Title

A Monte-Carlo Game Theoretic Approach For Multi-Criteria Decision Making Under Uncertainty

Keywords

Conflict resolution; Game theory; Monte-Carlo; Multi-Criteria Decision Making; Sacramento-San Joaquin Delta; Uncertainty

Abstract

Game theory provides a useful framework for studying Multi-Criteria Decision Making problems. This paper suggests modeling Multi-Criteria Decision Making problems as strategic games and solving them using non-cooperative game theory concepts. The suggested method can be used to prescribe non-dominated solutions and also can be used as a method to predict the outcome of a decision making problem. Non-cooperative stability definitions for solving the games allow consideration of non-cooperative behaviors, often neglected by other methods which assume perfect cooperation among decision makers. To deal with the uncertainty in input variables a Monte-Carlo Game Theory (MCGT) approach is suggested which maps the stochastic problem into many deterministic strategic games. The games are solved using non-cooperative stability definitions and the results include possible effects of uncertainty in input variables on outcomes. The method can handle multi-criteria multi-decision-maker problems with uncertainty. The suggested method does not require criteria weighting, developing a compound decision objective, and accurate quantitative (cardinal) information as it simplifies the decision analysis by solving problems based on qualitative (ordinal) information, reducing the computational burden substantially. The MCGT method is applied to analyze California's Sacramento-San Joaquin Delta problem. The suggested method provides insights, identifies non-dominated alternatives, and predicts likely decision outcomes. © 2011 Elsevier Ltd.

Publication Date

5-1-2011

Publication Title

Advances in Water Resources

Volume

34

Issue

5

Number of Pages

607-616

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.advwatres.2011.02.009

Socpus ID

79954933207 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/79954933207

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