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Title: Mating with Multi-Armed Bandits: Reinforcement Learning Models of Human Mate Search
Abstract Mate choice requires navigating an exploration-exploitation trade-off. Successful mate choice requires choosing partners who have preferred qualities; but time spent determining one partner’s qualities could have been spent exploring for potentially superior alternatives. Here I argue that this dilemma can be modeled in a reinforcement learning framework as a multi-armed bandit problem. Moreover, using agent-based models and a sample of k = 522 real-world romantic dyads, I show that a reciprocity-weighted Thompson sampling algorithm performs well both in guiding mate search in noisy search environments and in reproducing the mate choices of real-world participants. These results provide a formal model of the understudied psychology of human mate search. They additionally offer implications for our understanding of person perception and mate choice.  more » « less
Award ID(s):
1845586
PAR ID:
10614334
Author(s) / Creator(s):
Publisher / Repository:
MIT Press
Date Published:
Journal Name:
Open Mind
Volume:
8
ISSN:
2470-2986
Page Range / eLocation ID:
995 to 1011
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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