen )
        Page 61
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
        Page 67
        Page 68
        Page 69
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        Page 85
        Page 86
        Page 87
        Page 88
        Page 89
        Page 90
    Discussion (N. K. Roberts )
        Page 91
        Page 92
        Page 93
        Page 94
        Page 95
        Page 96
    Adoption of range improvement practices (Ronald D. Krenz )
        Page 97
        Page 98
        Page 99
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
        Page 107
        Page 108
        Page 109
        Page 110
        Page 111
        Page 112
        Page 113
        Page 114
    Discussion (Harry G. Sitler )
        Page 115
        Page 116
    Market effects on ranch adjustments (R. E. Seltzer )
        Page 117
        Page 118
        Page 119
        Page 120
        Page 121
        Page 122
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        Page 129
        Page 130
        Page 131
        Page 132
        Page 133
        Page 134
    Discussion (D. F. Jones)
        Page 135
        Page 136
    The national land reserve: adjustment in the range livestock industry (Harold R. Hockmuth)
        Page 137
        Page 138
        Page 139
        Page 140
        Page 141
        Page 142
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        Page 150
    Roster of attendance at the 1961 meeting of the range committee, Fort Collins, Colorado, August 11-12, 1961
        Page 151
        Page 152
Full Text






Report No. 3

Committee on the Economics of Range Use and Development of
Western Agricultural Economics Research Council



Report No. 3

Adjustments in the Range Livestock Industry

Conference Proceedings

Committee on Economics of Range Use and Development

of the

Western Agricultural Economics Research Council

Ft. Collins, Colorado, August 11 and 12, 1961,


The Committee on Economics of Range Use and Development of the

Western Agricultural Economics Research Council has met annually over

the last several years to consider problems and new areas of research in

Western range use and improvement. This is the second numbered report

and the fourth report of the series.

The Committee's first report, released in 1957, was entitled "Economic

Research in the the Use and Development of Range Resources--A Methodological

Anthology." The report was unnumbered.

The second report released in 1959 was entitled "Economic Research

in the Use and Development of Range Resources--Economics of Range and

Multiple Use." This report was identified as Report No. 2.

In January of 1961 the Range Committee met jointly with the Com-

mittee on the Economics of Water Resources Development of the Western

Agricultural Economics Research Council. The theme of this conference

was "Methodology for Measuring Change in Value of Products from Altered

Management of Multiple Use Watershed Lands." The proceedings of this

joint meeting was published as Report No. 9 of the Water Committee.

The report herein includes the papers and discussions presented

at the Range Committee's annual meeting in Fort Collins, Colorado, on

August 11 and 12, 1961. This report focuses attention on the current

adjustment problems of the range industry and individual ranch firms.

The program title was selected by the Range Committee at its

business meeting in Tucson, Arizona. At that time three research

proposals were submitted to the Committee. It was pointed out that two

of the proposals had been subjects discussed by speakers at previous meet-

ings. The third proposal had not. Therefore, the committee decided to

make this third proposal the subject for discussion at the meeting rep-

resented by this report.

The program subcommittee was primarily responsible for designing

the program and selection of speakers. Each member of the Range Committee

made constructive suggestions. The program subcommittee was J. R. Gray,

chairman, Calvin C. Boykin, Perry F. Philipp, D. F. Jones, and C. 0.

McCorkle, Jr. The report was processed under the direction of N. Keith

Roberts at Utah State University.

The attendance of committee members and guest speakers at these

and previous meetings of the Committee was made possible by a generous

grant from the Farm Foundation. Reproduction costs associated with

this report were paid from funds contributed by the Agricultural Experi-

ment Stations of the twelve western states and the ERS-USDA.

James R. Grey, 1961 Chairman
Committee on Economics of Range
Use and Development

N. K. Roberts, Editor,
Report No. 3






Experimental Design and Accumulation Problems
Wilbur R. Maki . . 1

Discussion D. D. Caton . . . 23

Analysis Techniques in Industry-Wide Adjustments
Don Bostwick . . .. 35

Discussion William G. Brown . . .. 57


Decision Theory and Range Livestock Operations
R. J. McConnen . .. . 61

Discussion N, K. Roberts . . 91

Adoption of Range Improvement Practices
Ronald D. Krenz . . 97

Discussion Harry G. Sitler . . 115


Market Effects on Ranch Adjustments
R. E. Seltzer . . ... 117

Discussion D. F. Jones . . 135

The National Land Reserve Adjustments in the
Range Livestock Industry
Harold R. Hockmuth . . .137

Roster of Attendance at the 1961 Meeting of the Range
Committee, Fort Collins, Colorado,
August 11-12, 1961 . .. 151


By Wilbur R. Makil

The operation of the cattle cycle illustrates an accumulative economic

process of broad national scope but yet of wide local diversity in its

impact on resource use. The 17 western states, for example, account for

60 percent of the beef cows on hand at the low point of the cattle cycle.

During the upturn of the cycle, however, these states account for 70 per-

cent of the net change in beef cow numbers in the United States, while

during the downswing of the cycle the decline in beef cow numbers in

these states is over 90 percent of the net decline for the nation as a


Much of the variability in the western cattle industry occurs on the

marginal range land. Moreover, the accumulation of cattle numbers and the

corresponding increase in stocking rates on range land actually may induce

drought conditions, thus sharply reducing the ability of these lands to

handle the existing cattle numbers. Because of this and related phe-

nomena the question might be asked: How can the man-land relation in the

West sustain the long-run ecological balance of plant and animal life in

the range areas? In response to this question, I would,like to focus our

discussion on the nature of the cattle cycle and its relation to the

development of appropriate research procedures for dealing with range

management problems. Finally, I would like to direct our attention to

the role of experimental research in improving decision making processes

in the livestock industries and particularly in the cattle industry of the


Associate Professor, Department of Economics and Sociology, Iowa
State University, Ames, Iowa.

Experimental Approaches in Economic Research

At the outset we can observe a remarkable lack of experimental research

on industry adjustment problems. Except for the interdisciplinary studies

in plant and animal nutrition/ and the controlled experiments in retail

merchandising,I/ empirical work in agricultural economics is almost wholly

dependent on the use of cross-sectional survey and time-series data. Never-

theless, the "systematic study of human relations by making observations

under conditions of control" is recognized by one eminent sociologist as

an important way to appraise, by scientific methods of research, the effec-

tivbness of specific means to achieve certain ends and to isolate cause-

and-effect relationships in the complicated area of human relations.4J

Analyses of industry-wide adjustment problems that involve observations of

economic phenomena under conditions of control (obtained by selecting two

groups of like elements subject to different treatment) would appear, also,

to be potentially important contributions to achieving desired patterns and

rates of change in our economic structures.

Before we attempt to examine the possible uses of experimental design

in dealing with accumulation problems, the case for a modified stimulus-

response causality will be reviewed. In this context, a recursive model

of the beef economy is presented as a system of causal relations. The

William G. Brown, Suggested methods of the analysis of experimental
data relating to production functions, Proceedings, Western Farm Economics
Association, Twenty-third Annual Meeting, August 23-26, 1960, pp. 356-372.

Murray A. MacGregor, Uniformity trial experiments in marketing
research, Methods in Research, Paper Number 6, Department of Agricultural
Economics, Cornell University, September, 1958.
4/ F. Stuart Chapin, Experimental Designs in Scicilo,:ic-l Research,
Harper and Brothers, New York, 1955, p. 29.

findings based on the aggregative analysis are presented as a series of

hypotheses to serve as a guide for more rigorous examination and analysis.

Finally, alternative experimental approaches are examined as part of re-

search strategy in data construction and utilization.

Control of a Multivariable System

Changes in the number of beef cows on hand, January 1, together with

changes in the number of steers on hand, account for subsequent variations

in commercial cattle slaughter and market prices. An increase in feeder

calf prices, which follows closely an increase in slaughter, steer prices,

encourages inventory accumulations of steers by the beginning of the

following year and of cows, heifers and calves 2 years later. The increase

in calves is associated with an increase in heifers a year later, while

the increase in heifers is associated with an increase in beef cows

during the same period. Using the least squared method, a direct. associ-

ation can be estimated between changes in feeder calf prices in a given

year and changes in beef cownumbers and an indirect association can be

estimated through changes in heifer and calf numbers; the estimated values

of the entire set of inventory, slaughter and price relationships are

summarized in tables 1 and 2. To lay the groundwork for further dis-

cussion of research strategy, the interacting components of the beef

economy are examined in detail.

Beef cattle inventories

Xn the statistical model of the beef economy (based on time series

data for the United States), the number of a specified class of cattle and

calves on farms and ranches, January 1, is related to (1) the number of

other specified cattle on farms a year earlier, (2) the number of cows

Table 1, Estimated change in total number of selected classes of cattle on hand, January 1,
a 1-unit change in specified explanatory variables, United States, 1949-1958.

associated with

Cattle on hand, January 1 Feeder calf price
Heifer and Constant
Beef cattle Beef calves, Beef heifers, Beef cows, Lagged Lagged cow, FIS term R
inventory lagged 1 year lagged 1 year lagged 1 year 1 year 2,years 3
H21t- H22t-i H23t-1 2t-1 P2t-2 2jt-1 1

Calves, H21t 0.660' 95.356 -10.8 0.964
21 (0.049) (32.611)

Heifers,H22t 0.232 53.531 780.2 0.892
22t (0.031) (14.763)

Cows, AH2t823"-/ -0.319" 2,902.0 0.841
(0.603) (0.127)

Steers, H24t 0.403 76.118* 4.6 0.909
(0.050) (23.997)

* Significantly

different from zero at the 0.05 probability level
different from zero at the 0.01 probability level

a/ First difference of yearly values (e.g.,aH22t-1 = H22t-l H22t-2)

Table 2. Estimated change in total slaughter of selected classes of cattle associated with a 1-unit
change in specified explanatory variable, United States, 1949-1958

Cattle on hand, January 1
Slaughter Beef Dairy Cow Constant
equation cows cows Steers slaughter Time term R
2 t
H23t H13t H24t C23t t 1

Heifers, C22t -0.742H 428.3H 1,377.2 0.974
(0.101) (77.4)

Cows, C23t -1.369 0.656 5,383.6 0.970
(0.099) (0.180)

Steers, -0.832" 0.986' 330.1 0.908
Steers, C2t (0.294) (0.217)

Bulls, C25t 0.056 63.3 0.875

Cattle, C2t -3.631 1.642 8,414.3 0.926
(0.703) (0.519)
Calves, C1t -1.952' 1.026" -13,103.3 0.884
(0.278) (0.398)

Significantly different from zero at the 0.0 probability level
Significantly different from zero at the 0.01 probability level.


and heifers slaughtered under federal inspection during the preceding year,

and (3) the average Kansas City feeder calf price during the last 2 years.

(The standard error of each regression coefficient in table 1 is shown

directly below the regression coefficient.) If ideal data were available,

year-to-year change in the number of beef cows on farms, January 1, would

be represented by an accounting identity; namely, year-to-year change in

beef heifers, lagged 1 year, and total disappearance (i.e., slaughter and

deaths) of beef cows and beef heifers, also lagged 1 year. Since the latter

data are lacking, estimates of federally inspected slaughter of all cows

and heifers serve as indicators of the desired variables--total slaughter

and total deaths of beef cows and heifers during the preceding year.

Two major considerations are involved in the estimation of yearly

variability in beef cow numbers: the diversion of heifers for slaughter

and the replacement rate for beef cows. If beef heifers were kept only

for breeding purposes, i.e., to replace beef cows, the cow-heifer ratio

would equal unity. The cow-heifer coefficient during the 1949-1958 period

was substantially more than unity--and quite appropriately in view of the

flexibility introduced into farm production planning by diverting'a rather

large potential number of heifer calves from feeding to breeding, and by

withholding older beef cows from commercial slaughter. In the short run,

therefore, changes in feeder calf prices and in cow and heifer slaughter,

rather than changes in the number of beef heifers, account for a substantial

part of the variability in the number of beef cows on farms.

To further evaluate the make-up of the beef cycle, the beef cow in-

ventory equation was transformed into an equivalent equation including only

lagged beef cow inventories and feeder calf prices. In its modified form,

the beef cow inventory equation in table 1 is represented by

AH23t = 1,758.5 0.117H13t_1 + 0.719AH231 0.719HH23t-2

+ 0.282AH23t3 + 97.589A4P + 40.414aP*
t-3 c2t-3 +2t4 ,

where ZAH23t-k -= .r-to -year change in total number of other cows and
heifers, 2 : and over, on farms, January 1, in
thousands head, (t-k)-th year;

H13t1 = total nuaiber :. cows and l.:-:., 2 years and over, kept
,IL.rJy for milk, on farms, January 1, in thousands of
head, t-th year;

2t-k = year-to-year change in average price of feeder calves
sold at Kansas City, in dollars per 100 pounds, (t-k)-th

Appropriate reaction intervals are denoted by the subscript k.

As shown by the values of the above coefficients, an increase in

either group of lagged explanatory variables-beef cows or feeder calf prices--

generally was associated with an increase in beef cow numbers on January 1.

To obtain the beef cycle, therefore, the inverse price-quantity relation-

ship at the priina; demand level rust be introduced into the causal chain

of events. The use of the price-.quantity coefficient is contingent, however,

on an estimate of total disposition of cattle for commercial slaughter.

Commercial cattle slaugteor

Two sets of slaughter prediction equations were derived to show the

association between feeder calf prices, cattle on hand, and cattle slaughter

(table 2). The first set of equations shows the association between the two

critical cattle inventory variables-beef cows and steers-on the total

federally inspected blu:ghLt- of specified classes of cattle. A change in

the second difference value of the beef cow inventory variable, 4YH23t'

during the 1949-1958 period was associated -ith a somewhat Saller inverse

change in federally inspected slaughter of heifers. An increase in tihe

annual rate of change of beef cows on hand involves the withholding of


calves, heifers and cows for breeding purposes rather than for slaughter,

which accounts for the negative inventory-slaughter relationship. An in-

crease in the number of steers on hand, however, offers only the single

possibility of increased sales of steers for slaughter. Thus, a change

in steers on hand during the 1949-1958 period was associated with an al-

most equal change in beef cows in federally inspected slaughter. In

total, therefore, a +1 unit change in beef cows on January 1 was associ-

ated with a -3.020 unit change in federally inspected cattle slaughter.

Year-to-year changes in the number of dairy cows and steers on January 1

accounted for most of the remaining explained variation in the slaughter


Feeder calf prices

Average annual Kansas City feeder calf prices were related to

average annual slaughter steer prices, average annual corn prices, and

the year-to-year change in steers on hand, January 1. Using annual data,

93 percent of the variation in feeder calf prices was explained. Only

one regression coefficient--slaughter steer prices--however, was signifi-

cant (at the 0.01 probability level). Moreover, variability in slaughter

steer prices introduced substantial instability to the feeder cattle mar-

ket, as shown by the regression relationship,

2t = -4.575 + 1.702**Pt 10.497Pt 0.001 24t R2 = 0.930,
(0.238) (4.789) (0.001)

where P2t = average annual price, in dollars per 100 pounds, of U. S. Choice
and prime grade feeder calves sold at Kansas City during the
t-th year;

P2t = average annual price, in dollars per 100 pounds, of U. S.
Choice grade beef steers sold at selected markets during t-th

P4t = average annual price, in cents per bushel, of No. 3 yellow corn
sold at Chicago during t-th year;

aH2t = year-to-year change in total steers on farms, January 1, in
millions of head, t-th year.

The potential instability in the feeder cattle market revealed by the

feeder price equation contributes to short-term pricing errors, particularly

when the major production changes lag price changes by 3 to 4 years. An

initial output response is attained, however, by slight gains in weight

of slaughter cattle, chiefly heifers and steers, and by short-term increases

in the number of cattle on feed. But the major production response is not

attained until the cows and heifers that were withheld from slaughter--as

a result of ah initial price increase--produce a calf crop and subsequently

yield an increased supply of slaughter cattle. During the intervening

period, the long-run historical pattern of increase in beef production must

be consistent with the increase in aggregate demand; otherwise, further

price changes can be expected in the primary livestock markets.-'

If beef supplies increase without compensating increases in aggregate

consumer demand, slaughter cattle prices decline and, consequently, feeder

cattle prices also decline. Thus, yearly changes in the feeding margin

are inversely associated with changes in beef production. Moreover, the

effect of a 1-unit change in beef production on feeder calf price is almost

twice as large as the corresponding effect on slaughter steer price.

