Abstract: We formalize a voting model for plurality elections that combines Iterative Voting and Calculus of Voting. Each iteration, autonomous agents simultaneously maximize the utility they expect from candidates. Agents are aware of neither other individuals’ preferences or choices, nor of the distribution preferences come from. They know only of candidates’ current expected vote shares (e.g. from a poll, network, etc) and with that calculate expected rewards from each candidate, pondering the probability that voting for each would alter the final outcome. We define the general form of those pivotal probabilities, then we derive both efficient exact calculations as well as heuristics. Lastly, we both prove formally that the model converges with asymptotically large electorates and show empirically (via simulations) that it nearly always converges even with very few agents.