Game Theory: The Language of Social Science?

The present paper tries in a largely non-technical way to discuss the aim, the basic notions and methods as well as the limits of game theory under the aspect of providing a general modelling method or language for social sciences.

An overview of economic applications of David Schmeidler`s models of decision making under uncertainty

This paper surveys some economic applications of the decision theoretic framework pioneered by David Schmeidler to model effects of ambiguity. We have organized the discussion principally around three themes: financial markets, contractual arrangements and game theory. The first section discusses papers that have contributed to a better understanding of financial market outcomes based on ambiguity aversion. The second section focusses on contractual arrangements and is divided into two sub-sections. The first sub-section reports research on optimal risk sharing arrangements, while in the second sub-section, discusses research on incentive contracts. The third section concentrates on strategic interaction and reviews several papers that have extended different game theoretic solution concepts to settings with ambiguity averse players. A final section deals with several contributions which while not dealing with ambiguity per se, are linked at a formal level, in terms of the pure mathematical structures involved, with Schmeidler`s models of decision making under ambiguity. These contributions involve issues such as, inequality measurement, intertemporal decision making and multi-attribute choice.Ellsberg Paradox, Ambiguity aversion, Uncertainty aversion

From Wald to Savage: homo economicus becomes a Bayesian statistician

Bayesian rationality is the paradigm of rational behavior in neoclassical economics. A rational agent in an economic model is one who maximizes her subjective expected utility and consistently revises her beliefs according to Bayes’s rule. The paper raises the question of how, when and why this characterization of rationality came to be endorsed by mainstream economists. Though no definitive answer is provided, it is argued that the question is far from trivial and of great historiographic importance. The story begins with Abraham Wald’s behaviorist approach to statistics and culminates with Leonard J. Savage’s elaboration of subjective expected utility theory in his 1954 classic The Foundations of Statistics. It is the latter’s acknowledged fiasco to achieve its planned goal, the reinterpretation of traditional inferential techniques along subjectivist and behaviorist lines, which raises the puzzle of how a failed project in statistics could turn into such a tremendous hit in economics. A couple of tentative answers are also offered, involving the role of the consistency requirement in neoclassical analysis and the impact of the postwar transformation of US business schools.Savage, Wald, rational behavior, Bayesian decision theory, subjective probability, minimax rule, statistical decision functions, neoclassical economics

The New Basel Accord and the Nature of Risk: A Game Theoretic Perspective

Basel II changes risk management in banks strongly. Internal rating procedures would lead one to expect that banks are changing over to active risk control. But, if risk management is no longer a simple "game against nature", if all agents involved are active players then a shift from a non-strategic model setting (measuring event risk stochastically) to a more general strategic model setting (measuring behavioral risk adequately) comes true. Knowing that a game is any situation in which the players make strategic decisions – i.e., decisions that take into account each other's actions and responses – game theory is a useful set of tools for better understanding different risk settings. Embedded in a short history of the Basel Accord in this article we introduce some basic ideas of game theory in the context of rating procedures in accordance with Basel II. As well, some insight is given how game theory works. Here, the primary value of game theory stems from its focus on behavioral risk: risk when all agents are presumed rational, each attempting to anticipate likely actions and reactions by its rivals --New Basel Accord,event risk,behavioral risk,rating,simple game,Nash-equilibrium,game theory

Towards Machine Wald

The past century has seen a steady increase in the need of estimating and predicting complex systems and making (possibly critical) decisions with limited information. Although computers have made possible the numerical evaluation of sophisticated statistical models, these models are still designed \emph{by humans} because there is currently no known recipe or algorithm for dividing the design of a statistical model into a sequence of arithmetic operations. Indeed enabling computers to \emph{think} as \emph{humans} have the ability to do when faced with uncertainty is challenging in several major ways: (1) Finding optimal statistical models remains to be formulated as a well posed problem when information on the system of interest is incomplete and comes in the form of a complex combination of sample data, partial knowledge of constitutive relations and a limited description of the distribution of input random variables. (2) The space of admissible scenarios along with the space of relevant information, assumptions, and/or beliefs, tend to be infinite dimensional, whereas calculus on a computer is necessarily discrete and finite. With this purpose, this paper explores the foundations of a rigorous framework for the scientific computation of optimal statistical estimators/models and reviews their connections with Decision Theory, Machine Learning, Bayesian Inference, Stochastic Optimization, Robust Optimization, Optimal Uncertainty Quantification and Information Based Complexity.Comment: 37 page