Hence, the price elasticity of demand for feeder calves would be substan-

tially less than the price elasticity of demand for slaughter steers.

Specifically, if the latter were -0.6, the former would be about -0.4. A

[/A modified form of the slaughter price equation was represented
merely by the price-quantity coefficient, -0.522, to account for the
inverse demand effect in consumption. This coefficient denotes the change
in _l- ..htler steer price associated with a +1 pound Chl-.-' in annual per
I.Lita beef production.


given price change thus appears as a less effective mechanism in changing

production patterns than in changing consumption patterns (insofar as the

criterion of effectiveness is specified in terms of the price elasticity

of demand, which is about -0.8 for beef at the retail level). In terms of

this presentation, moreover, the existence of an inverse margin-quantity

relationship in cattle feeding represents a substantial source of short-

term instability in the beef economy.

Initial conditions

From the available data, the time path of cattle inventories can be

generated as a function of slaughter cattle, feeder calf and corn prices.

Obviously, many other factors account for changes in cattle inventories,

and these factors may differ in their impacts on cattle production in

different geographical areas. For example, the 11 western states account

for 1 of every 4 beef cows on hand, but during the upswing or downswing of

the cattle cycle these states account for only 1 of every 6 beef cow

accumulations or decumulations. In the seven west northcentral states,

however, which account for approximately the same number of all beef cows,

year-to-year changes in beef cow numbers are twice as large as in the 11

western states.6J Alternative production opportunities in these two areas

differ and, hence, the production response to price will show corresponding

area differences. Because the economic model shows market prices as the

controlling influences on production, non-price factors presumably are

included implicitly in the constant terms. A regionally differentiated

6/ Only because of the inclusion of the 4 Plains States, North Dakota,
South Dakota, Nebraska and Kansas, the West Northcentral Staues reveal
greater variability on beef cow numbers than the 11 western states.

model of the beef economy could involve additional production inputs as

well as a different set of regression coefficients. In terms of the aggre-

gate representation of the beef economy, the effective constraints on

year-to-year variability in inventories, production and prices are

established through the aggregate price-quantity relationships. Differ-

ential rates of change in these relationships, however, may contribute

to short-term inconsistencies and instabilities in market behavior.

Feedback and market stability

The negative price-quantity relationship at the primary market demand

level and the positive output response to price changes provide for market

stability if the output response occurs simultaneously with the price

changes. Because of the tendency to use current market prices in pro-

duction planning and because of the rather long production period for

beef cattle, however, the output response to price involves a substantial

time lag and, thus, an output cycle of approximately 8 years duration.

While the feedback of price information from the slaughter cattle markets

to the feeder cattle markets is almost instantaneous, a corresponding

feedback of price information from the feeder cattle markets to the

rancher or producer is inadequate, therefore, from the standpoint of the

latter simply because of the nature of the production process (as con-

trasted to the pricing process). To more adequately satisfy production

planning requirements, the feedback of market information should include

prospective prices for both the feeder and slaughter cattle markets.

If production decisions were based on estimates of future market prices--

2 to 4 years ahead--then a higher degree of output stability would be

achieved in calf and fat cattle production. In the aggregate model of


the beef economy, therefore, price information represents the primary means

for controlling the yearly rate of change in beef cow inventories and cattle

production. The long-run changes in production and consumption must be

taken into account, however, in the preparation of annual and quarterly

forecasts for effective long-run production.planning in the beef economy.

Causal Chains in Beef Production

To paraphrase Dr. Herman Wold, a dynamic model of the beef economy

should solve three problems in one stroke; namely, "(1) the existence of a

limiting equilibrium between demand and supply, (2) the existence of stable

prices and quantities in the limit, and (3) the path of the prices and

quantities in their development towards the stable limit."- A system of

causal relations, moreover, requires that the direction of the causal

influences must be specified for each relation of the system and that vari-

ables subject to equilibrium relations must be distinctly observable for

the purpose of causal chain analysis. Finally, the several groups of

decision-makers--producers, consumers and marketing firms--are "autonomous"

in the sense that they are mutually free in their behavior patterns. With

this orientation, the relevant data are now presented to show the internal

mechanism of the beef output cycle.

The operation of the beef cycle can be presented quite simply by a

recursive chain of market and production variables. In this causal chain

of events, feeder calf prices are linked to cattle inventories and, thence,

to cattle slaughter and prices as follows:

/ Herman 0. A. Wold, Ends and means in econometric model building, In:
Probability and Statistics, Ulf Grenander (ed.), Stockholm, Geber, 1959,
p. 381.

8/ Herman 0. A. Wold, A case study of interdependent versus causal
chain systems, Review of the Intern. Statist. Inst., 26:5-25, 1959.

Market or
production variable Year t-2 Year t-1 Year t Year t+l Year t+2

Feeder calf price L- L r- L~ i-4

Other cattle on farms,
January 1: r-'i r'
Calves \\ 1--

Heifers \ -

Steers / ,-4 \ -J -J


Commercial cattle slaughter

Slaughter cattle price

Feeder calf price

Each successive year the change in feeder calf price induces a series of

changes that gradually modify the initial impact of the first price change

in year t-2. Exogenous factors affect slaughter cattle prices and, thus,

modify the rather simple pattern of generating the beef cycle described


As will be demonstrated later, changes in beef cow inventories perform

an extremely critical role in accounting for both the period and the ampli-

tude of the beef cycle. Because of the cumulative processes involved in

the initial phase of the cycle, an overestimation of equilibrium require-

ments may be corrected quickly by an increase in the disposition of cows

and heifers for slaughter. A small decrease in beef cow inventories on

January 1, for example, would signal a much larger increase in commercial

slaughter during the forthcoming year. Moreover, beef cows on January 1

may decline in total number because of an increase in cow and heifer slaugh-

ter during the preceding year.

To achieve an increase in cow and heifer slaughter, beef cow inven-

tories must have declined 2 years earlier, or at,least the variable,


H23-t-, must have declined during year t-2. Thus, a small change in
beef cow inventories generates a series of inventory adjustments of increas-

ing magnitude, except for the restraining influence of the inverse price-

quantity relationship. .The lagged price-quantity coefficients ultimately,

however, contribute to an increase in each component of cattle slaughter

until a net increase is attained in the total slaughter variable and, hence,

a net decrease is attained in the corresponding price variable. This decrease

. in price gradually induces a reduction in inventories'and, subsequently the

volume of each component of commercial slaughter, until a net decrease in

the total slaughter variable results in an increase in the price variable.

By including both (1) the cow and heifer slaughter variable and (2) the

feeder calf price variable as determinants of changes in the beef cow

inventory level, additional sensitivity is introduced into the prediction


For the most part, the explanation of internal relationships (which

was derived from highly aggregate time series data) in the beef economy

has been formulated as a series of hypotheses that must be subject to

rigorous investigation. To phrase these statements in the context of a

modified stimulus-response causality increases the burden of investigation

but also renders the task more fitting for an experimental approach in

testing the relevant hypotheses. In the following sections, several ex-

perimental approaches are considered in relation to accumulation problems

that are part of the beef economy and its internal mechanism.

Measurement of Causal Relations

The underlying behavioral process in the t,eration of the beef economy

is that of human beings making choices. A change in the stockihi rate on

some range land, for example, may be preceded by a period in which market

reports, conversation with neighbors and other bits of information on

future market prospects are acquired. This information may be organized

in some manner and processed into alternative courses of action. Finally,

a choice is made to change the existing stocking rate. Because of envir-

onmental conditions, however, the alternative choices may be quite limited;

or the value system in operation may contribute to changes that appear

inconsistent with economic objectives. Moreover, the uncertainties of

nature are known to affect outcomes adversely and, hence, the choice is

affected accordingly. Each decision-maker, finally, has a unique set of

rules or patterns of behavior that prescribes how information is used to

attain or avoid values and objectives. Thus, the aggregation of the

outcomes of producer decisions is quite simple when compared with the

aggregation of the variety of choice-making processes that can be ex-

pected to occur in the entire beef economy. Nevertheless, choices are

being made with incomplete information regarding critical factors that

have a known relation to the outcome and that are amenable to more precise

estimation. The estimation of these factors is reviewed with reference

to an experimental design framework, including industry simulation,

business gaming, longitudinal studies and learning theory.

Industry simulation

The advent of the "computer age" makes possible experimentation with

economic systems once the parameters and initial conditions of this

system have been specified. In a simulation study, the behavior of the

system is given (unlike a business game in which decision-makers act within


the simulated environment).- In this presentation, the model of the beef

economy provides the necessary data for an example of industry simulation.

In the simulation of the beef economy, an increase in consumer demand

equivalent to a $1 increase in feeder calf price is introduced as the

stimulus to start the beef cycle. The estimated effects of the $1 increase

in feeder calf price over a l0-year period, in terms of year-to-year changes

in cattle inventories, slaughter and prices, are summarized in table 3 for

the interested reader to examine and relate to the earlier discussion.

Because of secular growth in both supply and demand, and because of

initial conditions affecting the level of beef production and prices, the

predicted values for the period 1958-1965, corresponding with the data in

table 3, yield a somewhat different pattern of variability. As in table

3, however, the cattle inventory series shows a period that i:- roughly

twice the period of the beef production and price cycles. Similarly, the

potentially explosive nature of the over-all beef cycle, as revealed by

the gradually increasing amplitude of oscillations, is evident in the

predicted price and quantity series for the period 1958-1965.

An industry simulation problem, as suggested at the beginning of this

discussion, is more readily solved by using computer language and flow

diagramming than by using a verbal description of the beef economy and the

related equations in tables 1 and 2.1/ Moreover, the increasing flexi-

bility of computer operations will make possible the use of representative

firms on a regional basis in an aggregate analysis of the livestock and

meat industries. Thus, the effects of changes in behavioral relationships

9/ Martin Shubik, Simulation of the industry and .h: r"i::., P~z:ian
Economic Review, 50:908-919, December, 1960.

10/ International Business Machines Corpirt.ion, Programmer's Primer
for Fortran Automatic Coding System for the IBM 704, 1957.

Table 3. Predicted change from base year (t = 0) in specified cattle, in 1,000 head, associated
$1 increase in feeder calf price, by year

with an initial

Other cattle on farms, January 1 Commercial slaughter
Calves, Heifers, Cows, Steers, Under federal inspection Feeder
Year under 1 to 2 2 years 1 year calf
t 1 year years and over and over Total Total Steers Other Total price
H21 H2 H23 H24 C2 C2I I j P2

0 0 0 0 0 0 0 0 0 0 1.00
1 0 0 0 76 76 125 76 0 76 0.63
2 95 54 0 104 253 171 103 0 103 0.50
3 60 56 98 132 346 -137 49 -214 -165 1.4o
4 112 41 168 187 508 51 131 -154 -23 0.85
5 245 101 122 166 634 440 207 101 308 -0.29
6 162 102 153 133 550 103 110 -69 41 0.70

7 74 22 208 174 478 88 131 -119 12 0.74
8 204 54 77 142 477 708 254 286 540 -1.07
9 122 87 11 56 276 331 115 144 259 0.03
10 -95 -29 114 107 97 -198 25 -225 -200 1.58


within firms and among firms, as well as changes in initial conditions,

can be examined with respect to prospective developments among a speci-

fied group of firms.

Business gaming

A rapidly expanding literature on business games for teaching and

experimental purposes is available in a wide variety of professional and

trade journals.11- The authors of a recent.study in this area in which

the experimental method was used concluded thusly: "By so doing, we have

demonstrated that the amount of information available to a bargainer and

his level of aspiration are significant determinants of the price-quantity

contract which will be reached. We aver that only the experimental method

could have demonstrated the influence and importance of these determi-

nants.",' In the disaggregation of the beef economy into regions and

firms, an accurate specification of the various behavioral relations would

require new information about the decision-making process. Much of this

information can be acquired most readily in the laboratory situation where

those aspects of the problem which are irrelevant to the testing of*

hypotheses can be strictly controlled.

Longitudinal studies

Chapin cites two methods of controlled observation over time; namely, the

classical pattern of "before" and "after" experiments and the ex post facto

,j/ Martin Shubik, Bibliography on simulation, gaming, artificial
intelligence and allied topics, Journal of the American Statistical
Association, 55:736-751, December, 1960.

12/ Sidney Siegel and Lawrence E. Fouraker, Bargaining and Group
Decision Making, McGraw-Hill Book Co., Inc., New York, 1960, p. 73.

experimental design.- The benefit-cost analyses accompanying new

reclamation project proposals appropriately could use the projective form

of experimental design. An ex post facto design could be applied in a

study of two communities that at one time were alike in certain char-

acteristics but now are different because, let us say, of the beneficial

impact of an irrigation project on one of the two communities. Numerous

other designs are available for economic research. Generally, however,

longitudinal studies are not confined to the use of experimental de-


Control in longitudinal studies is achieved by selecting for ob-

servation two groups of like firms or households; for example, firms of

the same size in animal units or households of the same income bracket.

One group--the experimental group--may be provided with market information

regarding prospective price changes of demonstrated reliability, while

the other group--the control group--would not be provided this informa-

tion. If these two groups were cattle ranchers, then the number of

cattle and their disposition would be observed over a period of time.

Meanwhile, other factors affecting choice-making within each group

could be isolated. Later experiments could deal with the effects of

each of these additional factors, or their relation to the treatment

variables could be ascertained by covariance analysis.

Learning theory

With reference to the aggregate model of the beef economy, each

autonomous group of decision-makers is conceived as a "quantity adjuster"

l/ Chapin, oP. cit., pp. 29-33.

L_/ Nathan Goldfarb, An Introduction to Longitudinal Statistical
Analysis, The Free Press of Glencoe, Illinois, 1960.


or a "price adjuster." The cattle rancher, for example, adjusts the level

of cattle inventories as feeder calf prices change, while the feeder cattle

market adjusts to changes in slaughter steer prices. In the aggregate be-

havioral relationships, the outcomes of countless numbers of adjustments

to price or quantity changes occur with a high degree of regularity, on the

average, for the entire population.

Underlying the notion of causal chains is a complementary notion of

stimulus-response causality for each decision-making unit. This notion of

causality can be phrased in terms of a statistical theory of learning.1-

The latter, moreover, is amenable to experimental analysis,

In the context of learning theory, adjustments in cattle numbers can

be viewed (paraphrasing Estes) as follows:1-

1. Initially, a rancher responds to each major environmental change

by random choice of one of the permissible responses (on an all-

or-none basis), with probability one-half for each.

2. Over a period of weeks, a learning process is established whereby

the responses become conditioned to the stimulus (environmental

change) pattern with probability c, the parameter c being assumed

constant over weeks for each rancher.

3. Once conditioned to a given stimulus pattern, the correct response

henceforth occurs to that pattern with probability one or approach-

ing one.

These assumptions may be represented further by a mean error value for r

alternative changes in cattle numbers; namely,

I/ W. K. Estes, New Developments in statistical behavior theory:
differential tests of axioms for associative learning, Psychomuirica, 26:
73-84, March, 1961.

I6/ Ibid., pp. 82-83.

E xn) (1 ) 1 (1 c)
where xn= random variable which equals 1 or 0 accordingly as an error or
a correct response, respectively, occurs to a given item (cattle
inventories) on trial n for a total of N trials.

Finally, the last of the three assumptions may be stated, following Estes,

as a general matching law, which is "that, beginning at any point in a

learning series, the cumulative proportions of a given response and

corresponding reinforcing event tend to equality." 2/

One task of experimental design would be to obtain estimates of the

parameter "c" for different groups of decision-making units with respect

to specific objects of decision-making. Cumulative proportions of a

particular response--for example, an increase in beef cow numbers--

would be obtained over a period of time with reference to a particular

environmental change. It is quite possible that the learning theory

model offers an explanation for the persistent underestimation of future

change in prices and other market factors on the part of livestock pro-

ducers generally. In any event, the causal chains in the aggregate model

of the beef economy thus could be verified in terms of the individual

decision-making units of which the model is composed.

