ICES: Small: The Structure of Signals: Causal Interdependence Models and Bayesian Inference

ICES：小：信号的结构：因果相互依赖模型和贝叶斯推理

• 批准号: 1101465
• 负责人: Michael Wellman
• 金额: $ 40万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1101465&HistoricalAwards=false
• 关键词: ICES; Small; Structure; Signals; Causal

This interdisciplinary project investigates the detailed structure of information sources and processing that underly strategic interactions in games of incomplete information. Traditional economic models typically treat private information, or signals, as generated from some underlying state. However, multiagent interactions often pivot around situations where signals are actually based on alternative interpretations of available information, and such processes often produce qualitatively different results for the game. By developing a common language using probabilistic graphical models, the effort seeks insights about alternative models of signals, and computationally effective representations in support of strategic analysis. Specific technical developments will include extension of qualitative probabilistic reasoning methods, tailored to capture important distinctions in signal structure. The project further develops an expanded view of signals, that encompasses deliberate choices about what information to consider in complex decisions under uncertainty.Results from this project will have applications to many situations of economic significance. An example is the domain of auctions, which encompasses markets in electronic commerce (e.g., eBay and B2B exchange), internet advertising (keyword search, social media), financial securities (equities, commodities), and energy (electricity, fuels). Auctions are conventionally modeled in the game-theoretic framework employed in this research, but typically using much more restricted models than necessary. By bringing to bear new computational methods and extending the scope of analysis, the project may lead to innovative new market designs, and a better understanding of human and automated market behavior. The techniques employed bridge across computational and economic disciplines, and open the door to more comprehensive and operational models of decision making.

这个跨学科的项目调查的信息来源和处理的详细结构，在不完全信息的游戏策略的相互作用。 传统的经济模型通常将私人信息或信号视为从某种潜在状态产生的。然而，多智能体交互通常围绕信号实际上是基于对可用信息的替代解释的情况进行，并且这样的过程通常会为游戏产生质的不同结果。 通过使用概率图形模型开发一种通用语言，该努力寻求有关信号替代模型的见解，以及支持战略分析的计算有效表示。具体的技术发展将包括扩展定性概率推理方法，以捕捉信号结构中的重要区别。该项目进一步发展了信号的扩展视图，包括在不确定性下的复杂决策中考虑哪些信息的深思熟虑的选择。该项目的结果将应用于许多具有经济意义的情况。一个例子是拍卖领域，其中包括电子商务市场（例如，eBay和B2B交易所）、互联网广告（关键字搜索、社交媒体）、金融证券（股票、大宗商品）和能源（电力、燃料）。 拍卖是传统的建模在本研究中采用的博弈论框架，但通常使用更多的限制模型比必要的。 通过引入新的计算方法和扩展分析范围，该项目可能会导致创新的新市场设计，并更好地理解人类和自动化的市场行为。 所采用的技术跨越了计算和经济学科，并为更全面和可操作的决策模型打开了大门。