This is the old United Nations University website. Visit the new site at http://unu.edu

Decision-making models have been employed in a number of anthropological studies to describe the process by which observed distributions of behaviour are generated. From this perspective, individuals in all societies are seen to confront an array of problematic situations that require decision-making under conditions of varying degrees of uncertainty and risk. The solution sets they arrive at, in the form of the strategic choices they make, are assumed to be both rational and optimal within bounds. That is, they tend to be the best possible courses of action given available knowledge of alternatives, perceptions of a situation, and evaluations of surrounding constraints. Accordingly, observed behaviour patterns, frequencies, and regularities are conceived to represent the outcomes of a multiplicity of individual choices. The goal of inquiry is to discover and analyse the values and constraints that condition the actual decisions made, and in effect produce the patterns or the set of frequencies of the alternatives observed.

Toward this goal, several methodological paths are open. An especially promising one is to use mathematical models of optimization processes (e.g. linear programming, game-theory, and decision analysis). These supply both a logic and calculus for formulating and analysing peoples' values and goals as optimization criteria. And as White (1974, pp. 401-402) observes, they "provide a means of examining the predictions of different axiomatic models of optimizing behaviour, in comparison with behavioural outcomes or statistical distributions of behaviour within a population."

Several important food-related activities involve individual and small-group (e.g. domesticgroup) decision-making concerning such questions as: What foods should be procured? How frequently and in what quantities should certain foods be eaten? Even in relatively restricted situations, questions like these occur and require some choice between alternatives. Institutional diets, such as "dorm food," allow some consumer choice and are at least decided upon by someone (too often not the consumer). Likewise, cultural traditions and norms and seasonal scarcity and poverty can sharply curtail the range of choice, but some options are normally available. In brief, much nutrition behaviour is optative, and in relation to this fact some important questions arise for both the analyst and applied nutrition specialist. These include: What factors determine the specific choices made? What is the nature of the decision-making process? Do the foods procured represent maximum nutritional gains and optimal resource allocations (land, labour, wealth)? Is the selected diet the best combination of foods for simultaneously satisfying nutrient need and culturally constituted preferences at minimum cost?

**Linear Programming**

In this section we introduce linear programming, a mathematical technique for performing optimization analysis, which has been successfully applied to a large and diverse number of nutritional problems both inside anthropology and outside the field. A very useful introduction to the literature in anthropology can be found in Reidhead (1979). Outside of anthropology there are two notable studies clearly pertinent to nutritional anthropology. One, by the geographer Peter Gould (Gould and Sparks, 1969), uses linear programming to construct diets for Guatemala that are minimal in cost, culturally appropriate, and nutritionally adequate. Mapping regional variations in dietary cost-surfaces, he demonstrates the potential utility of linear programming for regional planning and family income determination. The second, by the economist Victor Smith (1964, 1975), is an excellent example of the comprehensive use of mathematical programming (linear and non-linear) for constructing optimal diets and foodproduction systems in Nigeria. His work is also remarkable for an abundance of procedural guidelines.

Broadly speaking, linear programming is a determinate decision-making model for finding the most favourable strategy to secure some preferred outcome. It is normally applied when we wish to find non-negative values of variables that optimize some objective (that is, that maximize the gains and minimize the losses in the process of achieving a goal), when the choice among strategies is restricted by one or more constraints. If the objective can be approximated by a linear function and the constraints expressed as linear equalities or inequalities, then the problem can be resolved mathematically with linear programming. Space prohibits discussing other kinds of mathematical programming (Spivey, 1962; Strum, 1972; Dantzig, 1963). As an illustration, and in the simple examples to follow, we will want to use non-negative values of two variables x and y that maximize or minimize an objective function of the form

(54) f(x, y) = ax + by

where a and b are given real numbers subject to certain constraints expressible as linear inequalities ( <; > ) in x and y.

*Example 5*

The first example concerns maximizing an objective function. The data come from an inquiry we are making into the dietary patterns and preferences of middle-income Americans. A 40year-old male wanted to know the best combination of meats to eat in order to get the most protein, with the fewest calories, at the least cost. Beef roast and pork roast were the meats he liked the most. He was willing to spend up to, but no more than, $1.50 for meat for one daily meal. Realizing that he needed about 2,800 calories daily and most of these would come from other ingestibles (e.g. beer), he wanted up to, but no more than, one-fourth of his required calories to come from meat (700 car). In July 1980, at the store where he shopped, a heel of round, lean and fat beef roast cost $2.39 per pound (14.9 cents per ounce), and pork roast cost $1.39 per pound (8.9 cents per ounce). A table of nutritive values of foods (Chancy and Ross, 1971, pp. 434-469) showed that per ounce, the beef roast contained about 8.33 g protein and 55 cal and the pork roast about 7 g protein and 103 cal.