Research Strategy in Experimental Design

Because of the considerable progress in the application of experi-

mental design to nutrition problems and production economics, and because

of the ample source books on experimental design techniques, it seemed

appropriate to deal essentially with the behavioral relations associated

~/ W. K. Estes, Of models and men, The American Psychologist,
12:609-617, October, 1957.


with problems of changing numbers of livestock. To establish the role of

experimental design in the estimation of behavioral relationships, moreover,

it seemed appropriate that we view the beef economy as an economic system

with an internal mechanism of its own to account for year-to-year variability

in its principal variables. In summary, therefore, elements of a research

strategy are presented as follows:

1. Learning theory to establish the quantitative characteristics of the

choice-making processes in the beef economy.

2. Longitudinal studies to identify and estimate changes over time of

economic relations accounting for year-to-year variability in

livestock numbers and prices.

3. Business gaming to obtain fruitful hypotheses for investigation and

development with reference to improving existing choice-making


4. Industry simulation to evaluate the probable impacts of changes

in specified variables and relationships on the over-all operation

of the beef economy.


by Douglas D. Caton-/

Dr. Maki argues that except for the unusual circumstances in which

conditions can be controlled or safely assumed to be sufficiently stable,

analysis of cross-section survey data and time-series data is usually in-

sufficient to isolate and explain causal relations adequately. Under-

standing an economic process and how it works is-always difficult. We

acquire knowledge in two different ways: as a result of direct experience

and from theoretical and empirical propositions. Each has its own range

of application and its own particular limitations. A concern about the

"experience" explanation of economic processes is its dependence on the

qualifications of the observer and the fact that the "error" of the

estimate cannot be statistically established. But users of more ob-

jective methods who depend upon distinctly observable variables and

formal analytical procedures frequently do not feel justified in

making predictions or in making deductions about causal relations be-

cause the observed variables may be indirect indicators or the observed

relations may be unstable.

Dr. Maki, therefore, argues for a more determinate theory of cause

and effect relations. He holds that economic behavior cannot be inter-

preted systematically without a thorough understanding of the underlying

conditions. Various ways of getting at the underlying conditions are

examined as to each method's possible contribution to data construction

and utilization. The concluding section is tied back to a subsection of

I/ Agricultural Economist, Farm Economics Division, Economic
Research Service, U. S. Department of Agriculture


the introduction entitled "Control of a Multivariable System." "Control," as

the word is used, does not mean control in the sense of constraint, but rather

"conceptual control" to permit better visualization of the component associations

and their interactions and isolation of the key factors. Among the key factors

are prices, inventory patterns, and the distinctions that must be made between

the short and the long run.

Before examining the possible uses of experimental design in dealing with

accumulation problems, I shall review the case for time-series analysis, in this

instance, a stimulus-response model. The equations identified are equations of

condition. I intend to go beyond an explanation of livestock inventory behavior

as a patterned sequence in time. The primary focus is on cause and effect.

Causal analysis is concerned with two main questions; "What caused this result?"

and "Given these conditions, what effect will follow?" In answering the first

question, we reason from effect back to cause, and in answering the second

question, from cause to effect.

Given the effect, time-series analysis can help in isolating the cause and,

given the cause, can help in estimating the effect under stated conditions. In

this regard, cause and effect can be determined only if a sufficient degree of

uniformity and regularity between an occurrence and immediately connected vari-

ables can be isolated. Dr. Maki strengthened his argument by specifying the

range of application, the rearrangement of variables necessary to account for

interaction, and the change in inventory behavior from stage to stage. He

gives primary attention to the fact that an event does not take place in com-

plete isolation. It takes place in a real environment, and many factors con-

tribute to its setting.-2 In most instances it is not possible to make an

2/ A condition is whatever factor in a situation that allows an event to


absolute distinction between cause and condition. The selection of one

factor as the cause is on the basis of immediate connection;-that is,

price-stimulus-response, and on the basis of what is the particular

interest. When we take one or more factors for the cause, we presume

the presence of the others. 2

Production adjustments to drought conditions is an illustration of

the importance of determining "conditions." The immediate cause of a

reduction in forage is lack of rainfall. Looking back, we can indicate

with some confidence whether what was done was the only thing that could

have been done or whether something else would have been preferable--

to preserve the grass, to minimize cost, or to give a desired distri-

bution of income. But not being able to predict rainfall lessens the

chances of guessing right. Consequently, each rancher does what he

can, considering what he can foresee and what he can afford to do. A

reliable method of predicting rainfall is desirable before effective

drought-adjustment procedures can be undertaken. It is necessary then

to know what the conditions will be before the relevancy of an adjust-

ment technique can be stated. It is also necessary to distinguish

between relevant factors and factors present as mere background. The

fact that something happens to be associated with something else in

time does not mean that the one or the other is either a cause or an effect.

/ A necessary condition is a factor situation when an event never
occurs in its absence. If at the low point of inventories a build-up in
inventory occurs as a result of a price increase, the price increase
might be taken as a sufficient condition, but a build-up can also occur
as a result of a decrease in the price of feed or an increase in soil
moisture that favors forage production. In this illustration, price is
a sufficient condition, but it is not necessary; some other factor
situation will do. Then, it seems obvious, the selection of causal
factors must always be done arbitrarily (on the basis of some interest)
because, in its fullest sence, the cause is the complex of factors that
has a direct or an indirect bearing on the event.

Reliance on association to mean that a cause-effect connection also

exists is one of the troublesome features of inventory analysis. The in-

ventory analysis of the livestock production cycles with which I am familiar

have slanted their arguments one way or another: that either the inventories

are self-generating, even though the necessity of the existence of an ex-

ternal shock variable is conceded; or that inventory changes are due to

certain exogenous variables, such as price, demand shifts, and substitute

product competition. To a considerable extent, livestock inventories are

self-generating, but at key points external variables become more important;

for example, Dr. Maki's calf price at the low point of the cycle. Both the

internal self-sustaining arguments and the external exogenous variable

arguments need to recognize the emphasis that must be given to different

variables and different conditions at different inventory stages.

The rate of growth of the livestock industry is subject to two main

limitations: competition and the ecological balance of plant and animal

life. To understand the process of growth, we must know the conditions

for plant and animal growth and the factors in the competitive situation.

The modified stimulus-response model of the paper explores possible ways

to isolate and to specify the form of connective relationships in a causal

sense. The model is partial, conditions are given, and the equations are

conditional equations. Each equation is a proposition to be believed or

not, depending upon the weight of the evidence. The sustaining logic of

a method of this kind is not so much how well it explains a single case

but rather how well a principle of uniformity can be established. Can we

expect the same inventory behavior time after time and what is the degree

of probability that certain events will be repeated? Regularity must have


been established and the conditions; that is, the principle that binds

the variables of the prediction equation together, must have been under-


Dr. Maki contends that the principle binding the components of the

livestock inventory together is the multiplier effect, which is due to

the reaction of certain inventory components to price. Prices affect

calf inventories. Calf inventories eventually affect heifer inventories

and a year later, cow inventories. However, partial independence of in-

ventory stages can be argued, even though considerable evidence exists

that overall time build-up sequence is self-generating.once it is

started. Calves can be used as feeders as well as breeding-herd re-

placements; culling rates can be changed and do change with a change in

replacement numbers; and slaughter rates change as do holding rates with

changes in prospects for feed and forage.

The reason for examining beef-cattle inventory behavior is to

explain economic behavior by isolating the elements of the inventory that

most nearly follow the perceived time path. Should the course of events

be repetitive, the specified variables may be sufficient indicators.

However, as experience has indicated, they seldom are sufficient, even

under the idealized data situation which, it is argued, would simplify

the progress of keeping track of developments through the use of an

accounting identity. Producer psychology, leakage in the indicator

variables, and disturbance factors cause inventory patterns to deviate

from expectations and, consequently, the indicator variables become

unstable. The failure, for example, of a change in replacement rates to

hold up will cause a graduated change in numbers, and allowance for


such an occurrence must be "built in" the production coefficients. In

the short run, a change in slaughter cattle prices account for most of

the year-to-year changes in cow numbers. In the long run, changes in cow

numbers must come from the calf-heifer replacement ratio. Each inventory

stage affects each succeeding stage, but beyond the first4- stage, inven-

tory behavior is guided by both condition variables and prices of feeder

and slaughter livestock. What this means is that the cycle has both self-

generating and external-stimulus features and that the transition from one

set of circumstances to the other is difficult to determine.

One limitation of estimates of time-series supply response analysis

is that few variables and a limited number of relationships can be taken

into account. A second limitation is that the relation of the unobserved

variables to the observed variables may not be close. In these instances,

whether the coefficients have any real structural interpretation may be

questioned because in each equation the value of the expectations (predic-

tion) is sensitive to the omission of relevant variables.3

4/ The low point in the cycle

j/ "Other factor" conditions point up the desirability of not depend-
ing entirely upon deductions from secondary data to explain an economic
process. Equally, it does not seem entirely realistic to presume that
once the pattern is fixed no interaction takes place between the elements
of the "constant" terms and the selected variables. This contention is
difficult to support directly but, for example, the supply and price of
feed can accent or limit year-to-year variability through their controlling
influence on production. There is also some evidence to support the argu-
ment that it is easier to reason from price to secondary production points
and back than from price to the primary production points. Production
response to price at the primary production points has on occasion (during
the war years) been a primary consideration, as has been argued by Breimyer;
but production at the farm level has many self-generating elements. Ex-
planation of inventory behavior, therefore, must depend upon examination
of the relationship and interaction between the inventory components.
See Brandon (2).

Because supply response is not a simple condition of the relation-

ship of price to output, the interdependence of causal relations and

predictive indicator analysis is stressed. This point has been emphasized

in other papers and research reports. Bachman and Nerlove stated that

the study of production functions on the firm level is both a necessary

and desirable supplement to time-series analysis of supply. The reason

is to help interpretation through an understanding of the problems that

arise in the structural analysis of an industry because of complementarity

and supplementarity of products and inputs, interfirm differences, multiple-

product interdependence, and identity and measurement. In this respect,

various types of "constructed" expectation models have been used--Hicks'

expectation model based on a study of behavior in situations involving

price expectations held with certainty (5), and Nerlove's concept of ex-

pected normal price (10). Other approaches that follow these lines are

the "statistical" method of aggregation suggested by Thiel (11); Hender-

son's short-run land use predictions (4) based on recursive analysis; and

Wold's system of economic relations (14) based on two properties: (1) the

development of the variables of a system up to time (t-l) determines the

values taken by the variables at time (t), and (2) the variables at time

(t) may be obtained one by one in a definite order. That is, knowledge

of p (tl-) enables us to determine q (t) from the supply relation, and

then p (t) can be determined from the demand relation (1).

Various arguments have been developed for and against each of these

approaches to an empirical explanation of supply response. Prices lagged

one year may not be the prices farmers take into account, or the prices

they choose to consider. Although the "expected normal price," seems to

work, it has little theoretical justification. The statistical approaches

have not as yet taken into account the fact that some macro-relations may

differ from micro-relations. Bachman and Nerlove contend that because

livestock production is of the "continuous input-continuous output type,"

it cannot be fitted into a recursive framework as long as the temporal net

of observation periods is as coarse as at present. In a review of Nerlove's

book (8),6' Ladd raised several questions about the applicability of simple

dynamic models to livestock supply questions. Apparently the qualifica-

tions as to the applicability of predictive models to livestock response

questions are based on the "continuance" of the production characteristic

when current output depends upon decisions made in more than one period

and upon expectations concerning present price that were formed over

several periods, and also upon the complexity of both the physical proper-

ties and the price relations of the livestock industry. Breimyer (3) brings

out many of these problems; and Kearl (6) brings out the diverse and com-

plex relationships between price and the cattle cycle.

6/ See also Ladd (7, 9).


Maki's main interest in his analysis, as I see it, is to "net out"

certain indicators which, in turn, can be used for initial establishment

of the causal conditions. On the basis of the relationships he worked

out, he selected the variables slaughter cattle, feeder calf prices, and

corn prices as the primary indicators of the time path of cattle inventories.

He points out, however, that these estimating aids need to be placed in the

proper context. The context for interpretation of causal relations

might be limited to a sector or an area analysis, depending on expected

response differences. In this regard, the paper brings out clearly the

importance of being able to specify the effect of the underlying behavior

processes on supply response. In my opinion, the simulation technique

he suggests and an even more extensive development of learning theory have

considerable merit. Still another approach that can be added to the list

of suggested experimental methods is the statistical decision function.

The central idea of this approach is that we perform experiments or

take observations as a basis for establishing hypotheses as to the probable

results of further action. These hypotheses would be used to consider

possible types of action, the risks involved in each course of action,

and the probabilities that the data and known statistical techniques will

provide acceptable answers (12).

From an examination of the price change behavior in the Kansas City

market for the period 1949-59, the price basis used by Dr. Maki, it is

difficult to contend that the inventory patterns can be explained by prices

or their derivatives alone. During the 1949-59 period, six different trend

situations occurred in the prices of stocker and feeder steers at Kansas

City, Missouri. These trends were abrupt--21 to 35 percent of the mid-range

value. The price changes can be grouped as follows: (1) in 1943-45,

1949-51, and 1956-59, prices were moving upward; (2) in 1948-49 and 1953-56,

prices were declining; and (3) in 1953-56 prices were stable.2/ These price

relationships indicate the frequency of occurrence, the abruptness of inter-

mediate time-period price changes, and the considerable instability in

year-to-year prices. Complicating features are the sensitivity of live-

stock prices to outside influences at high inventory numbers regardless of

the cycle phase, and the trend effect of changes in production technology

and in management practices.

Professor Maki rightly states that the path of the cattle cycle is

dependent upon both price and inventory behavior. Prices of cattle affect

most decisions, and prices and inventories including changes in the compo-

sition of inventories are mutually related. An answer to the question of

the effect of price on producer decisions would seem to require examination

of the anatomy of the cycle by means of the progressive balance sheet

or inventory analysis suggested by Breimyer and a comprehensive and

detailed analysis of beef cattle prices of the type developed in the

paper, combined with an appropriate condition framework for analysis.

2/ Price data and related materials were provided by W. G. Kearl, Dept.
of Agricultural Economics, University of Wyoming, Laramie, Wyoming.

8/ The instability of particular prices was emphasized by Maki, together
with the probable contribution of such instability to short-term pricing


(1) Bachman, K. L., and Nerlove, Marc, "Memorandum on the Analysis of
Changes in Agricultural Supply," Mimeograph paper, ARS, FERD, USDA,
undated, p. 9.

(2) Brandon, G. E., "A Note on the Nerlove Estimate of Supply Elasticity."
Jour. Farm Econ. 40:719-722, 1958.

(3) Breimyer, H. F., "Observations on the Cattle Cycle," Agr. Econ. Res.
7:1-11, 1955.

(4) Henderson, J. W., "The Utilization of Agricultural Land: A Theoretical
and Empirical Inquiry," Rev. Econ. and Statis., 41:242-259, 1959.

(5) Hicks, J. R., Value and Capital, 2nd ed. (Oxford: Oxford Univ. Press,

(6) Kearl, W. G., "Beef Cattle Prices for Ranchers' Decision Making."
(Unpublished manuscript).

(7) Ladd, G. W., "Effects of Shocks and Errors in Estimation: An
Empirical Comparison," Jour. Farm Econ., 38:485-495, 1956.

(8) Ladd, G. W., "The Dynamics of Supply: Estimation of Farmers'
Response to Price, by Marc Nerlov," Jour. Farm Econ. 41:452-455, 1959.

(9) Ladd, G. W., and Tedford, J. R., "A Generalization of the Working
Method for Estimating Long-run Elasticities," Jour. Farm Econ.,
41:221-233, 1959.

(10) Nerlove, Marc, The Dynamics of Supply: Estimation of Farmers' Response
to Price. Baltimore, Md., Johns Hopkins Press, 1958.

(11) Thiel, H., Linear Aggregation of Economic Relations. Amsterdam, North-
Holland Publishing Co., 1954.

(12) Wald, A., Statistical Decision Functions. New York, John Wiley and
Sons, 1950.

(13) Wald, A., and Kempthorne, 0. The Design and Analysis of Experiments.
New York, John Wiley and Sons, 1952.

(14) Wold, H., and Jureen, L. Demand Analysis, New York, John Wiley and
Sons, 1953.


by Don Bostwick2/


My direct experience with the range livestock industry consists

largely of having been a practicing cowboy for a while in western

Colorado. Applying a more recently acquired vocabulary, I believe that

my efforts were devoted to maximizing the intake of grub and to mini-

mizing the activity of being throwed from a horse. The first objective

presented no problems, as I recall, but failure sufficiently to mini-

mize the second led to my seeking other professional employment. An

economist might argue that these objectives were poorly chosen or were

perhaps more means than ends, but this wasn't the point at the time.

