Abstract
Recent work in iterative voting has defined the additive dynamic price of anarchy (ADPoA) as the difference in social welfare between the truthful and worst-case equilibrium profiles resulting from repeated strategic manipulations. While iterative plurality has been shown to only return alternatives with at most one less initial vote than the truthful winner, it is less understood how agents’ welfare changes in equilibrium. To this end, we differentiate agents’ utility from their manipulation mechanism and determine iterative plurality’s ADPoA in the worst- and average-cases. We first prove that the worst-case ADPoA is linear in the number of agents. To overcome this negative result, we study the average-case ADPoA and prove that equilibrium winners have a constant order welfare advantage over the truthful winner in expectation. Our positive results illustrate the prospect for social welfare to increase due to strategic manipulation.

Speaker Bio
Joshua Kavner is a doctoral student in the computer science department at Rensselaer Polytechnic Institute. He graduated from the University of Michigan, College of Engineering, in 2020, with a bachelor's degree in data science engineering and minors in complex systems and mathematics. Joshua's research interests include computational social choice and collective decision-making in multiagent systems.