When behavior matters: Games and computation in A Behavioral Theory of the Firm

Journal of Economic Behavior & Organization Vol. 66 (2008) 74–94

When behavior matters: Games and computation in A Behavioral Theory of the Firm Michael J. Prietula a,∗ , Harry S. Watson b a

Goizueta Business School, Emory University, 1300 Clifton Road, Atlanta, GA 30322-2710, United States b Department of Economics, George Washington University, 1922 F Street, NW, Suite 208, Washington, DC 20052, United States Received 1 January 2004; received in revised form 1 September 2006; accepted 1 December 2006 Available online 31 January 2008

Abstract A Behavioral Theory of the Firm presents a computational model of a duopoly that is based on observations of firm behavior and that incorporates a range of behavioral constructs. Because this model is starkly different from the traditional game-theoretic analysis of duopoly, it useful to compare the performance of a game-theoretic version of this model, shorn of all behavioral constructs, with the original Cyert and March paradigm. To do this we calibrate the game-theoretic model with all the economic components of the computational model, and we assume that firms could choose either cooperative or non-cooperative strategies. We find that the pricing strategy of the computational firms is similar to that found in a non-cooperative game-theoretic outcome and that the advertising choice of the computational firms is less than what noncooperative or cooperative game-theoretic behavior would predict. We also consider how initializing the choices of the computational firm with those of the game-theoretic firms affects their performance. Informing the computational firms in this way led to greater changes in advertising than pricing strategies; profits of the computational firms were greatest when their initial choices were those found in a non-cooperative equilibrium. © 2008 Elsevier B.V. All rights reserved. JEL classification: L13; L21 Keywords: Organizational modeling; Behavioral theory; Computational organization theory

∗

Corresponding author. Tel.: +1 404 727 8761; fax: +1 404 727 2053. E-mail addresses: [email protected] (M.J. Prietula), [email protected] (H.S. Watson).

0167-2681/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jebo.2006.12.005

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1. Introduction The duopoly model presented in the first edition of A Behavioral Theory of the Firm (Cyert and March, 1963) is interesting for historic and theoretical reasons. We suggest that there are three primary reasons that explain the uniqueness and relevance of this work. First, it defines a set of behavioral constructs and decision rules in terms of a coherent, general model of a firm’s price and output determination in a duopoly setting. In essence, it both describes a market and demonstrates how firms make certain economic decisions in that market. However, the decisions of the firms are influenced not solely by pure economic goals, but substantially augmented by aspirations and heuristic procedures resulting from conflicting goals, negotiation, accommodations to local interests, and constrained (informational and computational) resources. Second, the underpinnings of the model, as well as its parameter values, were determined by analysis of an actual duopoly. Finally, the form of their model itself was computational: it was instantiated as a computer program.1 Oligopoly theory then and now tends to specify theory as mathematical models that make simplifying assumptions that permit inferences based on coherent mathematical techniques, primarily game theory. A tradeoff exists between the robustness of the model and the power of the inference mechanisms. To the extent that the model sufficiently captures the parameters of interest, the mathematics can generate specific equilibria values. For the most part, oligopoly theory is specified at an abstraction level that does not (purposefully) encompass behavioral constructs of consequence.2 Organization theory often takes a slightly different route and purports behavioral mechanisms that can account for observations at the more macro level. Rather than presumed static or assumed away, behavioral mechanisms in organization theory are fundamental constructs underlying organizational choice. Although it is occasionally suggested that game theory affords organizational science important tools for research (Murnighan, 1994), the assumptions and simplicity of the models can quickly erode the capacity to accommodate behavioral components. Although the application of game theory has indeed led to insights into the manner in which firms may strategically interact (Tirole, 1988), it does so at the cost of limited reality in the inability to incorporate the findings (or the goals) of behaviorist researchers in organizational science. To the extent that behavioral constructs matter (e.g., either in effect or in characterization), the assumptions of game theory simply violate the observations of behavioral constructs, except under the simplest of assumptions (which is not to deny the potential importance of simplicity). Regarding the second observation, there is a rich history in science of documenting processes underlying complex phenomenon, and this necessitated explaining a theory of firm behavior as an organization. Cyert recalled this conclusion and the role that James March played: I had been doing quite a bit of reading and had come to the conclusion that we weren’t ever going to get a theory of how oligopoly priced without going inside the firm. And I talked with Jim about it and thought that we might be able to do something with organization theory and oligopoly theory. . . .that became the basis of deciding to do a book together. . . . That is really how the Behavioral Theory of the Firm got started. (Augier and March, p. 6–7) 1

For an excellent summary of the general theory and its implications, see Augier and March (2002). Of course, there is an argument sometimes promoted that at this level there are no behavioral constructs of consequence, as any variation is absorbed by economic assumptions. 2

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Accordingly, the model is based on an extensive study of a department in a large retail department store that competes in a duopoly. The general form of the decision processes (for sales, production, and pricing) was deemed to be sufficiently similar across organizations. Thus the organizational mechanisms contributing to process were defined. Finally, the use of a computational model is interesting. Because of the mathematical intractability of theoretical constructs and behaviors under investigation, concerns certainly existed regarding the validity and usefulness of game-theoretic approaches in explaining much of actual firm decision-making. At the time computer models of organizational science were rarely considered, much less rendered.3 However, Cyert and his students and colleagues were involved in exploring this approach within the context of organizations (e.g., Cohen and Cyert, 1961; Cyert et al., 1956; March, 1962; Simon, 1955, 1979). A realistic rendering of oligopolistic behavior would provide a unified treatment of the way in which firms deal with major incentive/contractual problems and would take into account the informational/computational aspects of the firms’ environment. Most of the economics literature on the incentive/contractual problems facing firms does not focus on oligopolistic firms per se, though most of the results could be applied to these firms.4 Given the complexity of the issues, it is not surprising that, until recently, most of this research examines specific incentive or contractual problems in isolation. For example, Williamson (1988), Grossman and Hart (1986), and Hart (1995) examine contracting problems that arise with asset specificity and the potential for opportunism; Jensen and Meckling (1976) and Holmstrom and Tirole (1993) consider the interaction between the firm’s financial structure and managerial performance while Milgrom and Roberts (1992) provide an extensive discussion of how the structure of compensation schemes affects employee performance. Holmstrom and Milgrom (1994) have used a principal-agent framework in which agents have multiple tasks to broaden the analysis of incentive and contractual problems within a firm, and Spulber (1999) has suggested that many of these issues are resolved by the intermediation efforts of firms and provide a useful review of much of this literature. While efforts have been made to broaden the modeling of firm behavior to take into account the range of incentive/contractual problems that firms face, there is still no paradigm that addresses a realistic collection of these problems while, at the same time, providing an explanation of how firms might: • learn about the possible strategies that might be used to deal with these problems, • generate the information needed to develop optimal strategies, and • cope with the computational burden of implementing the strategies. Most research that focuses of these sorts of informational/computational issues require a number of simplifying assumptions to ensure tractability. In many cases the specific strategy is given, and the model describes how firms might calibrate this strategy. For example, results concerning the process by which firms in an oligopoly might resolve uncertainty about demand for their output, assuming market demand is linear and that the firms are Cournot competitors, are discussed by Kirman (1995), and Gallego (1998) presents some experimental results concerning learning by oligopolists in similarly structured environment. Friedman and Mezzetti (2002) model dynamic 3 Early work on the use of computer models and decision-making was being reported (e.g., Forrester, 1958), but these did not embody specific organizational concepts. An analysis of the historical roots of C–M is given in Augier and Prietula (2006). 4 A good overview of the oligopoly literature can be found in Tirole.

