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: 0341702
• 负责人: Abhijit Gosavi
• 金额: $ 5.86万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0341702&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知识的单人随机决策问题，这已经研究了多年，开发新的方法，多人游戏，并测试他们在大规模的问题，从电子商务和供应链管理领域。 这种方法一旦得到发展和测试，将为企业界审查商业政策提供急需的资源。 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进行的竞争已经达到了一个史诗般的高度。 例如，考虑购房者对抵押贷款的请求，现在几乎每个贷款机构都可以获得抵押贷款（例如，世界上的银行。所有银行（玩家，在一般的博弈论术语中）都试图抓住这个客户，参与了一个随机博弈，他们在预期其他玩家的行动中形成自己的出价。根据他们的出价结果，玩家试图为随后的客户学习策略。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。建模和研究这些问题的主要目的是预测游戏的均衡点，游戏演变的路径，它告诉我们游戏在有限时间范围内的回报，以及游戏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直接。 标准的博弈论分析，如果可用的话，是为了描述均衡点。通过基于模拟的方法来研究随机游戏，如本项目所述，人们还可以研究游戏可以遵循的进化路径。