AbstractUnderstanding how to design agents that sustain cooperation in multi-agent systems has been a long-lasting goal in distributed artificial intelligence. Proposed solutions rely on identifying free-riders and avoiding cooperating or interacting with them. These mechanisms of social control are traditionally studied in games with linear and deterministic payoffs, such as the prisoner’s dilemma or the public goods game. In reality, however, agents often face dilemmas in which payoffs are uncertain and non-linear, as collective success requires a minimum number of cooperators. The collective risk dilemma (CRD) is one of these games, and it is unclear whether the known mechanisms of cooperation remain effective in this case. Here we study the emergence of cooperation in CRD through partner-based selection. First, we discuss an experiment in which groups of humans and robots play a CRD. This experiment suggests that people only prefer cooperative partners when they lose a previous game (i.e., when collective success was not previously achieved). Secondly, we develop an evolutionary game theoretical model pointing out the evolutionary advantages of preferring cooperative partners only when a previous game was lost. We show that this strategy constitutes a favorable balance between strictness (only interact with cooperators) and softness (cooperate and interact with everyone), thus suggesting a new way of designing agents that promote cooperation in CRD. We confirm these theoretical results through computer simulations considering a more complex strategy space. Third, resorting to online human–agent experiments, we observe that participants are more likely to accept playing in a group with one defector when they won in a previous CRD, when compared to participants that lost the game. These empirical results provide additional support to the human predisposition to use outcome-based partner selection strategies in human–agent interactions.