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oligopoly behavior by allowing firms to adjust their expectations to reflect past choices of competitors, an approach that allows them to incorporate optimization within a framework of bounded rationality. Chiarella and Szidarovszky (2004) model dynamic oligopoly behavior when a firm’s observations about the price and output decisions of its competitors occur within a time lag and show that such lags can affect the stability of the oligopoly. Finally, Cyert et al. (1995) suggest how the organizational structure of firms within an oligopoly may affect the degree of price competition. However, more general models that deal with all the hurdles listed above have yet to be developed in the economics literature. Thus we turn to a behavioral approach. A behavioral approach is not considered more appropriate, but an alternative. If behavioral constructs are essential to a particular theory (as explanatory variables) but cannot be sufficiently modeled under the standard economic paradigm, we propose that a computational approach may be used to instantiate and test such constructs for equilibrium-like results, an observation that has been discussed in economics for over 40 years (Cohen and Cyert, 1961; Shubik, 1958, 1960). Nevertheless, the motivation of this paper is not to articulate the potential limitations of economic approaches, but to explore certain differences (if any) between a game-theoretic and a behavioral rendering of the famous duopoly described in A Behavioral Theory of the Firm (Cyert and March). If there are differences (or commonalities) in the underlying theories of performance, be they behavioral or economic, then perhaps we can provide evidence by comparing them. One means of exploring the difference is to contrast the predictions (as equilibria) of a gametheoretic model and the performance (as decision values unfolding over time) of a computational model capable of realizing the behavioral constructs in the context of a firm’s economic decision making. The predictions and decisions of the models in this paper, center on the determination of prices, advertising expenditures and (consequently) profits. In general, a game-theoretic approach predicts that one of two strategies (at the extremes) may be pursued: non-cooperative or cooperative (Tirole).5 Solutions to a game-theoretic model will yield the equilibrium price and advertising levels appropriate for the relevant strategy choice, but subject to rather heroic computational and informational assumptions. A game-theoretic approach offers little to discuss regarding endogenous organizational constructs of interest to many behavioral-oriented organizational scientists, as the processes by which decisions are made are not part of the normal form of the game.6 Together, and with the data provided in the original reference, these afford sufficient information to express both a game-theoretic and a behavioral-computational formulation of the firms and their market behavior. On the other hand, a behavioral model of firm decision making focuses (but not exclusively) on the endogenous factors comprising the elements of the theory of internal events accounting for the same price and advertising choices. The assumptions underlying this approach are more realistic (in terms of the informational and computational assumptions), but are also more complex in terms of the underlying phenomena to be explained and modeled. Equilibria are not computable; rather, complete (or sufficient) process models must be created that realize the theory and generate data regarding choices over samplings of dynamic firm interaction. 5

Note that when they both choose a cooperative strategy, their choices mirror those of a monopolist. Exceptions are oligopoly models that include some aspects of agency in a dynamic framework (e.g., Cyert and Kumar, 1995). These come closest to embracing some aspects of organizational/behavioral issues, but are still simplified and restrictive in their assumptions in the interest of mathematical tractability (well-defined uncertainty, no learning, few behavioral constructs, firms are fully informed in an equilibrium). 6

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Two types of outcomes comparing price, advertising, and profit from the game-theoretic and computational-behavioral forms may be interpreted as follows. If the differences in the outcome measures (i.e., price, advertising, and profit) are significant, either the game-theoretic model is insufficient in capturing aspects of the critical decision making events of the firm as reflected in the behavioral procedures, or the computational-behavioral model is insufficient in explaining the events underlying the economic choices of the firm. On the other hand, if the differences are insignificant, then either the computational-behavioral model supports and explains the gametheoretic model, or both formulations are incorrect. It is the purpose of this paper to explore these issues by investigating a behavioral model well known to organizational science: A Behavioral Theory of the Firm (Cyert and March). The paper is organized as follows. We first describe a game-theoretic interpretation of the Cyert and March duopoly model. Necessarily, behavioral constructs are eliminated and simplifying assumptions are made, but this allows us to determine equilibrium strategy choices. Next, we describe a computational interpretation of the Cyert and March model that incorporates behavioral constructs eliminated from the previous formulation and similarly generates price and advertising outputs over time. Using parameter values from the original study, we then determine the game-theoretic equilibria for price and advertising, as well as the price and advertising values generated by the computational-behavioral model (over a series of simulations), and compare the results. We show that the price behavior of the behavioral-computational model approximates the non-cooperative equilibrium price of the game-theoretical model, but advertising expense is significantly less than either. We then show how the computational-behavioral model could be influenced by initial conditions reflecting the equilibrium prices and advertising for cooperative and non-cooperative strategies. While the game-theoretic firm is far more informed than its computational-behavioral counterpart, the relative difference in information is more pronounced for advertising than for pricing strategies. As will be seen below, the computational-behavioral firm is relatively more informed about a competitor’s pricing strategy than a competitor’s advertising strategy. In addition, the computational-behavioral firm will find it difficult to predict the impact of advertising outlays on sales. These limitations make advertising relatively less attractive from a strategic perspective, an outcome that is reflected in the computational-behavioral firm’s organizational routines: when goals are not met, the firm will be more willing to adjust its pricing strategy before considering a change in its advertising strategy. 2. The duopoly The general formulation of the Cyert and March duopoly (hereafter, C–M) is a fairly typical one and, as stated, was based on observations of an actual duopoly. The behavior that C–M observed was limited to a relatively brief time period, so their analysis was necessarily short run in nature. For this reason, issues involving the evolution of the duopoly, entry and exit of firms, changes in technology, and the like are not addressed. In the next section we describe the gametheoretic rendering of the C–M model. This rendering is shorn of all the organizational aspects of the C–M model but is sufficiently general to be calibrated with the all functional forms (such as firm demand), uncertainty, and the non-organizational strategies found in the C–M model. This rendering also follows the assumptions made in the C–M model concerning the structure of firm costs and the timing of firm decisions. In the subsequent section we provide an overview of the computational-behavioral version of the C–M model found in A Behavioral Theory of the Firm.

