ITR/AP: COLLABORATIVE RESEARCH: A Simulation Based Computational Approach using Machine Learning to Study Stochastic Business Games

Suny At Buffalo, Amherst NY

Investigators

Abstract

The objective of this research is to extend the knowledge of the single player stochastic decision problems, which have been studied for years, to develop new methodologies for multi-player games and test them on large-scale problems from the domain of e-commerce and supply chain management. Such a methodology, when developed and tested, will provide a much needed resource to the corporate world for examining business policies. The Internet revolution has brought about tremendous changes in the marketplace by tearing down the barriers of time and distance. The competition among the providers of goods and services for luring the customers has reached an epic height. Consider, for example, a homebuyer's request for a mortgage loan, which is now available to virtually every lending institution (e.g., a bank) in the world. All the banks (players, in generic game theory nomenclature) seeking to capture this customer are involved in a stochastic game, where they form their bids in anticipation of other players' actions. Based on the outcome of their bids, the players try to learn a strategy for the subsequent customers. In the above example, the game environment is highly stochastic and the game return is not necessarily of the zero-sum type. The main purpose of modeling and examining such problems is to foresee the equilibrium point(s) of a game, the path of the game evolution which tells us about the game returns in finite time horizons, and also the time taken by a game to reach an equilibrium point. In today's volatile market situation, short-term game returns could be of much more immediate importance. Standard game theoretic analysis, when available, is geared toward characterizing the equilibrium points. Through simulation-based approaches to the study of stochastic games, as presented in this project, one can also examine the evolutionary paths a game can follow.