Modeling Human Ad Hoc Coordination

TL;DR

This paper presents an exact algorithm for modeling the recursive beliefs necessary for rational coordination in uncertain environments and tests its application to human decision-making in coordination games.

Contribution

It introduces a novel algorithm for computing infinite belief hierarchies and applies it to model human coordination behavior in ad hoc multiagent settings.

Findings

The algorithm accurately models rational coordination in finite environments.

Modeling humans with this algorithm improves human-agent collaboration in simulations.

The approach provides insights into how humans form expectations for coordination.

Abstract

Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.