Bayesian game | EPFL Graph Search

Are you an EPFL student looking for a semester project?

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

In game theory, a Bayesian game is a strategic decision-making model which assumes players have incomplete information. Players hold private information relevant to the game, meaning that the payoffs are not common knowledge. Bayesian games model the outcome of player interactions using aspects of Bayesian probability. They are notable because they allowed, for the first time in game theory, for the specification of the solutions to games with incomplete information. Hungarian economist John C. Harsanyi introduced the concept of Bayesian games in three papers from 1967 and 1968: He was awarded the Nobel Memorial Prize in Economic Sciences for these and other contributions to game theory in 1994. Roughly speaking, Harsanyi defined Bayesian games in the following way: players are assigned by nature at the start of the game a set of characteristics. By mapping probability distributions to these characteristics and by calculating the outcome of the game using Bayesian probability, the result is a game whose solution is, for technical reasons, far easier to calculate than a similar game in a non-Bayesian context. For those technical reasons, see the Specification of games section in this article. A Bayesian game is defined by (N,A,T,p,u), where it consists of the following elements: Set of players, N: The set of players within the game Action sets, ai: The set of actions available to Player i. An action profile a = (a1, . . . , aN) is a list of types, one for each player Type sets, ti: The set of types of players i. "Types" capture the private information a player can have. A type profile t = (t1, . . . , tN) is a list of types, one for each player Payoff functions, u: Assign a payoff to a player given their type and the action profiles. A payoff function, u= (u1, . . . , uN) denotes the utilities of player i Prior, p: A probability distribution over all possible type profiles, where p(t) = p(t1, . . . ,tN) is the probability that Player 1 has type t1 and Player N has type tN.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related concepts (10)

Related courses (9)

Related lectures (41)

Signaling game

In game theory, a signaling game is a simple type of a dynamic Bayesian game. The essence of a signalling game is that one player takes an action, the signal, to convey information to another player, where sending the signal is more costly if they are conveying false information. A manufacturer, for example, might provide a warranty for its product in order to signal to consumers that its product is unlikely to break down. The classic example is of a worker who acquires a college degree not because it increases their skill, but because it conveys their ability to employers.

Information set (game theory)

The information set is the basis for decision making in a game, which includes the actions available to both sides and the benefits of each action.The information set is an important concept in non-perfect games. In game theory, an information set is the set of all possible actions in the game for a given player, built on their observations and a set for a particular player that, given what that player has observed, shows the decision vertices available to the player which are undistinguishable to them at the current point in the game.

Complete information

In economics and game theory, complete information is an economic situation or game in which knowledge about other market participants or players is available to all participants. The utility functions (including risk aversion), payoffs, strategies and "types" of players are thus common knowledge. Complete information is the concept that each player in the game is aware of the sequence, strategies, and payoffs throughout gameplay.

FIN-620: Game Theory

Game theory deals with multiperson strategic decision making. Major fields of Economics, such as Microeconomics, Corporate Finance, Market Microstructure, Monetary Economics, Industrial Organization,

FIN-608: Information and Asset Pricing

We study the role of information in equilibrium asset pricing models. We cover simple one-period models of incomplete and asymmetric information using competitive rational expectation equilibria and B

ME-429: Multi-agent learning and control

Students will be able to formulate a multi-agent decision-making problem as a game and apply relevant mathematical theories and algorithms to analyze the interaction of the agents.

Untitled ME-429: Multi-agent learning and control

Game Theory: Decision Making in Interdependent Environments MGT-454: Principles of microeconomics

Explores game theory, decision-making in interdependent environments, equilibrium concepts, and economic applications in management.

Introduction to Game Theory CS-430: Intelligent agents

Covers game theory fundamentals, Nash equilibrium, and strategic interactions in multi-agent systems.