This paper proposes an alternative analysis of the accrual anomaly. It checks whether the accrual anomaly documented by Sloan (1996) exists in the game-theoretic setting. The game-theoretic tests identify the accrual anomaly in the following sense: a trading strategy that is based on the accrual

We study the consequences of thruthful communication among agents in a game-theoretic setting. To this end we consider strategic games in the presence of an interaction structure, which specifies groups of players who can communicate their preferences with each other, assuming that initially each

We investigate the use of standard statistical models for quantal choice in a game theoretic setting. Players choose strategies based on relative expected utility, and assume other players do so as well. We define a Quantal Response Equilibrium (QRE) as a fixed point of this process, and establish

Models for the processes by which ideas and influence propagate through a social network have been studied in a number of domains, including the diffusion of medical and technological innovations, the sudden and widespread adoption of various strategies in game-theoretic settings, and the effects

Multi-Agent Influence Diagrams (MAIDs) are a compact modelling language for representing game theoretic settings.

Abstract not found

A system with multiple interacting agents (whether artificial or human) is often best analyzed using game-theoretic tools. Unfortunately, while the formal foundations are well-established, standard computational techniques for game-theoretic reasoning are inadequate for dealing with realistic games

, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

Abstract not found

We present an algorithm for improving the accuracy of algorithms for learning binary concepts. The improvement is achieved by combining a large number of hypotheses, each of which is generated by training the given learning algorithm on a different set of examples. Our algorithm is based on ideas