Learning to search collaboratively: how dyads overcome complexity and misaligned incentives in imperfect modular decompositions

We investigate the search processes that dyads engage in when each human agent is responsible for one module of a complex task. Our laboratory experiment manipulates global vs. local incentives and low vs. high cross-modular interdependence. We find that dyads endogenously learn to coordinate their joint search efforts by engaging in parallel and sequential searches that, over time, give rise to coordinated repeated actions. Such collaborative search emerges despite complexity and misaligned incentives, and without a coordinating hierarchy.