We study the use of Bayesian persuasion (i.e., strategic use of information disclosure signaling) in endogenous team formation. This is an important consideration in settings such as crowdsourcing competitions, open science challenges and group-based assignments, where a large number of agents organize themselves into small teams which then compete against each other. A central tension here is between the strategic interests of agents who want to have the highest-performing team, and that of the principal who wants teams to be balanced. Moreover, although the principal cannot choose the teams or modify rewards, she often has additional knowledge of agents' abilities, and can leverage this information asymmetry to provide signals that influence team formation. Our work uncovers the critical role of self-awareness (i.e., knowledge of one's own abilities) for the design of such mechanisms. For settings with two-member teams and binary-valued agents partitioned into a constant number of prior classes, we provide signaling mechanisms which are asymptotically optimal when agents are agnostic of their own abilities. On the other hand, when agents are self-aware, then we show that there is no signaling mechanism that can do better than not releasing information, while satisfying agent participation constraints.
Information structure design, also sometimes known as signaling or persuasion, is concerned with understanding the effects of information on the outcomes of strategic interactions (the descriptive question), and in characterizing and computing the information sharing strategies which optimize some design objective (the prescriptive question). Both questions are illuminated through the lens of algorithms and complexity, as evidenced by recent work on the topic in the algorithmic game theory community. This monograph is a biased survey of this work, and paints a picture of the current state of progress and challenges ahead. We divide information structure design into single agent and multiple agent models, and further subdivide the multiple agent case into the public channel and private channel modes of information revelation. In each of these three cases, we describe the most prominent models and applications, survey the associated algorithms and complexity results and their structural implications, and outline directions for future work.