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2.1. Game-theoretic formulation Crafting a game-theoretic model of the C–M duopoly entails creating an analytical apparatus that describes the strategic behavior of firms, an approach taken in the economics literature. In this form C–M is necessarily shorn of its behavioral aspects; indeed, this rendering of the model implicitly makes the following assumptions: Assumption 1. Firms have full information about the environment in which they function. Assumption 2. The internal functioning of firms (how firms cope with agency problems, communication problems, or conflicting goals) can be ignored. These assumptions eliminate many issues that are of concern to organizational science. The first assumption eliminates the need to describe how the firm learns about its environment and how what is learned is communicated within the firm; the second eliminates the need to be concerned with the manner in which a firm’s objectives are set and how they are met. On the other hand, they do make game-theoretic analysis tractable, so much so that in describing a game-theoretic model it is easy to overlook their significance. The issue of note, then, is the extent to which behavioral elements account for added and substantial variance in explaining firm behavior. This interplay between realism and tractability can be appreciated by considering the economic environment in which the C–M firms function. The demand for the output of the C–M firms is assumed to depend upon the prices chosen by both firms as well as on their advertising efforts and expenditures. Specifically, demand for the output of Firm 1 that competes with a second firm in a duopoly, say, is some function D1 [p1 ,p2 ,A1 ,A2 ], where pi are the product prices of the firms and Aj are the firms’ advertising expenditures. In keeping with Assumption 1 above, the firms are blessed with the function Di ; exactly how each firm learns how a rival’s strategy choices, the pi and the Aj , will affect the firm’s sales is not specified. There is a unit cost of production, ci , that is assumed to be uncertain, but the form of uncertainty is, via Assumption 1, well-defined; that is, its mean, variance, and the like are known by the particular firm. It is also assumed that a production decision must be made before unit cost is known. Faced with these economic circumstances, each firm is assumed to maximize the expected discounted value of future profits, which for Firm i will be equal to  ∞   1 [(pi (t) − ci )Di [pi (t), pk (t), Ai (t), Ak (t)] − Ai (t)] E (1) (1 + ri )t t=0

where ri is the discount rate for Firm i. While this objective is a commonplace in the economics literature, it relies on both Assumptions 1 and 2. For example, it is possible that while the owners of the firm could agree to maximize discounted expected profits, they may not agree on the form of uncertainty in unit costs, so it would be difficult for them to reach agreement on how discounted profits would be determined. This difficulty is ruled out by Assumption 1. Similarly, if manager behavior cannot be monitored closely enough, it may be necessary to use some proxy for manager performance, such as sales, which would mean that the firm’s objective might be a mix of profits and sales. However, Assumption 2 rules out the presence of agency problems so that this issue can be ignored. With these assumptions, it is possible to describe the strategy choices of the firms. It may be that they will choose strategies in a non-cooperative fashion, in which case each will choose a price and advertising level in every period to maximize the objective above. It turns out that this choice is quite straightforward: in each period Firm 1, for example, will choose pl and A1 to

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maximize profits, given the choices of Firm 2, or Firm 1 will maximize (p1 − Ec1 )D1 [p1 , p2 , A1 , A2 ] − A1

(2)

The simple nature of this result follows from the assumption that a production decision has to be made before unit costs are known, which means that c1 can be replaced by its expected value (Ec1 ). Furthermore, the strategies chosen by both firms will remain the same throughout time because per period profits are not a function of time. This choice of strategies also relies on Assumption 1 because in order for the firms to reach a non-cooperative equilibrium, each has to assume how the other will behave. Thus, it is not enough for each firm to be completely informed about its own circumstances; in addition each firm also has to be aware of the circumstances facing its rival if the firms are to reach a non-cooperative equilibrium. Alternatively, the firms may play cooperatively (collude and choose prices and advertising expenditures in such a way as to maximize their joint profits), which is the outcome that would be chosen by a monopolist. As is well known, such collusive arrangements tend to fall apart; the short-term gain from violating the collusive arrangement may offset the loss of future monopoly profits. However, provided the discount rate is not too large, such a collusive arrangement can be enforced with equilibrium strategies that entail punishments for violating the arrangement.7 In this equilibrium, no punishment is ever inflicted, and the firms earn monopoly profits. When the two firms cooperate, they will simply maximize the sum of the firms’ profits each period through the choice of pi and Ai . When firms behave non-cooperatively, their strategy choices will satisfy the following four equations: (p1 − Ec1 )∂D1 =0 ∂p1 + D1

(Firm 1’s choice of p1 ),

(3a)

(p2 − Ec2 )∂D2 =0 ∂p2 + D2

(Firm 2’s choice of p2 ),

(3b)

(p1 − Ec1 )∂D1 =0 ∂A1 − 1

(Firm 1’s choice of A1 ),

(3c)

(p2 − Ec2 )∂D2 =0 ∂A2 − 1

(Firm 2’s choice of A2 ).

(3d)

Solving these four equations simultaneously determines the four strategy choices of the firms. When firms behave cooperatively, the strategy choices are determined by conditions that are similar to those above, except that in this case each firm internalizes the impact of its choices on the other firm, (p1 − Ec1 )∂D1 (p2 − Ec2 )∂D2 + =0 1 ∂p1 ∂p1 + D

(Choice of p1 ),

(4a)

7 Cyert and Kumar show that in all oligopolies with a range of price and non-price strategies, such collusive equilibria cannot be sustained. Given the more complex nature of the C–M model, however, it is not clear that their results apply. For this reason, we consider the two extreme forms of strategic behavior.

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(p1 − Ec1 )∂D1 (p2 − Ec2 )∂D2 + =0 ∂p2 + D2 ∂p2

(Choice of p2 ),

(4b)

(p1 − Ec1 )∂D1 (p2 − Ec2 )∂D2 =0 + ∂A1 − 1 ∂A1

(Choice of A1 ),

(4c)

(p2 − Ec2 )∂D2 (p1 − Ec1 )∂D1 =0 + ∂A2 − 1 ∂A2

(Choice of A2 ).

