EAGER: Hybrid Socio-Technical Teams: A Theoretical Framework for Modeling and Design of Hybrid Networks of Human and Autonomous Agents

未来的许多关键系统将依赖于人类和智能、决策、自主代理的混合团队的协调合作。这种混合团队在各种各样的社会技术系统中变得越来越普遍。这种团队的成员是高度异质的，在快速变化和不确定的环境中工作，有不同的目标和动机，并且通常是空间分布的，很少有直接面对面的互动。这给团队级别的协调和合作带来了严重的挑战，并可能导致效率下降，甚至在某些关键情况下导致灾难性的失败。该奖项支持基础研究，以提供对人类代理网络的动力学、行为和协调机制的必要理解。该项目的成果将有助于更好地理解此类网络的行为，并更有效地设计未来的灾害响应、能源和交通智能系统，所有这些都将在未来依赖于一群人和自主代理网络之间的高效、动态协调。理解混合团队并解决他们的协调挑战需要结合计算和行为科学的跨学科方法。为此，本研究将复杂网络分析、博弈论等多智能体计算方法与团队行为的社会心理学相结合。该研究团队将创建参数化、上下文依赖、分析和计算模型，以了解团队级别的协调和合作行为如何受到团队成员组成、节点之间的通信网络结构、网络节点上的人和机器分布以及环境中的关键参数等变量的影响。分析方法，如图上的博弈论和可验证的基于代理的模拟，将用于从网络结构和代理组成到整个团队级别的性能参数中得出因果推论。研究还将验证团队网络的连通性结构可以作为一个有效的控制参数来动态引导混合社会技术系统的合作行为。为了验证开发的模型，研究小组将对灾害反应小组进行数据驱动的研究。它将调查在将自主代理引入人类灾害响应团队时对整体协调和效率的影响。

Many critical systems in the future will rely on coordinated teamwork of a hybrid group of humans and smart, decision-making, autonomous agents. Such hybrid teams are becoming increasingly common in a wide variety of socio-technical systems. Members of such teams are highly heterogeneous, work in rapidly changing and uncertain environments, have different goals and motivations, and are often spatially distributed with little direct face-to-face interaction. This creates serious challenges for team-level coordination and cooperation, and can result in decreased efficiency, or even disastrous failure, in some critical cases. This award supports fundamental research to provide needed understanding of dynamics, behavior, and coordination mechanisms of human-agent networks. Outcomes of this project will result in better understanding of the behavior of such networks, and more efficient design of future intelligent systems for disaster response, energy and transportation, all of which will in the future rely on efficient, dynamic coordination between a group of people and a network of autonomous agents. Understanding hybrid teams and addressing their coordination challenges require an interdisciplinary approach that combines computational and behavioral sciences. To this end, this research will combine multi-agent computational methods such as complex network analysis and game theory with social psychology of team behavior. The research team will create parametric, context-dependent, analytical and computational models to understand how team-level coordination and cooperative behavior are affected by variables such as composition of team members, communication network structure among nodes, distribution of humans and machines on networks nodes, and key parameters in the environment. Analytical methods, such as game theory on graphs, and verifiable agent-based simulations will be used to draw causal inferences from the network structure and agent compositions to overall team-level performance parameters. The research will also test the hypothesis that the connectivity structure of the team network can be used as an effective control parameter to dynamically steer the cooperative behavior of hybrid socio-technical systems. To validate the developed models, the research team will perform a data-driven study of disaster response teams. It will investigate the impact on overall coordination and efficiency when introducing autonomous agents into human disaster-response teams.