Choice and Action - CiteSeerX

Choice and Action

David

Lane,

Franco

Malerba, Robert Orsenigo *

Maxfield,

Luigi

November 23, 1994 SFI Working Paper 95-01-004 ABSTRACT In this essay, we argue that rational choice (RC) provides an inadequate foundation for a theory of economic action. After defining RC sufficiently broadly to encompass much of the bounded rationality literature as well as neoclassical optimization theory, we present three principal arguments against RC. The first is cognitive: economic actors are experts at what they do, and the cognitive processes that underlie expertise are not consistent with RC, descriptively, prescriptively or positively. The second argument begins with the observation that economic action takes place in and through relationships between agents, and these relationships may generate actions that cannot be localized to individual agents. We argue that these generative relationships are essential to understanding such fundamental economic phenomena as innovation, and the actions that result from them are not amenable to analysis from a RC perspective. Finally, we argue that most economic agents lack the judgement and execution coherence required by RC. In a companion paper, we propose an alternative foundation for a theory of economic action that builds on the critique of RC presented in this paper. ACKNOWLEDGEMENTS: We would like to thank the Santa Fe Institute and CNR (Italy) for support for our research. We benefited from discussions around the ideas presented here, as well as detailed comments and criticisms on previous drafts, from many people, including especially Brian Arthur, Michael Cohen, Jim Dickey, Stuart Dreyfus, Walter Fontana, Dick Nelson, John Padgett and Jim Pelkey.

* Lane, Department of Political Economy, University of Modena ([email protected]); Maxfield,

Department of Engineering and Economic Systems, Stanford University ([email protected]); Malerba and Orsenigo, Institute of Political Economy, Bocconi University ([email protected]).

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Economic theory is about what economic agents do. Any such theory must be predicated on a conception of what constitutes economic action. Most current economic theories are based on the presumption that every economic action that matters arises from a process of rational choice (RC). This paper challenges that presumption. In a companion paper, we develop an alternative conception that highlights those features of economic action that RC hides. The paper proceeds as follows. In section 1, we say what we mean by RC. Our definition of RC is much broader than optimization and Bayesian updating. For example, according to our definition, most of the bounded rationality literature falls under the RC rubric. The starting point of our critique of RC is the idea that the contexts in which economic action takes place cannot be resolved into the kind of abstract decision problems studied in economic textbooks. Section 2 describes two such contexts, which will provide examples for our subsequent arguments. Our critical discussion begins in section 3, where we show that some familiar arguments against the validity of particular RC-based theories do not constitute sufficient grounds for abandoning RC as the foundation of economic action. In contrast, the next three sections discuss three attributes of economic agents and their actions that, we argue, are inconsistent with RC. According to the argument in section 4, the cognitive processes that make economic actors1 good at what they do are not amenable to descriptions in terms of structured choice situations. As a result, no RC-based analysis can predict, much less explain, what actions these processes will lead to in many economically interesting situations. Moreover, if the actors were constrained to act according to the dictates of RC, they would in general perform less well than if they were left to their own cognitive devices. In sections 5 and 6, we examine some implications of the fact that most interesting economic actions are really interactions between different economic agents. In section 5, we argue that interactions between agents give rise to relationships, these relationships generate further actions -- and these actions, inextricably embedded in the relationships that produce them, cannot be understood from a RC perspective. In particular, we show how persisting patterns of interaction between agents can lead to 1

By "economic actor", we mean an individual human being engaged in some economic activity. Typically, this activity is part of an action carried out by some structured collection of economic actors -- a firm, for example. We use the term "economic agent" to refer to a locus of economic action. Thus, an economic agent is in general a structured collection of economic actors.

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innovations in how the agents see themselves and the artifacts around which their relationship is structured -and these innovations, which are rich in economic consequences, do not derive from processes of RC. In section 6, we focus on the internal structure of agents that are composed of other agents. We claim that RC imposes coherence conditions on such agents, and we argue that most interesting economic agents do not meet these conditions.

1.

Rational choice

Agents choose when they commit themselves to a particular course of action that is completely specified at the moment of choice. What we mean by RC are the presumptions about economic action contained in the following three assertions: RC1 Universality: Every significant economic action is the result of a choice. RC2 Context representation: To choose what course of action to take, the agent must construe the context in which the action is to take place in terms of a choice situation. A choice situation consists of a specification of a set of available acts and, associated with each available act, a set of consequences that describe what might happen should the agent choose that act. RC3 Rationality: The agent must select an act on the basis of a calculation of the value of the consequences associated with it. The algorithm guiding the calculation must be such that the agent obtains some pre-specified measure of value from the chosen act. The value may be specified in absolute terms, or relative to what can be attained from the other available acts. RC1 implies the primacy of choice over action itself, since what agents do is just what they choose to do. Thus, by analyzing how agents choose, a RC-based theory can purport to describe or prescribe the actions they take. RC2 and RC3 describe what choice entails. Taken together, the three assertions presuppose that the capabilities of rationally choosing agents must fit together with the contexts in which the agents operate in the following ways. First, the agents must have the capability to decompose the continuous stream of activities in which they are immersed into discrete choice situations. For each such situation, they have to represent their possible actions and the consequences of these actions (RC2). Then, they must be able to judge the value of the consequences as they have represented them (RC3). Finally, they must be capable of doing what their choice precommits them to do (RC1 again). The critical question that this paper addresses can be posed as follows: is the match between capabilities and contexts presupposed by RC a plausible assumption for

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economic agents acting in the contexts in which interesting economic actions take place? To help clarify what this question means, consider the following simple context. An evening at the casino: Suppose you spend an evening playing roulette at a casino, with a $1000 budget constraint. Suppose further that you are willing to ignore any effects that the size of your winnings will have on what you do in the future. What should you do? Representing this context as a choice situation, as required by RC2, is easy. There is only one agent that matters, you. Your possible actions are described by strategies that tell you how much to stake on which kinds of bets, as a function of time and your current winnings. The casino's rules determine what your possible bets are. For each of these bets, the stakes and possible payoffs are known in advance. Moreover, almost everyone believes that the ball is equally likely to come to rest in any of the 38 pockets, so the probability structure of each bet is essentially public knowledge. Thus, to choose rationally how much you want to bet, on what, all you need to do is determine the value of the possible sizes of your gains or losses at the end of the evening, and then apply your favorite decision algorithm. This algorithm might, for example, be “maximize expected utility”. But if you think that the dynamic programming problem of finding the strategy that maximizes expected utility is too hard for a plausible human agent to solve, you can substitute a decision algorithm based upon some sort of heuristic search through strategy space and a satisficing criterion function. Such a "boundedly rational" choice model is perfectly consistent with RC3. In this context, it is easy to suppose that agents have sufficient modelling and computational capabilities to do what choice requires. Moreover, it is also easy to suppose that choice is indeed the primary issue here. Any competence an agent might need for playing roulette is in choosing what to do, not in doing it, which only requires some trivial physical acts of pushing chips around on a table and the ability to count. As a result, RC1 seems obviously correct in this context.2 So far, we have argued that our agents are capable of rationally choosing what to do in their evening at the casino. Nothing guarantees that they will in fact exercise that capability. They may just place bets as their "animal spirits" move them. If so, to describe their actions, we would need a theory of "animal spirits", not a theory based on RC. While it is certainly an interesting question whether an RC-based theory describes what real economic agents actually do, that is not the question this paper addresses. 2

But note that this is not so facial expressions, table talk, all play a part in deciding the these elements can or should be

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evident in a game like poker, in which feelings between the participants, etc., outcome. It is not clear whether all chosen in advance. See section 4.

We ask only whether agents' capabilities are matched to their contexts in such a way that RC is possible; and, if so, whether it is plausible to suppose that agents need not act against what they regard as their own interests in order to do so. Moreover, we do not insist that agents themselves provide explicit representations of the choice situation and explicit calculations of which action to choose. We only require that their actions taken together be consistent with such an explicit analysis provided from "outside". From this point of view, it seems reasonable to conclude that RC provides an adequate basis for modelling an agent's evening at the casino. Gambling contexts like our evening at the casino are prototypical of RC -- so much so that they provide a metaphor that structures the ways in which we think and theorize about much of economic activity.3 The metaphor shows up in ordinary language, with such expressions as "what is at stake here is..." and "investing in a new plant now is a gamble. " Moreover, it appears also in formal theorizing: DeFinetti coherence, von Neumann-Morgenstern utility, and Savage's axiomatization of preference all rely on gambling metaphors. The "choice"4 to build economic theory on a RC foundation is tightly connected with a (generally implicit) judgment that the gambling metaphor fits all situations in which economic agents act, including contexts like the two examples we present in the next section. Sections 3 through 6 present arguments that call this fit into question.

2.

Contexts of economic action:

two examples

In this section, we present two examples that describe contexts in which economic action is taking place.5 These examples do not frame the kind of abstract decision problems that occupy space in economics textbooks: determining how much to ask or to pay or to make, deciding whether to contract out or to produce within. Indeed, the immediate problems in our two examples are not directly economic, but medical and technological respectively. Yet as we shall see, 3

See Lakoff (1992) for a discussion of the entire metaphor system underlying the concept of rational choice. The roulette context pits the gambler against impersonal chance. Clearly, the general gambling metaphor involves other players as well. Thus, game theory is also structured in an obvious way by the gambling metaphor. 4 For many reasons, some of them related to arguments developed in this paper, we find it very difficult to imagine that theorists really "choose" -- in the sense of RC -- which theories to construct. Some of these reasons involve the issue discussed in the previous paragraph: even if they could theorize in a way consistent with RC, they might not. But the deeper reasons deny the possibility of RC theorizing. 5 Both examples are based upon actual situations, which are reflected in the features of the contexts that we emphasize and in the actions that arise in these contexts, though not in the details provided in the descriptions of the example firms.