(4d)

The solution of this set of equations determines the equilibrium cooperative strategy. The cooperative and non-cooperative solutions described here are clearly not the only gametheoretic descriptions of oligopoly behavior in the literature. For example, Sutton (1991) analyzes the evolution of oligopolies within the framework of a multistage game in which advertising may affect entry decisions. While such an approach could yield additional insights, it would be difficult to apply to the current C–M model. The C–M model is short-run in nature with a fixed number of firms, and does not hypothesize any industry dynamics, but it would be interesting to augment the model so that Sutton’s approach could be applied in a way that would ensure comparability with the duopoly envisioned by C–M. 2.1.1. Determining equilibria Using the parameter values and functional forms assumed by C–M, it is possible to solve for these strategies. C–M assumes that the demand for, say, Firm 1’s product takes the form d3 d4 D1 = s1 [d0 WSEPd2 1 WPCL1 WPEX1 ]

(5)

where s1 is the share of the market for Firm 1, WSEP is a market share weighted average of sales effort (sales effectiveness pressure, a quantified behavioral construct),8 WPCL is a market share weighted average of the prices charged by the firms, WPEX is a market share weighted average of advertising expenditures, and d1 are constants. The market share for Firm 1 is determined by s1 = w1 RSEP1 − w2 RPCL1 + w3 RPEX1

(6)

where RSEP1 is advertising effort for Firm 1 relative to weighted total effort, RPCL1 is the price charged by Firm 1 relative to the weighted sum of the prices charged, RPEX1 is the advertising expenditure of Firm 1 relative to the weighted sum of such spending for both firms, and the w1 are constants. The same expression, adjusted for Firm 2’s relative measures, defines s2 . The variables WSEP and RSEP are organizational measures of advertising effort that have no counterpart in a game-theoretic construct. The values of these variables were set equal to the initial values chosen by C–M in their model and were assumed to be constant in the determination of the strategies above. Finally, for the unit cost variable, the distribution chosen by C–M was used in determining strategies.

8 Sales effectiveness pressure is a component of the general sales and marketing strategy, and refers to the “amount of sales effort” a firm will exert as a consequence of events (i.e., there are no decisions based directly on SEP). It is a construct that is separate from the dollar expenditures selected for sales promotion. Though organizational routines adjust SEP, it only affects market demand and market share. SEP and associated components could serve as foundations for additional routines that would address attention to market entry threats as suggested in longer-term models studying the evolution of oligopolies (Sutton).

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2.2. Computational formulation The rendition of the computational form starts with a significantly different set of initial assumptions than those articulated above: Assumption 3. Firms have limited information about the environment in which they function. Assumption 4. The internal functioning of firms (the sorts of organizational issues that arise with moral hazard and agency problems) must be accommodated. Assumption 5. Firms have limited computational resources to addresses 1 and 2. The third assumption states that C–M firms do not have access to (or cannot learn about in sufficient time) all the information in the environment required to make informed strategic decisions. Thus, the normal form of the game is neither known to nor learnable by either firm. For example, in the game-theoretic model, a firm knows the form of demand, uncertainty, and the like and can choose strategies based on this information. In contrast, the C–M firm’s information is limited to what it observes about its sales and the price charged by its competitor. Lacking information about the form of the demand and uncertainty, the C–M firm relies on routines, as described by C–M, to evaluate this limited information and to reach strategic decisions. The fourth assumption states that firms have competing or conflicting goals relevant to the strategy choices of the firm, and it matters how they are set, reflecting the existence of influential endogenous behavioral factors such as power and politics (March, 1962). The fifth assumption states that the amount of resources (e.g., information, computation, time) brought to bear on organizational decisions under Assumptions 3 and 4 are bounded, which means that there are necessary structural accommodations (goal/task decompositions, goal/task divergences, restricted information flows) within the organization (March and Simon, 1993; Simon, 1969) with all of the subsequent sideeffects (e.g., conflicting goals). Once these assumptions are made, the C–M model is in the realm of behavioral analysis of duopoly models. The immediate consequence is that the economic environment for the computational form must realize both the economic and organizational constructs (thus including SEP) defined in Eqs. (5) and (6). Furthermore, there must be a concomitant realization of the internal decisions of the firm that embodies the above assumptions and can address the requirements of the economic environment. In order to realize the C–M model within this context, we make a representational presumption incorporated by Cyert and March: elements of C–M are cast as assemblages of computational routines.9 This assumes (in this case) a representational adequacy by a symbolic system; specifically, the economic and behavioral elements of C–M can be articulated as computational formalisms in the form of computer programs (Burton and Obel, 1995; Carley and Prietula, 1994; Prietula et al., 1998). To mimic the C–M descriptions, the behavioral rendering of the model makes four additional assumptions, most of which are concerned with formulating internal behavioral constructs and

9 In general, routines can be viewed as “patterned sequences of learned behavior involving multiple actors who are linked by relations of communications and/or authority” (Cohen and Bacdayan, 1994). In the original C–M model, these were refereed to as “standard operating procedures” or “rules” that, when examining the content, are functionally equivalent. As March (1988, p. 8) put it, “Much organizational behavior, including choice behavior, involves rule-following more than calculation of consequences (p. 8).”

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behaviors that account (directly and indirectly) for the economic choices in the market: price, advertising, and sales effectiveness. Assumption 4a. Firms have conflicting objectives and unresolved conflict arising from those objectives. The C–M model articulates the firm as a coalition of members (who may also be coalitions) having differing goals with a consequential lack of an overarching, universally defined (or accepted) organizational goal. Furthermore, the preferences of the coalitions may be incommensurable with any specified organizational objective function, thus creating conflicting goal aspiration levels and negotiated solutions (March, 1962). In the model, there are five basic goals affecting decision-making: profit, inventory, production, market share, and sales. Conflict, in the sense used by C–M, arises in a firm when the aspiration level of one goal (e.g., market share) may interfere with the achievement of the aspiration level of another goal (e.g., profit). Despite recent economic formulations on how to resolve or address such disparate preferences or goals, empirical observation, as well as conventional wisdom, underscores the likelihood of continued unresolved within-firm conflict (Hart, 1995; Holmstrom, 1982). Unresolved, however, does not mean unaddressed. Latent goal conflict can and does exist without significant disruptive effects to the organization, as goals are considered as relatively independent constraints. The reason for this is that conflict is structurally and temporally accommodated by three mechanisms: local rationality (decisions are decomposed, made relatively independently based on partial information, and related to explicit goals, such as sales, marketing, production, and so forth), sequential attention to goals (potentially conflicting goals arising from decomposition are not handled simultaneously), and satisfying routines (decisions and information regarding goals are made based on incomplete information, limited search, and limited computational resources). Assumption 4b. Firms will develop mechanisms to avoid uncertainty in their decision-making. Organizations face uncertainty on a variety of fronts (internal and external) and deal with it in a variety of forms, while even the distributions are rarely known (or even knowable). In actuality, organizations exhibit explicit and varied strategies that expect and avoid uncertainty without resorting to either ignoring it or redefining it as some sort of certainty equivalent (e.g., long-term contracts). The C–M incorporates uncertainty avoidance by (a) using routines that focus on short-run decision horizons, (b) using routines that react to problems arising from short-run feedback events, and (c) stabilizing the routines used for (a) and (b). Thus, uncertainty is avoided by the routines of C–M in two general forms. First, the fundamental nature of the suite of adapted routines associated with the determination of an aspiration level affords a “first-level” adapted form. These rely on short-term data and fluctuations in environmental uncertainty to be handled by the adjustments in parameter values or the engagement of alternative routines. Second, routines may detect, but not address, problems in the short-run. Rather, they signal the problem to be handled by other routines.10 Those other routines, however, similarly rely on simple decision rules, short-run data, and local information. 10 There are also general similarities between this (and other choice mechanisms in the C–M model) and aspects of the “garbage can” solutions (Cohen et al., 1972) in that explicit non-decisions or choice abrogation (oversight, flight) and attentional shifting is represented in the C–M routines.

