May 1, 2024New Journal of PhysicsOpen Access

Decoding trust: A reinforcement learning perspective

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High levels of trust and trustworthiness emerge naturally when individuals value past experience alongside future payoffs in the trust game.
Internal Q-tables driving trustor and trustee actions show a dynamic crossover that mirrors psychological transitions during strategy updates.
Simulation using the Q-learning algorithm across pairwise interactions and a latticed population establishes stable behavioral boundaries.
Highlights how endogenous learning mechanisms alone explain cooperative human social behavior without needing exogenous imitation models.

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Implication

Simulation reveals trust and trustworthiness naturally emerge through reinforcement learning, indicating endogenous factors explain cooperative human behaviors.

Cite This Study

Zheng et al. (2024) studied this question.

synapsesocial.com/papers/68e6c03bb6db64358763facf https://doi.org/10.1088/1367-2630/ad4b5a

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Implication

Simulation reveals trust and trustworthiness naturally emerge through reinforcement learning, indicating endogenous factors explain cooperative human behaviors.

Decoding trust: A reinforcement learning perspective

Key Points

Discussion

Implication

Cite This Study

Decoding trust: A reinforcement learning perspective

Key Points

Discussion

Implication

Cite This Study

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