Optimal decision-making under uncertainties

Emmanuel M. Tadjouddine*, Xiaoyi Wu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

We consider stochastic games wherein players are striving to make optimal decisions but their decisions are subject to mistakes or random shocks. We assume that the players make decisions in the direction of higher payoffs and yet they are in a uncertain environment. The dynamics of this kind of evolutionary games can be described by stochastic differential equations, which are solved and the payoffs are calculated using a Monte Carlo simulation. Then, sensitivities are evaluated so as to assess the impact of changes in decisions. Numerical results have shown that noisy environments can lead to important payoff variations and higher payoff sensitivities with respect to a player's decisions. We also discussed equilibrium concepts that may result from the players' abilities to learn from mistakes and adopt successful strategies.

Original languageEnglish
Title of host publicationProceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012
Pages491-496
Number of pages6
DOIs
Publication statusPublished - 2012
Event2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012 - Chongqing, China
Duration: 29 May 201231 May 2012

Publication series

NameProceedings - 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012

Conference

Conference2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2012
Country/TerritoryChina
CityChongqing
Period29/05/1231/05/12

Keywords

  • logit equilibrium
  • sensitivity analysis
  • stochastic differential equations
  • stochastic games

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