Hard Obscurantism and Unrealistic Models in Economics

The philosopher and social scientist Jon Elster is well-known for his critical and insightful views about the (ir)relevance of rational choice theory (RCT) in the social sciences. Among his recent writings on the subject, Elster has published last year a paper in the philosophy journal Synthese concerning what he calls “hard obscurantism” in economic modeling (gated version here). By hard obscurantism, Elster essentially refers to a practice where “ends and procedures become ends in themselves, dissociated form their explanatory functions” (p. 2163). This includes many rational choice models, but also a part of agent-based modeling, behavioral economics and statistical analysis in economics.

Elster’s paper focuses on the case of rational choice models and builds on several “case studies” that are thought to illustrate the practice of hard obscurantism. These case studies include Akerlof & Dickens’s and Rabin’s use of cognitive dissonance theory, Becker and Mulligan’s accounts of altruism as well as Acemoglu & Robinson’s theory of political transitions. Beyond these examples, Elster underlines two general problems with rational choice models and more generally with RAT: first, theory is indeterminate, second it ignores the irrationality of the agents. Indetermination is indeed a well-known problem that is partly (though not equivalent) related to the existence of multiple equilibria in many rational choice models. According to Elster, it has three sources: (i) the fact that the determination of the optimal amount of information leads to an infinite regress (i.e. to compute the marginal utility of information requires to collect the information but whether or not to collect the information necessitates to know its marginal utility), (ii) brute and strategic uncertainty (the latter is of course closely related to the existence of multiple equilibria) and (iii) the agents’ cognitive limitations. The latter is regarded by Elster as the most important source and is somewhat related to the irrationality problem. In Elster’s words,

“How can we impute to real-life agents the capacity to make in real time the calculations that occupy many pages of mathematical appendixes in the leading journals and that can be acquired only through years of professional training?” (p. 2166)

Elster’s objection is hardly new and many different responses have been developed. It is not my intention to survey them. I shall rather on one issue that follows from Elster’s critique: can we learn anything with unrealistic models and how? There is an empirical disagreement among economists regarding the degree at which individual agents are truly irrational. Against the behavioral economists’ claim that individuals’ behavior and reasoning exhibit a long list of biases, other economists claim that this depends on the institutional setting in which individuals’ choices take place (for instance, it is probably not true that hyperbolic discounting is dominant in many market and many biases seem to diminish in importance if agents have the opportunity to learn). It is a fact however that individuals’ behaviors do not have the consistency properties that most rational choice models assume they have. Moreover, most rational choice models are unrealistic beyond their “behavioral” assumptions about agents’ reasoning abilities. They also make rather unrealistic “structural” assumptions such as for instance the number of players, the homogeneity of their preferences, the fact that features of the game are common knowledge, and so on. A good example among the case discussed by Elster is Acemoglu & Robinson’s theory of political transitions. The latter builds on a game-theoretic model with only two players which are thought to be representative of two groups of actors, the elites and the citizens. The preferences of the members of each group are assumed to be homogenous and, for the citizens group, to correspond to the median voter’s preferences. The model also makes several strong assumptions regarding what the players are knowing.

So, can we learn anything about real world mechanisms from such unrealistic models? The philosopher of social science Harold Kincaid has recently made an interesting suggestion for a (partially) positive answer. Kincaid rightly starts by indicating that it is vain to search for a general defense of unrealistic models in the social sciences and that each evaluation must be made on a case-by-case basis. Regarding perfect competition and game-theoretic models, Kincaid argues that may offer relevant explanations in spite of the fact that they build on highly unrealistic assumptions:

“The insight is that assumptions of the perfect competition and game theory models may just be assumptions the analyst – the economist or political scientist – uses to identify equilibria. However, in certain empirical applications, the explanations are equilibrium explanations that make no commitment to what process leads individuals to find equilibrium”

In my view, this account of the relevance of unrealistic models particularly works well in the case of mechanism design which is at the same time a highly theoretical but also applied branch of microeconomics. A typical approach in mechanism design is to consider that the right institutional design will entail equilibrium play from the players, even if the designer ignores the players’ actual preferences. The modeler does not make any commitment regarding how the players will find their way to the equilibrium. The model simply indicates that if the institutional set up has such or such characteristics (e.g. a continuous double bid auction), then the outcome will have such or such characteristics (e.g. allocative efficiency). It is then possible to check for this conjecture through experiments.

On this account, the model is thus merely a device to identify the equilibrium but has no use for explaining the mechanism through which the equilibrium is reached. It is not sure however that this account applies to rational choice models used in other settings, especially if experiments are impossible. For instance, Acemoglu & Robinson’s model highlight the importance of commitment to explain political transitions. Indeed, their theory aims at accounting for the change from a dictatorial equilibrium toward a democratic equilibrium. The elites’ ability to commit not to raise taxes in the future is the key feature that determines whether or not the political transition will occur. The model thus suggests that a highly general mechanism is at play but it is unsure which level of confidence we can have in this explanation given the highly unrealistic assumptions on which it builds. An alternative defense would be that the model’s value comes from the fact that it highlights a mechanism that may possibly partially explain political transitions. Thanks to the model, we perfectly understand how this mechanism works, even though we cannot be sure that this mechanism is actually responsible for the relevant phenomenon to be explained. In other words, the relevance of the model comes from the fact that it depicts a possible world which we are able to fully explore and that this world bears some (even remote) resemblance with the actual world. As I have argued elsewhere, many models in economics seem to be valued for this reason.

The problem with this last account is that, while it may explain why economists give credence to rational choice models, it is highly unlikely to convince skeptics like Elster that they are explanatory relevant. Indeed, as Elster has argued elsewhere, the academic value given to these models may itself result from the fact that the economic profession is trapped in a bad equilibrium.

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