Modeling Mate-Choice using Computational Temperature and Dynamically Evolving Representations

We present a model of mate-choice (KAMA) that is based on a gradual, stochastic process of representation-building leading to marriage. KAMA reproduces empirically verifiable population-level mate-selection behavior using individual-level mate-choice mechanisms. Individuals have character profiles, which describe a number of their characteristics (physical beauty, potential earning power, etc.), as well as preference profiles, that specify their degree of preference for those characteristics in members of the opposite sex. A process of encounters and dating serves to exchange information and allows accurate representations of potential mates to be gradually built up over time. Finally, individuals each have a “temperature”, which is the extent to which they are willing to continue exploring mate-space. “Temperature” (the inverse of mate “choosiness”) drives individual decision-making in this model. We show that the individual-level mechanisms implemented in the model produce population-level data that qualitatively matches empirical data.