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the implications of these problems are substantial, and an array of actions cutting across a lot of subject matter boundaries6 will be set in motion in order to deal with them. Abstract decision problems do not exist by themselves, but arise in contexts like those of our examples, in which many actions, involving many different agents, are taking place simultaneously. In this paper, we chose to start with context and see what that implies for decision, rather than the more traditional other way around. EXAMPLE 1: The agent is a large American pharmaceutical company, whose most profitable product is the current sales leader among nonsteroidal anti-inflammatory drugs (NSAIDs). This drug has been on the market for several years, and it has already been prescribed to several million patients. However, the company's adverse reaction surveillance group has recently received four case reports from credible practitioners that describe cases of agranulocytosis, a potentially fatal blood dyscrasia, in patients receiving the drug. These are the first reports linking the drug with agranulocytosis, yet on the basis of its analysis of these reports, the group has come to the conclusion that the drug may be causally implicated in these cases. The reports raise a potentially serious problem for the company. If it turns out that the company's drug indeed causes agranulocytosis and alternative NSAIDs do not, prescriptions for the drug will plummet -- and company prudence or regulatory action could even result in the withdrawal of the drug from the market. Moreover, even unsubstantiated suspicions can bring about these results, depending on who gets access to the information in the case reports and what they do with it. In the past, competitors, journalists, consumer activists and congressmen have all fanned the flames of popular outrage against "killer" drugs -- even when the evidence against the drugs was scanty and their benefits great. For NSAIDs, the vulnerability to this kind of attack is substantial, since there are many NSAIDs on the market, and none have benefits that clearly dominate all the rest. So it is relatively easy for prescribers to switch to a competitor if their current preference attracts unfavorable attention. Nonetheless, the company cannot simply ignore the case reports in a desire to escape the negative effects to which they might lead. At the very least, legally mandated reports must be submitted to the FDA, the federal regulatory authority. And for legal (not to mention moral) reasons, it is important that the company make efforts to determine whether its drug really can cause agranulocytosis -- and, if it can, what kinds of patients are at risk in which circumstances. Clearly, the last issue has marketing implications as well. 6

Medical, engineering, political, sociological, and economic, to name a few.

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Upon receiving the report on the cases from the surveillance group, the company's medical director has called a meeting to discuss what the company's response should be. The meeting is to take place in the office of the head of the company's legal department; other attendees include people from regulatory affairs, the group responsible for marketing the drug, the medical director and the adverse reaction surveillance group. It is expected that the meeting will decide whether any active response is called for, and if so, what it should be. Possible actions include: initiating discussions with the FDA about changes in labelling and package inserts; commissioning some kind of pharmacoepidemiological study to find out more about the problem; requiring the company's detail people to elicit additional case information from the physicians with whom they are in contact; drafting material to be circulated to prescribers and, if necessary, the general community giving the company's perspectives on the problem. EXAMPLE 2: The agent is a young but rapidly growing company competing in an expanding new submarket of the telecommunications industry, the automatic call distribution (ACD) market. The company manufactures and sells a computerbased system for answering a bank of incoming lines, using voice prompts that help callers to get access to recorded information or to the appropriate department. The company currently has the leading market share, but the market is growing very fast, with many competitors and rapid technological change. The company is about to introduce its next-generation product, which will provide a considerable increase in performance with a 20% reduction in manufacturing costs. The company has decided to push the new product aggressively: it will price it at 10% less than its current product, thus making this product obsolete. This necessitates a very difficult "flash cutover" of manufacturing. The company managers believe this step is justified, because they think that the new product can completely dominate the market. They know that the company's major competitor is working on a comparable product, but they think they have a year's advantage over the competitor, which in this environment would probably be decisive. Two months ago, the company embarked on a three month Beta test of the new product, putting it on-line with twenty selected customers, who have each agreed not to disclose any test-based information about product performance. Until last week, the system worked perfectly. As a result, the company has already confirmed its scheduled launch date for four weeks hence. All the preparations are in order: analysts, press, and current and prospective customers have already been invited to attend the unveiling of the product at the major annual trade show; advertising copy has been written and space purchased in various publications; the cover

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article of the major trade journal describing the product is ready to go to press. Three days ago, one of the Beta sites crashed catastrophically -- the system failed in such a way that it took out all phone service. For companies using ACD systems, downtime is visible and immediately translatable into lost revenue and profits. Fortunately, the test site was near our company's headquarters, so it was possible to get a crew of engineers and technicians there immediately, and the crew was able to limit the outage to thirty minutes. The company where the crash occurred is a long-standing customer, so it is willing to continue using the system, provided that our company assign it a technician full-time who (our company assures) can limit any further crash to less than a minute. In addition, a team of development engineers is trying to locate the cause of the crash. This morning, a second crash occurred. While the system was brought back on-line almost immediately, the engineers gained valuable information through the diagnostic logs they had activated in the software. They now believe that the problem is in the software, located somewhere in a block of code about 100,000 instructions long, and involves execution paths in this block that are used only in rarely encountered situations. No-one is willing to predict how long it will take to find and fix the error, nor whether the same crashes will occur at other customer sites. The company president is about to chair a meeting of the top management team -- the vice-presidents for engineering, finance, marketing, manufacturing, and customer support. The purpose of the meeting is to decide how the company should respond to this crisis. One extreme possibility is to cancel the launch, publicly admit the reason, and start over when the problem is solved. If the company did this, it would only be able to produce half the number of systems it had scheduled, due to parts shortages of the old product. Thus, it would have to introduce some kind of interim rationing scheme, and to decide whether to keep the scheme in place until the new product is ready or to re-develop full production capabilities for the old product. At the other extreme, the company can continue as planned. Then, the company managers must develop some procedure to contain the damage if crashes happen at other sites before the problem is diagnosed and solved. Clearly, both these possibilities are full of risks for the company. To make matters worse, this is a critical time for the company financially. The initial public offering of stock occurred only two weeks ago. The key security analysts published bullish reports, and the stock has doubled from the offering price. The financial community is now eagerly awaiting the next two quarterly reports. Some contexts of economic action might be described as "business as usual" -- that is, they consist of familiar situations that tend to resemble at least in broad outline

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many other situations which the agent has already encountered. Such contexts can give rise to what Nelson and Winter (1982) call organizational routines, which may substitute for choice as a foundation of economic action in these contexts. The contexts in our two examples are not "business as usual". They call for innovative, situationspecific responses. One might even be tempted to say: they call for choices to be made. We will argue, though, that the actions that will arise in our two examples cannot be understood as arising from processes of RC.

3.

Some arguments we will not make

In this section, we briefly describe three arguments that have been made against particular RC-based economic theories. Each of these arguments derives its strength from features of economic contexts (like our two examples) that make them more complicated than an evening in the casino. We introduce these arguments primarily to distinguish them from the ones we make in the next three sections. We do not think that the three arguments presented in this section offer a compelling reason to abandon RC itself, at least in their usual forms, whatever be their merits in countering the particular RCbased theories against which they are directed. 1) The combinatorial explosion argument Think how many consequences the ACD company president will have to take into account when he chooses the company's strategy for dealing with the crashes. If the company goes ahead with the launch, how many other crashes will there be? How effectively will the plan for providing service to new crash sites work? How will prospective customers and the financial community react if the system's propensity to crash becomes known? And if the company postpones the launch, what kinds of reactions can they expect from customers, competitors, investors and bankers? How quickly can they re-tool their plants to turn out more of the current product? How many orders will they receive for the current product, after the pre-publicity for the new one and the announcement of the postponement? It is a feature of contexts like our two examples that the set of possible consequences from any of the possible actions is extremely large. Given this, it is reasonable to ask whether the problem of choosing amongst these actions on the basis of their consequences may exceed the agent's capabilities to handle it. Can the ACD company's president really be expected to take all the consequences mentioned above into account -- and then calculate which action the company ought to take? Surely there are some limits on his information processing and computational capabilities, and the complexity of this problem may be such that these limits are surpassed. If so, how can he rationally choose what to do? This is the argument that underlies much of the literature on bounded rationality. But the challenge of this literature

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is not really to RC itself. Rather, the literature shows how informational and computational constraints affect the ways in which agents structure choice situations (RC2) and the algorithms they use to calculate their choices (RC3). Moreover, the bounded rationality perspective is not the only RC-based solution to the problem of combinatorial explosion. It is conceivable that the human brain is sufficiently powerful to compute neoclassical, optimizing solutions, even in the presence of very large consequence spaces -- and even if the computation is not consciously accessible.7 2) The Knightian uncertainty argument This argument focuses on the problem of uncertainty assessment. Many of the consequences in our two examples have a “one-off” character that makes any probability assessment based on observable frequencies impossible. Trying to figure out how a particular customer will respond to a new kind of system crash or how fast a competitor can develop a new product (or what the resemblance will be between the product it does develop and your new product -- or how future customers will compare the two) all have this character. Recent work by Brian Arthur (1994) calls attention to another source of Knightian uncertainty: when heterogeneous agents all try to predict an aggregate quantity, like price, which is actually formed in response to the behavior their predictions generate, the actual dynamics of that quantity can be extremely complicated (one is tempted to say: unpredictable). Such arguments may tell against the possibility of agents forming rational expectations. In the “one-off” case, it is not even clear what rational expectations means; in the aggregation problem, it is hard to imagine how the agents can jointly arrive at an understanding of a mechanism as elusive as what Arthur and others have described. But the argument has no implications for RC itself, since the Knightian distinction between risk and uncertainty is essentially irrelevant in this context. Agents’ probabilities are just agents’ probabilities, and the problem of whether they are calibrated or not does not affect how agents use them to form their choices. Moreover, while some kind of uncertainty assessment must play a role in any formulation of RC, it need not take the form of a quantitative measure satisfying the axioms of probability. 3) The evening at can happen allowable examples,

unanticipated consequences argument In the the casino, there can be no real surprises. What is completely determined by the house rules for bets. In contrast, in contexts like our two something totally unexpected always seems to

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According to Lewis (1985), for this to be so the brain would need more computational capability than a Turing machine.

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happen. Reports on agranulocytosis cases might right now be piling up in the files of a competing manufacturer, implicating another drug that all the patients were receiving along with their NSAID. Or -- an unanticipated consequence with longer-term implications -- another pharmaceutical company might right now be developing a new drug that will make all NSAIDs obsolete. How can agents choose rationally, when they cannot even foresee what might happen? Especially given that they know that they will encounter unanticipated consequences, even as they structure the choice situation in which those consequences can play no role! There is a simple answer to the unanticipated consequences argument from the RC perspective. Choice requires agents to set up a model of the future, and to make their choices on the presumption that this model is true. It does not require that the model be true for the choice to be rational. You do the best you can. When agents encounter consequences they had not foreseen at the moment of choice, they can decide whether the consequence matters enough to modify their current strategy. If so, they can structure a new choice situation that incorporates the implications of the previously unanticipated consequences and choose to commit themselves to a new line of action. Two of the arguments we present in sections 4 and 5 may be interpreted as extensions of the unanticipated consequences argument. These arguments start from two caveats about the validity of the RC rejoinder sketched in the previous paragraph. We conclude this section by stating these caveats and pointing to the arguments against RC to which they lead. First, it is clear that the time horizon for which future action is determined by a particular choice ought to depend on the rate at which unanticipated consequences tend to arrive. That is, it would hardly be worthwhile for an agent to go through an RC process to commit to a particular strategy of action designed on a time scale of months, if the strategy is derailed by some unanticipated consequences arising in the first few days. But establishing the time horizon for a choice situation is not a free "choice": it depends upon how far into the future the relevant consequences are foreseen to extend. Thus, it might be the case that the arrival rate of unanticipated consequences is simply incompatible with the possibility of engaging in RC. We return to this issue in section 4. The second caveat relates to the source of the unanticipated consequences, and the agent’s involvement in bringing them about. So far, we have talked about unanticipated consequences as though they were some sort of exogenous "shock". Clearly, this is an incomplete picture; the “shocks” need not be exogenous. The agent's actions may be a contributory or even sufficient cause of an unanticipated consequence, but the agent may have lacked this causal knowledge at the time of choice. Suppose instead,

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however, that agents intend, even w i s h , to encounter consequences that they cannot forsee, and embark on a particular course of action for precisely this purpose. In this case, RC breaks down. Agents cannot rationally choose a course of action, if they omit from their representation of the choice situation the very consequences they want that course of action to bring about. But if they cannot foresee what these consequences are, how can they assess the values on which RC calculations depend? We explore this seemingly paradoxical situation further in section 5.

