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Socially relevant situations that involve strategic interactions arewidespread among animals and humans alike. To study these situations,theoretical and experimental works have adopted a game-theoretical perspective,which has allowed to obtain valuable insights about human behavior. However,most of the results reported so far have been obtained from a populationperspective and considered one specific conflicting situation at a time. Thismakes it difficult to extract conclusions about the consistency of individuals'behavior when facing different situations, and more importantly, to define acomprehensive classification of the strategies underlying the observedbehaviors. Here, we present the results of a lab-in-the-field experiment inwhich subjects face four different dyadic games, with the aim of establishinggeneral behavioral rules dictating individuals' actions. By analyzing our datawith an unsupervised clustering algorithm, we find that all the subjectsconform, with a large degree of consistency, to a limited number of behavioralphenotypes (Envious, Optimist, Pessimist, and Trustful), with only a smallfraction of undefined subjects. We also discuss the possible connections toexisting interpretations based on a priori theoretical approaches. Our findingsprovide a relevant contribution to the experimental and theoretical effortstowards the identification of basic behavioral phenotypes in a wider set ofcontexts without aprioristic assumptions regarding the rules or strategiesbehind actions. From this perspective, our work contributes to a fact-basedapproach to the study of human behavior in strategic situations, that could beapplied to simulating societies, policy-making scenario building and even for avariety of business applications.

Humans display a reduced set of consistent behavioral phenotypes in dyadic games