My research activity has been concerned with managerial problems

of dryland grain farmers, particularly those that arise from uncertain

weather and crop yields. In effect, I lack the background, training,

and professional experience that would seem to be required for an ade-

quate discussion of the assigned topic. But this doesn't prevent my

talking about it; indeed, it constitutes a very interesting and challenging

opportunity to try my hand at a somewhat alien activity. Having taken

care of this necessary confessional, let me proceed to the business at


The first order of business is a bit of explicit fence building.

I assume that my job here is not to present data or research results,

but to discuss techniques of analysis that might be appropriate to a

study of adjustments in the range livestock industry. I shall include a

1/ Agricultural Economist, Farm Economics Division, Economic Research
Service, U. S. Dept. of Agriculture, Bozeman, Montana.


number of possibilities for your consideration, and leave it to you to

decide which, if any, are pertinent to your problem.

I have organized the discussion around a series of basic models. I

shall treat the assumptions that attach to each model and the analytical

tools and data that I believe are required for the proper use of each

model. I shan't worry over details, such as the mathematics that may be

involved, or the specific sources of data, etc. I will only suggest the

presence of these things and then pass on. I will try to be somewhat

selective of techniques that might be appropriate to an aggregate model

of the complexity of the range livestock industry. In short, this dis-

cussion will be a broad-brush job; I leave the rest to you who are experts.


I want to define what I think are the necessary characteristics of

a desirable model. A model is supposed to bear some recognizable re-

semblance to a segment of the real world. This requires both internal and

external logical consistency and distinguishes a model from a paradigm,

the latter of which requires only internal logical consistency.

We sometimes distinguish between descriptive and predictive models.

The difference is primarily one of time orientation. The descriptive model

says, in effect, "these are the phenomena that have been observed, and the

apparent relationships between them." Any uncertainty in a descriptive

model lies in the cause-effect relationships; the observed events, being

ex post, are certain, except for errors of observation. A predictive

model is a series of if-then predications based generally on ex post de-

scriptions, and causal relationships derived from them. Descriptive models

are verified by historical data; predictive models may be checked against

such data but are verified only by ex ante or future events. Predictive

models therefore include probabilistic statements throughout, since

both events and their relationships are uncertain. If we are working

in a static framework, we may choose to assume that certain classes of

events or relationships are certain within specified time limits, and still

produce a predictive model within these limitations.

It is necessary to modify the requirement of logical consistence,

lest we try to build a model that reflects real events down to the rela-

tive minutia. One of the arts of the successful model builder is his

ability to account for events and relationships just sufficient for the

application desired. It is impossible completely to specify everything

involved in a very simple model. We must be content to specify the level

of generality with which we are prepared to live, and then to select data

and techniques that will satisfy just these conditions and possibly no

more. We must be artists enough to select just those simplifying assump-

tions that allow for the desired results, being neither too general nor too


It is generally agreed that, given the explicit purposes of the model,

a simply conceived model is more desirable than an ornate one. We must bow

first to our own limited powers of simultaneous comprehension. We must bow

also to the restrictions of data processing and computation. In the end, we

must also bow to the need for communication with people who have not our own

experience of and interest in the particular problem with which the model

deals. It is conceivable that members of this committee could successfully

construct a model of the range livestock industry comprehensible by perhaps

a dozen other people in the country, and no more. This might be desirable,

but I doubt it. I think that you want a model that will be communicable

to most other researchers interested in the general problem area.

We must be able to acquire data and to devise analytical techniques

by which the hypotheses of the model may be tested. This is a shoal on

which much theoretically well-conceived research founders. I find it

difficult to limit my theoretical constructs to the often pedestrian

constraints imposed by available data and analytical tools. This

accounts for a lot of what I prefer to call "creative abortions." A

certain amount of these hopeful creations are exciting, but eventually

one must still back up to modifications in the model which are susceptible

to testing, assuming that the expected abortion does occur. I like to

make some allowance for this kind of activity in the work plan, but the

allowance must be made in terms of the possible benefits, should the

attempt succeed, and the penalties in time, money, and human resources,

should it fail. Such a determination is a probabilistic, expectations

model in its own right. The probabilities might be estimated by the

researcher, but the payoffs are set by the research administrator, who

characteristically dislikes to lose. This sort of Cloud 9 activity

should be considered before the research is started, not after it is

underway and an abortion and salvage operation is imminent.

In summary, I would characterize a desirable model as follows:

it is logically consistent in its internal logic and its representation

of selected real-world phenomena; it is as simple as possible for the

specified level of generality; it requires only data that can be ob-

tained within the restraints of time and money; and it calls for the

use of analytical tools that are available and that permit rigorous

testing of the hypotheses of the model. The criterion of reasonableness

is implied in all cases, allowing for the fun and games associated with a

bit of research uncertainty. It is possible, of course, to build models

for the purpose of testing the adequacy of available data and/or the

nature of the data needed but not available. This gets into the

province of methodological research, one remove from the more mundane

research on practical problems of the range livestock industry.

Constraints for industry-adjustment models

The purpose of an industrywide adjustments model is, I presume,

to indicate possible innovations, ranked according to some scale of

socio-politico-economic criteria, and probable results if given adjust-

ments should take place. It would be necessary explicitly to establish

the goals of adjustment, then to list all possible means of adjusting

toward these goals, and last, to apply acceptability criteria to these

possible means, establishing a desirability continuum. The three

processes operate as constraints on the activities of the adjustments


It is usual to assume the single-valued criterion of welfare

maximization in economic models, but this fails to represent the real

world. I have never known a person,real or corporate, with such an un-

complicated goal structure. The range livestock industry is an ab-

straction, and as such does not have goals. It represents a gaggle of

people who do have goals--multivalued, often poorly enunciated, and

frequently conflicting, but withal, quite real. A practical adjust-

ments model should reflect these characteristics, except perhaps for

the "poorly enunciated" bit. So much for this suggestion that a means-

ends framework is necessary; the job has been done so often now, that

I shall only refer you back to the literature with which you are

already familiar. Let me discuss some of the constraints more or less

peculiar to the range livestock industry.


Geographic dispersion

The industry is situate here and there over the western half of the

United States. This leads to dissimilarities of topography; of rainfall

both as to amount and pattern of occurrence; of soil structure, fertility

level, and moisture-holding capacity; of temperature in terms of degree-day

totals, or seasonal ranges; of dominant forage type; of association with

areas of dryland or irrigated crop production, or with such other uses

as recreation, forest products, watershed control, etc.; of relationships

to primary markets, meaning feeder buyers, auctions, etc.; to population

centers that influence demands; etc.

This suggests that the most acceptable industry adjustments model

might well be an aggregate of models specific to the dozen or more

disparate areas in which range livestock are produced. I can't imagine

an equilibrium adjustment that would be equally suitable for such dissimilar

areas as the Northern Plains, the plateau country of western Colorado-

eastern Utah, and the Red Desert of Wyoming.

As an example, consider three bases of stratification which are, in

effect, a definition of constraints on the area model, in relation to an

area such as the Red Desert. Stratify the constraints first on the basis

of such physical features as topography, climate, and forage associations.

Second, consider the location of the area relative to major primary mar-

kets, transportation facilities, and demographic features. Third, con-

sider the alternative use of the resources employed in the industry.

A range area like the Red Desert is exceptionally vulnerable to rather

modest variations in the amount and timing of rainfall. The carrying

capacity for 6 months grazing might range from 50 to 60 acres per cow unit

in a "wet" year up to a section or so in a "dry" one. (I suggest that

this latter figure is a practical definition of infinitely poor range,

excluding the Sahara). Range utilization is limited by the availability

of stock water, drift fencing, and the ownership pattern of the land.

Land use may well be limited to the alternatives: range live-

stock or nonuse. A certain amount of oil extraction hardly competes,

nor does antelope production which, if expanded, would put things in the

nonuse category above for practical economic purposes. The combination

of altitude (6,000 to 7,000 feet in the main), aridity (something like

8" to 12" of rainfall per year), and latitude (taking Rock Springs,

Wyoming, as a mean), has led to a pattern of heavy winter feeding re-

quirements. Cattle are fed about 5 months on hay raised in the rare

irrigated river bottoms, while sheep producers rely on rather scroungy

winter range plus hay in reserve.

The nearest primary markets are the feeding areas along the Platte

River in eastern Colorado and western Nebraska, several hundred miles

away. In recent years, the feeder markets on the West Coast have

developed as a primary market for range animals from this area, and this

is considerably farther in miles, and a bit further in transit time.

Without going into any great detail, I suggest that an adjustments

model must include these limitations; and that a model which does so

would hardly be suitable for other producing areas.

Industrywide constraints

Some factors are more or less peculiar to the range livestock

industry generally. These factors might enter as constraints on the

aggregate adjustments level, applying with only minor modifications to

all of the area models. The first that occurs to me is the competitive

situation of the industry. It seems reasonable to assume that no in-

dividual producer is able to affect his market significantly. This


might want some modification if the pooled marketing operations of wool

growers are considered. One might consider the case for prices admini-

stered by buyers in primary markets. Cooperative effort by buyers may

be evident in stockyards, etc., but they may still depend upon prices

established in a proximate sense by a market beyond the significant

control of local buyers. This matter deserves more than just specula-

tion. But lacking the data of a research effort, the assumption of

competitive markets might not be too far from reality.

The wool market is peculiar in that it is affected both by a

governmentally administered base price, and by well-developed marketing

pools. This no doubt affects, or should affect, the production and

marketing decisions of the range sheep producer. Again, these are in-

tuitive statements which need the support or refutation of research

findings before incorporation into a model.

A second industrywide constraint has to do with the ability of

producers to adjust through innovations; with the pressures toward

adjustments of various kinds and with the aggregate of producers' desires

to adopt what might be defined as desirable adjustments. Areas of possible

innovation might include the technological, such as machinery, range

improvement and feeding and breeding practices; economic organization,

such as insurance, price controls, cooperative marketing, and integration

and specialization of production; and effective political action, in

the large sense, which is the means toward economic-technological innova-

tionary goals.

It seems to me that innovations in these areas have had a more rapid

rate of adoption among other groups of agricultural producers, for in-

stance, dryland wheat growers, and specialized truck crop and fruit

growers, than among range livestock producers. I don't know for certain


whether this is so, why it is so, or even if it is desirable. But I

read the general subject of this meeting as an explicit recognition that

some kind of adjustment is desirable. I suggest that the implied malad-

justment might have to do with pressures toward innovation in the in-

dustry, and perhaps with a set of attitudes toward innovation held by

the dominant group of range livestock producers. An effective adjust-

ments model must include these pressures, limitations, and attitudes in

the set of constraints. It does not simplify the methodology to admit

that these constraining factors are, in all likelihood, interrelated.

I suggest that the range livestock industry is still motivated

largely by a philosophical remnant of the 19th century. The public

press maintained in the name of the dryland wheat industry loudly

proclaims the still politically virulent virtues of agricultural

fundamentalism. But dryland wheat producers, individually and in

bunches, have begun the adjustment to the somewhat unique milieu of

the 1940's and 1950's. The consumer and the centralized industries

that have captivated him, are in the saddle. With all deliberate

alacrity, dryland wheat producers have adopted technological and marketing

innovations, along with rapid adjustments in scale.

I suggest that the range livestock industry has not shown an equal

grasp of current realities. This may be due to a permissive lag in

individual motivational adjustments, in the pressures exerted by the

entrepreneurial few, or by the adjustment choices suggested by research-

ers in the field. In any event, I believe that the dryland grain pro-

ducers, though not by any means up to date are ahead of their large-

hatted and long-booted brethren in adjusting to nascent economic


Techniques and Data

I come now to a discussion of various analytical techniques that

might be appropriate to an industry adjustments model. Ancillary to this

emphasis, I shall discuss some of the types of data appropriate to the

various techniques.


Budgeting is an old friend of all of us, I am sure. It is a static

technique which we often use in comparing the results of alternative sets

of assumptions. For instance, we might be concerned with aggregate pro-

duction of certain classes of cattle, under a series of alternative stocking

rates, holding price and management constant. A budget study is often

useful in suggesting the most likely equilibria positions, given certain

adjustments in resources, technology, etc. The technique can say little

about the adjustment processes, the time required, or the path that would

most likely be followed during adjustment.

Perhaps the greatest utility of budget analysis in an aggregate adjust-

ments problem is to allow a comparison between assumed goals and those

goals mostly likely to be achieved by a range of alternative modifica-

tions in the resources bundle. The data required for budget analysis are

input-output coefficients for the activities involved in the range of

alternatives to be considered.

Linear programing

Linear programing is a way of arriving at the same general conclusions

possible with budgets, though not probable except with considerable luck. A

linear programing solution guarantees an optimum if there is a solution at

all. Budgeting is more of a hit or miss affair, with no tightly controlled

test for an optimum, and no procedure that ensures that an optimum will

be recognized even if it is calculated.

The linear programing technique assumes linear and independent vari-

ables whose possible values are limited by a series of linear inequalities.

Like budgets, linear programs provide point estimates of equilibrium

positions. The solution adjustment is innocent of time dimension and of

data on the adjustment processes involved in reaching the final equilibrium

position. One advantage of linear programing over budgeting is its simul-

taneous consideration of allowable activities and levels of activity. A

large number of variables (100 or more) can be included in the possibilities

space, and the solution can be computed in a short time using numerous

available electronic computer programs.

The data required for a linear program do not differ greatly from

those required for a budget solution. It is necessary to have input-output

coefficients and a reasonable set of linear formulae describing the activi-

ties and constraints on them.

It is possible to replace the original values of the constraints

and recalculate the problem, noting the effect of these new values on the

optimum solution each time. The next step is parametric linear programing,

which treats the values of the constraints as parameters in the problem.

These values are allowed to vary over continuous range, for the purpose

of observing the transitions that occur in the system. The technique per-

mits analysis of the optimal incremental behavior of the system, as well

as of the effect of changes in these parameters on the final solutions.

Spatial equilibrium

Spatial equilibrium analyses look at price and demand interrelation-

ships between areas. The problem under attack is a price-demand equilibrium


solution on a geographical basis. This technique assumes: a perfectly

competitive market (in space, time, and form); the maximization of net

profits as the sole objective of the firms involved; supply sources and

markets to be single points; transportation costs between any two points

to be independent of volume; and the commodity produced and marketed to

be homogenous. The quantity available from an area, population character-

istics, and disposable income are taken to be exogenous variables within

the assumed time period of one year. There is no allowance for inventory

management between years. The market demand schedules for each area are

known, but they are not necessarily the same.

The problem in a spatial equilibrium analysis is to maximize the

profit at each shipping point, by minimizing the transportation and

associated costs involved. The problem fits the general framework of a

linear programing solution technique. The solution, if there is one,

indicates a price for each shipping point and market, and the amount of

the commodity that will be shipped over each path.

Varying the demand, price, or production limits for areas and resolv-

ing this new problem allows a prediction of the effects likely to flow

from similar changes in the actual situation, if the assumptions of the

technique are acceptable. The data required for this technique include

aggregate production possibilities, price, amount, and paths of available

transportation, for each area involved in the analysis. It is also neces-

sary to have the demand schedule for the various markets to be considered,

in terms of population levels, disposable income, and effective prices.

Such an analysis might be useful in indicating the marginal or submarginal

livestock-producing areas under specified price and productivity assump-

tions, indicating areas of possible adjustment.

Game theory

Game theory might have some utility in an industry adjustments study,

but utility would be limited. This technique assumes two or more rational

opponents engaged in the selection of countervailing strategies, or modes

of behavior, toward a profit-maximizing or loss-minimizing goal. If the

losses in a game exactly equal the gains, the game is said to be zero-sum.

This can be converted and solved in a linear programing format. If the

losses do not exactly offset the gains, the game is non-zero-sum, and a

rigorous solution technique may be hard to devise.

It is easy to visualize a game situation involving a livestock producer

and a country buyer, quite possibly zero-sum in nature. It is also possible

to visualize an aggregate game with the opposing interests represented on

the one hand by large buyers or a market, and on the other by some aggre-

gate of livestock producers. In this case, each strategy would represent

a segment of the aggregate possibilities, the probability assigned to the

strategy being derived from the frequency of individual sellers or buyers

normally following the designated strategy.

