Integrating Models of Trust, Gossip, and Emotion for Artifical Agents

该奖项支持使用基于有限理性主体的模型，通过将信任和情感模型整合到这些主体中，来研究在基本社会群体环境中多主体“可信信息结构”的出现。由于这些代理只交换信息，这些可信结构的出现代表了一种原始的（但基本的）组织学习和适应形式。然而，社会环境使问题复杂化，因为这些结构的调整也纯粹是信息性的——通过对其他代理人的八卦，选择信任或不信任其他代理人，以及对特定类别事件的情感类型反应。该模型的计算公式整合了心理学、社会学和人类学的理论。本研究在社会科学和计算科学的间隙中推进了知识，提供了对互联网上出现的紧急形式的建议网络的见解，探索了情感的基本形式如何改变代理（和群体）行为，并有助于将代理属性与紧急群体和组织的学习和行为联系起来。

This award supports the use of boundedly-rational agent-based models to study the emergence of multi-agent "trusted information structures" in a rudimentary societal group setting by incorporating models of trust and emotion in these agents. As these agents exchange only information, the emergence of these trusted structures represent a primitive (but fundamental) form of organizational learning and adaptation. However, the social setting complicates matters, as the adjustment of these structures is also purely informational - through gossip about the other agents, choices to trust other agents or not, and emotional-type responses to particular categories of events. The computational formulation of the model integrates theories from psychology, sociology, and anthropology. This research advances knowledge at the interstices of social and computational sciences, provides insight into the emergent forms of advice networks occurring on the Internet, explores how elementary forms of emotion alter agent (and group) behaviors, and contributes to the linking of agent properties to emergent group and organizational learning and behaviors.