Game theory‐based influence diagrams

Many decisions are made in interactive situations in which decision makers interact and may also affect each other's decision outcomes. In order to model decision makings in such interactive situations, this paper incorporates game theory into influence diagrams and presents a new approach, called game theory-based influence diagrams (GIDs). GIDs consider an extra factor, that of the choice of strategies made by other decision makers, to the list of determinants that influence the decision-making process of each decision maker, so that decision makers can make more rational decisions. As a result of integrating influence diagrams and game theory, GIDs benefit from the simplicity and efficiency of influence diagrams for modelling complex decision problems as well as the rationality and suitability of applying game theory for making decisions in dynamic interactive scenarios. This paper also introduces genetic algorithm-based methods to evaluate GIDs. Experimental studies have been performed for validation and evaluation.

[1]  Marek J. Druzdzel,et al.  An Efficient Sampling Algorithm for Influence Diagrams , 2004 .

[2]  Christopher R. Houck,et al.  A Genetic Algorithm for Function Optimization: A Matlab Implementation , 2001 .

[3]  C. D. Hurt,et al.  Influence diagrams and multiple experts: a preliminary model , 2010, Expert Syst. J. Knowl. Eng..

[4]  Régis Sabbadin,et al.  Complexity results and algorithms for possibilistic influence diagrams , 2008, Artif. Intell..

[5]  Prakash P. Shenoy,et al.  Multistage Monte Carlo Method for Solving Influence Diagrams Using Local Computation , 2004, Manag. Sci..

[6]  Panlop Zeephongsekul,et al.  A game theory approach in seller-buyer supply chain , 2009, Eur. J. Oper. Res..

[7]  Ross D. Shachter Evaluating Influence Diagrams , 1986, Oper. Res..

[8]  Anders L. Madsen,et al.  Solving linear-quadratic conditional Gaussian influence diagrams , 2005, Int. J. Approx. Reason..

[9]  Play or not to play—An analysis of the mechanism of the zero-commission Chinese outbound tours through a game theory approach , 2009, Tourism Management.

[10]  D. Wu,et al.  Modeling technological innovation risks of an entrepreneurial team using system dynamics: An agent-based perspective , 2010 .

[11]  Nevin Lianwen Zhang,et al.  Probabilistic Inference in Influence Diagrams , 1998, Comput. Intell..

[12]  Ross D. Shachter,et al.  Dynamic programming and influence diagrams , 1990, IEEE Trans. Syst. Man Cybern..

[13]  Ross D. Shachter,et al.  Decision Making Using Probabilistic Inference Methods , 1992, UAI.

[14]  Prakash P. Shenoy,et al.  Decision making with hybrid influence diagrams using mixtures of truncated exponentials , 2004, Eur. J. Oper. Res..

[15]  K. Komathy,et al.  Trust-based evolutionary game model assisting AODV routing against selfishness , 2008, J. Netw. Comput. Appl..

[16]  Desheng Dash Wu,et al.  Enterprise risk management: coping with model risk in a large bank , 2010, J. Oper. Res. Soc..

[17]  Daphne Koller,et al.  Multi-Agent Influence Diagrams for Representing and Solving Games , 2001, IJCAI.

[18]  Daphne Koller,et al.  A Continuation Method for Nash Equilibria in Structured Games , 2003, IJCAI.

[19]  Yoav Shoham,et al.  Simple search methods for finding a Nash equilibrium , 2004, Games Econ. Behav..

[20]  Khaled Mellouli,et al.  Qualitative possibilistic influence diagrams based on qualitative possibilistic utilities , 2009, Eur. J. Oper. Res..

[21]  Robert Wilson,et al.  Computing Nash equilibria by iterated polymatrix approximation , 2004 .

[22]  Yoav Shoham,et al.  Ranking games , 2009, Artif. Intell..