4.

Expert action and rational choice

The argument we present in this section is about the individual human beings who comprise and act for economic agents. The argument consists of three claims. First, these economic actors are experts in their domain of action. Indeed, their value to the agents for whom they act lies precisely in their expertise. Second, the cognitive processes through which experts acquire and exercise their expertise are different from and even inconsistent with those required by RC. Consequently, RC-based models cannot be expected to generate accurate predictions about expert action. Third, if experts were constrained to act in accordance with the dictates of RC, they would have to surrender the advantages their expertise confers upon them and settle for decreased levels of performance. Thus, RC fails as the basis for a prescriptive theory of economic action, when the action is carried out by experts. 4.1 Expertise As we saw, our evening in the casino did not require any skills in acting, just in deciding how to act. Given information about allowable bets, monetary outcomes and probabilities -- which could be obtained just as easily from someone else as from personal experience -- the gambler would fare just as well if he had never played roulette before as if he had played it every evening for the last ten years. This is not true for the vice-presidents of engineering or finance of the ACD company, or for the physicians and statistician that make up the pharmaceutical company’s adverse reaction surveillance group. They have all received extensive training preparing them for what they do -- and spent many years doing it and other closely related activities. The expertise they have accumulated in their respective domains of action is what makes them valuable to their companies, which could replace them only with difficulty and even then only by people with similar training and experience. It is important to clarify the sense in which these people are experts. The word is commonly used in two different senses. In its first sense, it refers to someone who has repeatedly engaged in a particular kind of activity (generally after a more or less extended period of

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instruction) and has attained an acknowledged mastery of this activity. The kind of activity that lends itself to expertise in this sense are embedded in contexts which provide opportunities for feedback: actors can observe at least some of the effects of their actions, and they can modify how they act in the future on the basis of these observations. The prototypical example of an expert in this sense is a grand master in chess. He has studied the huge chess literature and has personally engaged in thousands of games in his career; he knows if he wins or loses a game, and generally can figure out which moves were the crucial ones in determining the game’s outcome; and his comparative mastery is neatly and publicly summarized by his master’s rating. Other examples abound: athletes, artisans, professional people and managers can all qualify as experts in this sense. The second sense of “expert” applies to someone who studies what others do, without personally engaging in the activity he observes. It is in this sense that academic political scientists or economists are described as experts in international relations or industrial development. We will use the word only in its first sense. For us, feedback from experience in the activity itself is the hallmark of contexts in which expertise is possible. In the sense in which we use the word, “expert” observers are not experts in the realms of experience they observe, although they may have acquired expertise in the use of the data sources, models and other techniques that are the tools of their trade. 4.2 Cognition and expert action The literature on how experts think and act is vast.8 In addition, it is full of contradictions and unresolved issues. In the following discussion, we draw from it a few general conclusions that we think are consistent with the major part of this literature. Our account draws heavily on the interpretative categories of Shanteau (1992), Dreyfus’ and Dreyfus’ (1986) model of skill, and Suchman’s (1987) distinction between plan and strategy. We will briefly describe three levels of cognition involved in expert action. The first level is the primary mode in which experts operate. It functions by means of a categorization-action system, through which expertise may be said to express itself directly in action. The categorization-action system operates subcognitively, and its workings are only partially accessible to consciousness. The other two levels, deliberation and planning, introduce a partial conscious control to the categorization-action system.

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We have relied on Bereiter and Scardamalia (1993 ), Chi et al (1988), Ericcson and Smith (1991), and Wright and Bolger (1992) to orient us to this literature.

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1) Categorization-action system This system manages the feedback between past experiences and present action that is the essence of expertise. It works by comparing the current situation to the expert’s memories of past experiences. These comparisons draw upon a huge number of situations in the domain of expertise, together with the actions taken in each situation and valuations of the effects of the actions.9 These situations come from the expert’s own experience, or they are assimilated from the experiences of colleagues or competitors, as when chessplayers replay published games or businessmen exchange “war stories”. Even when the experience is not first-hand, the ability to comprehend what “really happened” in these experiences depends on the mutual expertise of the actual actor and the assimilator. To novices, most of the moves in grand master chess games are simply incomprehensible, and so cannot inform their own practice. When confronted with a new situation requiring action, the system categorizes the situation according to patterns motivated by previously experienced situations. 10 The categories are associated with particular actions: the association depends upon the valuations of the effects of the actions taken in past situations that were categorized similarly to the present situation. The categorizationaction system then generates an action on the basis of this association. Roughly speaking, if previous actions in situations similar to the present situation led to good results, the new action is modelled on them, while actions that led to bad results are avoided. The comparisons and valuations that make this system work are subcognitive and essentially instantaneous.11 Moreover, the system does not seem to carry out extensive numerical calculations to determine the action it suggests. In fact, expert performance can proceed effectively, even improve, under circumstances in which time or other constraints make calculation impossible. For example, Dreyfus and Dreyfus (1986) report an experiment in which an international master 9

Note that this implies that the system is value-driven. It also must have some method for solving the credit assignment problem -- that is, for determining which actions to credit for results that happen after the action is taken. 10 Interesting computational models for how these categorizations form can be found in Holland et al (1986) and Elman (1990). Edelman (1992) and Damasio (1994) propose neurobiologally-based theories for memory, categorization and action with which our proposals are consistent. 11 While the neural basis of the recognition-action system is not yet well understood, there exist a number of models in the machine learning literature that reproduce most or all of the system properties described above. These include Kohonen’s self-organizing memory (1977), Holland’s classifier systems (Holland et al., 1986), and reinforcement learning schemes (Sutton, 1988). These models do not qualify as “experts” themselves, of course, because they operate in much simpler environments than do expert economic actors. Yet they provide insight into the kind of non-RC mechanisms that can give rise to expert action.

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chess player performed well against a slightly weaker, but still master level player, even though the international master was required to move within five seconds and, simultaneously, to add a list of numbers presented to him at the rate of one per second. The workings of the categorization-action system are only partially accessible to consciousness. When queried about the reasons for their actions, experts generally experience great difficulty in explaining these judgments. In particular, they find it hard to describe which features of previous experiences make them relevant to the present one, or to lay out a goal structure that is consistent with the judgments about which relevant prior actions produced good results and which bad. Nonetheless, the answers experts give to the researchers’ queries have some interesting features. Typically, experts list fewer action alternatives than do novices engaged in the same task, and they claim to look no farther into the future than do novices to anticipate the effects of the actions they do consider. 2) Deliberation The categorization-action system may fail to generate an action, either because it cannot categorize the present situation, or because it cannot associate the category in which the situation is placed with any valuable action, or because this category is associated with contradictory actions with roughly similar values. For example, the circumstances surrounding the phone system crash were sufficiently unlike anything they had previously encountered that the engineers assigned to find the cause could not at first even determine whether it was a software or a hardware problem. And while the information on the time course of NSAID treatment and the onset and progression of agranulocytosis in the four case reports was very suggestive of drug causation, the fact that no report linking the drug with agranulocytosis had been received previously, even though so many patients had already been exposed to the drug, pointed to the opposite conclusion. In such situations, the expert experiences a feeling of ambiguity about what he ought to do. Generally, the response is to enter a conscious deliberation mode, in which the expert weighs the action alternatives facing him. The form this deliberation takes, however, is different from what RC would lead one to expect. In these situations, what experts tend to do is to focus on precedents: that is, they recall situations from their own experience or solicit anecdotes from others that bear some relation to the current context. For example, the surveillance group gathered information about four previous incidents in which new hematological adverse reactions were discovered for drugs that had been on the market for relatively long periods of time. Experts then try to analyze how the precedents are similar and how different from the current situation, and on the basis of these retrospective reflections to find hints for

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appropriate action built on analogies to what worked (or failed to work) in the past. That is, they carry out a conscious version of the work of the categorization-action system. The aim is to reason about the kind of material that that system operated on subcognitively. In the course of deliberation, the expert may try to figure out what the consequences of particular contemplated actions might be. But he does not do this as the novice does, by constructing an abstract representation of all possible future contingencies. Rather, he constructs parallels with explicit situations in the past, and projects the future by analogy with what happened in these situations. The more an expert encounters a particular type of situation, the more will the categorization-action system succeed in categorizing new instances of the type and generating appropriate action, and consequently the less will the expert rely on deliberation to guide his actions. As skilled athletes and fighter pilots have always maintained, the more you have to think about what you are doing, the less able you are to do it well. It is the function of expertise to hew out a domain of the familiar and to build in a repertoire of appropriate responses, leaving the conscious mind free to deal with the genuinely novel or with broader questions of general orientation. 3) Planning Planning is the conscious insertion of value into the situations in which experts find themselves. The purpose of a plan is to get somewhere, to obtain objectives: thus, unlike the categorization-action system or deliberation, planning is forward- rather than backwardlooking in time. A plan interprets the situation in which the expert finds himself, in terms of a series of tasks for which his expertise equips him and which he intends to carry out. These tasks are expected to gain the value that it is the purpose of the plan to achieve. A plan does not specify the details of actions the expert intends to take. It functions as an orientation to a context, not as a pre-committment to a particular sequence of acts. The situations in which experts act are simply too complicated for any detailed action blueprint to produce a successful outcome. Instead, they require exquisite responsiveness to the contingencies of the context as they arise. The categorization-action system, augmented if necessary by deliberation, has the responsibility for actually carrying out the tasks delimited by the plan. In this sense, a plan can be seen as “controlling” the categorization-action system by shaping some of the valuations that, together with situational comparisons, drive that system. But the categorization-action system controls planning as well, in the following sense. The ability to formulate plans is an expert skill. Which situations require planning and what constitutes a task are categories whose structure is