To formulate his question as a linear programming problem we rephrase it to read: "How many ounces of beef roast and pork roast should he purchase (and eat) to obtain the maximum number of grams of protein?" Since there are two variables, we let x = the number of ounces of beef roast, and y = the number of ounces of pork roast. The objective function to be maximized is given by the linear function z = f (fx. y).

(55) max z = 8.33x + 7y

This is the number of grams of protein in each ounce of beef roast (x) and pork roast (y). Since he wants to spend no more than 51.5(), and beef is 14.9 cents per ounce and pork 8.69 cents per ounce:

(56) 14.9x + 8.69y < 150

Likewise there is a constraint on the total number of calories he wants from these meats, so that

(57) 55x + 103y < 700

which is the number of calories per ounce in beef (x) and pork (y).

Finally, since negative amounts of meat are irrelevant, we write a non-negativity constraint that

(58) x, y > 0

Since only two variables are involved, a simple and convenient technique for finding the optimal solution to this problem is to use a graphical method. With more than three variables an algebraic method called the simplex algorithm, or some variation of it, is usually necessary (Strum. 1972). In the graphical method we regard an ordered pair (x, y) as rectangular coordinates of a point in the x, y plane. We then graph the equational form of each inequality constraint (56) and (57). We do so only in the positive quadrant (I) because of the nonnegativity requirement (58) on x and y. For example, the equational form of inequality (57) is

(59) 55x + 103y = 7(X)

which can be graphed by rewriting it

(60) y = 6.796 - .534x

Then, real numbers can be substituted for x, and the values of y can be plotted. The same is done for (56). This describes the two straight lines which intersect in figure 2. The shaded area satisfies the inequality constraints (56-58) and contains an infinite set of solutions within it and on the boundaries. The shaded region has four corner (extreme) points labelled, 0, A, B. and C. Each of these is a basic feasible solution.

An extreme point theorem can be proved to show that the objective function (55) will attain a maximum value, subject to constraints (56-58), when the x, y values that define one of these corner points are inserted into it. The insertion of the x, y values defines the optimal solution. At corner point 0, x = 0 and y = 0 (eat neither) and we ignore it because, while it is a feasible constraint, it produces no protein. At corner point A, x = 0 and y = 6.796. Substituting these values for x and y into (55) produces 47.57 g protein. At corner point C, x = 10.067 and y = 0, and substituting these values into (55) produces 83.858 g protein. Finally, at B. x = 8.66 and y = 2.06, and these values, when substituted into (55), produce the maximum value of z, which is 88.2 g protein. (Notice that it is usually necessary to solve the constraint equations simultaneously to define this point exactly.) Thus, we conclude that the optimal strategy for this person is to eat 8.86 ounces of beef roast and 2.06 ounces of port roast per day, which will provide 699.5 calories and cost $1.50. Another way to phrase this solution is to say that about 81 per cent of his meat should be beef roast and 19 per cent pork roast.

Records kept on this person for 43 days reveal that in fact he ate beef and pork for 32 days, and that 53 per cent of his evening meals were beef and 47 per cent pork, although not always in the form of roasts. More extensive studies comparing how well optimal patterns, deduced from a linear programming model, fit actual, observed patterns can provide important information on who is and is not optimizing. Those who show large disparities from the optimum might be studied further to determine whether this is so, and how they differ in other respects from those closer to the optimum. In an applied setting they might be targeted for benevolent intervention, e.g. nutrition and budgetary education and/or resource supplements.

We should mention that a large amount of additional information can also be obtained from a post-optimality analysis of the solution to linear programming problems. For example' the marginal value of each input constraint can be evaluated. This tells us how much the objective function would change as a result of a unit increase in the constant value of each constraint. In this example, cost has a marginal value of .447, and calories a marginal value of .03. Thus, increasing the cost constraint by I cent increases the protein gain .477 g, whereas increasing the caloric constraint by I cal only adds .03 g protein. This relationship can be made clearer by noting that the objective function z is equal to the sum of the products of the marginal and constant values of each constraint' or

(61) z = .447(150) + .03(2000) = 88.05 g protein

We could also perform a sensitivity analysis to determine what effect changes in coefficient values of the objective function and constraint inequations would have on the optimal solution' and what would happen if one or more constraints were added or removed (Strum, 1972).

