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

这个跨学科项目调查了信息来源和处理的详细结构，这些信息是不完全信息博弈中潜在的战略互动。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，多智能体互动通常围绕着信号实际上是基于对可用信息的不同解释的情况，并且这种过程通常会为游戏产生不同的质量结果。通过开发一种使用概率图形模型的通用语言，努力寻求对信号的替代模型的见解，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定性概率推理方法的扩展，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，包括在不确定的复杂决策中考虑哪些信息的慎重选择。该项目的成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，它包括电子商务（例如，eBay和B2B交易）、互联网广告（关键字搜索、社交媒体）、金融证券（股票、商品）和能源（电力、燃料）等市场。拍卖通常是在本研究中采用的博弈论框架中建模的，但通常使用的模型比必要的要限制得多。通过采用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，以及对人类和自动化市场行为的更好理解。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。

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.