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first set by instruction and later extended with experience. This process, like that for any other kind of expertise, is achieved through the operation of the categorization-action system. In particular, there is no outside view of economic action from which certain problems can be defined as “choice situations”, independently of the experience of the economic actors who are supposed to solve those problems. What these actors see as problems to be solved and what tools they can conceive as relevant to solving them are emergent outcomes of their past immersion in domain-specific situations. We stopped our descriptions of both the contexts of section 2 just before a meeting was to take place. What happens in a meeting is a good example of the relationship between plans, deliberation and the categorization-action system. Think about the members of the surveillance group as they prepare to enter the medical director’s office. The group has a strong committment to maintaining the safety of the company’s products. On the basis of their careful study of the information in the case reports, in comparison with the data they found on the other examples of delayed drug-event reporting, they think that the company’s NSAID may well have caused the reported cases of agranulocytosis. As a result, they want the company to follow-up on these reports -- by soliciting further reports through the marketing group’s contact with physicians, commissioning an epidemiological study, or even listing agranulocytosis as a possible adverse reaction in the drug’s package insert. But they have a good deal of experience in similar situations, and they know that the marketing, regulatory and legal people at the meeting will have different points of view on these questions. So they go into the meeting with a plan, the purpose of which is to convince the others of the potential seriousness of the problem and to win support for follow-up action. Part of the plan is to present technical arguments in support of their conclusion on causation; they have also anticipated some of the likely counter-arguments and proposals of the other attendees, and they have prepared some refutations and counter-proposals, to use as needed. During the meeting, all of the participants will note what the other participants say and do. They will operate partly in the deliberation mode, as they form and reform their opinions about what is going on -- and why. For the most part, they will just act: restraining themselves at one point, interjecting a comment at another. And their actions will consist of a great deal more than just what they say -they will include the tones of voice they use, the expressions on their faces, their bodily movements. What prompts a participant to do one thing rather than another in general are nuances of phrasing and manner by the other participants that he had certainly not foreseen -- and which he may even fail to notice even as he responds to them. Both the conscious interpretations of and the subcognitive responses to what is going on depend on the participant’s

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particular history, and it may turn out that members of the surveillance group, who all went into the meeting with the same plan, actually end up operating at cross-purposes to one another. In summary, what the participants actually do in the meeting is determined by their experience in this kind of situation, operating through the categorization-action system, modulated by deliberation and oriented by their respective plans. The outcome of the meeting will depend on how the participants act in the meeting -- not just on the plans they each bring into it.12 4.3. Why RC does not describe expert action The essence of choice is precommittment to action (RC1), based on anticipation of value (RC3), calculated with respect to a representation of future consequences (RC2). According to our description, expert action is generated differently. It is not precommitted; even the expert’s planning mode provides only an orientation to the essentially reactive categorization-action system. Expert cognitive processes do not rely on explicit and exhaustive representations of future consequences. Rather, they operate on representations of the concrete details of past experiences. And they do not calculate the value to be gained from possible actions. Instead, they rely on judgments of similarity with past situations, the value of whose associated action has already been assessed experientially. None of the three assertions that we used to define RC seem to apply to our description of expert action. 4.4. Why RC would not improve expert action Even though expert action does not proceed from RC, perhaps it should. After all, there is a substantial body of empirical evidence suggesting that, in many contexts, experts err. 13 Might experts perform better as economic actors if they engaged in RC, instead of relying on the cognitive processes of expertise described above? 12

Nelson and Winter (1982) share our view that economic actors are experts -- or "skilled", in their vocabulary. We agree with the two main points they make in their fourth chapter: that skill suppresses choice, and that much of what constitutes a skill is tacit -- that is, not consciously appreciated by the actor himself. Our account of expert cognition differs from theirs in a number of respects. In particular, the idea of a categorization-action system, as Dreyfus and Dreyfus (1986) argue at length, is very different from the Simonian "skill as program" metaphor that underlies the Nelson-Winter account. But the greatest difference between our treatment and theirs is in section 4.4 below, where we argue that expert performance is likely to degrade if experts trade in their expert cognitive modes for the prescriptions of RC. In contrast, Nelson and Winter conclude their chapter with the claim that the conservative and relative context-independent nature of skill makes economic agents prone to behaviors that are sub-optimal, in comparison with the strong RC injunctions of Friedman and Machlup. 13 See Shanteau (1992) for a review.

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A standard positive answer to this question refers to socalled prescriptive arguments, like those of Ramsay (1926) or Savage (1954). These arguments prove that "reasonable" or "consistent" decision-makers must select acts in accordance with some RC-based prescription, like "maximize expected utility". However, these arguments begin by placing the decision-maker in an acknowledged choice situation and requiring from him a context representation at least as detailed as that specified by RC2. Thus, they can only claim to show how, not whether, to engage in RC. Consequently, the claim that RC would improve expert action, based upon prescriptive arguments, is circular: it begins by assuming what it pretends to conclude. For many people (especially economists), it is difficult to imagine how the quality of actions can fail to improve if the actor rationally chooses what to do, instead of "just doing it". Hence, it is worth considering a simple example of a situation in which "just doing" seems clearly superior to RC: EXAMPLE: Someone has just thrown a ball in your direction, and you want to catch it. To do so, you must move your arm so that your hand intercepts the trajectory of the ball, open your hand before interception, and close it around the ball within about 14 msec after interception.14 Moreover, the success of these actions depends on the speed, direction and spin of the ball. Given the intricate perceptual and motor requirements of the actions you perform to catch the ball, and the severe timing constraints under which you must take these actions, it is clear that you cannot hope to succeed by making a conscious pre-committment to the whole sequence of actions you will take, based on a consideration of all possible motions you could make and the anticipated consequences of each of them. But most of us can just reach out and catch the ball. Presumably, that is because we are the products of a long evolutionary history in which catching moving objects was an important survival skill, and so we embody some evolutionarily determined solution to the problem of catching. In fact, clever experiments with infants as young as 18 weeks reveal rudimentary catching skills, which improve with age (von Hofsten, 1980). Moreover, these skills can be greatly sharpened by experience with particular catching activities, as anyone who has worked to attain competence as a baseball shortstop, a soccer goal-keeper or a juggler can attest. Of course, it is possible that even if we do not consciously engage in RC when we catch the ball, our catching skill functions "as-if" we were doing so. In that case, the question of whether we could catch better with RC would not arise, at least if we take an "outside" view of what constitutes RC. 14

Much less, if the ball is thrown fast -- see von Hofsten (1987).

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However, a number of facts, reviewed in von Hofsten (1987), make it clear that catching skill cannot be modelled from the "outside" as a RC process, any more than it is experienced from the "inside" as one. For example, if you were rationally choosing where to move your arm, you would need to carry out a calculation based on information about the ball's position. Whatever information you used in this calculation would also be available to you if you were contemplating another action that also depended upon it. In fact, though, subjects asked to locate a flashing target on a light-board are unable to verbally report its location with any accuracy, even after training -- while they are able to smash it with a hammer. Again, when children are asked to intercept a target dot on an oscilloscope with a cursor operated by a slide control, they make timing errors more than an order of magnitude greater than that of infants grabbing at a moving target. Catching does not seem to be decomposable into distinct stages of context representation, consequence valuation and calculation of anticipated value, as required for RC. Instead, it is a highly context-specific skill that inextricably integrates perception, valuation and action. We claim that catching is a better metaphor for expert action than is the evening at the casino. The experientially-based feedback process on which expertise is built works like catching: it directly connects context recognition with appropriate action. In contrast, it generally does not equip experts with any special ability to construct abstract representations of future consequences, evaluate the attractiveness of these consequences, or estimate the frequencies of particular classes of events. In fact, much of the literature that purports to show how flawed expertise can be really only shows how poorly experts carry out these latter, RC-required tasks. Expert judgement is not the issue for us; expert action is. It is hard to see how performance would improve by requiring experts to abandon the cognitive modalities in which their expertise is embedded for procedures that depend on judgemental tasks that they seem neither evolutionarily or experientially equipped to handle. Think of catching again: if a friend hits a baseball towards you, practice and embodied skill make it possible for you to catch it, even though you have only the haziest ideas about how far away your friend is, how fast the ball is travelling, and how high the ball gets at the top of its flight. The above argument does not imply that there are no situations in which the judgemental tasks required by RC can be mastered with experience and then profitably applied to decision-making. In such situations, a prescriptive argument for RC may be justified. Our argument does imply, however, that such situations are quite special. To determine what they are -- and thus how RC fits into the broader canvas of

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economic action -- a more general theory of economic action, one not based on the universality assertion RC1, is required. 4.5. Are experts economic actors? It might be argued, in rebuttal to our claims about expert action, that the experts we are describing are not economic actors at all. Rather, the actors who matter are the decision-makers that “in the end” decide what to do, like the presidents of our ACD or pharmaceutical companies. Perhaps these decision-makers do engage in RC, relying on experts from within and outside their companies to provide information that they then use to represent their choice situations and make the judgements about uncertainty and value that RC requires. This argument fails, for two reasons. First, as we will see in next two sections, the actions of service representatives, engineers, financial officers, drug safety officers and medical directors matter -- and they cannot be interpreted solely in terms of how they influence the strategies determined by company presidents and other “pure” decision-makers. Second, the presidents of our companies are experts too, in planning and coordinating actions carried out in the name of their companies. Everything we have said about experts in general applies to them too, so why should we expect that they typically engage in RC, any more than other experts do?

5.

Generative

relationships

Economic agents do not just act, they interact. Often, interactions between particular sets of agents take place in recurring patterns that persist over time. These interactions may give rise to relationships between the participants that we call generative relationships (GRs). It is through their participation in GRs that economic agents come to understand their world and how to act in it. Moreover, GRs are the structures in and through which economic innovation takes place. The most important characteristics of GR interactions are summarized in the following three claims: GR1. GRs are g e n e r a t i v e : Interactions amongst the participants in a GR can induce changes in the way the participants conceive of their world and act in it, and they can even give rise to new entities, such as artifacts, agents or institutions. GR2. GR constructions are emergent: The attributions, competences and entities that are constructed from GR interactions cannot be predicted from a knowledge of the characteristics of the participating agents alone, without knowledge of the structure and history of the interactions that constitute the GR.