The linear constraints and strategy sets and the necessary omission

of a time variable seem to limit the applicability of game solutions to

industrywide problems. A complete list of available strategies is required

for both game participants, as is a payoff function for each strategy

combination. These data may well be available only as the result of

preliminary research of a descriptive nature. They are equivalent to the

input-output data used in the budgeting or linear programing techniques.

Decision theory

This is a technique often described as a game between nature and a

rational player. The decision maker is assumed to be a rational animal,


with some range of choices to be made in the strategy he chooses. His

opponent (Nature) also has a range of possible actions, but these occur

according to some probability derived from a study of historical events.

The choice criteria are somewhat more flexible than the min-max re-

quired of a player in the game situation. These may include such criteria

as maximum marginal net payoff, expected value of payoffs, the minimum

opportunity loss, or expected loss, maximum likelihood, etc. The

proper criterion is a function of the decision makers concept of the

problem involved, of his available choices under various probable events,

and perhaps most critically, of the probabilities he assigns to the

various possible actions of Nature.

Decision theory might be useful in the establishment of optimum

long-term stocking rates on extensive grazing land, of feeding and breed-

ing practices, shipping, culling, and replacement practices, etc., for

an aggregate representing all reasonable choices in a producing area.

Markov chains

Markov chain analysis assumes that there is a probabilistic relation-

ship between levels of a studied activity in two successive time periods,

e.g., that successive paired observations are auto-correlated. This seems

to be the case for many human as well as natural phenomena, including

yields of grain on dry land, perhaps cattle prices on a central market,

range capacities from year to year, support prices based on a moving

average, etc.

The data required for a Markov chain analysis are paired sequential

observations of the variable under study. From this, it is relatively

simple to compute the probabilities of transition from a given activity

state to any other in one period, or any number of periods in the future

up to a steady state, where the probabilities no longer change with

increments of time. The process will not describe the course of events

between the current activity state and any given future state. It will

report the mean number of trials required to arrive at the selected

state, the probability of being in any given state n trials in the

the future, and the mean number of trials required to arrive for the first

time back t the state from which the process took off.

Marko, chain analysis is a neat and computationally rather simple

technique that might be used to estimate probable events in a wide range

of situations where there is reason to believe that the events are auto-

correlated over successive time periods.

Signal flow

Signal flow theory is an analytical tool that isn't entirely de-

pendent on the researchers' ability to express salient relationships in

mathematicsl terms. It is permissible here to reason by analogy, so that

this techn ue is probably a special case of simulation. This tool was

developed for application in engineering research, especially circuitry,

but I believe that it can be adapted to certain problems in economic

research as well.

The procedure is one of constructing a network of activities

connected in various ways by relational propositions. One enters the

system with a specified level of the studied activity and traces the

effect on the final result, of modifications of activity level and perhaps

type arising from the action of the network. It is possible to define

interrelationships within the system to any degree of complication that

the researcher's knowledge and computing ability will allow. This

includes the possibility of feedback, or dampened oscillations. Any


infinite series oscillation prevents a solution (a constant loop feedback),

and an exploding oscillation of course destroys the system.

Each activity node is defined in terms of its subject and limits. The

relational propositions are generally rather straightforward mathematical

formulae, but they needn't be. Mathematics may break down when it comes to

structuring the network, leaving the researcher with a set of logical but

nonarithmetic structural statements. These do not prevent computer solutions,

although they may complicate the programing a bit.

The value of signal-flow analysis would lie in testing structural or

relational hypotheses for exogenous logical consistency, prior to their

inclusion (or exclusion) in an econometric model. Once a signal-flow model

is operational, it is possible to change activity levels or hypothecate

changes in the structure and to attempt to predict by analogy the results

of similar changes in the real-world situation. This technique, of course,

has all the built-in pitfalls associated with any argument by analogy, and

it should be used with these limitations in mind.

Attitudinal scaling techniques

Most formulations of economic relationships assume away such factors

as attitudes, learning ability, and state of knowledge, and assorted "sub-

jective" processes of evaluation and decision-making. I think that the

basis for this is the difficulty encountered in earlier times of getting

objective data in these areas. The difficulty is rapidly becoming less

acute, so that we are in position to incorporate smae of the human variables

explicitly in our economic analysis. This should lead to a fuller under-

standing of the economic processes we study and consequently to better

predictive results.


The breakthrough in the provision of such data is due to the rapidly

increasing kit of psycho-dynamic research techniques. One of the earliest

of these techniques, and the one with which I am most familiar, is the

Guttman scale. This technique allows for the placing of individuals,

relative to a group, on an attitudinal continuum ranging from least to

most favorable. Study attitudes may be anything about which it is possible

to make statements that can be agreed or disagreed with by individual

respondents. A Guttman scale may be used as one variable in correlation

and regression studies. A scale that meets established tests for

statistical validity may also be used to predict attitudes for the popu-

lation from which the scale sample was drawn.

Statistical techniques, such as rank correlation and paired com-

parisons, may also be useful in establishing attitudes and preferences

as objective data. These and related techniques are useful in studying

some of the causal factors in consumer demand; the adoption of new tech-

nology; the participation in cooperative marketing or range livestock

grazing such as pooled grazing permits; or cooperation with various

aggregate programs sponsored by various governmental agencies. I am sure

that a careful search of psychometric and sociological literature would

turn up a number of techniques of this general kind that have interesting

applications for an industry adjustments study.

Leontief input-output systems

The Leontief system assumes a high degree of interdependence between

the various production, processing, marketing, and service activities in

an economy. There are certain primary factors, especially labor, which

are required in all other activities, but are not themselves outputs from

any other activity.

The system consists of a description of inputs used in the output

of each studied activity, including the internal consumption of part of

an output (some cows are required as inputs for the output of cows).

A matrix format called the technology matrix, allows the summation of

inputs to all studied activities, from each studied activity. This sum-

mation is the total output and the allocative consumption of that par-

ticular activity. Similarly, a summation of inputs to each activity

from all studied activities is the total of resources used by that activity.

The elements of the system may be expressed in value terms, so that

marginal-value productivities, prices, profits, losses, etc., can be


In the simplest Leontief systems, final consumption is assumed to

be the only human good and labor the only human cost. The system can

be fitted into a linear programing solution, maximizing consumption for

a minimum level of labor use.

The system can be complicated with the inclusion of a time variable,

thus becoming a dynamic but still linear system. Outputs are then

characterized as additions to stocks required for current production;

for maintenance of capital goods used in output activities; or held for

input in the next, or some future, time period. This is essentially

an economy flow model and allows for balanced growth within the system.

Such a system is causally indeterminate. This forces exogenous choices

of rate of growth, preferences for one use of a resource, or one activ-

ity over another, etc.

Leontief input-output systems might provide a way of estimating the

current position of the range livestock industry in the larger economy.

More important, such a technique might suggest possible adjustments that

could be expected in the economy at large that would impinge on the

range livestock industry, forcing adjustments in one area or another.

There adjustments could then be incorporated as constraints in an

optimizing model for the industry.


I have talked about my idea of a proper industry adjustments

model, and of techniques and data that might go into one. I want to

conclude with a few general remarks intended to fill some of the more

obvious lacunae in this discussion. These are statements so obvious

that they are usually ignored.

I believe that any approach to an adjustments model for the range

livestock industry must begin with a carefully logical structuring of

the problem. There must be an explicit definition of the goals, an

exhaustive listing of the possible and appropriate means to them, and

of constraints and limits that might apply. The logic of the structure

must be consistent with external reality or the result is likely to be

intellectualized gibberish. The logic of the structure must also be

internally consistent, or there will be built-in errors that destroy

the effectiveness and purpose of the research effort.

A model that assumes away or ignores institutional or attitudinal

restraints on human action, for instance, is likely to contribute

neither to our understanding of real events nor to our ability accurately

to predict their consequences. The range livestock industry is not an

extensive game of chess, an aggregation from model phenomena, or a

living case study of linear mathematics. The industry is a loose

gaggle of live people whom we cannot fully understand, whose goals and

means are largely inarticulate, and with whom we can barely communicate

at times.

My personal feeling is that a model built on probabilities, hunches,

and a careful observation of human actions, may be closer to the truth than

a very neat and precise list of arithmetic equations based on a careful

reading of extant economic treatises. I do not insist that you include

the possibility of errant human behavior in an industry adjustments model--

this would be both presumptuous and contrary to accepted practice. I only

suggest that your results, whatever the quality of the methodological

tinsel, will not be worth much if you do not do so.

Let me reiterate what I tried to say earlier about data. A model

depends upon data as a sheepherder depends upon his sheep. A county full

of nice, high-quality sagebrush desert surrounding a faithful Basque and

his dog is picturesque. But is is worthless without sheep. Similarly,

a model without data is just a pretty picture. We can afford a few people,

perhaps, who devote their entire efforts to the structuring of models.

But we can afford these people only if there are others who can plug

in the proper data, and crank out results that we can all examine and

perforce improve upon. I have personally failed on this account more than

on any other and it is not an especially pleasant way to abort.

I have suggested that the range livestock industry is especially

infected with the image of the 19th century entrepreneur. We face the

spectacle of some thousands of livestock producers in 1961, trying to

emulate the philosophy of cattle companies that failed around 1890.

Such rampant individualism may be admirable, but I suggest that its

extension to current industry-operating procedure is not an equilibrium

situation. Sooner or later the range livestock industry will adjust

to the current economic and political realities, even as the wheat in-

dustry in the Northern Plains is doing.


I conceive the purpose of research to be one of suggesting adjust-

ment processes that maximize the probability that this individualist will

survive another generation or so, while still competing effectively in

the rapidly coalescing milieu of the politically and economically

powerful consumer. The analytical techniques and sources of data that

I have observed (a limited sample) in research on the adjustments open

to the range livestock industry are not adequate for this job.

My condolences to you who are charged with this research job, and to

your constituents whom you may not know, but whose children may be fore-

doomed to a factory because of your and their joint failure to compre-

hend the adjustments and the choices still open, and those that the

future portends.

Reference s


Irving F. Fellows, Editor; Budgeting. Storrs Agric. Expt. Sta.,
U. of Conn., Bul. 357, Aug. 1961.

Linear Programming:

Robert Dorfman, Paul A. Samuelson, & Robert M. Solow; Linear
Programming and Economic Analysis. New York, McGraw-Hill Book Co.
Inc., 1958.

Spatial Equilibrium:

George C. Judge; A Spatial Equilibrium Model for Eggs, No. 7 of
the series, Competitive Position of the Connecticut Poultry
Industry, Storrs Agric. Expt. Sta., U. of Conn., Bul. 318, 1956.

Game Theory:

R. Duncan Luce & Howard Raiffa; Games & Decisions, New York, John
Wiley & Sons, Inc., 1958.

Decision Theory:

Howard Raiffa & Robert Schlaifer; Applied Statistical Decision
Theory, Division of Research, Graduate School of Business Ad-
ministration, Harvard University, Boston, 1961.

William J. Baumol, Economic Theory & Operations Analysis, New
York, Prentice-Hall, Inco., 1961.


Markov Chain Analysis:

John G. Kemeny & J. Lowrie Snell; Finite Markov Chains, Princeton,
N.J., D. Van Nostrand & Co., 1960.

A. T. Bharucha-Reid; Elements of the Theory of Markov Processes and
Their Application, New York, McGraiHill Book Co., Inc., 1960.

Signal Flow:

T. R. Nisbet & W. W. Happ; Flow Graph Analysis, Technical Report,
LMSD 48357, Lockheed Missiles & Space Division, Sunnyvale, California,
Dec. 1958.

Attitudinal Scaling:

M. G. Kendall; Rank Correlation Methods, Hafner Publishing Co., 1955.

Allen A. Edwards; Techniques of Attitude Scale Construction, New
York, Appleton-Century-Crofts, 1957.

J. P. Guildord: Psychometric Methods, New York, McGraw-Hill, 1954.

Don Bostwick, James Esmay, & Gordon Rodewald: Attitudinal Research
Relating to Farmers Use of Short Term Credit. In process of publl-
cation by ERS, USDA, Washington, D. C.

Leontief Input-Outut Systems:

Robert Dorfman, et al., a. cit.


by William G. Brown

Mr. Bostwick is to be commended for presenting his paper on

"Analysis Techniques in Industry-Wide Adjustments," in an interest-

ing and entertaining manner. Although I had thought that Mr. Bostwick's

topic was too broad when first reading his paper, it is now apparent

that his paper is very helpful in relating and integrating the other

papers of the program. The program chairman has done well in obtaining

a balance between depth, generality, and detail in the papers presented.

To briefly review the paper's format, after his introductory re-

marks, Mr. Bostwick discusses criteria for the selection of "models."

He then lists what he calls "constraints" for industry-adjustment

models. Techniques and data are then presented and discussed in his

interesting "rapid-fire" manner in the last major section.

In his paper, Mr. Bostwick made the following statements: "A model

is supposed to bear some recognizable resemblance [sic to a segment of

the real world. This requires both internal and external logical con-

sistency, and distinguishes a model from a paradigm, the latter re-

quiring only internal logical consistency."

As discovered by Mr. Bostwick (on page 3 of my manuscript)

"external" logical consistency turns out to be a troublesome term to use.

True "external" logical consistency would require perfect prediction

and/or description of some part of the real world. Such a state of

affairs is rare indeed, especially for economic models. Hence, it would

seem preferable to substitute a term such as "predictive" or "descriptive"

power for "external logical consistency" as a criterion for choosing

among alternative models.

Mr. Bostwick's distinction between descriptive and predictive models

is not so clear-cut as might be inferred. A "predictive" model is often

used for describing as well as predicting. Similarly, many so-called

descriptive analyses are of interest as far as the future outlook is con-

cerned, at least by implication.

Returning to the problem of criteria in selecting among alternative

models, Mr. Bostwick states, in effect, that it is generally agreed that

a simple model is often preferable to a more complicated one. The prin-

ciple of parsimony or of Occam's Razor is indeed accepted by philosophers

of science. However, some of the reasons given by Mr. Bostwick for

preferring a simpler model are somewhat surprising. It is true that

we must defer to general considerations of "cost," including data pro-

cessing. But then the statement is made that, "In the end, we must also

bow to the need for communication with people who have not our own

experience of and interest in the particular problem with which the

model deals." This kind of idea has often been thrown around by econo-

mists and administrators but it is absolutely untenable, unless one is

primarily a journalist rather than a researcher. Think of how Einstein

would have had to have withheld his model because it was more complex in

many respects than the classical theory. He had real difficulty in

communicating his ideas to other researchers interested in the same

problem area. However, because the predictive and explanatory power of

Einstein's model was superior, his more complex model gained acceptance.

The following statements were made in Bostwick's paper:

"We must be able to acquire data and to devise analytical tech-
niques by which the hypotheses of the model may be tested.
This is a shoal on which much theoretically well conceived
research founders."

However, if the hypotheses of the model are not operationally testable,

the research could not be considered as theoretically well conceived.

In Mr. Bostwick's next section on "Constraints for Industry-

Adjustment Models," the word "constraint" was used fairly often rather

than some other word such as "characteristic." As an example, consider

the following statement: "Let me discuss some of the constraints more

or less peculiar to the range livestock industry." Perhaps the Industry-

Adjustment Model must take account of some of the characteristics noted,

such as geographic dispersion, but I do not see how these can be entered

into a model as simple "constraints" in the mathematical sense. If they

are not constraints in the usual sense, then the model itself must be

specially designed to somehow reflect the salient characteristics of the

situation. I fear that much of our use of words such as "constraint"

merely adds further to our professional kit of jargon.

It should be noted that the section on "Constraints" for Industry-

Adjustment Models is about five typewritten pages long, yet in the

next section on Techniques and Data, Bostwick fails, for the most part,

to relate these so-called "constraints" to the various models discussed.

Hence, the section on "constraints" is not very well integrated with the

rest of the paper.

In the last major section, Techniques and Data, nine different

techniques are discussed. In my opinion, this section is the best of his

paper as the techniques are presented in an interesting but concise

manner. A possible shortcoming of this section is that in discussing this

many techniques, there is insufficient time and space to relate in detail

any one of the nine techniques to industry-wide adjustments in the range

livestock industry. However, more detailed treatment of certain types

of models is given by other papers on the program.