*Example 6*

The next example concerns a cost-minimization
problem and is derived from fieldwork among rural and urban
Baganda, the largest ethnicity in Uganda. The main dietary staple
of the Baganda is the plantain, which is consumed almost daily,
constitutes the main bulk of meals' and is used to manufacture a
widely imbibed, mildly intoxicating wine produced on nearly every
rural farm. In the major city of Kampala, expenditure studies
show that the plantain is the biggest single food purchase made
by non-producing urbanities. An edible 100 g portion of plantain
(Platt, 1962), contains 128 calories, 31 g of carbohydrates, and
20 mg of ascorbic acid. However, plantain is a poor source of B
vitamins, especially of thiamine (B_{1}) for there is
only .05 mg of B_{1} per 100 g portion. Thiamine is
essential for the utilization of carbohydrate; it is generally
recommended that about .5 mg of thiamine be ingested per 1,000
non-fat calories (Latham, 1965; Passmore et al., 1974). Like many
tropical agriculturalists, Baganda ingest a large number of
carbohydrate calories, not only from plantains, but also from
sweet potatoes, cassava, and yams. One possible source of
thiamine in the Bagandan diet is the ground-nut, which is the
most common ingredient of sauces served with plantains
(Rutishauser, 1962), and which is an excellent source not only of
thiamine (.5 mg/100 g) but also of protein (15 g/100 g) and
calories (332 cal/100 g). However, using ground-nuts as a
thiamine source has a drawback; they are not extensively grown
and are very expensive to purchase.

Linear programming may be useful in answering two questions: (a) What is the optimal, lowest-cost combination of plantains and ground-nuts for obtaining an adequate amount of both calories and thiamine? (b) How do actual use patterns compare with this optimum?

We will answer these questions from the perspective of a low-income, unskilled male worker in Kampala earning about 140 shillings per month in 1970, who must purchase his food. We have chosen this perspective since have the most useful statistical data on prices and food expenditures for this group of workers (1970 Statistical Abstract, Statistics Division, Ministry of Planning and Development, Uganda).

With respect to question (a), the objective function "c," to be minimized is

(62) min c = 3x + 20.3y

where x = 100 g of plantain at a cost of 3 cents, and y = 100 g of ground-nuts at 20.3 cents (100 cents = 1 shilling = 14 cents US) in Kampala in December 1965. In this problem we want to make the value of "c" as small as possible, subject to the following constraints:

(63) 128x + 332y 2 2000

where 128x = the number of calories in 100 g of plantain, and 332y = the number of calories in 100 g of ground-nuts. The constraint value 2,000 represents 80 per cent of the recommended 2,500 calories for an average, active male (weight 55 kg) in East Africa (Latham, 1965, p. 234). We assume that at least 500 additional calories will come from other sources. Constraint (64) specifies that at least one mg of thiamine is needed to utilize 2,000 carbohydrate calories in (63). And .05x = the number of mg of thiamine per 100 g of plantain and .5 may = the number of mg of thiamine per 100 g of ground-nuts.

(64) .05x + .5y > 1

Constraint (65) requires that we consider only non-negative amounts of plantains (x) and ground-nuts (y).

(65) x, y > 0

Had we wished, we could have specified positive, non - zero values for x and/or y. For example, because of food preferences we might have restricted the solution to x > 8 and y > 1, which would guarantee that at least 8 x 100 g of plantain and 100 g of ground-nuts would be contained in the final solution. Food preferences can be important because often optimal diets from a cost and/or nutritional standpoint are unpalatable. Differences in the values of objective functions when food preferences are put in or left out can provide useful measures of "cultural cost" (Gould and Sparks, 1969).

As before, we graph the equational form of (63) and (64) (fig. 3). Again, only the positive quadrant is used because of (65). Notice that this time the shaded, feasible region is on, and to the right of, the intersecting lines. The basic feasible solutions occur at corner points, labelled A, B. and C. Successively inserting the x, y values at these points in the objective function (62) shows the minimum cost function, c, attains its smallest value at point B .543. The optimal solution at this point is x = 14.093 and y = .5907. Thus, to minimize cost, and get at least 2,000 calories and 1 mg of thiamine, the worker should purchase and consume 14.093 x 100= 1409.3 g of plantains and 5907 x 100 = 59.07 g of ground-nuts, at a cost of .543. Rephrasing, we could say that, proportionally, about 96 per cent of his intake should be plantain and 4 per cent ground-nuts.

