Proceedings:
No. 1: AAAI-19, IAAI-19, EAAI-20
Volume
Issue:
Proceedings of the AAAI Conference on Artificial Intelligence, 33
Track:
AAAI Technical Track: Game Theory and Economic Paradigms
Downloads:
Abstract:
Decision making under uncertainty is a key component of many AI settings, and in particular of voting scenarios where strategic agents are trying to reach a joint decision. The common approach to handle uncertainty is by maximizing expected utility, which requires a cardinal utility function as well as detailed probabilistic information. However, often such probabilities are not easy to estimate or apply.To this end, we present a framework that allows for “shades of gray” of likelihood without probabilities. Specifically, we create a hierarchy of sets of world states based on a prospective poll, with inner sets contain more likely outcomes. This hierarchy of likelihoods allows us to define what we term ordinally-dominated strategies. We use this approach to justify various known voting heuristics as bounded-rational strategies.
DOI:
10.1609/aaai.v33i01.33012077
AAAI
Proceedings of the AAAI Conference on Artificial Intelligence, 33