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Mean-field Game Theory
Mean-field game theory is the study of strategic decision making by small interacting agents in very large populations. It lies at the intersection of game theory with stochastic analysis and control theory. The use of the term "mean field" is inspired by mean-field theory in physics, which considers the behavior of systems of large numbers of particles where individual particles have negligible impacts upon the system. In other words, each agent acts according to his minimization or maximization problem taking into account other agents’ decisions and because their population is large we can assume the number of agents goes to infinity and a representative agent exists. In traditional game theory, the subject of study is usually a game with two players and discrete time space, and extends the results to more complex situations by induction. However, for games in continuous time with continuous states (differential games or stochastic differential games) this strategy cannot be us ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Agent (economics)
In economics, an agent is an actor (more specifically, a decision maker) in a model of some aspect of the economy. Typically, every agent makes decisions by solving a well- or ill-defined optimization or choice problem. For example, ''buyers'' (consumers) and ''sellers'' ( producers) are two common types of agents in partial equilibrium models of a single market. Macroeconomic models, especially dynamic stochastic general equilibrium models that are explicitly based on microfoundations, often distinguish households, firms, and governments or central banks as the main types of agents in the economy. Each of these agents may play multiple roles in the economy; households, for example, might act as consumers, as workers, and as voters in the model. Some macroeconomic models distinguish even more types of agents, such as workers and shoppers or commercial banks. The term ''agent'' is also used in relation to principal–agent models; in this case, it refers specifically to someone de ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Linear–quadratic–Gaussian Control
In control theory, the linear–quadratic–Gaussian (LQG) control problem is one of the most fundamental optimal control problems, and it can also be operated repeatedly for model predictive control. It concerns linear systems driven by additive white Gaussian noise. The problem is to determine an output feedback law that is optimal in the sense of minimizing the expected value of a quadratic cost criterion. Output measurements are assumed to be corrupted by Gaussian noise and the initial state, likewise, is assumed to be a Gaussian random vector. Under these assumptions an optimal control scheme within the class of linear control laws can be derived by a completion-of-squares argument. This control law which is known as the LQG controller, is unique and it is simply a combination of a Kalman filter (a linear–quadratic state estimator (LQE)) together with a linear–quadratic regulator (LQR). The separation principle states that the state estimator and the state feedback can be ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Collège De France
The Collège de France (), formerly known as the ''Collège Royal'' or as the ''Collège impérial'' founded in 1530 by François I, is a higher education and research establishment (''grand établissement'') in France. It is located in Paris near La Sorbonne. The Collège de France is considered to be France's most prestigious research establishment. Research and teaching are closely linked at the Collège de France, whose ambition is to teach "the knowledge that is being built up in all fields of literature, science and the arts". It offers high-level courses that are free, non-degree-granting and open to all without condition or registration. This gives it a special place in the French intellectual landscape. Overview The Collège is considered to be France's most prestigious research establishment. As of 2021, 21 Nobel Prize winners and 9 Fields Medalists have been affiliated with the Collège. It does not grant degrees. Each professor is required to give lectures where ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Potential Game
In game theory, a game is said to be a potential game if the incentive of all players to change their strategy can be expressed using a single global function called the potential function. The concept originated in a 1996 paper by Dov Monderer and Lloyd Shapley. The properties of several types of potential games have since been studied. Games can be either ''ordinal'' or ''cardinal'' potential games. In cardinal games, the difference in individual payoffs for each player from individually changing one's strategy, other things equal, has to have the same value as the difference in values for the potential function. In ordinal games, only the signs of the differences have to be the same. The potential function is a useful tool to analyze equilibrium properties of games, since the incentives of all players are mapped into one function, and the set of pure Nash equilibria can be found by locating the local optima of the potential function. Convergence and finite-time convergence of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Quantal Response Equilibrium
Quantal response equilibrium (QRE) is a solution concept in game theory. First introduced by Richard McKelvey and Thomas Palfrey, it provides an equilibrium notion with bounded rationality. QRE is not an equilibrium refinement, and it can give significantly different results from Nash equilibrium. QRE is only defined for games with discrete strategies, although there are continuous-strategy analogues. In a quantal response equilibrium, players are assumed to make errors in choosing which pure strategy to play. The probability of any particular strategy being chosen is positively related to the payoff from that strategy. In other words, very costly errors are unlikely. The equilibrium arises from the realization of beliefs. A player's payoffs are computed based on beliefs about other players' probability distribution over strategies. In equilibrium, a player's beliefs are correct. Application to data When analyzing data from the play of actual games, particularly from laborato ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Evolutionary Game Theory
