Incomplete information about the partner affects the development of collaborative strategies in joint action

Physical interaction with a partner plays an essential role in our life experience and is the basis of many daily activities. When two physically coupled humans have different and partly conflicting goals, they face the challenge of negotiating some type of collaboration. This requires that both subjects understand their opponents state and current actions. But, how would the collaboration be affected if information about their opponent were unreliable or incomplete? Here we show that incomplete information about the partner affects not only the speed at which a collaborative strategy is achieved (less information, slower learning), but also the modality of the collaboration. In particular, incomplete or unreliable information leads to an interaction strategy characterized by alternating leader-follower roles. In contrast, more reliable information leads to a more synchronous behavior, in which no specific roles can be identified. Simulations based on a combination of game theory and Bayesian estimation suggested that synchronous behaviors denote optimal interaction (Nash equilibrium). Roles emerge as sub-optimal forms of interaction, which minimize the need to know about the partner. These findings suggest that physical interaction strategies are shaped by the trade-off of between the task requirements and the uncertainty of the information available about the opponent.\n\nAuthor summaryMany activities in daily life involve physical interaction with a partner or opponent. In many situations they have conflicting goals. Therefore, they need to negotiate some form of collaboration. Although very common, these situations have rarely been studied empirically. In this study, we specifically address what is a optimal collaboration and how it can be achieved. We also address how developing a collaboration is affected by uncertainty about the partner. Through a combination of empirical studies and computer simulations based on game theory, we show that subject pairs (dyads) are capable of developing stable collaborations, but the learned collaboration strategy depends on the reliability of the information about the partner. High-information dyads converge to the optimal strategies in game-theoretic sense. Low-information dyads converge to strategies that minimize the need to know about the partner. These findings are consistent with a game theoretic learning model which relies on estimates of partner actions, but not partner goals. This similarity sheds some light on the minimal computational machinery which is necessary to an intelligent agent in order to develop stable physical collaborations.