  Socially relevant situations that involve strategic interactions are
widespread 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 population
perspective and considered one specific conflicting situation at a time. This
makes it difficult to extract conclusions about the consistency of individuals'
behavior when facing different situations, and more importantly, to define a
comprehensive classification of the strategies underlying the observed
behaviors. Here, we present the results of a lab-in-the-field experiment in
which subjects face four different dyadic games, with the aim of establishing
general behavioral rules dictating individuals' actions. By analyzing our data
with an unsupervised clustering algorithm, we find that all the subjects
conform, with a large degree of consistency, to a limited number of behavioral
phenotypes (Envious, Optimist, Pessimist, and Trustful), with only a small
fraction of undefined subjects. We also discuss the possible connections to
existing interpretations based on a priori theoretical approaches. Our findings
provide a relevant contribution to the experimental and theoretical efforts
towards the identification of basic behavioral phenotypes in a wider set of
contexts without aprioristic assumptions regarding the rules or strategies
behind actions. From this perspective, our work contributes to a fact-based
approach to the study of human behavior in strategic situations, that could be
applied to simulating societies, policy-making scenario building and even for a
variety of business applications.