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Assumption 4c. Search for solution alternatives is problem-driven and constrained. Search in the C–M refers to the firm’s attempts at finding a solution to a problem. A problem is defined locally in terms of a particular aspiration level (e.g., profit goal). The implication is that the “normal” suite of adapted routines has not been able to address the situation sufficiently, so alternative measures must be taken. Although the firm may have a set of possible solutions identified, there is uncertainty as to which solution is most appropriate.11 As a consequence, an explicit decision rule cannot (yet) be articulated and sustained. Therefore, the firm explores (i.e., searches) each solution as the need arises. This search is characterized as being simple (i.e., nonexhaustive and local) and biased (i.e., reflecting historical success or failure, reflecting preferred choice alternatives). Assumption 4d. Organizations exhibit learning. Though there is wide variance in the interpretations of organizational learning, in the C–M organizational learning is operationally defined as explicit adaptation in certain components of organizational decision-making. Lack of full information and non-insignificant information costs require an adaptive component sensitive to exogenous signals, but also linked to internal decisions (in response to exogenous signals). Specifically, the C–M addresses three types of adaptation in the theory of organizational decision-making: goals, attention rules, and search rules. Goals, for example, are adapted periodically as a function of prior performance (aspiration with respect to actual) and behavior (reflected in attention parameter). Consider the following adjustment rule for the profit goal (ProfitGoal) of a firm: Profit Goalt = (1 − β1 )Profit Goalt−1 + β1 Profitt−1 . Adjustment of the current profit goal (at time t) is a joint function of the prior goal (at time t − 1), the prior profit (at time t − 1), and an attention parameter (β1 ) for both. Attention parameters such as β1 , are adjusted via attention rules in an attempt to link prior decisions to subsequent effects. A firm learns through experience to reduce or to increase its attention to recent success through such rules. Consider an associated attention rule for β1 , if{Profit Goalt > Profitt } then β1 = β1 + η(1 − β1 ). Note that the contingent adjustment of the attentional parameter β1 is augmented by a learning parameter, η, controlling the rate at which β1 is adjusted (the attentional response given to recent goal achievement). Search alternatives are also influenced by their success or failure, but in a less rigorous manner. By its very nature, search in the C–M model is the weakest form of organizational problem solving and relies simply on a generated set of possible solution alternatives, systematically sampling from set and adjusting their likelihood of application according to their success or failure (Levinthal and March, 1981).12 11 This could also reflect unresolved organizational conflict, a fundamental condition of firms defined by multiple actors with inconsistent preferences (March, 1962). 12 By “weak form” we mean less specified and less integrated into rigid organizational procedures. Metaphorically, this is the form of organizational problem solving that is similar to the weak methods of individual problem solving, where the methods are more generally applicable and transferable, but less powerful within the context of a particular application (Newell, 1969). Interestingly, by not committing to a rigid rule or rule set, this search mechanism is also a flexible method that can incorporate additional solutions easily and even benefit from diverse and random application of solution components, depending on the nature and stability of the decision landscape and conditions (see Solis and Wets, 1981).

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Table 1 Economic equilibria and initial values for C–M simulation Firm values

Non-cooperative equilibrium

Cooperative equilibrium

C–M initial conditions

Unit price ($) Advertising ($) Period profit ($)

1.2 632.9 341

0.8 334 779.5

1.0 100 100

3. Comparative analysis As noted, one means of exploring the difference between the standard economic paradigm of oligopoly behavior and a more behavioral rendering of oligopoly behavior is to contrast the predictions of the game-theoretic form with the performance of the computational-behavioral form of the C–M model. This comparison provides an indication of how closely the gametheoretic paradigm can predict organizationally complex (and internally influenced) strategy, how effectively organizational issues (such as structure and decisions) can predict price strategy choices and profit levels in an oligopoly, and how a game-theoretic rendering can inform a behavioralcomputational one. Using the parameter values and functional forms assumed by C–M reported in Cyert and March, it is possible to solve the game-theoretic model for the equilibrium cooperative and noncooperative strategies, as discussed in Section 2.1. As noted, the variables WSEP and RSEP are organizational measures of advertising effort that have no counterpart in a game-theoretic construct. The values of these variables were set equal to the initial values chosen by C–M in their model and were assumed to be constant in the determination of the strategies. Finally, for the unit cost variable, the distribution chosen by C–M was used in determining strategies. The equilibrium values for both the cooperative and non-cooperative cases were determined by solving the simultaneous systems of Section 2.1 with the equation solving routine in Mathcad 6.0, which relies on a modified Newton method to seek solutions. The equilibrium conditions indicate that as long as firms face the same expected cost shock (as assumed by the C–M model), they will choose symmetric strategies whether they compete or collude. These conditions, when calibrated with the parameter values chosen by C–M (e.g., d1 and w1 terms), suggest that when firms play non-cooperatively, each will choose a price of $1.20 and a per period level of advertising expenditures of $600. On the other hand, if they play cooperatively, each will choose a price of $0.80 and a per-period level of advertising expenditures of $330.13 With these strategies it is possible to determine the output levels as well as actual profits (that is, profits for a given cost shock) for any time period. The results of the derivation of the equilibria determined under these two strategies are shown in Table 1, along with the initial values of the original C–M model. A computational version of the C–M model was written in Excel and defined two firms interacting over 50 periods, each period corresponding to one month, which parallels the period duration of the original model. C–M incorporates 96 parameters; details of the parameter values, distributions, and routines can be found in Cyert and March and are also available upon request from 13 In more elementary models of oligopoly that do not permit advertising, the cooperative pricing strategy will always exceed that found with competition. Once advertising strategies are allowed, this result will generally be reversed, as it is here. In effect, firms that cooperate will be able to avoid costly advertising wars and, by choosing a lower price, significantly increasing their sales.

