People solve a myriad of coordination problems without explicit communication every day. A recent theoretical account, virtual bargaining, proposes that, to coordinate, we often simulate a negotiation process, and act according to what we would be most likely to agree to do if we were to bargain. But very often several equivalent tacit agreements — or virtual bargains — are available, which poses the challenge of figuring out which one to follow. Here we take inspiration from virtual bargaining to develop a cognitive modeling framework for dynamic coordination problems. We assume that players recognize their common goal, identify one or more possible tacit agreements based on situational features, observe the history of their partner’s choices to infer the most likely tacit agreement, and play their role in the joint plan. We test this approach in two experiments (n = 125 and n = 133) based on a dynamic coordination game designed to elicit agreement-based behavior. We fit our model at the individual level and compare its performance against alternative models. Across four different conditions, our model performs best among the set of models considered. Behavioral results are also consistent with players sustaining coordination and cooperation in the task by converging on tacitly agreed strategies or “virtual bargains”.
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