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Open Access Publications from the University of California

Fishing Free-Riders using Altruism: Zero-Sum Fitness Competition in Prey-Predator System


How altruistic behavior evolves despite its evolutionary cost is still an intriguing question. Using Neural Network and Gradient descent algorithms, we proposed a mixed computational model of fitness competition among three artificial agents (predator, altruistic prey, recipient prey), in the zero-sum game environment. We found that altruism emerged without direct reciprocity when the predator invested in altruism aiming to use the prey’s altruistic behavior as “bait” to fish for more prey. For the decisive factor of this mechanism, we demonstrated that the long-term decision-making of a predator enhanced its investment in the prey's altruistic behavior, which leads to a significant increase in altruism and fitness in altruistic prey. We interpreted our findings from economic, evolutionary, and psychological perspectives, connecting zero-sum economies, K-selection, and third-party emotional decision-making to the emergence and maintenance of altruistic behavior.

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