On Adaptive Emergence of Trust Behavior in the Game of Stag Hunt

We study the emergence of trust behavior at both the individual and the population levels. At the individual level, in contrast to prior research that views trust as fixed traits, we model the emergence of trust or cooperation as a result of trial and error learning by a computer algorithm borrowed from the field of artificial intelligence (Watkins 1989). We show that trust can indeed arise as a result of trial and error learning. Emergence of trust at the population level is modeled by a grid-world consisting of cells of individual agents, a technique known as spatialization in evolutionary game theory. We show that, under a wide range of assumptions, trusting individuals tend to take over the population and trust becomes a systematic property. At both individual and population levels, therefore, we argue that trust behaviors will often emerge as a result of learning.