Maarten Janssen

Many recent developments in artificial intelligence research are relevant for traditional issues in the philosophy of science. One of the developments in AI research we want to focus on in this article is diagnostic reasoning, which we consider to be of interest for the theory of explanation in general and for an understanding of explanatory arguments in economic science in particular. Usually, explanation is primarily discussed in terms of deductive inferences in classical logic. However, in recent AI research it is observed that a diagnostic explanation is actually quite different from deductive reasoning. In diagnostic reasoning the emphasis is on restoring consistency rather than on deduction. Intuitively speaking, the problem diagnostic reasoning is concerned with is the following. Consider a description of a system in which the normal behavior of the system is characterized and an observation that conflicts with this normal behavior. The diagnostic problem is to determine which of the components of the system can, when assumed to be functioning abnormally, account for the conflicting observation. A diagnosis is a set of allegedly malfunctioning components that can be used to restore the consistency of the system description and the observation. In this article, this kind of reasoning is formalized and we show its importance for the theory of explanation. We will show how the diagnosis nondeductively explains the discrepancy between the observed and the correct system behavior. The article also shows the relevance of the subject for real scientific arguments by showing that examples of diagnostic reasoning can be found in Friedman's Theory of the Consumption Function. Moreover, it places the philosophical implications of diagnostic reasoning in the context of Mill's aprioristic methodology.

On many occasions, individuals are able to coordinate their actions. The first empirical evidence to this effect has been described by Schelling (1960) in an informal experiment. His results were corroborated many years later by Mehta et al. (1994a,b) and Bacharach and Bernasconi (1997). From the point of view of mainstream game theory, the success of individuals in coordinating their actions is something of a mystery. If there are two or more strict Nash equilibria, mainstream game theory has no means of explaining why people tend to choose their part of one and the same equilibrium. Textbooks (see, e.g., Rasmusen, 1989 and Kreps, 1990) refer to the fact that players may use focal points (see Schelling (1960)) or social conventions (see Lewis (1969)). Both notions cannot easily be incorporated into mainstream game theory, however. The notion of social conventions has recently been extensively studied in the context of evolutionary game theory where a population of agents interacts with each other. The central focus of this paper, however, is on situations where a few players play a game only once and I study how they may coordinate their actions.

Focal points seem to be important in helping players coordinate their strategies in coordination problems. Game theory lacks, however, a formal theory of focal points. This paper proposes a theory of focal points that is based on individual rationality considerations. The two principles upon which the theory rest are the Principle of Insufficient Reason (IR) and a Principle of Individual Team Member Rationality. The way IR is modelled combines the classic notion of description symmetry and a new notion of pay-off symmetry, which yields different predictions in a variety of games. The theory can explain why people do better than pure randomization in matching games