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the authors. An overview of the C–M model is given in technical appendixAppendix B. The C–M firms in the duopoly had identical routines (that is, they were organizationally equivalent), and their cost shocks were drawn from independent and identical distributions. Embedded in the routines were 96 variable parameters representing elements of the decision processes (e.g., attentional parameters, search states), decision results (e.g., suggested profit levels), intermediate states (e.g., temporary profit decisions), past states (e.g., profit level last period, decision made last period), signals (e.g., significant price changes by competitor), and market components (e.g., demand, exogenous shocks). Furthermore, all the parameter values in the organizational routines and the values used to calibrate the model were those originally chosen by C–M. It should be noted that while the C–M firms are identical from the organizational perspective, they will not generally choose the same strategies in each period because their cost shocks will not be the same. To provide a reasonable sample of behavior for the C–M firms, each 50-period simulation was run 26 times using different cost shock vectors randomly selected and applied to the two firms. 4. Results As each C–M firm makes 50 choices of price and advertising within each simulation run, it is necessary to create some summary measure of these strategies. In the case of the pricing strategy, the per period market share weighted price, averaged over 50 periods, was used as the measure of the C–M firms’ pricing strategy, while for advertising expenditures, the 50 period average of these expenditures for both firms was used as a summary measure of C–M advertising strategy. The results of the pricing comparisons are shown in Fig. 1, and the advertising comparisons are shown in Fig. 2. The data in Figs. 1 and 2 indicate that the C–M firms are not consistently choosing to behave in a manner that the game-theoretic paradigm might suggest. Were the firms to cooperate, the pricing strategy predicted by the game-theoretic paradigm is significantly below (z = 3.1) that chosen by the C–M firms (Fig. 1, cooperative); conversely, the advertising strategy predicted by the game-theoretic paradigm is significantly above (z = −35.5) that chosen by the C–M firms

Fig. 1. C–M simulation results plotted against non-cooperative and cooperative price equilibrium values.

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Fig. 2. C–M simulation results plotted against non-cooperative and cooperative advertising expenditures equilibrium values.

(Fig. 2, cooperative). Were the firms to compete, the pricing strategy predicted by the gametheoretic paradigm is not significantly different (z = −1.56) from that chosen by the C–M (Fig. 1, non-cooperative), but the level of advertising is indeed significantly higher (z = −10.14) than that chosen by the C–M firms (Fig. 2, non-cooperative). The results above clearly indicate that the C–M firms are not cooperating. For the noncooperative case, the game-theoretic paradigm comes close to predicting pricing strategies but fails to predict advertising strategies. The impact of the predictions of the game-theoretic paradigm becomes more apparent if one considers a measure of firm performance. One obvious measure of performance is the level of discounted (50-period) profits of the firms, which was calculated for each simulation. Similar profit figures were generated by the game-theoretic strategies using the same cost shocks as those used by the C–M firms. The results are shown in Fig. 3. According to the game-theoretic paradigm, were the firms to compete they would each make, on average, about 300% more in discounted profits than a C–M firm. Were they to cooperate, the level of discounted profits would be about 700% greater.

Fig. 3. C–M simulation results plotted against non-cooperative and cooperative discounted profit per firm.

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4.1. Will strategic information matter? The differences in strategic choices between the C–M behavioral model and its game-theoretic counterpart are, in part, results of the differences in the information firms are assumed to possess. By providing the C–M firms in the behavioral model with the initial strategic choices predicted by the game-theoretic paradigm, it is possible to observe how valuable this information is and how it might affect behavioral outcomes were the firms able to learn more over time. In order to this, a second series of analyses was conducted among three sets of C–M simulations as follows. The first set consisted of the results from the 26 C–M simulations used in the prior analyses. In this set, the initial values of the C–M simulation were obtained from those used in the original C–M study. Two other sets were defined by choosing the initial conditions suggested by the game-theoretic model for either cooperative or non-cooperative behavior. Each set consisted of 26 simulations using the same cost shock vectors of the C–M simulations in the prior analysis. The purpose of this analysis was to determine whether knowledge of game-theoretic equilibria (represented as initial choices for these parameters) could differentially impact the results of subsequent firm decisions. In short, and in context, it was a test to see whether gametheoretic equilibria could inform the firm’s decisions. The results of these simulations are shown in Figs. 4 and 5. When the C–M firms start with the initial values they would have if they were playing cooperatively, they choose a pricing strategy (Fig. 4) that is not significantly different from the original C–M simulation (z = 1.20). However, these firms choose an advertising strategy (Fig. 5) that is significantly higher than the original C–M values (z = 6.18) and are equivalent to the cooperative level predicted by the game-theoretic model (z = −2.63). When firms start with the initial values they should have were they playing non-cooperatively, prices are significantly higher (Fig. 4) than in the original C–M simulation (z = 4.39) and differ significantly from the non-cooperative price predicted by the game-theoretic paradigm (z = 4.49). Similar to the cooperative results, the non-cooperative advertising levels (Fig. 5) are significantly higher than the original C–M simulation (z = 6.18), and approach the cooperative prediction, but remain substantially less than the non-cooperative prediction.

Fig. 4. C–M informed simulation results for price decisions, showing non-cooperative (NCE) and cooperative (CE) equilibria.

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Fig. 5. C–M informed simulation results for advertising expenditures, showing non-cooperative (NCE) and cooperative (CE) equilibria.

Apparently, even when the C–M firms begin competing with the information that economists assume that they should have, their subsequent behavior does not strictly follow the game-theoretic paradigm, but it is informed by it. As in the earlier analysis, the effects of the influences of the game-theoretic paradigm are illustrated by a comparison of discounted profits for the firms. The discounted profit profiles for the three sets of simulations, each over 50 periods, are given in Fig. 6. Comparing the results in Fig. 6 with those in Fig. 3 indicates that, even when informed, the profits of the C–M firm are substantially below their game-theoretic counterparts (Kruskal–Wallis H(2,78) = 65.6, p < 0.001). The profit rankings in Fig. 6 clearly suggest that being informed with non-cooperative strategies dominates being uninformed and being uninformed dominates being informed with cooperative strategies. As cooperative strategies, at least in a game-theoretic setting, produce higher profits, one might expect that C–M firms to be most profitable if they were

Fig. 6. C–M informed simulation results for discounted profit over 50 periods.

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informed with cooperative strategies. However, profits are significantly higher than when the firm is uninformed (z = 6.11) or when the firm is informed with cooperative strategies (z = 6.18). 5. Discussion In this paper we have compared a game-theoretic model of oligopoly with a behavioralcomputational one. They are based, of course, on differing assumptions, but how and how far would these models diverge, and could the economic model differentially inform the computational model? The results are summarized in Table 2. In the game-theoretic model, cooperating firms would limit advertising but maintain demand via low prices; conversely, a non-cooperative equilibrium is characterized by higher prices with correspondingly higher advertising outlays. In comparison, the uninformed C–M firms choose a pricing strategy in line with the non-cooperative outcome but an advertising strategy that is more conservative than either game-theoretic prediction. The similarity of the of the C–M pricing strategy and the non-cooperative pricing strategy results from the characteristics of the C–M routines and the choices of the computational parameter values, values that Cyert and March selected on the basis of their observations of firm behavior. Specifically, changes in pricing strategy are primarily reactive: a C–M firm will alter its pricing strategy only in response to changes in price–cost margins, sales/profit performance, or its competitor’s pricing strategy (Appendix B), and, while changes in pricing strategy do not change frequently, the C–M firm is quite sensitive to its price–cost margin and avoids prices that are too close to what the firm perceives to be its expected unit cost. This sensitivity appears to create a lower bound on its pricing strategy. As firms are symmetric (apart from cost shocks), one might expect that this reactive behavior would allow prices to stabilize around a level that is sufficiently above the firms’ expectation of unit cost to meet sales/profit goals but not so far above that a firm might risk a significant drop in demand. These characteristics of the routines that alter pricing strategy, coupled with the parameter values chosen by Cyert and March, lead to a price strategy close to that found in a non-cooperative equilibrium. Though the pricing strategy of the C–M firm is similar to that found in a non-cooperative equilibrium, the level of advertising is far less. C–M firms are relatively less informed about the impact of advertising than the impact of prices on sales, which may make investments in advertising less attractive. Furthermore, when a C–M firm’s profit performance is not adequate, it begins a search over strategies, including cutting advertising outlays that will restore profits. This search procedure is affected by the success of previous searches so that if cutting advertising in the recent past has restored profits, this strategy is more likely to be invoked again. The combination of the uncertainty about the effectiveness of advertising and the willingness of the firm to rely on Table 2 Summary of informed simulation results compared to game-theoretic equilibria