In Mr. Bostwick's conclusion he reiterates statements about data which,

while true in themselves, tend to obscure an important point. Bostwick

states that "A model depends on data like a sheepherder on his sheep."

Surely, we would all agree that nearly all our models are very dependent

upon the data. However, a model may eventually be extremely useful even

though we might not have data for the model at the time it is proposed.

The reason is that the model itself specifies the type of data required.

And contrary to the possible implication of Mr. Bostwick's statements,

data are not necessarily fixed or given for all time.

If a new model is proposed which promises to be logically superior,

it may be possible to secure the new type of data specified by means of

new surveys, experiments, or statistic collecting procedures. This

important possibility must not be overlooked if we are to improve the

reliability of our results.

Although many of my comments have been on the critical side, most

of these comments have been concerned with minor aspects of the paper.

I think that we are all in agreement with the major portion of Mr. Bost-

wick's paper and appreciate his considerable efforts in obtaining and

presenting his material in an interesting and straightforward manner.


by R. J. McConnen, Montana State College

If the range livestock operator had perfect knowledge about the

future levels of price, feed production, and performance of livestock,

his decision-making would be a more simple task than it is today. If

he also had perfect knowledge about how actions in one year would affect

the feed production and livestock performance in future years, there

would be precious little art left to the science of the management of

range livestock operations. It is likely that the big "ifs" will remain

with us. Range livestock operators must make decisions on the basis of

less than perfect knowledge about future levels of prices, feed pro-

duction and livestock performance.

In 1921, Professor Frank Fnight's Risk, Uncertainty and Profit was

published.1/ Knight not only developed the basis for the modern theory

of profits, he also distinguished between the knowledge situations of

risk and uncertainty. Knight's risk involves situations where well defined

probabilities can be attached to the alternative outcomes. Knight's

uncertainty involves knowledge situations where it is not possible

to attach quantitative probabilities to alternative outcomes. This

classification of knowledge situations which will be referred to as

classical risk and uncertainty, was often helpful in conceptualizing re-

search problems and undoubtedly stimulated much thought. Now, when

economists are faced with problems of less than perfect knowledge about

future conditions, they could conveniently drop the problems into one

1/ F. H. Knight, Risk, Uncertainty and Profit, Houghton-Mifflin,
Boston, 1921.

of two baskets.2 Knight's classification of knowledge situations could

certainly be used to characterize many of the problems of a range livestock

operation. Most of the situations would be classified under uncertainty.

While Knight's modern theory of profits was extremely useful to the

economist, his classification of knowledge situations led, in my knowledge,

to no significant research in the area of range livestock operations.

Indeed, the concepts of risk and uncertainty, after being clarified by

Knight, were sometimes confused and the issues were clouded rather than

clarified. This was the case in my own Master's thesis, which dealt with

range development

2Z Perfect knowledge is when a single future outcome would occur with
certainty. If a future situation could have several outcomes and perfect
knowledge existed about the probabilities attached to these outcomes
(provided that the probability for any one outcome did not equal one),
less than perfect knowledge exists about the particular outcome occurring
at some future time. The phrase "less than perfect knowledge" is used
differently here than by Heady. See E. 0. Heady, Economics of Agricultural
Production and Resource Use, Prentice Hall, New York, 1952 p. 443. Knight
states, ". it is unnecessary to perfect, profitless imputation that
particular occurrences be foreseeable, if only all the alternative possi-
bilities are known and the probability of the occurrence of each can be
accurately ascertained. Even though the business man could not know in
advance the results of individual ventures, he could operate and base his
competitive offers upon accurate foreknowledge of the future if.quantitative
knowledge of the probability of every possible outcome can be had. For
by figuring on the basis of a large number of ventures (whether in his own
business alone or in that of business in general) the losses could be
converted into fixed costs." (Knight op. cit., pp. 198-199). But in many
cases, the occurrence takes place only once, and although the probability
distribution for the occurrences is known with certainty, an individual
entrepreneur cannot properly analyze the case by considering the problem
of risk as one of fixed costs. The case is one where, as Knight states,
". .. the practical problem may relate to the degree of knowledge rather
than to its presence or absence in toto . The essence of the
situation is action according to opinion, of greater or less foundation
and value, neither entire ignorance nor complete and perfect information,
but partial knowledge." Ibid., p. 199.

/ R. J. McConnen, Risk and Uncertainty in the Appraisal of Sunken
Investments in Range Development, Master's Thesis, Department of Agri-
cultural Economics, Montana State College, Bozeman, 1953, Mimeo.

With the publication of Value and Capital in 1939, economists were

provided with a systematic outline of how to deal with problems of

dynamic economics.-/ In building these pioneer dynamic models, Hicks

found himself confronted with situations where knowledge was less than

perfect. Hicks restricted himself to dealing with the knowledge situation

that Knight would classify as risk. Hicks determined equilibrium situ-

ations by using the value of the most probable, that is model, price and

technical expectations.- Hicks' model has often been modified to the

extent that the mathematical expectations of price and technical outcomes

have been used to calculate equilibrium or optimum conditions.a Hicks'

method of dealing with risk (along with certain modifications mentioned

above), when combined with his methods of handling sunken investments

and a flow of future incomes, has served as the basic model for most of

the work published under the auspices of the W-16 Technical Committee.z

Economic research in the area of range livestock operations has been able

to deal with the knowledge situation which is defined as the case of

classical risk. Cases of knowledge situations which fall into the

categories of either classical uncertainty or that crevice that exists

between classical uncertainty and classical risk, are seldom explicit

subjects for research. The researcher is often forced to deal with

4/ J. R. Hicks, Value and Capital, An Inquiry Into Some Fundamental
Principles of Economic Theory, Second Edition, 1946, Oxford at the
Clarendon Press, especially Part III and Part IV.

J/ Ibid., pp. 124-127.

/ For a discussion of these modifications, see Fredrick and Vera
Lutz, The Theory of Investment of the Firm, Princeton University Press,
Princeton, 1951, Chapter XV, "Treatment of Risk and Uncertainty," p. 179.

Z/ A good exposition of the economic theory is presented by the


these knowledge situations, however, Usually he deals with them, often

after paying lip service to the problem of uncertainty, by either (1)

ignoring the problem of uncertainty thereafter or (2) implicitly treating

such knowledge situations as cases of classical risk. Later in this

paper, I will treat such knowledge situations as risk. This risk will

be defined somewhat differently than the classical risk. 'However, I main-

tain that the crime will not be as great because (1) this will be done

explicitly and (2) it will be done for a definite purpose.

One day economists started to hear about game theory. Some agricultural

economists naively believed that game theory could be used to deal with

problems of uncertainty. However, they soon found that after you advanced

beyond the two-person zero-sum game, they had reason to doubt the honesty

of the author who stated in the preface, "No knowledge of mathematics

beyond high school algebra is required ." Also, game theory could

only be used when all players were "rational" and there were conflicts of

interests. Few would regard the sources of uncertainty (price, climate,

etc.) as being governed by a player who opposed us. Finally, game theory

had been developed to deal with risk, not uncertainty anyway.8j

Now decision theory. Can it be used to deal with problems of un-

certainty? "Decision theory has been developed to deal with problems of

choice or decision-making under uncertainty, where the probability figures

required for the utility calculus are not available."12 Thus, decision

8~ For a discussion of game theory see R. Duncan Lace and Howard
Raiffa, Games and Decisions, Introduction and Critical Survey, John
Wiley and Sons, New York, 1957, Chapter 1.

2/ W. J. Baumol, Economic Theory and Operations Analysis, Prentice,
Hall, 1961, p. 368.

theory as defined by Baumol differs from the Hicksian analysis and

from game theory. However, many of the terms used in decision theory

are borrowed from game theory.

Several proposed criteria for decision-making will be outlined

shortly. The decision-making criterion used extensively in this paper

is, however, closely related to the basic method of analysis outlined

by Hicks. Why then refer to decision theory rather than the general

model outlined by Hicks? First, some methods of analysis which cannot

be considered as an outgrowth of Hicks' model will be considered.

Secondly, most of the developments in the application of decision

theory have been made in the general field referred to as operations

research. The philosophy of operations research is encompassed in the

definition of the field given by Saaty. "Operations research is the art

of giving bad answers to problems to which otherwise worse answers are

given."-10 The philosophy of operations research seems to correspond

more closely to Samuel Gompers' philosophy of, "Each day, more and more."

The philosophy of much economic research seems to hue more closely to the

Marxist philosophy of Lenin, "All or nothing." When dealing with the

problems of range livestock operations, the philosophy of Gompers seems

more appropriate. Therefore, I intend to give no final answers in this

paper. My purpose is to outline a method which I believe will give

". .bad answers to problems to which otherwise worse answers are

given." And in the spirit of the philosophy of this paper, I would proph-

esy that if you use the method outlined in this paper, each year your

results will become, to paraphrase Gompers, "better and better."

10/ Thomas L. Saaty, Mathematical Methods of Operations Research,
McGraw-Hill Book Co., Inc., New York, 1959, p. 3.

Part II

Decision theory

Management plays a "game" against nature. However, it must be assumed

that nature is not a "diabolical Miss Nature."ll/ If decision theory is

to be used, what is required? First, the manager or "player" has to be

capable of: (a) making a list of strategy alternatives which are available

to his "opponent," Miss Nature (these strategy alternatives are referred

to as "states of nature"), (b) making a list of strategy alternatives

available to himself, 2/ and (c) constructing a "pay-off matrix" on the

basis of the possible states of nature and available strategies. Secondly,

the player must have considerable information about himself. He must know

what his attitude is towards taking chances in order to select an appropri-

ate criterion for decision-making.

The pay-off matrix below will be used to illustrate the criteria

presented below.

A 100 2 1
B 99 198 0

A and B are strategies for the player while C, D, and E are states of

nature. The elements in the matrix are the "pay-offs." For instance,

if the manager plays A and the state of nature C occurs, the pay-off will

be 100.

11/ Luce and Raiffa, oE. cit., p. 279.

12/ Strategy alternatives are a reflection of the flexibility avail-
able to the decision-maker. For a discussion on flexibility, see Heady,
op. cit., pp. 282-284, 524-525, and especially pp. 345-348 and 792-793.

Baumol lists six criteria as proposed decision rules.1-1 (1) The

maximum criterion defines the strategy with the "least worst" possible

outcome as the "best." Using this criterion, strategy A would be the

"best" strategy. (2) The maximax criterion defines the strategy with

the best possible outcome as the "best." Again, strategy A would be the

best strategy. (3) The Hurwicz O
the maximum weighted average (weights reflect the player's psychology)

of minimum and maximum pay-off as "best." If the minimum pay-off is given

the weight of Q(= 3/4 and the maximum pay-off weighted at 1/4, strategy

A would again be the "best" strategy. (4) the Bayes criterion defines

that strategy with the highest expected pay-off as the "best." If we

have no knowledge about relative probabilities of the states of nature;

C, D, and E; equal probabilities are assigned. In which case, A has a

value of 34 1/3 and B has a value of 65 2/3. B would be the "best"

strategy. A variant of the Bayes procedure, which will be used exten-

sively in the remainder of the paper, is for the decision-maker to assign

subjective probabilities to the states of nature. (5) The minimax regret

criterion defines that strategy with the minimum maximum regret as the

"best." The pay-off matrix has to be in terms of utility to use this

criterion. Assume this is so. A new matrix is constructed from the

original pay-off matrix. The "utils" for each element in a column are

subtracted from the element in that column which has the greatest number

of "utils." The regret matrix will appear as

A 0 96* 0
B 1* I 0

12/ Baumol, OP. cit., Chapter 19, The discussion which follows is
paraphrased from Baumol.

The maximum regret in each row is starred and that strategy with the mini-

mum maximum regret is the "best" strategy. (6) Mixed strategies permit

some combination of two or more strategies. For instance you could use

half of your resources to play strategy A and the other half to play strategy


If meaningful probabilities could be attached to the states of nature,

we have a knowledge situation which Knight would refer to as risk. If no

such probabilities could be attached, the situation would be one of uncer-

tainty. If the probabilities .2, .6, and .2 were attached to the states of

nature C, D, and E in the pay-off matrix used above, it would be possible

to define a "Hicks criterion." D is the most probable state of nature.

The "best" strategy for the decision-maker using this criterion would be

strategy B.

The manager has the job of picking either one of these or some other

criterion as the proper criterion. The proper criterion will change from

manager to manager and for any one manager over time as his psychology,

social position, and economic outlook and position change. Therefore, the

economist can choose no "correct" criterion. However, with this in mind,

I will make the following hypotheses. "The Bayes criterion will not prove

to be the most appropriate criterion in outlining the approach for re-

searchers working in the area of decision theory." Once the hypothesis

has been stated in this form, I find it possible for me to put on my armor,

designed by John Dewey, and strive to reject such a hypothesis.

As stated earlier, decision theory was developed to deal with decision-

making under uncertainty. Luce and Raiffa state,

"The field of decision-making is commonly partitioned according
to whether a decision is made by (i) an individual or (ii) a
group and according to whether it is effected under conditions

of (a) certainty, (b) risk, or (c) uncertainty. To this last
we must add (d) a combination of uncertainty and risk in the
light of experimental evidence."l,1
This paper will be concerned with individual decision-making. "Any

decision-maker--a single human being or an organization--which can be

thought of as having a unitary interest motivating its decisions can

be treated as an individual in the theory."l-
Now, lets look at the states of knowledge. The condition of

certainty requires a particular type of probability distribution and

can be treated as a special case of risk. What is required for decision-

making under risk?

"In general, if an a priori probability distribution over the
states of nature exists, or is assumed as meaningful by the
decision-maker, then the problem can be transformed into the
domain of decision-making under risk."l6/

In order for any element of a set of conceivable states of nature

to be possible, the probability of this state of nature occurring must be

greater than zero. But this isn't enough. It is both conceivable and

possible (though not at all very probable) that the annual precipitation

at Fort Collins next year will exceed 100 inches. However, the prob-

ability at Fort Collins next year will exceed 100 inches. However, the

probability of the occurrence of this state of nature, while greater than

zero, is so small that we would not consider this state of nature as

possible for pragmatic decision problems. This decision had to be made

with at least some subjective guess about the quantitative nature of at

least a portion of the probability distribution of unknown states of

l4/ Luce and Raiffa, oP. cit., p. 13.

I/ Ibid., p. 13.

6/ Ibid., p. 277.


nature. Ignoring for a moment the condition (d) "a combination of uncer-

tainty and risk in the light of experimental evidence," the two remaining

conditions, (b) risk and (c) uncertainty, must be combined. The only re-

maining condition is that of risk. Risk in this case is a continuum from

a well defined and stable probability distribution for the states of nature

(the classical case of risk) to an ill-defined and unreliable probability

distribution for the states of nature (an approach to the classical case

of uncertainty).

Classical uncertainty corresponds closely to the "complete ignorance

case."-1/ It's the contention of this paper that complete ignorance rarely

exists. If the case of complete ignorance persists, the decision-maker

makes the decision to make no decision (an inactive situation) until he is

propelled into a forced action situation.18/ It appears that many range

livestock operations needlessly go through this cycle when disastrous market

conditions and/or drought occur. Cases of complete ignorance are often

incorrectly labeled as cases of optimism. Complete ignorance and rational

action are incompatible. We must know something about the future in order

to act rationally. At least complete ignorance for the researcher shouldn't

exist by the time a project statement is drawn up. Partial ignorance, or

partial knowledge if you will, describes the area between complete ignorance

and classical risk.12/ Risk as used above, includes both classical risk and

partial ignorance. If the Von Neumann-Morgenstern utility theory is accepted,

it may be argued that since probability distributions are available for

17/ Luce and Raiffa, o.p cit., p. 295.

18/ L. A. Bradford and G. L. Johnson, Farm Management Analysis, John
Wiley & Sons, New York, 1953, p. 29.

