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

该奖项支持使用有限理性的基于代理的模型，通过将信任和情感模型纳入这些代理中，来研究多代理“可信信息结构”在基本社会群体环境中的出现。由于这些代理只交换信息，这些可信结构的出现代表了组织学习和适应的原始（但基本）形式。然而，社会环境使问题复杂化，因为这些结构的调整也是纯粹的信息-通过对其他代理人的八卦，选择信任其他代理人或不信任其他代理人，以及对特定类别事件的情感型反应。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进步的知识，提供了洞察到互联网上发生的建议网络的紧急形式，探讨情感的基本形式如何改变代理（和组）的行为，并有助于代理属性的链接到紧急组和组织学习和行为。

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.