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GR3. Expectation of benefits not presently conceived: The agents that participate in GRs may do so precisely because they expect the GR to generate emergent constructions from which they will benefit. When they enter into the relationships, however, they cannot even conceive the form that these constructions and benefits will take. In this section, we present examples that clarify and support these claims, and we argue that RC provides an inadequate basis for understanding the genesis and economic importance of GR interactions. The section is organized as follows. In 5.1, we introduce the key concepts underlying GR1-3 in a simple, noneconomic setting. In 5.2, we extend these concepts to economic contexts, through an example in which a GR consisting of a manufacturer and a customer generates a mutually beneficial innovation that would be literally inconceivable by the participating agents operating alone. Then, in 5.3, we present an argument, generalizing the example of 5.2, that RC provides an inadequate basis for understanding GR interactions. Finally, in 5.4, we consider some implications of the fact that most economic agents engage in relationships with many other agents, giving rise to cross-cutting networks of GRs. We show how many economic actions can best be interpreted in terms of the structure of these networks, a point of view from which a RC perspective has little to offer.15 5.1 Games worth playing To introduce the idea of a GR, we begin with a simple setting analyzed by Leifer (1991). Two agents engage each other in a zero-sum game that is, in Leifer's terminology, "worth playing". Some games, like tic-tac-toe, can be thought through, ex ante, all the way to the end -- that is, the players can easily analyze the games and select their preferred strategies on the basis of their analysis, before 15

Oliver Williamson (1985) is also concerned with the problem of the formation of relationships between agents, when the agents know that they cannot specify all the future consequences that will arise from their interactions. Our point of view is quite different from his, which we would place within the RC framework we are criticizing. In particular, we emphasize several aspects of relationships between agents that are not considered in Williamson's work. First, Williamson does not discuss the idea that agents may enter into relationships in order to construct presently unforeseen situations from which they might mutually benefit (GR3). Second, a primary issue for us is the way in which agents' identities, including their goal structures, change as a result of their participation in relationships; this issue plays no part in Williamson's discussions. Third, the aspects of relationships we emphasize may stand completely outside any contractual basis the relationship may have. For example, while the relationship featured in the innovation example of section 5.2 might not exist without a maintenance contract, there is nothing in that contract that relates to product innovation. In that story, both sides gain, but no money changes hands.

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play ever starts. Since the selected strategies determine the outcomes, nothing is gained by actually playing these games. In contrast, for games like chess, bridge or go, while strategy sets and value formally exist, no one has (yet, anyway) gotten to the end of thinking about them, ex ante. So to determine what happens in these games, the games have to be played. Think now about two players who begin a game worth playing, say chess.16 Before play starts, both players have a global goal: they want to win the game. But the initial position favors neither of them. If the players are both experts, and roughly equally matched, it will be some time before they can come to a position that one or the other of them will recognize as providing an opportunity for achieving victory. In the meantime, they cannot actively pursue their contradictory global goals, because as far as they can look ahead, they arrive only at board positions the values of which are virtually impossible to assess. How can they proceed? Leifer argues that the more experienced and skilled at the game the players are, the more they act cooperatively in this stage of the game, jointly searching to construct an unforeseen outcome in which one or the other of them, recognizing that he has attained an advantage over his opponent, will develop and execute a strategy aimed at victory. Acting cooperatively means executing coordinated series of moves, comprehensible to both players, that are likely to lead to novel, strategically exploitable board positions. Players' moves during the cooperative stage of the game cannot be interpreted from a RC perspective. In the first place, the particular moves the players’ make do not derive from their individual global goals of victory. Second, the players' primary "local" goal is to bring about some consequence that neither can envision precisely, even as they engage in the move sequences that will bring it about. Third, according to Leifer's empirical investigations, the players' orientation to what they are doing during this stage of the game is primarily ex post, not ex ante. When they pause for reflection, they are not thinking primarily about what they will do, but rather about what the relationship they are developing with the other player is revealing of his style, character, abilities and intentions, and about how the other player's characteristics mesh with their own. If Leifer is right, expert chess players enter into a GR, which he calls a “minimal social organization”. Through this GR, the players find out about each other (always in relation to themselves), and they build a world together that will serve as the arena for their strategic struggle, which begins 16

Several of our readers have been puzzled by our characterization of expert chess play. Michael Cohen has suggested another example that such readers might find less counter-intuitive: bicyclists competing in the Tour de France. We retain the chess example in our text and refer our puzzled readers to Leifer’s compelling arguments and data.

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as the GR is dissolved. During the stage of their game in which they participate in the GR, they execute moves drawn from a repertoire of move sequences that have proved their worth in the past for generating novel, strategically exploitable board positions, without giving away too much to their GR partners (and global opponents) about how they might respond once such a position is attained. Their mastery of this repertoire, and the ability to use it cooperatively with the other player, have been gained through long experience in playing and studying chess at the expert level.17 Moreover, what works here and now may not work somewhere else or later on, when different conventions may been established in the relevant community of expert players. As a result, the ability to enter into and sustain this kind of GR needs constant updating, not just with passive "information", but actively, through practice and experience in a particular community of other players. The primary elements of Leifer's analysis carry over into the contexts in which economic agents act. Like expert chess players, economic agents must also understand the other agents with whom they interact -- their suppliers, their competitors, their customers, their bankers, governmental regulatory authorities. This kind of understanding can be obtained only through persisting patterns of interactions, as illustrated by the examples in 5.2 and 5.4. These examples also show how these relationships lead to novel situations that the participants construct together in anticipation of mutual benefits that they cannot clearly foresee, even as they build the relationships that will give rise to them. There are, however, important differences between economic contexts and Leifer's games worth playing. Games worth playing are, after all, still games: they have unchangeable rules that determine who plays, what moves are allowed, and what constitutes a victory.18 Chess players cannot suddenly decide that queens can execute knight's moves, or that the players give up trying to win in order to achieve a particularly aesthetic board position. But in economic contexts, competitors can turn into partners when they form joint research and marketing consortia or when they establish such mutually beneficial institutions as trade associations 17

Novices, when asked to provide commentary on experts' games, generally find the experts' moves in this stage of the game incomprehensible. The novices tend to view all moves in a chess game as though they actually aim at some particular outcome that the player believes will give him an advantage over his opponent. Thus, novices lack both the knowledge of the relevant action repertoire and an understanding of intention to make sense out of what the experts are doing as they construct their CR. 18 At least, that is, at the time they become codified --- and even then, some codes can actually change over time. Chess, after all, like any other social institution, evolved into its present form. Here, we are really contrasting the world of economic action with the notion of “game” as formalized by game theory.

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or industry-wide worker training programs.19 In these, and in many other economic relationships, all participants in the relationship benefit, perhaps in different ways and to different extents. Thus, GRs provide the framework for cooperation of indefinite duration, unlike the chess players who must sooner or later play against rather than with one another. As a result, economic contexts provide much more scope for generative relationships than do games worth playing. GR interactions between economic agents can lead to enduring changes in the identity of economic agents and to the very rules that govern their relations with one another. 5.2 GR construction: an example In this section, we present an example that shows how a GR can generate an innovation. The participants in the GR are employees of our ACD company and one of its customers, a rapidly growing financial services company. The innovation that is the focus of our example is the most important new performance feature of the ACD's next-generation product. The key idea behind this feature came from interactions generated by the GR of our example. The idea seems simple, but it requires a new way of thinking about what an ACD system is. To set the stage for our story, we first describe the old way that our company conceived of an ACD system. Next, we describe the GR that generated the innovation. Then, we tell the story. Finally, we discuss the implications of the story for RC. WHAT IS AN ACD SYSTEM? According to the conception on which our company's current product is based, an ACD system is an interface between its user and the outside agents who contact the user by telephone. This interface must efficiently route calls, either to an appropriate prerecorded tape or to a telephone on an appropriate employee's desk. That is, the system either presents public information to parties interested in that information, or it connects outsiders to an insider who can provide what is needed. THE COMPANY AND THE CUSTOMER AFTER THE SALE: Every ACD system our company sells is customized to the needs of the customer who orders it. When the sale is concluded and the new system is assembled and installed, the maintenance of the system and its reconfiguration to meet the customer’s changing needs becomes the responsibility of the Customer Support Division. The Marketing Division, which managed the interactions with the customer through the sale, also assigns a representative, who calls the customer's telecommunications manager every six months for an update on the system's performance. 19

See for example Sabel and Zeitlin (1993), for a discussion of different patterns of emergent organization among competitor businesses in industrial districts.

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The Customer Support operations work as follows. First, a Customer Support Representative (CSR) is assigned to the new system. The CSR's responsibilities include coordinating and installing the software changes for minor system upgrades and reconfigurations; generating comprehensive statistical performance reports from the event data captured by the system computer; and training customer employees to handle simple reconfigurations, trouble shooting, and statistics reporting by themselves. Initially, the CSR will be in almost daily contact with the employees who operate the system for the customer. Afterward, he remains on call and checks in periodically with the system operators to share ideas he and his colleagues have come up with in their work at other sites. In addition, Customer Support has a group of application engineers, who advise customers on system expansion and design reconfigurations when necessary. These engineers respond to requests from the customer’s telecommunications manager, or they may initiate contact with him if the CSR or the Marketing Division representative thinks that the customer’s system needs changes. In either case, the standard practice is for an engineer to visit the customer, together with the CSR. During this visit, they meet with the telecommunications manager and the people who actually operate the system, and they physically inspect the sites where the anticipated expansion will take place. These arrangements lead to the development of a set of coordinated working relationships between people working for the ACD company (the CSR, the applications engineer, the Marketing representative) and people working for the customer (the system operators and the telecommunications manager). These relationships constitute our example GR. Through this GR, the people from the ACD company find out how their system is used by the customer, in the context of the customer's day-to-day work. As they work together, the people from the two companies form a shared view of what the ACD system is -what it is supposed to do, how it does it, how it fits together with the other means (human and machine) for carrying out the customer's business, what kinds of problems it can develop. The ACD company managers established its Customer Support procedures primarily to ensure that a system functions to the satisfaction of the customer who purchased it. But they also looked for ways that the company could obtain addition benefits from their Customer Support operations by learning more about the system's problems and potential, information that might lead to improvements in their product design. The particular arrangements they instituted were designed to accomplish this by maximizing the opportunity for relationships to develop between company and customer employees, and amongst the different company employees in contact with the customer. For example, if the only issue was to design expansions that customers wanted, it would be more time-efficient if the applications engineers respond to

