Managing the Game: Game Theory in Management Science

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Game theory can be applied to many different fields – simply as a tool of mathematical economics. Because its major use is to model competing behaviors of involved parties, game theory’s particular combination of behavioral economics and industrial organization theory lends a great deal to management science. Shubik states that game theory is “a method for the study of decision-making in situations of conflict”, and that the process usually “involves individuals with different goals or objectives whose fates are interlocked” (40). This ultimately means that the manager (the ‘decision-maker’) must account for these differing goals and objectives to make the best decision for the form, organization, or group. In essence, the manager “faces a cross-purpose maximization problem” (Shubik 45). This is exactly why game theory can be useful as a trend in management.

The management science approach to game theory (also known as “quantitative measurement approach”) is regarded as a problem-solving mechanism, using the aid of mathematical tools and techniques. Operations research, mathematical tools, simulations and models are the basic methodologies to solve managerial problems. This assumes that every activity in operational management can be quantified through data and mathematical symbols, while at the same time allowing for some aspects of human behavior (which cannot always necessarily be quantified). Using game theory in this approach has provided an “exactness in management discipline” by developing an orderly thinking in analyzing and understanding management (Myerson 9). While applying game theory in management does make for a more exact understanding of strategy, the fact that one must account for the ‘human factor’ means that its application is relatively limited.

Usual papers on game theory are in the abstract – presenting a game approach to a specific economic situation. So what is the best use that this information should be put? Economists and business academics suggest two primary ways of turning abstract game theory to application: descriptive and prescriptive. A descriptive approach describes how individuals and organizations act, while a prescriptive approach suggests the way in which they should act. This second abstraction-turned-application is the most important for “managing the game” in management science. As Smith states, “decision analysis is primarily a prescriptive discipline, built on normative and descriptive foundations” (561). This prescriptive approach focuses on helping people “make better decisions” using normative understanding, while at the same time accounting for the limitations and realities of human judgment.

While there is not room here to explore the application of game theory to management science in depth, there are several illuminating examples that give a picture of the usefulness of game theory. The first is shown by Charnes and Cooper’s examination of zero-sum games in management. The authors discuss a hypothetical advertising campaign, where the goals of the competing firms are “diametrically opposed” – what one gains, the other loses (Charnes and Cooper 46). Using game theory, both firms put their money into television advertising, because they have no other choice – the result being “that they make the same as they would if neither advertised, but neither can risk not advertising” (47). This is perhaps the most extreme form of a zero-sum game: both parties must act, even without a gain, to keep the opposing party from gaining instead.

Another brief example of game theory in management decision-making is that of statistical decision-making in games of incomplete information. Shubik discusses the cost of gathering and formulating information so as to inform a decision strategy. While it costs money, it also “cuts down on the possibility of making a wrong decision” (Shubik 49). These are made in situations that John Harsanyi deems games of incomplete information – “in which some or all players lack full information about some essential features of the game, including perhaps knowledge about the other players’ payoff function or available actions” (Harsanyi 161). In other words, players must either engage in statistical decision-making if they are going to do more than make a ‘best guess.’ It is this type of “game” in which most management decisions are made – in advertising, competitive bidding, and negotiation in business (Smith 569).

A final aspect of game theory important to management science is the differentiation between “is” and “ought”, as discussed by Joseph Kadane and Patrick Larkey. The authors stipulate that both prescriptive and predictive theories are necessary to management, but that researches clearly define which theoretical type and purpose they are using (1367). This distinction is important because it has to do with describing to managers what the norms are versus prescribing to managers what action they ought to take. Kadane and Larkey state that the ultimate goal of game theory in regards to management science is to “strive for more prescriptively useful theories and more predictively useful theories, recognizing that these theories are apt to differ from each other” (1377). This is exactly what is useful to managers: predicting others’ actions while at the same time prescribing to them what actions to take. Defining and differentiating these two is the benefit of game theory to management science.

Works Cited

Charnes, A., and Cooper, W. W., “An Example of Constrained Games in Industrial Economics,” Econometrica, 22, October 1954, p. 526.

Harsanyi, J. C. “Games with Incomplete Information Played by “Bayesian” Players. Management Science. 14 (3, Theory Series) 1967. 159-182.

Kadane, Joseph & Larkey, Patrick. The Confusion of Is and Ought in Game Theoretic Contexts. Management Science. 29 (12). Dec., 1983. Pp. 1365-1379.

Myerson, Roger. Game Theory: Analysis of Conflict. Harvard University Press. 1991.

Shubik, Martin. “The Uses of Game Theory in Management Science. Management Science. 2 (1). Oct., 1955. Pp. 40-54.

Smith, James & von Winterfeldt, Detlof. “Decision Analysis in ‘Management Science’”. Management Science. 50 (5). May, 2004. Pp. 561-574.