Spite, costly behavior that harms others, presents an evolutionary puzzle: given that both the actor and recipient do worse, how could it emerge? We show that dynamically evolving interaction networks provide a novel explanation for the evolution of costly harm. Previous work has shown that anti-correlated interaction (e.g., negative assortment or negative relatedness) among behavioral strategies in populations can lead to the evolution of costly harm. We show that these approaches are blind to important features of interaction brought about by a co-evolution of network and behavior and that these features enable the emergence of spite. We analyze a new model in which agents can inflict harm on others at a cost to themselves, and simultaneously learn how to behave and with whom to interact. We find spite emerges reliably under a wide range of conditions. Our model reveals that when interactions occur in dynamic networks the population can exhibit correlated and anti-correlated behavioral interactions simultaneously, something not possible in standard models. In dynamic networks spite evolves due to transient and partial anti-correlated interaction, even when other behaviors are positively correlated and average degree of correlated interaction in the population is low.
Zach is a third-year PhD student working with Dr. Chris Riedl as part of the CSS Lab. Zach is primarily interested in dynamic networks, search behavior, and organizational theory. His research utilizes simulation modeling and experiments to better understand how groups of people process information to adapt, learn, and innovate. Currently, he is undertaking research on the role of incentives and group structure in parallel problem solving, and the outcome of games on dynamic networks. Zach received his B.S. from the University of Pittsburgh majoring in Mathematics and Economics and minoring in Computer Science and Statistics.