情绪影响下选时博弈模型及其最优策略

依据Quiggin的秩依期望效用理论研究经典选时博弈问题。通过引入可以刻画局中人在博弈中情绪状态的非线性决策权重函数,将RDEU有限策略博弈扩展到连续博弈,构建了RDEU选时博弈模型。基于Riccati微分方程的解法,求出博弈模型中局中人的最优策略。最后,通过数值仿真,分析了不同情绪状态对局中人博弈决策行为的影响。研究发现,情绪对混合策略意义下的局中人最优策略有着显著的影响,在乐观情绪状态下,局中人对混合策略极易产生自信和较高的信任倾向,表现出"风险爱好者"行为;在悲观情绪状态下,局中人往往对混合策略缺乏自信和信任,表现出“风险厌恶者”行为。     

Algorithms for discounted stochastic games

In this paper, a two-person zero-sum discounted stochastic game with a finite state space is considered. The movement of the game from state to state is jointly controlled by the two players with a finite number of alternatives available to each player in each of the states. We present two convergent algorithms for arriving at minimax strategies for the players and the value of the game. The two algorithms are compared with respect to computational efficiency. Finally, a possible extension to nonzero sum stochastic game is suggested.This research was supported in part by funds allocated to the Department of Operations Research, School of Management, Case Western Reserve University under Contract No. DAHC 19-68-C-0007 (Project Themis) with the U.S. Army Research Office, Durham, North Carolina. The authors thank the referees for their valuable suggestions.     

A single-shot game of multi-period inspection

This paper deals with an inspection game of Customs and a smuggler during some days. Customs has two options of patrolling or not. The smuggler can take two strategies of shipping its cargo of contraband or not. Two players have several opportunities to take an action during a limited number of days but they may discard some of the opportunities. When the smuggling coincides with the patrol, there occurs one of three events: the capture of the smuggler by Customs, a success of the smuggling and nothing new. If the smuggler is captured or no time remains to complete the game, the game ends. There have been many studies on the inspection game so far by the multi-stage game model, where both players at a stage know players’ strategies taken at the previous stage. In this paper, we consider a two-person zero-sum single-shot game, where the game proceeds through multiple periods but both players do not know any strategies taken by their opponents on the process of the game. We apply dynamic programming to the game to exhaust all equilibrium points on a strategy space of player. We also clarify the characteristics of optimal strategies of players by some numerical examples.     

Multilayers in a modulated stochastic game

We are concerned with an antagonistic stochastic game between two players A and B which finds applications in economics and warfare. The actions of the players are manifested by a series of strikes of random magnitudes at random times exerted by each player against his opponent. Each of the assaults inflicts a random damage to enemy's vital areas. In contrast with traditional games, in our setting, each player can endure multiple strikes before perishing. Predicting the ruin time (exit) of player A, along with the total amount of casualties to both players at the exit is a main objective of this work. In contrast to the time sensitive analysis (earlier developed to refine the information on the game) we insert auxiliary control levels, which both players will cross in due game before the ruin of A. This gives A (and also B) an additional opportunity to reevaluate his strategy and change the course of the game. We formalize such a game and also allow the real time information about the game to be randomly delayed. The delayed exit time, cumulative casualties to both players, and prior crossings are all obtained in a closed-form joint functional.     

An agreeable collusive equilibrium in differential games with asymmetric players

We study a class of collusive equilibria in differential games with asymmetric players discounting the future at different rates. For such equilibria, at each moment, weights of players can depend on the state of the system. To fix them, we propose using a bargaining procedure according to which players can bargain again at every future moment. By choosing as threat point the feedback noncooperative outcome, the corresponding solution, if it exists, is agreeable. An exhaustible resource game illustrates the results.     

Tandem antagonistic games

We study an antagonistic sequential game of two players that undergoes two phases. Each phase is modeled by multi-dimensional random walk processes. During phase 1 (or game 1), the players exchange a series of random strikes of random magnitudes. Game 1 ends whenever one of the players sustains damages in excess of some lower threshold. However, the total damage does not exceed another upper threshold which allows the game to continue. Phase 2 (game 2) is run by another combination of random walk processes. At some point of phase 2, one of the players, after sustaining damages in excess of its third threshold, is ruined and he loses the entire game. We predict that moment, along with the total casualties to both players, and other critical information; all in terms of tractable functionals. The entire game is analyzed by tools of fluctuation theory.     

Tandem antagonistic games

We study an antagonistic sequential game of two players that undergoes two phases. Each phase is modeled by multi-dimensional random walk processes. During phase 1 (or game 1), the players exchange a series of random strikes of random magnitudes. Game 1 ends whenever one of the players sustains damages in excess of some lower threshold. However, the total damage does not exceed another upper threshold which allows the game to continue. Phase 2 (game 2) is run by another combination of random walk processes. At some point of phase 2, one of the players, after sustaining damages in excess of its third threshold, is ruined and he loses the entire game. We predict that moment, along with the total casualties to both players, and other critical information; all in terms of tractable functionals. The entire game is analyzed by tools of fluctuation theory.     

