ECOO,~AICS & SOCIO-LOGY
,OLO~,UO STATE UIj.4ERITY.
rO~T COLIS, OOUO
ECONOMIC RESEARCH IN THE
USE AND DEVELOPMENT OF
ADJUSTMENTS IN THE RANGE LIVESTOCK INDUSTRY
Report No. 3
Committee on the Economics of Range Use and Development of
Western Agricultural Economics Research Council
ECONOMIC RESEARCH IN THE USE AND DEVELOPMENT
OF RANGE RESOURCES
Report No. 3
Adjustments in the Range Livestock Industry
Committee on Economics of Range Use and Development
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
ECONOMICS & SOCIOLOGY
COLORADO STATE UNIVERSITY
FORT COLLINS, COLORADO
TABLE OF CONTENTS
PART I TECHNIQUES OF RESEARCH IN INDUSTRY WIDE ADJUSTMENT PROBLEMS
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
PART II IMPACT OF ADJUSTMENTS AT THE FIRM LEVEL
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
PART III MARKET AND POLICY EFFECTS ON RANCH ADJUSTMENTS
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
EXPERIMENTAL DESIGN AND ACCUMULATION PROBLEMS
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.
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
Steers, H24t 0.403 76.118* 4.6 0.909
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
H23t H13t H24t C23t t 1
Heifers, C22t -0.742H 428.3H 1,377.2 0.974
Cows, C23t -1.369 0.656 5,383.6 0.970
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
Calves, C1t -1.952' 1.026" -13,103.3 0.884
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
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.
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,
8/ Herman 0. A. Wold, A case study of interdependent versus causal
chain systems, Review of the Intern. Statist. Inst., 26:5-25, 1959.
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.
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.
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.
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.
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-
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.
DISCUSSION: EXPERIMENTAL DESIGN AND
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.
(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."
(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.,
(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
(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
ANALYSIS TECHNIQUES FOR INDUSTRY-WIDE ADJUSTMENTS
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.
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.
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-
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
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 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 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 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.
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 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
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 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
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
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
Irving F. Fellows, Editor; Budgeting. Storrs Agric. Expt. Sta.,
U. of Conn., Bul. 357, Aug. 1961.
Robert Dorfman, Paul A. Samuelson, & Robert M. Solow; Linear
Programming and Economic Analysis. New York, McGraw-Hill Book Co.
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.
R. Duncan Luce & Howard Raiffa; Games & Decisions, New York, John
Wiley & Sons, Inc., 1958.
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.
T. R. Nisbet & W. W. Happ; Flow Graph Analysis, Technical Report,
LMSD 48357, Lockheed Missiles & Space Division, Sunnyvale, California,
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.
DISCUSSION OF "ANALYSIS TECHNIQUES
IN INDUSTRY-WIDE ADJUSTMENTS"
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
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.
DECISION THEORY AND RANGE LIVESTOCK OPERATIONS
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,
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
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.
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
C D E
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
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
C D E
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
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
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-
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.
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
productivity I' I II III III'
Interval AUD AUD AUD AUD AUD
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'
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.
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
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
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 7. Gross profits and expected gross profits with an April price of 20 cents
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
High Strong 121.94
Table 8. Gross profits and expected gross profits with an April price of 30 cents
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
High Strong 183.38
Table 9. Future gross profits for
possible states of nature and their
and high precipitation
States of nature
1 2 3 4 5 6 7 8 9
decrease Low Low Low Med. Med. Med. High High High
weight High Med. Low High Med. Low High High Low
31 July $10,080
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.
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
DISCUSSION OF PAPER
"DECISION THEORY AND RANGE LIVESTOCK OPERATIONS"
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.