Now let us look at the second question, "How do actual use patterns compare with this optimism? Using the nearest month for which urban food-expenditure statistics are available (February 1964), we find that 13.45 shillings of the monthly food expediture of a typical unskilled worker in Kampala were spent on plantains, and 3.6 shillings were spent on groundnuts. At the December 1965 prices quoted above, these sums would buy 44.83 kg of plantains and 1.77 kg of ground-nuts. The relative proportion of actual use indicated by these purchases is thus 96.2 per cent plantains and 3.8 per cent ground-nuts, which is strikingly close to the optimum deduced from the model.

Unfortunately, the only other data we have on actual consumption patterns are from the nutritionist Rutishauser's study (1962) of the composition of meals and recipes in Buganda. In this study it was found that, typically, the ratio is 2 ounces of ground-nuts to 28 ounces of plantain per serving. Thus, the relative proportion is 93.3 per cent plantain and 6.7 per cent ground-nuts, which is also close to the optimum. If these figures are at all representative, and admittedly they leave a lot to be desired, then it appears that low-income urban workers in Kampala are probably securing a nutritious mix of calories and thiamine from these sources at near optimum cost.

Many food-related activities involve flows and exchanges. Food procurement, for instance, frequently involves the flow of food among diverse, specialized sectors of a socio-economic system. Meat from hunters goes to gatherers, while vegetables go from gatherers to hunters. At a more basic level, energy itself can be considered the currency for transactions among the components of an ecosystem (Hannon, 1973; Johnson, 1978, pp. 75-95). What is often difficult to identify, let alone describe and analyse, is the structure of the direct and indirect relations of interdependence among: (a) a set of components represented as an endogenous system; and (b) the relation of this system to exogenous environmental variables. Returning to our example we might ask: How dependent are hunters on gatherers for the food energy necessary for them to produce food energy for themselves, the gatherers, and nonproductive dependents? How dependent are gatherers on each other, hunters, and the environment? What is the nature of these interrelationships through time? Is an equilibrium point ever attained? And if so, what is it?

Even with regard to "simple" systems, these are complex questions and investigating them requires, inter alia, a precise language for representing patterned relations of interdependence among a set of elements. Many branches of mathematics (e.g. matrix algebra and graph theory) provide this language.

Here we briefly describe and present an attenuated illustration of input-output analysis (IOA), a mathematical model for analysing relations of interdependence in an e-component system. It was developed originally by econometricians (Leontief, 1966) to examine intersectorial relations in complex, national economic systems. It is also being vigorously used by ecologists to study the structure and dynamics of ecosystems (Hannon, 1973; Finn, 1976; Richey et al., 1978). An IOA can provide: (a) definitions and representations of the structure of the direct and indirect flows among the n - components of an endogenous system; (b) information on the way in which direct, exogenous inputs to, and demands on, the endogenous system ramify directly and indirectly throughout it; and (c) information on the nature of equilibrium conditions for system maintenance. IOA can take many forms: linear-non-linear, open-closed, static-dynamic. We will focus on the linear, open, static version.

*Example 7*

To illustrate an IOA, we will present a
much-abridged description of !Kung calorie production and flows,
analysed in detail by Carlson (1978), using data from Lee (1969).
All values are calories x 10^{3}.

An IOA begins with a flow matrix, F. Putting
the value of each element in the i-th row and jth column, the f_{ij}
elements denote the output of the i-th row component (source of
supply) to the j-th column component (destination). In the open
model we append another column, D, the d_{i} elements of
which represent the direct demand from the i-th row source to an
exogenous sector. In economic production systems the matrix F
normally represents inter- and intraindustry flows, and D
represents the non-producing consumer sector which makes direct
purchase demands on it. Thus, each row and column of F represents
a finite set of einterdependent industries and the values of the
f_{ij} elements are flows among them. D represents the
outside demand on the system and the values of the di elements
are the demands on each specific industry. The total flow, or
output of the system, X, is a column vector, the x, elements of
which represent the total amounts of output required from each
i-th row source (or industry) to meet both system and outside
requirements. Thus,

(66)

For the !Kung this is:

M N

where f_{11} = 69 is the amount of meat
calories provided by hunters and consumed by hunters; f_{12}
= 48.02 is the amount of meat calories provided by hunters and
consumed by nut-gatherers; f_{21} = 126 is the amount of
mongongo-nut calories provided by nut-gatherers and consumed by
hunters; and f_{22} = 87.7 is the amount of nut calories
provided by nut-gatherers and consumed by nut-gatherers. The
amount of meat calories used by non-procuring dependents (e.g.
children and old people) is d_{1}, which equals 494.04,
and d_{2}=902.16 is the amount of nut calories used by
non-procuring dependents. Finally, x1, the total output of meat
calories required from hunters by hunters and gatherers (f_{11}
+ f_{12}) and dependents (d_{1}) is (69 + 48.02)
+ 494.04 = 611.06. And x_{2} is interpreted similarly.
Thus, reading down each column of F gives the required inputs
from each i-th source to a j-th industry. Reading across the rows
of F gives the j-th destination of the outputs from each i-th
industry.