1_/ Luce and Raiffa, op. cit., pp. 299-316.

the states of nature, even though they may be subjective and perhaps

unreliable, the appropriate criterion to use is the Bayes criterion.2L1

The rejection of the original hypotheses will not, however, hinge on

such a sophisticated argument. The rejection is based on the following

reasoning. If adequate knowledge is available to permit the construction

of subjective probability distribution for the states of nature, it is

possible to use the Bayes criterion by utilizing this distribution. If

it is possible to use the Bayes criterion, and if the decision-maker

knows his own attitudes, there is enough information to use any of the

other criteria. By basing research on the needs of the Bayes criterion,

the researcher does not have to name the "right" criterion. If the re-

searcher uses the Bayes criterion as the basis for research in the area

of decision theory, he will be forced to attempt to improve the reliability

of the subjective probability distributions. Instead of dealing with

the case of "complete ignorance," the researcher deals with a case of

"partial ignorance." The purpose of the research is to (a) adequately

describe the pay-off matrix and (b) to decrease the degree of ignorance

about the probability distribution of the states of nature. The economist

is forced to deal with the cause-effect relationship and treat this

relationship as deterministic. The objective is to find out more about

this relationship. The researcher can no longer shrug off his responsi-

bilities merely by classifying knowledge situations. However, experi-

mentation does have a cost. How much experimentation is of significant

help is itself a problem for decision theory.2./

2~/ Ibid., p. 304 and Baumol, op. cit., pp. 381-384.
2~/ Luce and Raiffa, .. cit., "13.8 Statistical Decision Theory,
Experimentation Not Fixed," p. 313.


This does not mean that no criterion but the Bayes criterion has any

meaning for the researcher. The other criteria can be of definite value,

particularly in setting up "reasonable" criteria to establish rules for

strategy dominance. Before discussing this point, a short discussion on

the usual meaning of dominance will be given.

Assume there are two reasonable strategies, S1 and S2, available to

decision-maker. Say S1 dominates S2. Then the pay-offs of S1 and S2 for

the difference states of nature are not all identical and the pay-off for

S1 for each state of nature is either more desirable than or equally de-

dirable to the corresponding pay-off for S2. S2 would then be an inad-

missible strategy.-2 Strategies which have identical pay-offs for all

states of nature are equivalent strategies and may be considered as ad-

missible strategies. Usually, only one of a number of equivalent strategies

should be selected to use in the decision model. A side remark here. A

considerable increase in the understanding of a problem would probably occur

merely from the construction of a pay-off matrix and the selection of ad-

missible strategies.

For particular problems, some one or more.criteria may be used to

augment the specifications to be met by admissible strategies. For instance,

admissible strategies could be required to have a maximum loss of not more

than some fixed amount.

All this may seem somewhat nebulous. However, it is important to (1)

remember that the primary objective of decision theory should be to give

". bad answers to problems to which otherwise worse answers are given,"

and (2) believe "that each year your results will become, to paraphrase

Compers 'better and better.'"

22/ Herman Chernoff and Lincoln E. Moses, Elementary Decision Theory,
John Wiley and Sons, New York, 1959, pp. 123-124.

Decision theory, leaning heavily on a variant of the Bayes criterion,
will now be used to explore two problems in range livestock operations.

The first problem will be to select the "best" rate of stocking for a

predominantly cow-calf operation. The second problem will be to select
the "best" time to sell yearlings grazed on leased land. If this paper

were not an exploratory effort, I might be accused of having a disease

called quantophrenia, an uncritical devotion to faulty statistics. I must

apologize for the fact that the first example is also used in a contributed

paper that I will give during the American Farm Economics Association
Meetings next week.

Part III

Selected rates of stocking

Cattle ranchers in the Northern Great Plains have relatively few
alternative strategies which can be used to minimize the costs associated

with climatic variability. The costs may be either direct consequences

of unfavorable climatic conditions or opportunities not realized when

favorable climatic conditions occur. It will be assumed in this paper

that three different rates of stocking are the only admissible short-run

strategies available to a particular rancher. The flexibility available
to the rancher is described by these three strategies. The problem is

to pick the "best" of the three strategies. These strategies may in

practice be dominated by other strategies.

The assumed goal of this hypothetical rancher is to select that
strategy which will maximize the expected gross ranch profit for the

current year.- The impact of the present rate of use of this range

resource on the rate of flow in future intervals has not been considered.

23/ It is assumed that the price of cattle will remain constant.
Different expected price levels for livestock will affect the expected

The number of animal unit days is used as a measure of range produc-

tivity. The results of a grazing intensity study were used as a basis for

establishing the relationship between range productivity and the level of


For the purposes of constructing budgets for each of the three levels

of stocking, range productivity is considered to fall within one of five

levels. The interval for each level of productivity is given in table 1.

The gross ranch profit for each level of stocking for five levels of range

productivity is given in table 2.~1

gross ranch profit for any one strategy. The impact of different expected
prices on the choice of strategies could be considered. One method of
handling expected prices would be to define the goal as that of maximizing
expected net worth. A functional analytical model would have to be more
sophisticated than the illustrative model used here.

24/ The experiment was conducted at the U.S.D.A. Range-Livestock
Experiment Station at Miles City, Montana, from 1933 to 1959. The functional
relation used to predict range productivity on the basis of precipitation
was X1 = a + b, loge X + B2 (logeX3)2 + b4\/- X1 is the level of range
productivity, X is the precipitation in March, April and May. The term
\-E is a modified trend variable. The heavy rate of stocking was
the only level of stocking which could be used to measure range produc-
tivity. Cattle were taken off from all pastures when all forage was
grazed in the heavily stocked pasture. The\F t variable recognized
the decreased rate of flow from this resource in later intervals as the
result of heavy grazing in the forecasting model and was used for pre-
dictive purposes only.

25/ The basic data used to calculate these 15 gross ranch profits
were taken from R. 0. Wheeler and R. J. McConnen, Costs. Returns and Organ-
izational Characteristics--Three Sizes of Commercial Family-Operated Cattle
Ranches--Northern Great Plains-1959, Bulletin 557, Montana State College,
Agricultural Experiment Station, Bozeman, Montana, 1961. The large size
ranch with 309.2 animal units on hand January 1, 1959, was used as a basis
for these budgets. The experimental data were used as a basis for ad-
justing the gross ranch profit as the stocking rate and the level of range
productivity changed. The medium rate of stocking would be 309 animal units.
In order to achieve the light rate of stocking, it is necessary to reduce
the number of animal units to 210. This is approximated by selling all
the cattle not in the producing breeding herd. The heavy rate of stocking
required the purchase of 144 head of long yearling steers. These changes
were made the last of May.

Table 1, Levels of range productivity in terms of animal unit days
for each 12 acres

Level of
productivity I' I II III III'

99 or less 100-149 150-199 200-249 250 or more

Table 2. Gross ranch profit associated with three different rates of
stocking and five different levels of range productivity

Rate of Level of range productivity
stocking I' I II III III'
dollars dollars dollars dollars dollars

A. Heavy 4,000 605 7,645 11,389 11,389

B. Medium 200 2,833 7,165 8,527 8,527

C. Light 304 2,604 2,939 3,261 3,261

The prediction equation was used to estimate the expected level of
range productivity for any observed level of precipitation in March,

April and May., It was assumed that the deviations of the observed

levels from any expected level of range productivity had a mean value of

zero and were normally distributed. The standard error of estimate was

used as a basis for attaching probabilities to the five levels of range

productivity.2- This was done for each expected level of range produc-

tivity by using a cumulative normal distribution table. The probabili-

ties assigned to each level of range productivity for three different

levels of precipitation are given in table 3.

26J It would seem that the appropriate statistics to use in assign-
ing probabilities to the levels of productivity would be the standard error
of forecast and tolerance intervals. The probabilities for the levels
of range productivity calculated using the standard error of forecast

Table 3. Probabilities assigned to each level of range productivity when
precipitation in March, April and May equals 4, 3 and 2 inches

Level of range
productivity I' I II III III'

Proabilities when
precipitation equals

4 inches .0011 .0968 .5400 .3430 .0202

3 inches .0094 .2484 .5953 .1439 .0030

2 inches .0968 .5586 .3244 .0201 .0000

It is assumed that the probabilities assigned to each level of range

productivity can be assigned to the gross ranch profit for that level of

range productivity.2-/ The expected gross ranch profit for each of the

three strategies was calculated by multiplying the five possible gross

ranch profits by the probability for each level of range productivity and

summing the products. Values of the expected gross ranch profit for each

strategy using the probabilities in table 3 are presented in table 4.

and confidence intervals were not acceptable as "good" subjective probabili-
ties. The probabilities assigned to the levels of range productivities when
precipitation in March, April, and May equaled four inches where, I' .15,
I .18, II .23, III 20, and III' .24. The probabilities assigned
when precipitation in this period equaled three inches where, I' .19,
I .24, II .36, III .22, and III' .06. The probability of the
lowest level of range productivity is greater with four inches of pre-
cipitation than with three inches of precipitation. The oddity occurred
because I' and III' are open-end classifications and because in this
case, of the rapid increase in the value of the standard error of forecast
as the values of these independent variables deviate from their mean

2~/ There is a
profit for levels of
value of each level,
a special case.

certain weakness in this assumption. The gross ranch
range productivity were calculated by using the central
The central values will be the expected value in only

Table 4. Expected gross ranch profits for each of three rates of stocking
and three levels of precipitation

Rate of Expected gross ranch profit when precipitation equals
stocking 4 inches 3 inches 2 inches
dollars dollars dollars

A. Heavy 8,319 5,998 2,660

B. Medium 7,240 6,249 4,059

C. Light 3,024 2,878 2,503

Using the Bayes Criterion, the "best" strategy for this hypothetical

rancher would be A when precipitation equals four inches, B when precipi-

tation equals three inches and B when precipitation equals two inches.

Sequential decisions--the "best" time for selling

This illustration is perhaps more naive than the first. It illustrates

what essentially is a quasi-sequential decision-making model which is used

to make the "best" decision with respect to time of selling.-8

The range livestock operator used in this case runs 100 head of

yearlings on rented pasture. This phase of his operation is assumed to be

separate from any of his other activities. He pays a grazing fee in advance

each month of one dollar per head per month. He feels he should have a six

percent return on the value of the yearlings and his investments in graz-

ing fees. The value of the yearlings at the end of a month are used as

the value in the succeeding month for purposes of computing interest

charges. There are no other variable costs with respect to time associ-

ated with the operation.

28/ For a discussion of sequential decision procedures see, Robert
Schleifer, Probability and Statistics for Business Decision and Intro-
duction to Managerial Economics Under Uncertainty, McGraw-Hill Book Co.,
Inc., New York, 1959 Chapter 38 and Luce and Raiffa op. cit., section 13.8.

The "rules of the game" are listed below. He can only sell all or

none of the yearlings. They are sold as feeder-stockers. He can only sell

the yearlings on the last day of the month. If he sells the yearlings, he

does not have to pay the grazing fees for the succeeding months. He owns

the yearlings the last of April. Their average weight is 400 pounds. He

can sell the cattle in any of the months of April through October. On the

last day of October, "the game" terminates, if it hasn't been terminated

earlier by the sale of the yearlings.

In order to escape some of the responsibility for this model, I will

play reporter, Mr. Higgins operates a ranch not far from Fort Collins,

Colorado. He knows something about decision theory and would like to use

it to help him in his managerial chores. The "rules of the game" are a

fairly reasonable description of one phase of his operation.

Higgins decides to use decision theory and the Bayes criterion to

help him decide the "best" time of selling. He knows that the two major

factors in determining "best" time of selling are (1) the market price,

and (2) the weight of the yearlings.

Higgins feels that the major factor affecting animal gain is avail-

able feed. The amount of available feed on range is, in his mind, largely

a function of physical features of the range site, the plant cover, and

the climatic conditions existing for any particular year. The range land

Higgins rents is remarkedly similar to the range used in a grazing intensity

study at the Central Plains Experiment Station located nearby. He has a

publication reporting the result of this grazing study.2-2 Higgins feels

22/ G. E. Klipple and David F. Costello, "Vegetation and Cattle
Responses to Different Intensities of Grazing on Short-Grass Ranges of
the Central Great Plains." U.S.D.A. Technical Bulletin 1216, United
States Government Printing Office, Washington 25, D. C., July 1960,

his rate of stocking

presented by Klipple

as those used in the

corresponds closely to the assigned moderate use

and Costello. His animals also weigh about the same

experiment. He then took the average monthly cattle

gain per head for the pastures with an assigned treatment of moderate use

from table 22 and

constructed table 5

Table 5. Average weight of yearlings by month

Year April' May June July August September October

1940 400 483 561 624 673 698 713

1941 400 491 541 601 651 696 682

1942 400 477 533 604 635 672 663

1943 400 470 539 597 656 683 697

1944 400 464 538 593 652 685 659

1945 400 464 531 573 635 663 663

1946 400 460 518 569 602 658 655

1947 400 479 538 590 637 676 672

1948 400 446 480 545 604 626 638

1949 400 461 524 591 644 672 676

a/ assumed constant

Higgins assumed the differences in the average gain could be ex-

plained by differences in accumulated precipitation. Therefore, he

constructed table 6 from the information presented by Klipple and Costello

in their table 1.22/ He then constructed scatter diagrams by plotting

O/ Ibid., p. 58.

L/ Ibid., p. 64.

22/ Ibid,, p. 7.



the average weight for each month against the accumulated precipitation by

month. He didn't bother to plot, for instance, cases like accumulated pre-

cipitation for June against average weight for May.

Table 6.

Accumulated precipitation by month

1 2 3 4 5 6
March + Col..1 + Col. 2 + Col. 3 + Col. 4 + Col.5
Year April Ma-y June July August Sept.

1940 1.2 3.9 4.8 7.6 8.5 13.0

1941 3.5 5.2 10.6 14.4 17.1 18.5

1942 1.6 3.9 6.3 7.2 7.4 7.8

1943 1.4 4.6 6.2 6.7 7.2 7.4

1944 3.3 4.2 4.9 7.1 7.5 7.8

1945 1.9 3.0 5.9 6.7 9.2 10.7

1946 .8 3.2 4.1 6.3 9.0 9.2

1947 1.6 3.5 7.5 10.6 11.6 12.6

1948 .7 1.2 3.2 4.6 6.3 6.5

1949 1.6 5.3 9.0 10.3 11.4 11.6

Average 1.7 3.8 6.0 7.9 9.5 10.4

Next Higgins defined two climatic conditions, High Precipitation (above

the 1939-53 average) and Low Precipitation (below the 1939-53 average).

Average precipitation didn't occur between 1940 and 1949. Then, for each

month, he classified the average weight into either a High, Medium, and

Low ranges which are states of nature. Then, he divided each of the 28

scatter diagrams into six parts as indicated below.

He used the scatter diagrams to attach probabilities to the three

stages of nature, High Weight, Medium Weight, and Low Weight. He found


that his model was too simple to use the added information provided by

the accumulated precipitation after May. Therefore, he only used the

relationship between average weights and accumulated precipitation in

March April, and May. By using the mid-point of the ranges for average

weight, Higgins calculated the expected average weight for each month

for the climatic conditions of Low and High Precipitation.

States of nature Average weight

High weight

Medium weight

Low weight

Low precipitation High precipitation precipitation

Figure 1. States of nature related to accumulated precipitation

In the rest of the model, Higgins acted as if the expected did in fact

occur. He realized the danger of this, but had neither the time nor the

data to modify his model. What Higgins should have done was to calculate

conditional probabilities for average weights in future months based on

the actual average weight in the present month.

Next Higgins turned to the question of price. On 30 April, when he

starts to "play the game," the price is known with certainty. Higgins

wants to know how this price will change. He sells the yearlings on the

basis of Kansas City prices for feeders and stockers.

Higgins claims he can, without error, classify market conditions on

the 30th of April, as either Weak or Strong. I asked him how he did this.


When pressed, he said he utilized U.S.D.A. market reports, the U.S.D.A.

index on range feed condition for his area, and a tenacity index for

ground squirrels. Then Mr. Higgins launched into quite a tirade. He was

extremely critical of marketing people because they didn't give him the

kind of information about price expectations that he needed for a decision

model. I tried to quiet him down. I told him how busy marketing people

were and how they've been heavily criticized for some of the price fore-

casting work they've done in the past. I tried to impress him with the

fact that marketing people are a fine lot. In fact, I think I ended by

saying, "Why some of my best friends are marketing people."