Unit price ($) Advertising ($) Discounted profit ($)

Original C–M

Informed with non-cooperative

Informed with cooperative

Approximates NCE Less than CE, much less than NCE Less than NCE, much less than CE

Remains near NCE Increases, approximates CE

Remains near NCE Increases, approximates CE

Slightly increases, still much less than NCE, much less than CE

Slightly increases, still much less than NCE, much less than CE

NCE, non-cooperative equilibrium; CE, cooperative equilibrium.

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reductions in advertising when profit expectations have not been fulfilled suggests why advertising levels are relatively low. Not surprisingly, the profitability of C–M firms is less than their game-theoretic counterparts. The firms observed by Cyert and March do not focus solely on profits, are abysmally ignorant of their environment, and cannot avoid internal conflicts. When C–M firms are informed with either the cooperative or non-cooperative strategies, the resulting price strategy remains around the non-cooperative equilibrium value. As this value is close to what uninformed C–M firms choose, the absence of any significant change is not surprising. However, the cooperative pricing strategy is substantially below that chosen by uninformed C–M firms, so the lack of a response is more significant. The discussion above noted that pricing strategy is reactive and the C–M firm’s choice is likely to stabilize around a value that is sufficiently greater than a firm’s expectation of unit cost to satisfy the firm’s profit and sales goals. The cooperative initial values, though, include a price strategy that is much closer to the firm’s (unobserved) expected cost so that the firm, at times, will find that price falls below unit cost. When this occurs, the firm’s price adjustment routines will increase price enough to avoid this problem, and this leads to a higher price than that suggested by a cooperative strategy. C–M firms are relatively uninformed about the impact that advertising might have on sales, so information provided by the non-cooperative or cooperative strategies levels of advertising potentially could have greater consequences. Whether the firm is informed with the cooperative or non-cooperative initial values, advertising does rise significantly relative to the level chosen by an uninformed firm. It appears that the behavioral routines do allow the firms to recognize the benefits of non-price in expanding the industry’s market (Porter, 1980). This is borne out by a comparison of market demand in the three regimes: the ordering cooperative > non-cooperative > C–M original is statistically significant (Scheffe’s, ρ < 0.001). While advertising does rise with additional information, it is close to the level suggested found in a cooperative equilibrium, which is less than the level found in a non-cooperative equilibrium. Thus, while the C–M firm’s informed pricing strategy is generally close to the non-cooperative equilibrium value, its informed advertising is closer to the equilibrium cooperative advertising strategy. This seeming inconsistency is explored in the discussion of profits below. The best measure of the value of information for the C–M firms is profit. There are several reasons why being informed may not have a significant impact on profit. In particular, having the “correct” initial values does not translate immediately into the realization that these will maximize profits. Even if a firm believes that its rival is playing the right strategy (that is, even if one ignores strategic interaction) the routines through which firms cope with their environment have not been changed. For example, the initial values are predicated on knowledge of expected unit cost (i.e., firms simply use the expected value of unit cost in their determination of the optimal strategies), but in the C–M computational model this information is not available. Indeed, the firms have routines that are designed to cope with ignorance about unit costs, and these routines have not changed character simply because initial values have been changed. Even if the firms knew that they should be focusing on expected unit costs, they would have to learn its value, which would take a number of periods to do, and a substantial change in behavior. The “correct” initial values do not eliminate conflicts among the firm’s goals. For instance, in the cooperative case the initial price is quite low, which would appease those coalitions that are interested in sales. However, a low price combined with uncertainty concerning the correct level of inventories can have disastrous consequences for profits, which would create conflicts with those coalitions who focus on profits. The choice of the appropriate initial values does nothing to

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resolve these conflicts, so it would not be surprising to find that these choices yield poor results in terms of discounted profits. Both these factors can be used to explain differences among the informed C–M models. For example, consider the case when the C–M firms are given the appropriate initial values suggested by cooperative strategic behavior. In this case, the firms should choose a low price and limit advertising expenditures: the firms avoid the waste of competitive advertising but maintain demand with a low price. This strategy fails to perform well because firms do not know enough about their environment to appreciate the value of this strategy. Because C–MC–M firms know little about how sales will respond to these initial choices, they cannot predict future sales and therefore cannot determine the appropriate future output levels. Consequently, the cooperative strategy is not coupled to the correct output strategy, and firms find that sales and output do not match, leading to significant variations in inventory and concomitant holding costs. The variability in holding costs leads to variability in the gap between price and unit cost. From the firms’ perspective, this appears to be a risky strategy. Routines are used to lessen this uncertainty and these routines are sufficiently conservative that the firms move away from the choices that would, if they were fully informed, provide greater profits. The cooperative initial values also create some conflicts among the firms’ goals, conflicts that, in combination with the firm’s ignorance, move the firm away from the sorts of decisions that they should be making were they to maximize profits. The low initial price leads to heightened expectations with regard to sales goals, and these expectations cannot be maintained unless prices remain low. This pressure to keep prices low would appear to be exactly what the cooperative strategy requires, but this is true only if all the other decisions of the firm follow what is entailed by the cooperative strategy. As suggested above, the decisions of the firm do not support the cooperative strategy. In fact, ignorance and the resulting uncertainty induce the firm to adopt conservative output strategies, which perform poorly when the firm chooses a low price. Because the firm faces a relatively narrow gap between price and unit cost, it cannot generate significant profits unless sales are high, and while demand is high, sales are not realized because the firm’s output is too limited. 6. Conclusion While there appears to be a significant disparity between the way C–M firms behave and the way economists might expect them to behave, it might be argued that this disparity would disappear over time as firms learned more about their environment and their rival’s behavior. Put differently, when confronted with these results, an economist might suggest that if the C–M firms were allowed to interact over a sufficiently long time period, their behavior might come to resemble that predicted by the economic paradigm. As the C–M model was never intended to describe firm behavior in the long run, it is difficult to determine whether this convergence would occur. However, it is reasonable to ask whether changes in the behavioral routines (changes that preserve the structure envisioned by C–M but which permit variations in weights assigned to goals and the speed at which goals are reassessed, for example) would produce results more in line with the economic paradigm over the short run. In the same vein, creating asymmetries between some aspects of the firms’ behavioral routines beyond those considered by C–M might yield interesting comparisons with what is predicted by the economic paradigm in the short run. These possibilities are the subject of ongoing research. We conclude with an observation regarding the role of computational models in organization theory. Computational modeling is but one method to gain insight into the complex nature of