Evolutionary game theory (EGT) is the application of game theory to evolving populations in biology. It defines a framework of contests, strategies, and analytics into which Darwinian competition can be modelled. It originated in 1973 with John Maynard Smith and George R. Price's formalisation of contests, analysed as strategies, and the mathematical criteria that can be used to predict the results of competing strategies. Evolutionary game theory differs from classical game theory in focusing more on the dynamics of strategy change. This is influenced by the frequency of the competing strategies in the population. Evolutionary game theory has helped to explain the basis of altruistic behaviours in Darwinian evolution. It has in turn become of interest to economists, sociologists, anthropologists, and philosophers. History Classical game theory Classical non-cooperative game theory was conceived by John von Neumann to determine optimal strategies in competitions between adversa ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Differential Game
In game theory, differential games are a group of problems related to the modeling and analysis of conflict in the context of a dynamical system. More specifically, a state variable or variables evolve over time according to a differential equation. Early analyses reflected military interests, considering two actors—the pursuer and the evader—with diametrically opposed goals. More recent analyses have reflected engineering or economic considerations. Connection to optimal control Differential games are related closely with optimal control problems. In an optimal control problem there is single control u(t) and a single criterion to be optimized; differential game theory generalizes this to two controls u_(t),u_(t) and two criteria, one for each player. Each player attempts to control the state of the system so as to achieve its goal; the system responds to the inputs of all players. History In the study of competition, differential games have been employed since a 1925 art ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Complex Adaptive System
A complex adaptive system is a system that is ''complex'' in that it is a dynamic network of interactions, but the behavior of the ensemble may not be predictable according to the behavior of the components. It is ''adaptive'' in that the individual and collective behavior mutate and self-organize corresponding to the change-initiating micro-event or collection of events. It is a "complex macroscopic collection" of relatively "similar and partially connected micro-structures" formed in order to adapt to the changing environment and increase their survivability as a macro-structure. The Complex Adaptive Systems approach builds on replicator dynamics. The study of complex adaptive systems, a subset of nonlinear dynamical systems, is an interdisciplinary matter that attempts to blend insights from the natural and social sciences to develop system-level models and insights that allow for heterogeneous agents, phase transition, and emergent behavior. Overview The term ''complex a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Aggregative Game
In game theory, an aggregative game is a game in which every player’s payoff is a function of the player’s own strategy and the aggregate of all players’ strategies. The concept was first proposed by Nobel laureate Reinhard Selten in 1970 who considered the case where the aggregate is the sum of the players' strategies. Definition Consider a standard non-cooperative game with ''n'' players, where S_i \subseteq \mathbb is the strategy set of player ''i'', S=S_1 \times S_2 \times \ldots \times S_n is the joint strategy set, and f_i:S \to \mathbb is the payoff function of player ''i''. The game is then called an ''aggregative game'' if for each player ''i'' there exists a function \tilde_i:S_i \times \mathbb \to \mathbb such that for all s \in S : : f_i(s)=\tilde_i \left( s_i,\sum_^n s_j \right) In words, payoff functions in aggregative games depend on players' ''own strategies'' and the ''aggregate'' \sum s_j. As an example, consider the Cournot model where firm ' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Wiener Process
In mathematics, the Wiener process is a real-valued continuous-time stochastic process named in honor of American mathematician Norbert Wiener for his investigations on the mathematical properties of the one-dimensional Brownian motion. It is often also called Brownian motion due to its historical connection with the physical process of the same name originally observed by Scottish botanist Robert Brown (Scottish botanist from Montrose), Robert Brown. It is one of the best known Lévy processes (càdlàg stochastic processes with stationary increments, stationary independent increments) and occurs frequently in pure and applied mathematics, economy, economics, quantitative finance, evolutionary biology, and physics. The Wiener process plays an important role in both pure and applied mathematics. In pure mathematics, the Wiener process gave rise to the study of continuous time martingale (probability theory), martingales. It is a key process in terms of which more complicated sto ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stochastic Differential Equation
A stochastic differential equation (SDE) is a differential equation in which one or more of the terms is a stochastic process, resulting in a solution which is also a stochastic process. SDEs are used to model various phenomena such as stock prices or physical systems subject to thermal fluctuations. Typically, SDEs contain a variable which represents random white noise calculated as the derivative of Brownian motion or the Wiener process. However, other types of random behaviour are possible, such as jump processes. Random differential equations are conjugate to stochastic differential equations. Background Stochastic differential equations originated in the theory of Brownian motion, in the work of Albert Einstein and Smoluchowski. These early examples were linear stochastic differential equations, also called 'Langevin' equations after French physicist Langevin, describing the motion of a harmonic oscillator subject to a random force. The mathematical theory of stochasti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Social Planner
In welfare economics, a social planner is a hypothetical decision-maker who attempts to maximize some notion of social welfare function, social welfare. The planner is a fictional entity who chooses allocations for every agent in the economy—for example, levels of consumption and leisure—that maximize a social welfare function subject to certain constraints (e.g., a physical resource constraint, or incentive compatibility constraints). This so-called planner's problem is a mathematical constrained optimization problem. Solving the planner's problem for all possible Pareto weights (i.e., weights on each type of agent in the economy) yields all Pareto efficiency, Pareto efficient allocations. Connection with the fundamental welfare theorems Any Pareto efficient allocation is a solution to a planner's problem. However, the planner is a purely fictional entity; solving the planner's problem requires knowledge of consumers' preferences and all physical resource constraints in the e ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
