Stochastic game

About: Stochastic game is a research topic. Over the lifetime, 9493 publications have been published within this topic receiving 202664 citations.

“In-Group Love” and “Out-Group Hate” in Repeated Interaction Between Groups

Abstract: Costly individual participation in intergroup conflict can be motivated by ‘‘in-group love’’—a cooperative motivation to help the in-group, by ‘‘out-group hate’’—an aggressive or competitive motivation to hurt the out-group, or both. This study employed a recently developed game paradigm (Halevy, Bornstein, & Sagiv, 2008) designed specifically to distinguish between these two motives. The game was played repeatedly between two groups with three players in each group. In addition, we manipulated the payoff structure of the interaction that preceded the game such that half of the groups experienced peaceful coexistence and the other half experienced heightened conflict prior to the game. Enabling group members to express in-group love independently of out-group hate significantly reduced intergroup conflict. Group members strongly preferred to cooperate within their group, rather than to compete against the out-group for relative standing, even in the condition in which the repeated game was preceded by conflict. Although both ‘‘in-group love’’ and ‘‘out-group hate’’ somewhat diminished as the game continued (as players became more selfish), choices indicative of the former motivation were significantly more frequent than choices indicative of the latter throughout the interaction. We discuss the implications of these findings for conflict resolution. Copyright # 2011 John Wiley & Sons, Ltd.

Transforming the dilemma

Abstract: How does natural selection lead to cooperation between competing individuals? The Prisoner's Dilemma captures the essence of this problem. Two players can either cooperate or defect. The payoff for mutual cooperation, R, is greater than the payoff for mutual defection, P. But a defector versus a cooperator receives the highest payoff, T, where as the cooperator obtains the lowest payoff, S. Hence, the Prisoner's Dilemma is defined by the payoff ranking T > R > P > S. In a well-mixed population, defectors always have a higher expected payoff than cooperators, and therefore natural selection favors defectors. The evolution of cooperation requires specific mechanisms. Here we discuss five mechanisms for the evolution of cooperation: direct reciprocity, indirect reciprocity, kin selection, group selection, and network reciprocity (or graph selection). Each mechanism leads to a transformation of the Prisoner's Dilemma payoff matrix. From the transformed matrices, we derive the fundamental conditions for the evolution of cooperation. The transformed matrices can be used in standard frameworks of evolutionary dynamics such as the replicator equation or stochastic processes of game dynamics in finite populations.

Game Theory and Multi-agent Reinforcement Learning

Abstract: Reinforcement Learning was originally developed for Markov Decision Processes (MDPs). It allows a single agent to learn a policy that maximizes a possibly delayed reward signal in a stochastic stationary environment. It guarantees convergence to the optimal policy, provided that the agent can sufficiently experiment and the environment in which it is operating is Markovian. However, when multiple agents apply reinforcement learning in a shared environment, this might be beyond the MDP model. In such systems, the optimal policy of an agent depends not only on the environment, but on the policies of the other agents as well. These situations arise naturally in a variety of domains, such as: robotics, telecommunications, economics, distributed control, auctions, traffic light control, etc. In these domains multi-agent learning is used, either because of the complexity of the domain or because control is inherently decentralized. In such systems it is important that agents are capable of discovering good solutions to the problem at hand either by coordinating with other learners or by competing with them. This chapter focuses on the application reinforcement learning techniques in multi-agent systems. We describe a basic learning framework based on the economic research into game theory, and illustrate the additional complexity that arises in such systems. We also described a representative selection of algorithms for the different areas of multi-agent reinforcement learning research.

Simple Stochastic Parity Games

Abstract: Many verification, planning, and control problems can be modeled as games played on state-transition graphs by one or two players whose conflicting goals are to form a path in the graph. The focus here is on simple stochastic parity games, that is, two-player games with turn-based probabilistic transitions and ω-regular objectives formalized as parity (Rabin chain) winning conditions. An efficient translation from simple stochastic parity games to nonstochastic parity games is given. As many algorithms are known for solving the latter, the translation yields efficient algorithms for computing the states of a simple stochastic parity game from which a player can win with probability 1.

Bargaining Theory and Portfolio Payoffs in European Coalition Governments 1945–83

Abstract: Coalition formation has been the subject of much theoretical work in the past decade or so. The theories that have been way or another, upon assumptions about the ways in accruing to a particular coalition is distributed among its less empirical work has been done on the process of payoff some of the fundamental assumptions of coalition theories, at their practical application to coalition governments, have tested. Several theories of payoff distribution have been however. It is the purpose of this article to test the application theories to the practice of coalition government in Europe. We begin by looking in more detail at the role of payoff theories formation. We then review both the theoretical and coalition payoffs, especially those payoffs denominated in posts. This sets the scene for a comparative testing of three proportional payoffs, the bargaining set and the kernel, in terms of each theory to predict portfolio payoffs in twelve post-systems. This study is conducted both on a general and a basis, since past analyses have demonstrated that differences are at least as significant as those between theories.