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written requests from the customer's telecommunications manager specifying the number, nature and location of desired new entry or destination ports. Instead, the procedure guarantees personal contact between the applications engineer and the telecommunications manager -- and other customer employees who use the system in their work. Naturally, the company managers could not foresee in advance just which concrete benefits they might obtain from the relationships their arrangements were designed to facilitate. THE PROBLEM: One year after installing the new system, the telecommunications manager of the financial services company is troubled by the system’s response time. Part of the trouble is that the company is receiving a larger volume of calls than the system was originally designed to handle. Many of these calls are from clients, requesting information about the status of their accounts. Since account information is not public, the system passes these calls to an employee. The employee obtains an account number and password from the caller and enters these on his terminal. The company’s mainframe computer then searches the relevant data base and passes the required information back to the terminal screen, and the employee reads the data to the caller. The manager thinks that these calls could be handled much more quickly if the ACD system could forward them directly to his company's IBM mainframe computer. He has asked the Information Systems manager (his boss) to develop software that would give the mainframe the capability to process these touch-tone requests and provide voice-responses to them. However, there is a huge backlog of requests for mainframe applications, and the necessary software could not be developed for at least three years. As a result, the telecommunications manager has asked one of our company’s applications engineers to discuss with him the feasability of reconfiguring the ACD system to allow for more employees to respond to calls about account information. A POSSIBLE SOLUTION: At this point in the story, the nature of the GR becomes critical. The applications engineer and the CSR come to meet with the relevant customer employees. By now these people all know each other, and their conversation ranges more widely than the immediate problem of expansion. In particular, the manager tells the engineer about his idea for an “internal” solution to his problem, and why it failed to go anywhere. When the application engineer watches an employee enter account data on a terminal, he has an idea. Why not build an interface in the ACD system that could connect directly to the customer’s mainframe? The central processor in the ACD system could be programmed to emulate the terminal that the employee uses to enter data to the mainframe. Then, the system could use its voice prompt and

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touch-tone detection capabilities to obtain the account number and password from the caller, pass it on to the mainframe, capture the output data provided by the mainframe, and convert it to voice for the caller. In this way, these calls could be processed automatically, with a great savings in time and operating costs, with no modifications to existing mainframe software. The extra cost of the interface in the ACD system ought to be small by comparison. At first sight, the application engineer’s idea might seem like a minor modification to the telecommunication manager’s proposed “internal” solution. But in fact it is very different. In particular, if implemented, it would change the very nature of the ACD system, which would have access to data that the financial services company regards as internal or private, not public. The system would no longer be just an interface between a company and “outsiders”; it would now provide tools for the company to deliver customized services to the outsiders. Why stop at account information? Might the system automate certain kinds of financial transactions as well? Once the interface is in place, what other kinds of functions might the system perform, for other kinds of users? This kind of change in the attribution of a product’s nature is one of the fundamental mechanisms in product innovation. Attributions about the nature of the products they make are vitally important to a company; they inform the work of development engineers and marketing directors, not to mention CEO’s. As a result, changing these attributions is not easy. In our story, the initial impetus for change came from an applications engineer, a problem-solver who by the nature of his particular work had no need to develop a strong committment to the company’s conception of its product. Rather, his view of the product was shaped by his interactions with the customers on whose projects he worked. The particular idea that was to lead to a change in the company's view of its product arose in the context of what he heard and saw during his visit to the financial services company. Thus, the structure of the interactions that comprise and arise from our GR created the conditions under which the engineer’s idea was possible, even natural. Clearly, the company managers did not anticipate that their Customer Support procedures would lead to changes in the way they viewed what their product was -- it would be hard for anyone imbued in the old conception to imagine that a n y new attribution of the product’s identity was possible. In this sense, the new attribution is an emergent construction of our GR. AN INNOVATION: Of course, the application engineer’s idea is only an idea. Can it be made to work? The engineer knows that the communication protocol between the mainframe and the terminal is in the public domain, and that many companies make IBM-compatible terminals to connect to IBM mainframes. He remembers that one of the company’s development engineers

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previously worked for one of these companies, and so would know the technical details of the interface. He tries the idea out on her, and she confirms that it would be feasible. The two write a memo proposing a new project and send it to the VPs for Marketing and Engineering. The VP for Marketing, utilizing others of the companycustomer GRs, asks several salesmen to discuss the idea confidentially with telecommunication managers at some customer companies who may have similar problems, and they all report great enthusiasm. Within a month, the new project is under way, and the ACD-mainframe interface will become a key feature in the next-generation product the company is about to launch. The new ACD system will be marketed not as a voice-only system, but as an integrated voice-data system that can significantly increase the communications productivity of businesses in many industries. INNOVATION AND RATIONAL CHOICE: What does RC have to do with this story? The engineer's idea is a connection -between the data-entry process he just observed, the problem he had just heard about from the telecommunications manager, the ACD product with which he works, and a technique with which he was familiar through his training and experience as a software engineer. The idea cannot be construed as a choice. We can understand it by placing it in the context of GR interactions, not by analyzing it in comparison with a set of alternative solutions he never envisioned. Certainly, the company managers made choices when they decided to commit resources to the new project and then when they decided to incorporate the interface in the nextgeneration product. But by the time they made these choices, the context had already changed already changed substantially and with it the framing of “choice situations”, because of the technical implementation the engineer’s idea had undergone and the attributional shift it had brought about. In general, we claim that a RC perspective is an inadequate basis for understanding where innovations come from and how they get instantiated in new products. In these fundamental economic problems, RC processes play at most a subordinate role, while an understanding of the structure of GRs and the interactions to which they give rise is primary. 5.3 RC and GRs: an incompatability argument In this section, we show that a RC perspective cannot provide a basis for understanding GR interactions, as characterized by GR1-3. The argument implies that a RC perspective is inadequate to understand the relation between the actions that build and foster a GR and the benefits that participating agents derive from the GR's emergent constructions. The key to the argument is the difference between a consequence and a GR. A consequence is a particular state of the world. It can be valued, because its description conveys to the valuing agent just what the agent will have gained and

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lost, relative to the current state, at some pre-specified time in the future. To evaluate consequences, agents presume that their value judgements will be the same in the future as they are now, or at least that the agents can predict how their values will change and can make judgements now from the predicted future viewpoint. In contrast, a GR is a process, capable of bringing about states of the world presently not conceivable by the agents participating in the GR . Indeed, the GR may change the very identities of these agents, and the categories in terms of which they (separately and jointly) conceive of “states of the world”. GRs, then, cannot be valued in the same way as consequences. Realistically, agents cannot pretend to know what the future of a GR will be, nor how their own values will be altered by the experience of their participation in it. Rather, the value of GRs to an agent depends on their history and their structure, the kinds and intensity of interactions they currently support. An agent may wish to foster a particular GR or, conversely, to withdraw from one, and, as we will see in 5.4, the agent may embark on a course of action designed to bring such a change about. But neither the wish nor a successful plan to implement it can originate in a prospective comparative evaluation of future prospects, with and without the GR, as required by RC. The argument runs as follows. If agents choose acts i n order to build and foster a GR in which they partipate, then they must expect to accrue benefit from unforeseen consequences. According to RC3, these benefits must be evaluated prospectively as part of the choice process. However, if the benefits are acknowledged to be unforeseen a priori, such an evaluation is by definition impossible. On the other hand, if an analyst, taking an "outside" view, regards the GR and its constructions as unanticipated sideeffects of the actions giving rise to them, then the benefits that accrue from these actions cannot be construed as the intended outcomes of a n y choice process, rational or otherwise. Thus, as in the example of 5.2, a RC perspective will fail to provide any insight into how the agents act to obtain these benefits. 5.4 Relationships as constraint and opportunity Economic agents are typically engaged in interactions with a variety of other agents. The space of agents thus acquires a structure induced by the cross-cutting network of relationships that link agents in recurring patterns of interactions. A single action may affect many different relationships, directly and indirectly. In this section, we examine the implications of this kind of situation for RC. Our discussion focuses on what happened in the two meetings we described in section 2. In both meetings, it turned out to be exceedingly difficult to find any possible course of action that did not affect ongoing relationships with other agents in unacceptable ways. And in both meetings, the

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solutions that finally emerged depended crucially on the possibility of working through particular existing GRs to create new, mutually beneficial patterns of interactions. Thus, the network of relationships with other agents in which our companies participated constrained the set of possible actions they could take, but also provided the opportunities by means of which they could simultaneously satisfy these constraints. We first describe the plans for action that came out of the two meetings. Then, we show why these plans cannot be adequately interpreted from a RC perspective. EXAMPLE 1: The meeting about the four case reports began with a discussion about the kinds of reactions the company might anticipate to the possible implications of the reports. The marketing people were worried about the effects on prescribers. Through their detail people who regularly visit prescribers in their offices and clinics, the marketing people feel that they understand the attitudes of prescribers to NSAIDs and to safety problems. Prescribers tend to avoid drugs tainted by suspicions of safety problems, as long as there exist untainted alternatives with comparable efficacy. NSAIDs differ in efficacy, but not enough to prevent prescribers from switching away from one they believe may cause agranulocytosis. The marketing people believe the company should down-play the reports as much as possible, consistent with their legal reporting obligations. They want to assign detail people to go to the reporting physicians to gather additional evidence that might suggest other causes, and to emphasize this evidence and the extensive history of problem-free prescribing in an explanatory report to the FDA monitors. They oppose any further action, which they fear might draw attention to the problem. However, if for some reason, the reports do attract attention, or more reports come in, then it is important to convey to prescribers a sense that the company is taking decisive action to find out whether a problem really exists. In this way, even though there will be some prescribers who will switch NSAIDS, those who prefer the drug strongly enough may stick with it until the facts are in. At the least, their fears of successful liability claims will be reduced, since the company's vigorous actions to find out if the drug can cause agranulocytosis help constitute a defense against negligence on the part of the prescribers. The attitudes of the adverse reaction surveillance group reflect their experiences as part of the international drug safety community, which contains workers from other pharmaceutical companies as well as national regulatory authorities, including the FDA. The members of this community meet frequently at conventions and workshops on safety issues sponsored by organizations like the Drug Information Association, where they learn about surveillance methods and develop mutual expectations about what kinds of

