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

本研究的目的是扩展已研究多年的单人随机决策问题的知识，为多人博弈开发新的方法，并在电子商务和供应链管理领域的大规模问题上进行检验。这种方法在开发和测试后，将为企业界提供审查商业政策所急需的资源。互联网革命打破了时间和距离的障碍，给市场带来了巨大的变化。商品和服务提供商之间为吸引客户而展开的竞争达到了史无前例的高度。例如，考虑购房者的抵押贷款请求，现在世界上几乎所有的贷款机构(例如银行)都可以获得抵押贷款。所有试图抓住这一客户的银行(在一般博弈论术语中是玩家)都参与了一个随机博弈，在这个博弈中，他们根据其他玩家的行为形成报价。根据他们的出价结果，参与者试图了解后续客户的策略。在上面的例子中，博弈环境是高度随机的，博弈回报不一定是零和类型。建模和研究这类问题的主要目的是预测博弈的均衡点(S)，即博弈演化的路径，它告诉我们博弈在有限时间范围内的回报，以及博弈达到均衡点所需的时间。在当今动荡的市场形势下，短期游戏回报可能更为重要。如果有标准的博弈论分析，则倾向于描述均衡点的特征。通过基于模拟的方法来研究随机游戏，如本项目所示，人们还可以检查游戏可以遵循的进化路径。

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.