The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote (...) the belief that games represent an excellent tool for the project of computational psychology (CP). To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind. To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this. The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked. (shrink)

There is a tendency in decision‐making research to treat uncertainty only as a problem to be overcome. But it is also a feature that can be leveraged, particularly in social interaction. Comparing the behavior of profitable and unprofitable poker players, we reveal a strategic use of information processing that keeps decision makers unpredictable. To win at poker, a player must exploit public signals from others. But using public inputs makes it easier for an observer to reconstruct that player's strategy and (...) predict his or her behavior. How should players trade off between exploiting profitable opportunities and remaining unexploitable themselves? Using a recent multivariate approach to information theoretic data analysis and 1.75 million hands of online two‐player No‐Limit Texas Hold'em, we find that the important difference between winning and losing players is not in the amount of information they process, but how they process it. In particular, winning players are better at integrative information processing—creating new information from the interaction between their cards and their opponents’ signals. We argue that integrative information processing does not just produce better decisions, it makes decision‐making harder for others to reverse engineer, as an expert poker player's cards act like the private key in public‐key cryptography. Poker players encrypt their reasoning with the way they process information. The encryption function of integrative information processing makes it possible for players to exploit others while remaining unexploitable. By recognizing the act of information processing as a strategic behavior in its own right, we offer a detailed account of how experts use endemic uncertainty to conceal their intentions in high‐stakes competitive environments, and we highlight new opportunities between cognitive science, information theory, and game theory. (shrink)