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reports and follow-up studies are appropriate in which situations. Thus, they want to make sure that the company at least acts in accordance with their understanding of what the FDA expects in such situations. Moreover, they know that the clinical research group is right now engaged in some very delicate discussions with the FDA about the admissibility of some safety evidence concerning a new anti-depressant drug currently going through the approval process. This evidence comes from clinical data collected in Germany, where the drug is already licensed for sale. If the FDA agrees to accept this evidence, the company will save substantial money and time in a clinical trial now under design. If the company becomes embroiled in a dispute about how it should have reacted to the four case reports, the FDA may be less inclined to accept the company's interpretation of the German safety data. The head of the legal department also wants to be sure that the company stays inside FDA expectations. But she has a different view of these expectations, which is based on a close reading of formal regulations. For example, the adverse reaction group believes that the company's standing with the FDA will suffer if they fail to develop some active follow-up to the case reports. The lawyer does not agree with this conclusion. Rather, since she must worry about the company's position in any future liability litigation, she wants to make sure that nowhere in the company files will there be any documents that can be construed as an admission that the NSAID causes agranulocytosis. As a result, she is concerned about the possible wording of any documents that suggest the need for a follow-up study. The head of the marketing group reminds the others of another set of relationships that matter. The companies that produce rival NSAIDs stand to gain substantial market share if the suspicions about the company's drug become public knowledge, and through their detail people they may have already heard about some of the reported cases -- or others, if the drug really does cause agranulocytosis. They will certainly try to find a way to publicize such cases if they learn about them, although they may fail to find a prescriber who has observed such a case willing to write it up for a medical journal, if the company enjoys the respect amongst prescribers it thinks it has -- and is seen to be carrying out its own investigation of the problem in an unbiased way. At this point, the conflicting constraints imposed by prescribers, regulators, competitors and the legal system look impossible to satisfy. To maintain its current standing with the FDA as a safety-conscious company that can be trusted to interpret safety data in an objective way, the company cannot just dismiss the reports as temporal coincidences. Yet any positive action the company takes may lead to undesirable publicity that will adversely affect their relationship with prescribers, unless the action leads to a quick and convincing exoneration of the drug's role in

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the agranulocytosis cases, which cannot be guaranteed a priori -- and, if it is too quick, would probably fail to be convincing anyway. Moreover, any internal documents expressing suspicion that the drug can cause agranulocytosis may jeopardize the company in future liability claims -- but putting together a study without expressing any suspicions whatsoever can look suspiciously like a white-wash to the FDA, prescribers, and competitors. And if the company is tempted by all this back into inaction, there is the spectre of competitors raising the issue themselves, which would have disastrous effects on all the relationships they value. What is really needed is a way to initiate an investigation that can conclusively determine whether or not their NSAID causes agranulocytosis, without in any way implying that it might do so -- and to guarantee that prescribers will not switch to an alternative product until the investigation yields its answer. When the issue is formulated in this way, the medical director thinks he may have an answer. Over the past several years, the company has awarded grants and contracts to academics in the fledgling profession of pharmacoepidemiology, on the assumption that "clean" scientific data might give a better handle on which safety issues are real than does the current system based on case reports, anecdotes and the occasional press uproar. But the relationship is still tenuous, since the more efficiently, thoroughly and publicly the pharmacoepidemiologists ferret out new safety problems, the more frequently will the drugs under study be at risk for regulatory action or market losses -- even if the newly discovered problems are more "statistically signficant" than they are important from a public health point of view. However, the present situation might be an ideal opportunity to embark on an ambitious pharmacoepidemiological project that will advance the profession and benefit the company at the same time. Why not respond to the four case reports by announcing that the company's postmarketing surveillance system, regarded as one of the industry's best, has uncovered evidence suggesting that, in rare instances, NSAIDs may cause agranulocytosis -and that, as a result, the company intends to sponsor a large, multicenter prospective case-control study evaluating the link between agranulocytosis and all NSAIDs, their own as well as their competitors'? The study design will be developed by a committee consisting of academic pharmacoepidemiologists together with eminent hematologists, clinical pharmacologists, statisticians, and classical epidemiologists; and the study will be administered by the senior member of the current group of pharmacoepidemiologic researchers. By its size, objectives and the complexity of its design, the study will be path-breaking, and it is likely to significantly advance the current state (and prestige) of pharmacoepidemiology. Based on preliminary statistical power calculations, the study will take several years to generate enough cases to find a causal link, if one exists.

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Because of the company's past support for the research of the pharmacoepidemiologist targetted to head up the study, the medical director envisions little difficulty in convincing him to take on the project. Given his prestige in the medical research community, and other connections the company has through its many clinical research projects and outside consultancies, it should not be difficult to put together a blue-ribbon committee to plan and monitor the study. The study will be expensive, of course, but the cost will be small compared to the profits the company might lose if their drug were to become the victim of widely circulated (even if unfounded) suspicions about the drug's safety. What can the company expect to gain from this plan? Most importantly, the plan shifts the focus of the problem from a possible connection between their NSAID and agranulocytosis to a possible connection between NSAIDs and agranuolocytosis. This can work, only to the extent that the company's surveillance system is acknowledged by experts in the drug safety community (and, by extension, to the medical profession and hence prescribers generally) as state-of-theart, so that the fact that the company came up with the first hints of a connection should occasion no surprise, particularly since their drug is the most widely used NSAID. If the company is widely regarded as safety-conscious and technically advanced, the competitors will be unable to shift the focus back to the particular NSAID that inspired the reports. (In fact, at least two of the patients described in the reports used more than one NSAID in their treatment regimens, and the company will make every effort to find out more about the medication histories of all four patients, as well as ferret out more cases, from anecdotes and the literature, in which patients taking NSAIDs have been afflicted with agranulocytosis.) Second, assuming that the design and monitoring committees will be staffed by eminent scientists and that the leading pharmacoepidemiologist agrees to direct the study, the study will generally be regarded as the way to establish a conclusive, scientific answer to the "question" of the link between NSAIDs and agranulocytosis. Past experience has shown that in this kind of situations, prescribers are willing to suspend judgement and wait until the answer is in before change their prescribing habits. Third, assuming that the FDA will see the study as a positive committment to drug safety rather than as a delaying device, the company will be credited by the FDA with a major public health initiative. And this favorable interpretation of the company's intentions is more consonant with the history of the interactions the company has had with the regulatory agency than is the unfavorable one. Finally, the company will have provided a great opportunity for pharmacoepidemiology. If the study is successful, it will mark a new "gold standard" for drug safety studies. This study, and any others that adopt its basic design, will be expensive, and so the problem of funding will loom larger

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for young researchers anxious to make a career in the profession. As has been the case in the past, and as this study will reinforce, a major share of that funding will come from the pharmaceutical industry, with our company in the vanguard. In this way, the company is well positioned to contribute to the future determination of the role of pharmacoepidemiology in an overall drug safety system, and even to the ongoing process of defining what constitutes adequate scientific proof of drug causation of specific adverse events. Whether the plan that emerged from the meeting will succeed or not depends on how the study and the company's intentions are interpreted by the FDA, prescribers, pharmacoepidemiologists and others -- and this depends in turn on the history and structure of the relationships the company has had with these agents. The issue is not whether the company can trick any of these agents into incorrectly attributing the company's motives in initiating the study. In fact, we cannot say that the decision to initiate the study is motivated solely or even predominantly by the desire to preserve the profit stream generated by its NSAID; certainly, legitimate doubts that the drug really can cause agranulocytosis and a commitment to ensuring the safety of the company's products play important roles as well. The fact is that in this situation, the company has a variety of goals, each appropropriate to different roles it plays in relation to other agents, and these goals, while seemingly inconsistent with each other, are all consistent with the plan that emerged from the meeting. EXAMPLE 2: The ACD company managers are worried about the effects of their decision on the company's key relationships, inside and outside the company. These relationships put completely contradictory constraints on what the company can do and still expect to come out of this crisis intact. Here are some of the relationships the managers have to consider, and the constraints they impose on what the company can do: 1. Customers: Each ACD system is custom configured, and working out the configuration issues calls for a close collaboration between salesmen and engineers for the ACD system and the potential customer's telecommunications manager and his staff. Usually, the potential customer will seek bids from more than one vendor. Despite strict rules to the contrary from the VP Marketing, several salesmen have been informally telling key customers about the new product if they are in a tough sales situation with competitors, in order to delay decisions until the new product is announced. Postponing the launch will probably result in the loss of these sales. On the other hand, the VP Customer Support is extremely worried about what will happen with the customers who buy the new system if the launch takes place before the problem is

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fixed. ACD systems grow with the customers who buy them, and so the relationship between our company and a customer becomes deeper and certainly longer-lived after a system is sold than it was in the period leading up to the sale. Selling a system capable of creating disastrous crashes is not a good way to foster these relationships. The relationship with the test-site company where the crash occurred was already sufficiently strong that it could survive the crash, although heroic Customer Support measures were required to make sure that it did. Customers less strongly attached to our company could not be expected to be so forebearing. 2. Distributors: In some areas, our company works with distributors, independent regional sales/installation/service contractors. The relationship with these distributors is important to the company, not only because they allow the company to sell more systems than company personnel alone could sell and maintain, but because the company has already learned quite a bit and expects to learn more from some of the sales and service procedures the distributors have developed in the context of their own particular markets. The VP Marketing has scheduled a briefing for these distributors two days before the launch. A delay will plant seeds of doubt about the company that could undermine some of these relationships. The distributors can always defect to the competition. 3. Suppliers: Our company has to compete with other companies, not only its product competitors, to establish long-term relationships with the best specialized technology companies and suppliers of components and sub-systems. The VP Engineering is especially worried about a relationship he has established with a small startup company that will supply a key component to the new system. A year ago, he convinced the startup to work exclusively with our company for a period of one year, on the grounds that in this way the startup could prove the value of the component and get it into production more quickly -- and that the royalty stream from the "flash cutover" would give the startup enough cash to pursue other opportunities of interest to them. As a result, the startup is dependent for survival over the next year on the royalties payable as each new system is shipped. If the launch is delayed, the royalty stream will dry up, and the startup may not survive; yet the new system depends on the component that only that company currently knows how to make. Moreover, the VP Engineering has been counting on the startup to work with our company on a new project he thought uniquely suited to its capabilities. Even if the startup survives the dry period, could the company expect it to cooperate in the future?

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4. Financial analysts: Four security analysts are currently following our company, and their judgements carry great weight with the institutional investors who are the primary holders of the company's stock. The VP Finance has been doing everything he can, within SEC regulations, to ensure that these analysts form favorable opinions about the company's future, and in fact the analysts are all currently enthusiastic about the company's prospects. It will be difficult to put a positive spin on the news of a launch delay, but it is important that the analysts not lose faith in the company. In particular, it is essential that none of them stop following the company, since this would nearly inevitably lead to a great drop in the stock price. 5. Company employees: The company managers also must worry about the effect of their decision on their own employees. If the launch is not delayed, the key software engineers assigned to fix the problem will work around the clock until the problem is fixed, but this type of intermittent problem is the hardest to diagnose, and if it drags on there is the real possibility of burnout to key people, in addition to slips in other project schedules. Moreover, many engineers are imbued with a kind of quality ideology, and they will be upset if the company ships the new system before the problem is fixed. Talented engineers are the lifeblood of every high technology company, and recruiting and retaining the limited supply of them is a top company priority. Many other high technology companies in the area would be delighted to hire any of the engineering team our company has assembled, if they become disaffected with the company. After several hours of difficult and sometimes heated discussion, the managers work out a plan of action. They decide that they cannot afford to postpone the launch, so it will go ahead on schedule. Fortunately, the preliminary information they supplied to the press is sufficiently vague that they can introduce some major modifications in their original intentions. The new product will be priced at a 15% premium and positioned as an addition to the product line for customers who need enhanced features and performance. Customers who order the new product will be told that there is limited availability for the first three months due to normal manufacturing ramp-up considerations. The Marketing division will determine the priority with which new orders are met. Moreover, Marketing is authorized to offer a substantially reduced price on the new product to customers with which the company enjoys a strong relationship. Thus, the company hopes to maximize the probability that, before the problem is fixed, the new product will only be delivered to customers the company can count on to work constructively with it to overcome any problems that might result from additional system crashes.

