The field of social choice theory deals with aggregating the preferences or opinions of individuals towards a collective decision; voting is a paradigmatic example. This rich space of problems has long been studied in economics and mathematics, leading to a slew of striking theoretical results. But real-world applications have been sparse. From the AI viewpoint, the study of computational social choice is seen by many researchers as a central component in the effort to build the foundations of multiagent systems. This research extends this theoretical work in computational social choice to enable people to make joint decisions. This project aims to put human decision making at the center of computational social choice, while leveraging the very approaches and techniques developed for voting in multiagent systems. The research plan is directly motivated by the not-for-profit website, which enables people to implement whichever voting methods appear to be best based on analysis and empirical evidence. This project will realize the full potential of RoboVote for education, outreach, and societal impact with the aim of transforming the way people make group decisions in a wide range of applications.

The specific challenges that will be tackled are divided into two subsets, corresponding to the two fundamentally different types of polls currently modeled on RoboVote. 1. Subjective preferences: Preferences are subjective when the desirability of each alternative is a matter of taste. RoboVote aggregates subjective rankings by assuming that voters have latent utilities for the alternatives, and selecting an outcome that maximizes the sum of utilities by using the reported rankings as a proxy for those utilities. An immediate gap that must be addressed is that the approach does not extend to the case where the outcome is a ranking. A second, far-reaching challenge is to rethink the way voters express their preferences, in order to obtain more useful information regarding their actual utility functions while keeping the cognitive burden low. Finally, the project includes a study of the axiomatic properties of optimal aggregation methods in the foregoing framework. 2. Objective opinions: In this scenario, some alternatives are objectively better than others, but this objective comparison is not known to voters. The solutions deployed on RoboVote aim to handle worst-case noise. The research aims to create and test algorithms that improve upon naïve approaches, especially by building on synergistic advances in the design of fixed-parameter tractable algorithms.

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Harvard University
United States
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