Preference Evolution under Partner Choice
We present a model that investigates preference evolution with endogenous matching. In the short run, individuals' subjective preferences influence partner selection and behavior in strategic interactions, which affect their material payoffs. These payoffs, in turn, determine how preferences evolve in the long run. To properly model the "match-to-interact" process, we combine stable matching and equilibrium concepts. Our analysis shows that endogenous matching gives rise to the evolutionary stability of a class of preferences that exhibit both affinity bias and efficient play. Such preferences stand out in the evolutionary process because they are able to force positive assortative matching and joint payoff maximization. Under incomplete information, a strong form of affinity bias, parochialism, is necessary for a preference to prevail in evolution, because stronger incentives are required to engage in self-sorting with information friction.
Code (0)
등록된 구현이 없습니다.
Tasks
FrictionSimilar Papers 제목 키워드 기반
On the Dynamics of Mating Preferences in Genetic Programming
Several mating restriction techniques have been implemented in Evolutionary Algorithms to promote diversity. From similarity-based selection to niche preservation, the general goal is to avoid premature convergence by no…
DiversityEvolutionary AlgorithmsMonte Carlo Planning method estimates planning horizons during interactive social exchange
Reciprocating interactions represent a central feature of all human exchanges. They have been the target of various recent experiments, with healthy participants and psychiatric populations engaging as dyads in multi-rou…
Evolution of female choice under intralocus sexual conflict and genotype-by-environment interactions
In many species, females are hypothesised to obtain 'good genes' for their offspring by mating with males in good condition. However, female preferences might deplete genetic variance and make choice redundant. Additiona…
AI emotional support is better only when chosen, but shifts preferences even when it is not
People increasingly face a novel decision when seeking emotional support: human or AI. In existing studies, AI's empathic messages are rated as well as or better than humans'. But these studies either assigned the suppor…
Large Language Model Few-Shot Prompting with Dilemma Training Outperforms Human Surrogates in Predicting Patient Preferences
In serious illness, human surrogates often struggle to accurately predict patient preferences (68% accuracy), causing decision conflict. Personalized Patient Preference Predictor (P4) agents offer a potential solution, b…