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Prior to the delivery of each new system, the customer will be informed about the possibility of rare outages of less than two minutes duration. They will then be given the option of taking delivery of the old product, to be traded up later at no cost when the problem is fixed. The company is counting on its "friendship" with the customers it has selected for delivery not to publicize the price discount they received, the warning about potential problems with the new system, or any outages that may actually occur. Customer Support will supply a "baby-sitter" technician for each new system that is delivered before the problem is fixed. The technician pool will consist of existing support personnel, together with temporaries who can be trained to detect crashes and re-initialize the system. The availability of these "baby-sitters" and the ability of Manufacturing to produce both new and old products at the same time will determine the number of new systems Marketing will be authorized to deliver. The managers hope that as many old systems as can be built will be shipped and as many new systems as can be supported will be accepted by "friendly" customers. If things go well, total revenues may be around 80% of those anticipated in the pre-crisis plan. As soon as the problem is fixed, a new plan will be developed to announce price reductions and make other changes designed to return as much as possible to the original plan. The plan provides only a sketch of what will actually happen. There are too many uncertainties to try to specify detailed actions in advance. Many adjustments and supplementary ideas will be required, and the managers count on having them emerge in the context of the relationships where they arise. For example, the VP Engineering may need to find a way to supplement the reduced near-term royalties on which the startup is depending for survival. He can use the plan's limited "breathing room" to try to arrange (and enforce) relatively humane work schedules for the sofware engineers. Because of SEC regulations, the VP Finance must report to the public that revenues and profits for the next one or two quarters will almost surely be below prior expectations, but in his report he can cite extraordinary marketing and manufacturing startup costs in justification -and he must interact intensely with the four analysts to try to insure that they do not overreact to the announcement. Manufacturing will have to put extraordinary pressure on its suppliers to make major changes to parts delivery schedules. Customer Support will have to deal with existing customers facing unaccustomed delays, as a large fraction of Customer Support resources are funnelled into the new system installations. The success or failure of the plan -- and the company with it -- will depend on how the interactions in all these relationships proceed, and that in turn will depend on the structure, history and generative power of the relationships themselves.

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RC AND NETWORKS OF RELATIONSHIPS: Here are two reasons why a RC perspective cannot provide an adequate basis for understanding how our two companies will respond to their respective crises. First, even though both meetings resulted in an action plan, "choice" played a minor role. The real accomplishment was the development of the action plan itself. From a RC perspective, an action plan is just one of the ingredients of the context representation mandated by RC2: it represents a possible decision, which must be weighed against its alternatives with respect to their respective consequences. A RC analysis starts with a representation of context as choice situation. Thus, it offers no insights or explanation about where such representations come from. As we saw, to understand the action plans that emerged from the meetings, we needed to understand the structures of the relationships that bound our companies to other agents -- the constraints these relationships imposed on what the company could do, and the opportunities they afforded for generative action. Second, the action plans did not specify in advance every detail of the companies' responses to their respective problems, nor could they do so. The actions that implement the plan will be worked out through the various relationships that groups and individuals inside the company maintain with outside agents. There is no locus from which these relationships can be simultaneously observed and comprehended and the interactions to which they give rise be predicted, let alone controlled. Hence, the pre-committment to action that is fundamental to RC is just not possible in contexts where cross-cutting networks of relationships between agents matter.

6.

Coherence and rational choice

RC imposes strong coherence requirements on agents. According to RC3, an agent chooses what to do on the basis of judgements about the relative value of the various consequences of its possible actions. Thus, in order to act, an agent must resolve any ambiguity about those value judgements before a choice can be made. We call an agent that is capable of achieving an unambiguous assessment of value for the consequences of a projected course of action judgement coherent. According to RC1 and the definition of choice, agents precommit their future actions at the moment of choice. For agents consisting of many component agents, precommittment requires coordination of the actions of the component agents. Agents that have the capability to achieve this coordination we call execution coherence. To be capable of RC, an agent must be judgement and execution coherent. We now argue that most interesting economic agents, including our two example companies, lack both these properties.

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6.1 Economic agents are not judgement coherent Most economic agents have component parts that can themselves be regarded as economic agents. For example, the head of the pharmaceutical company's legal department, its medical director, and its adverse reaction surveillance group are each the loci of economic actions, and hence are economic agents. Clearly, the component agents may differ in their values, reflecting differences in the training and background of the people of which they are composed, the kind of work they do, and the other agents with whom they interact. We have already seen examples of such diffences in the examples of section 5.4. Mere value differences between component agents is not enough to make an agent judgement incoherent. These differences have to affect the agent’s actions in ways that are incompatible with RC. Let us suppose that the agent’s actions are based upon a single, unambiguous set of value judgements, which we personalize as the values of a "decision-maker". The decision-maker -- who might in reality be a single person or a group 2 0 of people -- has the responsibility of choosing which action the agent will take. We suppose that the decision-maker acts in accordance with RC2 and RC3. That is, the decision-maker represents the context of the problem as a choice situation, applies his value judgements, and then chooses which action to take. Where do the other component agents fit into this decision process? Some of these agents are experts, who supply the decision-maker with information on the basis of which he constructs his representation of the choice situation. This information will affect which consequences the decision-maker considers, how likely he thinks each consequence is to happen, and even his judgement of their value. Thus, the choice he makes will depend on the information supplied to him. But this information is not value-free. For example, consider the causality assessment submitted by the adverse reaction surveillance group. These assessments are based on standards of evidence originating in the group members' epidemiological and statistical training and committment to public health, rather than the standards of evidence of a lawyer defending against a liability suit in court. The contradictions between the values of different component agents would pose no problem for RC if the decision-maker could peel away the “pure” information from the value judgements in the reports he receives from these agents. But this is impossible. In particular, the underlying value judgements are frequently hidden behind a 20

We are not interested here in the familiar difficulty of how to aggregate the value judgements of a set of different people into a common measure of value. We suppose that there is in fact a single locus where consequences are valued, the DM, and that at this locus there is an unambiguous, coherent set of value judgements for all the consequences of all the contemplated possible actions.

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veil of technical language. For example, the adverse reaction surveillance group's causality assessments consist of detailed analyses of the clinical data in the case reports. The report bristles with terms and techniques from pharmacology, hematology, epidemiology and statistics. There is no explicit discussion of the meaning of "cause" or "probability" that underlie the analysis; the report’s technical language would not allow such a discussion, even if the group were inclined to write one. The decision-maker knows too little of the world the language is meant to describe to penetrate behind the veil to the values underneath.21 Thus, the values of the component agents that supply information to the decision-maker will partly determine the action he ends up choosing, through their effects on the decision-maker’s representation of the choice situation. As a result, the agent’s actions will not be based just on the decision-maker’s coherent value judgements, but on an ambiguous mixture of the values of all the component agents on whose reports the decision-maker relies. Even though the decision-maker may be judgement coherent, the agent for whom he chooses is not. 6.2 Economic agents are not execution coherent Suppose that the decision-maker of a composite agent makes a choice and thus precommits the agent to a particular, fully specified course of action. The course of action is to be implemented by various component agents. What guarantees that they execute the actions assigned to them, exactly as specified by the decision-maker’s choice? As we have seen, different component agents may have different values and hence different interpretations of what is in the best interests of the composite agent. Authority and reporting relations are designed to channel the activities of component entities so that their actions correspond to the decision-maker’s interpretations of the composite agent’s interests. We claim that these relations cannot suffice, for three reasons. First, many component agents are engaged in their own particular networks of relationships with other agents. As discussed in 5.4, the interactions that take place in these relationships may give rise to novel situations. The component agents will respond to these situations in accordance with their own interpretation of the composite 21

The people who wrote the report are habituated to their technical language from their training and professional experience. For them, it is the only way to describe the world about which they have their specialized knowledge. Thus, they may be just as blind to the values embedded in the language as are outsiders like the DM. (The difference is that they all share these values, while the DM, if he encountered them explicitly, might not.) Thus, they need have no intention to mislead the DM; their values will be inserted into the decision process all the same.

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agent’s interest, which may lead to actions that, from the point of view of the decision-maker’s values, are at crosspurposes with the decision-maker’s chosen course of action. For example, while prepared texts of talks given by members of the adverse reaction surveillance group at Drug Information Association meetings are screened in advance by the company’s legal department, the discussions that follow these talks cannot be effectively controlled a priori; yet they may play an important role in shaping the perceptions of competitors and F.D.A. officials about how the company intends to respond to new safety problems. Second, the actions of component agent cannot be monitored completely effectively, because of limitations on the monitor’s time and experience-based knowledge of the relevant sphere of activity. Neither the legal department nor marketing know enough about the possible mechanisms of druginduced agranulocytosis to control the causality assessments of the adverse reaction surveillance group. Authority cannot be exercised in the absence of effective monitoring. Third, conflict between different component agents can lead to mutiny or defection. When this happens, not only will expected actions fail to occur, but other actions, hitherto unforeseen, will be required to respond to attributions other agents make about the reasons behind the observed personnel changes. What would F.D.A. officials think -- and do -- if the whole adverse reaction surveillance group were suddenly to resign? These three factors limit the extent to which the decisionmaker can exert authority over the component agents whose actions he seeks to control. As a result, no decision-maker can be assured of sufficient authority to guarantee that his pre-specified course of action will be executed by component agents, exactly according to plan.

7.

Conclusion

In this paper, we have presented two kinds of arguments against the appropriateness of a RC-based theory of economic action. The first kind of argument is cognitive: economic agents are not the kind of entities that conceptualize their world in the ways required by RC. The second kind of argument is structural. Economic agents experience and understand their worlds only through their interactions with other agents. Interactions amongst agents give rise to networks of relationships, which generate actions that cannot be understood from a RC perspective -- and may even be incompatible with RC. Our arguments do not imply that economic agents never engage in RC or that RC never offers analytic insights into economic actions. Rather, they suggest that some alternative, more general foundation for economic theory is desirable, especially to shed light on such generative economic phenomena as innovation. In a companion paper, we embark on the construction of such a foundation.

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8.

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