Well anyway, come to find out, a Weak Market happened to occur when

the percent decrease between the April and October price for feeders and

stockers, all weights, at Kansas City was 14.9% or more. Using monthly

prices for the Kansas City market for 1951-1960 (there were five years of

Strong Market Conditions and 5 years of Weak Market Conditions), Higgins

plotted first the percent change from April prices for each month, then

the percent change from May prices and so forth. Prices showed a marked

downward seasonal trend' for both market conditions. The percent prices

changes were then classified into the three ranges for each month of Low,

Medium and High, which are states of nature. The scatter diagrams were

then used as a basis for establishing probabilities to attach to each state

of nature. The mid-point of each range and the probabilities of each

range were used to establish the expected percentage change in price.

Once again, Higgins realized that he should have been dealing with con-

ditional probabilities. What is the probability distribution of percentage

changes in price for September and October given a 3% decrease in price

from July to August? But Higgins didn't have the time.


Higgins then calculated the Gross Profits and Expected Gross Profits

for two different April prices, 20 cents per pound and 30 cents per pound,

for four possible situations. (1) Low Precipitation--Strong Market, (2)

Low Precipitation--Weak Market, (3) High Precipitation--Strong Market,

(4) High Precipitation--Weak Market. In tables 7 and 8, the gross profits

to the right of the dark line are expectations and to the left of this

line the gross profits are known with certainty. The 30 April calcula-

tions are made on the basis of the information available on 30 April.

If any one of the expected gross profits are greater than the 30 April gross

profit, the decision is made not to sell on 30 April. If this decision is

made, on 30 May another set of calculations are made using the information

available on 30 May. Once again, if any one of the expected gross profits

is greater than the 30 May gross profit, the "game" continues. In only

one case, a 20 cent April price, Low Precipitation, and Weak Market, would

the game terminate before 31 October. In this case, it would terminate on

30 September.

I criticized Mr. Higgins for using such a crude model. I told him he

should have defined more states of nature for both percent price change

and average weights and developed a more precise relationships between

these states of nature and the climatic and market conditions. I also

told him he should be more concerned about the concept of conditional

probabilities. Higgins agreed and felt pretty badly about it. He said

he would try to do better next year.

But Higgins is persistent. He wanted to take one more look at his

data. He said, "Suppose it's July and I face the conditions of a Weak

Market and High Precipitation. How could I use some of the other criteria

I've heard about to help me make a decision?" The data is presented in

table 9.

Table 7. Gross profits and expected gross profits with an April price of 20 cents

Time of
calcu- Climatic Market
nation condition condition April May June July August September October

30 April Low Strong 80.00 88,83 95,36 98.49 113.54 117.17 116.18
Weak 80,00 88,83 90.16 95.18 98.25 103.39 105.23
High Strong 80.00 90.10 97.84 103.40 115.29 118.98 117.17
Weak 80,00 90.10 92.51 99.92 99.76 105,09 106.01

30 May Low Strong 89.28 91.37 96.80 106.71 114.54 113.30
Weak 89.28 87.80 93.42 97.38 100.12 99.80
High Strong 92.85 97.76 101.99 111.97 117.71 118.14
Weak 92.85 93.94 98,43 102,20 102.90 104.10

30 June Low Strong 92.82 98.28 110.11 113.45 114.34
Weak 89.2.5 94,31 102.02 102.95 100.69
High Strong 99.23 103.50 115.49 116.58 119.17
Weak 95.41 99.32 107.02 105.78 104.95

31 July Low Strong 99.75 108.50 113.74 111.36
Weak 97.76 99.15 102.55 98.33
High Strong 105.00 113.75 116.86 116.03
Weak 100.80 103.95 105,37 102.47

31 August Low Strong 110.00 114.64 112.32
Weak 100.63 106.06 104.48
High Strong 115.28 117.77 116.98
Weak 105.46 108.97 108.83

30 Sep- Low Strong 116.20 116.55
tember Weak 107.57 103.14
High Strong 1119.35 121.34
Weak ("game" terminates) 110.48 109.01

31 October Low Strong 118.14
Weak 106.26
High Strong 121.94
Weak 109.57

Table 8. Gross profits and expected gross profits with an April price of 30 cents

rime of
calcu- Climatic Market
Nation condition condition April May June July August September October

30 April Low Strong 120.00 133.75 143.81 149.83 170.74 176.69 175.71
Weak 120.00 133.75 136.53 144.28 149.73 158,45 160.09
High Strong 120.00 139.67 144.90 152.50 167.56 175.86 177.71
Weak 120.00 139.67 140.00 146.61 152.61 154.57 156.30

30 May Low Strong 133.92 137.05 146.53 161.59 173.19 171.52
Weak 133.92 132.46 140.87 147.29 152.23 151.93
High Strong 133.92 146.59 154.20 169.48 177.92 179.80
Weak 133.92 141.69 148.35 154.50 156.40 158.36

30 June Low Strong 138.72 147,07 165.26 170.36 173.24
Weak 134.13 141.94 154.06 156.61 153.72
High Strong 148.29 154.85 173.26 175.03 180.49
Weak 143.39 149.58 161.53 160.99 160.17

31 July Low Strong 148,77 162.63 170,99 167,92
Weak 143.64 149,52 154.52 149.02
High Strong 156.60 170.48 175.63 174.89
Weak 151.60 156,75 158.72 155.23

31 August Low Strong 164.38 172.14 172.48
Weak 151.25 152,95 149.57
High Strong 164,38 176.81 176.18
Weak 151.25 156.12 157.89

30 Sep- Low Strong 173.97 174.35
tember Weak 154.71 149.51
High Strong 173.97 181.48
Weak 154.71 157.54

31 October Low Strong 176.22
Weak 153.12
High Strong 183.38
Weak 159.34


Table 9. Future gross profits for
probability--weak market

possible states of nature and their
and high precipitation

States of nature
1 2 3 4 5 6 7 8 9
Price change
decrease Low Low Low Med. Med. Med. High High High

weight High Med. Low High Med. Low High High Low

Selling time
31 July $10,080
probability 1

31 August $10,533 $10,048
probability .50 .50

30 September $10,754 $10,279 $9,854 $9,517
probability .32 .48 .08 .12

30 October $10,818 $10,184 $9,898 $9,313
probability .10 .50 .07 .33

Higgins felt he would like to see what the results would be if he used

the criteria titled (1) Hicks, (2) Maximim, (3) Maximax, (4) HurwiczX,

and (5) Higgins. The Hicks criterion could not be used in August since there

is no one most probable state of nature. However, this criteria would say

do not sell now in favor of selling in either September or October. The

maximum would consider state of nature 5 in August, 8 in September, and 8

in October. This criterion would say sell now. The maximax criterion would

consider state of nature 4 in August, September, and October. This criterion

would say do not sell now. For the Hurwicz 0 criterion, Higgins favors

weighing the minimum values at .7 and the maximum values at .3. This cri-

terion would give a value for August of $10,194, for September of $9,888 and

$9,965 for October. This criterion would say do not sell now in favor of

selling in August. The Higgins criterion does not lend itself to a rigorous

description. Higgins said, "There's a fifty percent chance I'll lose money


if I sell in August, a twenty percent chance if I sell in September and

a forty percent chance if I sell in October. I don't like those kinds of

odds. I'll sell now."

Even here, there was no answer. Except in this case, the Higgins

criterion is the criterion that will be used. Mr. Higgins feels

strongly about this.

Part IV


Decision theory is designed to deal with the problem of uncertainty.

Many of the most important problems faced by the managers of range live-

stock operations are problems associated with uncertainty. It seems

appropriate to note that decision theory can be of considerable value as

a research tool when dealing with the problems of range livestock opera-

tions. It seems likely that decision theory will also be of considerable

value in extension work.

The definition of a problem within the decision theory framework re-

quires (1) a definition of the states of nature, (2) a definition of the

strategies that are available, and (3) the construction of a pay-off matrix.

As in the case of linear programming, considerable insight into a problem

is acquired just in the process of defining it in terms of a new an-

alytical technique and getting the required data. We'll seldom be able

to bet all the required data we need. This will require that we (1) ad-

vocate additional research of a particular kind and (2) use available

data in a more imaginative way.

In defining and discovering appropriate decision-making criterion, we

will have to deviate from the classical economic models. In this process,


we may find out more about (1) the classical economic models we have dealt

with so long, and (2) the nature and peculiarities of the research area we

have staked out.

It seems a certainty, if there is a future, that we will come to know

more about the weather. Particularly about the predicting of the weather

and maybe about controlling it. The knowledge of probability distributions

about future states of nature with respect to weather will give us new

parameters of knowledge. If this information can be combined with knowledge

about the relationships between climate and agricultural production and

adequate information is available about possible market performance, the

very nature of agriculture could change. This is probably more true for

crop production and certain livestock production than it is for range

livestock production. I think that it is important that we look closely

at this tool. New works are continually being published in this area.

Recently, a new book came into my possession, Applied Statistical Decision

Theory, by Raiffa and Sclaifer.22 On the basis of reading the lengthy

preface and introduction, I'd say that this book has much to offer despite

the fact that some of its pages look like a collection of character brands.

There are many other sources of help. The surface has just been scratched.

The purpose of this meeting was to explore possibilities of research

in the area of range livestock industry. We need to explore in more de-

tail both the available analytical techniques and the available data. We

should not be satisfied with this. We also need to develop new analytical

techniques and new sources of usable data. We have some of the analytical

3/ Howard Raiffa and Robert Sclaifer, Applied Statistical Decision
Theory, Division of Research, Graduate School of Business Administration,
Harvard University, Boston, 1961.


tools, some of the data we need. The purpose of this paper was to explore

the possibilities of using decision theory as a research tool. The models

I used were crude. It is possible to do better. Our initial answers may

not be too good. But we should strive to give ". .. bad answers to

problems to which otherwise worse answers are given," and believe that

each year our answers will become to paraphrase Gompers, "better and



by N. K. Roberts

McConnen has presented and illustrated decision theory as a tech-

nique for more adequately handling ranch management problems where un-

certainty is an important aspect. He also traced briefly the development

of risk and uncertainty from Knight to Luce and Raiffa and Baumol. We

are reminded "that the primary objective of decision theory is to give

bad answers to problems to which otherwise worse answers are given,"

and that, if we use decision theory "each year your results will be-

come better and better." The requirements of decision theory were

classified concerning nature, the player, and his abilities. Six

criteria for making decisions within decision theory framework were

discussed briefly. Except to illustrate how answers differ when using

these decision making criteria, McConnen did not discuss the underlying

differences among the criteria. He rather easily decides that the Bayes

criterion is most applicable to ranching situations, though he does

give a glimpse of what happens when the others are used.

Two important ranch problems involving uncertainty, selecting

stocking rates and time of selling, were used to demonstrate how

decision theory can be used. In the latter case McConnen introduced us

to the imaginary Mr. Higgins, an uncommonly ordinary rancher, who

seems to have all the qualifications necessary to use decision theory with

a modified Bayes criterion. However, in the end, after working long

and hard at his problem, he threw it all over and used his own criterion

for deciding when to sell. Mr. Higgins is such a remarkable fellow and

played such an important part in McConnen's paper that I feel Montana

State College should award him an honorary degree.

All in all McConnen did a creditable job and achieved his purpose

which was to introduce us to a relatively new technique for range and

ranch management research. I agree with him that one purpose of these

meetings is to introduce and debate methods of solving range and ranch

management problems. With this in mind I will ignore the temptation to

dwell upon his examples, for, as he said, they served his purpose.

Besides, they will get "better and better" in the future as method and

data improve. I will spend the rest of my time raising questions about

decision theory and McConnen's presentation of it. Possibly the questions

I raise will stimulate discussion later.

Before I get too serious, let me take a semi-facetious crack at

those in our's and related professions who insist on congering up such

morbid and depressing terms as, for example, "least-worst" and "minimum

regret." When an economist recommends to a friend in trouble that he.

select the "least-worst" alternative, he is immediately placed in the

class with the undertaker who tries to sell a sick friend a cemetery lot.

I can agree with McConnen.when he implies that we should not wait

for the time when we will be able to give perfect answers to range and

ranch problems before we begin to work on them. The philosophy of doing

our best even if it means "giving bad answers to problems to which other-

wise worse answers are given" is sound, provided we do not misrepresent

our answers as being perfect or use the philosophy as an excuse to stop

looking for better answers. There is no doubt but that it is the phil-

osophy we have to accept in our area of research if we hope to feel any

satisfaction in the work.

McConnen did not draw a sharp enough distinction between decision

theory as such and the use to which it is put in applied research. He

says, to quote again, "o that the primary objective of decision

theory is to give bad answers to problems to which otherwise worse

answers are given." He seems to say decision theory is designed to give

bad answers. I think what he means to say is that the data we have to

plug into this problem solving model are imperfect; therefore, we get

bad answers which are better than those obtained if the model were not

used. The question that arises is this: If perfect data could be

had, would the decision theory model yield better answers than any

other known theoretical construct designed to solve the same problems?

In other words, a theoretical problem solving model need only

stand the fire test of pure logic to receive consideration irregardless

of the imperfections in its application to real life problems.

At least five basic criteria must be met in testing a theoretical

model for acceptance by applied researchers. First, it must resemble

some phenomenon in the real world. Second, it must be able to with-

stand all external attacks on its logical form. Third, it must apply

at the theoretical level to all cases in the problem class for which it

was designed to solve. Fourth, it must be internally complete and

explicit leaving nothing unsaid, Thus, a sound theoretical model

lends itself to the precision of a mathematical expression. Though I

am not a mathematician, I recognize the value of mathematics in expressing

our economic concepts. As McConnen implies, art plays a big part in

applied research on the dynamic problems of reality. Even so, our claim

that economics as a science is justified because our body of theoretical

laws are consistent though not complete, general, and can be expressed

with the precision of mathematics or symbolic logic.

Does decision theory meet the criteria of logical soundness, or is it

designed to give "bad" answers because of internal weaknesses in the theory?

From what little I have read, I believe it is logically sound and does

apply generally to the class of problems for which it was designed to

solve. Thus, it is worthy of our attention.

Still, another question arises: If decision theory is not the only

model designed to solve the problems of uncertainty, how do we decide whether

it should replace the old? There are some criteria to guide an applied

researcher in his choice of concepts. First, a theoretical problem solv-

ing model must lend itself to modification so it will fit closely the con-

ditions of the specific problem. Generally, the model which reflects

most closely the real world without sacrificing logical soundness, gener-

ality of application, and preciseness is best. Second, the model must be

simple enough in its data and analytical requirements so that it can be

used by the men and facilities available. Generally, the less complicated

model will be least costly to use.

Is decision theory a simpler, more realistic model than game theory

or one of the other logical systems designed to handle problems of uncer-

tainty? McConnen seems to think so. I wish he had had time to compare

it more fully with the competing theories in this respect.

McConnen raised another question in my mind: Is the Bayes criterion

(or a variant) for making ranching decisions given the "payoff" matrix,

always the appropriate one even for researchers? Could some of the others

be useful for purposes other than selecting admissible strategies? He

listed six criteria--(1) maximin, (2) maximax, (3) Hurwics, (4) Bayes,

(5) minimax regret, and (6) mixed case.

McConnen tells us that the answers to ranching problems involving

uncertainty obtained from decision theory will apply only to the ranch

being studied. If this is so, it is an uncomfortable truth we face

as researchers. Though the answer may be specific, the method is still

general. It would be better if we could generalize both method and

answers when dealing with uncertainty. If we are not to generalize our

answers, isn't it possible that in some specific cases a criterion for

selecting the strategy other than Bayes' is most appropriate to use

with decision theory?

Baumol makes some pertinent observations about some of the criteria

that McConnen didn't have time to discuss.!/ Baumol would call a

rancher who selects the maximin criterion a coward when he is dealing

with unmotivated nature because he would select the "least-worst"

strategy. I see three possible cases when this criterion might be

most appropriate. The first is Baumol's c6ward or the person who just

won't take a chance on losing. He is probably a small unimaginative

rancher with a low income and a quarter of beef hanging in a sack in the

garage and a wife with few economic demands.

The second is the rancher who has an average inclination to

gamble but runs on production credit and never seems to have any of

his own money in the bank. He may not be able to chance losing on a

gamble for high returns if the strategy requires more credit. If he

loses two or three years in a row, he is out of credit opportunities

and out of business. He continues to operate conservatively, hoping

that someday he'll make a killing in spite of himself so he can get

ahead of his creditors.

1/ W. J Baumol, Economic Theory and Operations Analysis, Prentice
Hall, 1961, pp. 370-375.

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