The blind bartender's problem

We present a two-player game with restricted information for one of the players. The game takes place on a transitive group action. The winning strategies depend on chains of structures in the group action. We also study a modification of the game with further restrictions on one of the players.     

Dynamic competition over social networks

We propose an analytical approach to the problem of influence maximization in a social network where two players compete by means of dynamic targeting strategies. We formulate the problem as a two-player zero-sum stochastic game. We prove the existence of the uniform value: if the players are sufficiently patient, both can guarantee the same mean-average opinion without knowing the exact length of the game. Furthermore, we put forward some elements for the characterization of equilibrium strategies. In general, players must implement a trade-off between a forward-looking perspective, according to which they aim to maximize the future spread of their opinion in the network, and a backward-looking perspective, according to which they aim to counteract their opponent’s previous actions. When the influence potential of players is small, we describe an equilibrium through a one-shot game based on eigenvector centrality.     

A differential game with jump process observations

We introduce a stochastic differential game with jump process observations. Both players obtain common, noisy information of the state of the system only at random time instants. The solutions to this game and its continuous observations in noise counterpart are obtained. Some earlier results dealing with the effect of changes in system parameters on the optimal cost for the continuous observations case are extended to the game with jump process observations.This work was supported by a 1978 Summer Faculty Fellowship from the University of Maryland, Baltimore County.     

Nonrandomized strategy equilibria in noncooperative stochastic games with additive transition and reward structure

In this paper, we study a discounted noncooperative stochastic game with an abstract measurable state space, compact metric action spaces of players, and additive transition and reward structure in the sense of Himmelberget al. (Ref. 1) and Parthasarathy (Ref. 2). We also assume that the transition law of the game is absolutely continuous with respect to some probability distributionp of the initial state and together with the reward functions of players satisfies certain continuity conditions. We prove that such a game has an equilibrium stationary point, which extends a result of Parthasarathy from Ref. 2, where the action spaces of players are assumed to be finite sets. Moreover, we show that our game has a nonrandomized (- )-equilibrium stationary point for each >0, provided that the probability distributionp is nonatomic. The latter result is a new existence theorem.     

Mechanism design for a multicommodity flow game in service network alliances

We study a collaborative multicommodity flow game where individual players own capacity on the edges of the network and share this capacity to deliver commodities. We present membership mechanisms, by adopting a rationality based approach using notions from game theory and inverse optimization, to allocate benefits among the players in such a game.     

Semi-markov strategies in stochastic games

For a stochastic game with countable state and action spaces we prove, that solutions in the game where all players are restricted to semi-markov strategies are solutions for the unrestricted game. In addition we show, that if all players, except for one, fix a stationary strategy, then the best the remaining player can do, is solving a markov decision problem, corresponding to the fixed stationary strategies.     

A Dynkin game with asymmetric information

We study a Dynkin game with asymmetric information. The game has a random expiry time, which is exponentially distributed and independent of the underlying process. The players have asymmetric information on the expiry time, namely only one of the players is able to observe its occurrence. We propose a set of conditions under which we solve the saddle point equilibrium and study the implications of the information asymmetry. Results are illustrated with an explicit example.     

Zero-Sum Stochastic Games with Partial Information and Average Payoff

We consider a discrete time partially observable zero-sum stochastic game with average payoff criterion. We study the game using an equivalent completely observable game. We show that the game has a value and also we present a pair of optimal strategies for both the players.     

Pure subgame-perfect equilibria in free transition games

We consider a class of stochastic games, where each state is identified with a player. At any moment during play, one of the players is called active. The active player can terminate the game, or he can announce any player, who then becomes the active player. There is a non-negative payoff for each player upon termination of the game, which depends only on the player who decided to terminate. We give a combinatorial proof of the existence of subgame-perfect equilibria in pure strategies for the games in our class.     

Rankings and values for team games

In this article we attack several problems that arise when a group of individuals is organized in several teams with equal number of players in each one (e.g., for company work, in sports leagues, etc). We define a team game as a cooperative game v that can have non-zero values only on coalitions of a given cardinality; it is further shown that, for such games, there is essentially a unique ranking among the players. We also study the way the ranking changes after one or more players retire. Also, we characterize axiomatically different ways of ranking the players that intervene in a cooperative game.     

Cooperative Markov decision processes: time consistency, greedy players satisfaction, and cooperation maintenance

We deal with multi-agent Markov decision processes (MDPs) in which cooperation among players is allowed. We find a cooperative payoff distribution procedure (MDP-CPDP) that distributes in the course of the game the payoff that players would earn in the long run game. We show under which conditions such a MDP-CPDP fulfills a time consistency property, contents greedy players, and strengthen the coalition cohesiveness throughout the game. Finally we refine the concept of Core for Cooperative MDPs.