Next, we use the elements in F and X to construct an input-output matrix, A. Each aij element of A denotes the fractional amount of the output of a row-component industry i, used by a column-component industry j, to produce a unit of j. (These aij's are often called "technological co-efficients.") Or

(67) a_{ij} = f_{ij}/x_{i}

where x_{i} is the total output of
industry i.

In the !Kung example

Meat Nuts

Thus, a_{11} = .113 is a ratio of meat
calories used to meat calories provided and means that .113
calories of meat are used by hunters to provide a calorie of
meat; and a_{21} = .206 is the ratio of nut calories used
by hunters to meat calories provided by hunters in order for them
to provide one calorie of meat. Column 2 is interpreted
similarly. Thus, A defines the equilibrium or maintenance
conditions of the food-procurement system. For an open system to
operate feasibly (i.e. meet industry and outside requirements),
at least one column of A must sum to < 1. Otherwise, it will
operate at a loss.

With the data in this form, we will ask two questions of the model: (a) What level of production is needed from these industries, both separately and together, to maintain the production system and satisfy the demand from the sector of non-producing dependents? (b) What level of production would be needed from these industries, both separately and together, to maintain the production system (i.e. maintain equilibrium) and satisfy the demand of the non-producing dependent sector if the nature of their demands changes in a specifiable way?

To answer question (1) we will represent the model as a system of linear equations.

(68) x_{1} = a_{11}x_{1}
+ a_{12}x_{2} + d_{1}

x_{2} = a_{21}x_{1} + a_{22}x_{2}
+ d_{2}

where x_{n} are the elements of X,
specifying the total output required of each industry; a_{nn}
are the elements of A specifying proportionally co-efficients of
output required of each industry from each industry; and d_{n}
are elements of D specifying the outside demand on each industry.
Solving for x_{n} provides the answer to question (a) -
the total output required from each industry to meet industry
needs and outside demand. For the !Kung

(69) x_{1} = .113x_{1} + .043x_{2}
+ 494.04

x_{2} = .206x_{1} + .079x_{2}
+ 902.16

where x1 = total required output of meat calories and x2 = total required output of nut calories. Rewriting with the outside demand on the right, and collecting terms, we have

(70) (1 - .113)x_{1} - .043x_{2}
= 494.04

-.206x_{1} + (1 - .079)x_{2} =
902.16

The solution, x_{1}, = 611.07 meat
calories, and x_{2} = 1,115.75 nut calories, comes as no
surprise, for we already know the total output of each industry,
which is given by X. The system of equations (69) represents the
structure of the system in precise terms.

The answer to question (b) reveals the full
power of the model for extrapolation. Consider a change in the
values of the outside demand, from 494.04 cal for meat to 600 cal
and from 902.16 cal for nuts to 500 cal (perhaps attributable to
changes in such factors as food preferences, trade, and
resources). This change, obviously, would call for an overall
decrease of 296.2 x 10^{3} calories. But what level of
production would be required from each industry to meet both the
revised outside demand and industry requirements? The answer to
this question is not so obvious? and certainly would not be
obvious in a model composed of several dozen industries.

Fortunately, the answer is easily obtained by inserting these new demand values into equations (69):

(71) x_{1} = .113x_{1} + .043x_{2}
+ 600

x_{2} = .206x_{1} + .079x_{2}
+ 500

Rewriting with the demands on the right and
collecting terms as before, the solution is: x_{1} =
710.446 and x_{2} = 701.499. In other words, in order for
the system of industries to meet the new combined total of 1,100
x 10^{3} calories of outside demand and remain in
equilibrium (maintain inter-industry flows as in A), meat calorie
production (x,) would have to increase from 611.07 to 710.446 and
nut calorie production (x_{2}) would have to decrease
from 1,115 86 to 701.499.

We could continue to substitute e-different D values into (69) to explore n-alternative equilibrium solutions that might occur under various theoretically expected conditions. These projections, however, all depend on an assumption of stability in A, and for this reason we have used the label static for this model. More intricate, dynamic models can be developed which allow for changes in A. Closed models (without an outside demand) and models involving nonlinear equations can also be constructed. Finally, it should be noted that matrix algebra and notation provide a more compact representation of the input-output model, and greatly relieve the computational burden, especially when the systems have components. Matrix algebra is commonly used when electronic computers are programmed to perform calculations (Leontief, 1966).