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

ITR/AP：协作研究：使用机器学习研究随机商业博弈的基于模拟的计算方法

• 批准号: 0113946
• 负责人: Tapas Das
• 金额: $ 21万
• 依托单位: University of South Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0113946&HistoricalAwards=false
• 关键词: ITR; AP; COLLABORATIVE; RESEARCH; Simulation

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.

本研究的目的是扩展已经研究多年的单人随机决策问题的知识，开发多人博弈的新方法，并在电子商务和供应链管理领域的大规模问题上进行测试。这样的方法在开发和测试后，将为企业界检查业务策略提供非常需要的资源。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。商品和服务供应商之间的竞争已经达到了一个史诗般的高度。例如，考虑一个购房者的抵押贷款请求，现在世界上几乎每个贷款机构（例如银行）都可以获得抵押贷款。所有的银行（游戏邦注：用博弈论的术语来说就是玩家）都在参与随机博弈，他们根据其他玩家的行为来制定自己的出价。根据他们的出价结果，玩家们试图为随后的顾客学习一种策略。在上面的例子中，游戏环境是高度随机的，游戏回报不一定是零和类型。建模和检查这类问题的主要目的是预测游戏的平衡点，游戏进化的路径，这告诉我们游戏在有限的时间范围内的回报，以及游戏达到平衡点所花费的时间。在当今动荡的市场形势下，短期游戏回报可能更为重要。标准的博弈论分析，在可用的情况下，是针对均衡点的特征。通过基于模拟的随机游戏研究方法，就像在这个项目中所呈现的那样，人们还可以检查游戏可以遵循的进化路径。