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firm decision making and market performance. In addition to game-theoretic models, the use of human laboratory studies incorporating stylized firms in simple markets have generated interesting insights into market performance (Holt, 1995), as have observational studies (Cyert et al., 1956). All methods are based on somewhat differing assumptions, but all also have commonalities and the capability, at some level, to inform each other. The ability to inform researchers in different fields as well as to argue persuasively that a computational-behavioral approach to the analysis of the firm would provide valuable insights is one of the enduring aspects of A Behavioral Theory of the Firm. Acknowledgements We thank the anonymous reviewers for their input. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jebo.2006.12.005. References Augier, M., March, J. (Eds.), 2002. The economics of choice, change and organization: Essays in memory of Richard M. Cyert. Edward Elgar, Northhampton, MA. Augier, M., Prietula, M., 2006. Historical roots of the ABTOF model at GSIA. Paper presented at the Carnegie-Bosch Institute Forum on A Behavioral Theory of the Firm: Forty Years and Counting, 26–27 May 2006, Carnegie Mellon University. Burton, R., Obel, B., 1995. The validity of computer models in organizational science from model realism to purpose of the model. Computational and Mathematical Organization Theory l, 57–71. Carley, K., Prietula, M. (Eds.), 1994. Computational Organization Theory. Erlbaum, Hillsdale, NJ. Chiarella, C., Szidarovszky, F., 2004. Dynamic oligopolies without full information and with continuously distributed time lags. Journal of Economic Behavior and Organization 54, 495–511. Cohen, M., Bacdayan, P., 1994. Organizational routines are stored as procedural memory: evidence from a laboratory study. Organizational Science 5, 554–568. Cohen, K., Cyert, R., 1961. Computer models in dynamic economics. Quarterly Journal of Economics 75, 112–127. Cohen, M., March, J., Olsen, J., 1972. A garbage can model of organizational choice. Administrative Science Quarterly 17, 1–25. Cyert, R., Kumar, P., 1995. Organizational strategy and tacit collusion in oligopoly with agency. Computational and Mathematical Organization Theory 1, 9–38. Cyert, R., March, J., 1963. A Behavioral Theory of the Firm. Prentice-Hall, Englewood Cliffs, NJ. Cyert, R., Simon, H., Trow, D., 1956. Observation of a business decision. Journal of Business 29, 237–248. Cyert, R., Kumar, P., Williams, J., 1995. Impact of organizational structure on organizational pricing. Journal of Economic Behavior and Organization 26, 1–15. Forrester, J., 1958. Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review 36, 37–66. Friedman, J., Mezzetti, C., 2002. Bounded rationality, dynamic oligopoly, and conjectural variations. Journal of Economic Behavior and Organization 49, 287–306. Gallego, A., 1998. Oligopoly experimentation of learning with simulated markets. Journal of Economic Behavior and Organization 35, 333–355. Grossman, S., Hart, O., 1986. The costs and benefits of ownership: a theory of vertical and lateral integration. Journal of Political Economy 94, 691–719. Hart, 0., 1995. Firms, Contracts, and Financial Structure. Oxford University Press, Oxford. Holmstrom, B., 1982. Moral hazard in teams. Bell Journal of Economics 13, 324–340. Holmstrom, B., Milgrom, P., 1994. The firm as an incentive system. American Economic Review 84, 972–991. Holmstrom, B., Tirole, J., 1993. Market performance and liquidity monitoring. Journal of Political Economy 101, 678–709.

94

M.J. Prietula, H.S. Watson / J. of Economic Behavior & Org. 66 (2008) 74–94

Holt, C., 1995. Industrial organization. In: Kagel, J., Roth, A. (Eds.), Handbook of Experimental Economics. Princeton University Press, Princeton, NJ. Jensen, M., Meckling, W., 1976. Theory of the firm: managerial behavior, agency costs, and ownership structure. Journal of Financial Economics 3, 305–360. Kirman, A., 1995. Learning in oligopoly: theory, simulation, and experimental evidence. In: Kirman, A., Salmon, M. (Eds.), Learning and Rationality in Economics. Basil Blackwell, Cambridge, MA. Levinthal, D., March, J., 1981. A model of adaptive organizational search. Journal of Economic Behavior and Organization 2, 307–333. March, J., 1962. The business firm as a political coalition. Journal of Politics 24, 662–678. March, J., 1988. Introduction: a chronicle of speculations about decision-making in organizations. In: March, J. (Ed.), Decisions and Organizations. Blackwell, Cambridge, MA, pp. 1–21. March, J., Simon, H., 1993. Organizations, 2nd Ed. Blackwell, Cambridge, NIA. Milgrom, P., Roberts, J., 1992. Economics, Organization, and Management. Prentice-Hall, Englewood Cliffs, NJ. Murnighan, J., 1994. Game theory and organizational behavior. Research in Organizational Behavior 16, 83–123. Newell, A., 1969. Heuristic programming: ill-structured problems. In: Arnofsky, J. (Ed.), Progress in Operations Research. Wiley, New York, NY. Porter, M., 1980. Competitive Strategy: Techniques for Analyzing Industries and Competitors. Free Press, New York, NY. Prietula, M., Carley, K., Gasser, L. (Eds.), 1998. Simulating Organizations: Computational Models of Institutions and Groups. AAAI/MIT Press, Cambridge, MA. Shubik, M., 1958. Simulation of the firm. Journal of Industrial Engineering 9, 391–392. Shubik, M., 1960. Simulation of the industry and the firm. American Economic Review 50, 908–919. Simon, H., 1955. A behavioral model of rational choice. Quarterly Journal of Economics 69, 99–118. Simon, H., 1969. The Sciences of the Artificial. MIT Press, Cambridge, MA. Simon, H., 1979. Rational decision making in business organizations. American Economic Review 69, 493–513. Solis, F., Wets, R., 1981. Minimization by random search techniques. Mathematics of Operations Research 6, 19–30. Spulber, D., 1999. Market Microstructure, Intermediaries and the Theory of the Firm. Cambridge Press, Cambridge, UK. Sutton, J., 1991. Sunk Costs and Market Structure: Price Competition, Advertising, and the Evolution of Concentration. MIT Press, Cambridge. Tirole, J., 1988. The Theory of Industrial Organization. MIT Press, Cambridge, MA. Williamson, O., 1988. The logic of economic organization. Journal of Law, Economics, and Organization 4, 65–94.