Author name cluster

Ben Armstrong

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

AAMAS Conference 2026 Conference Paper

Optimizing Voting Rules for Social Welfare and Beyond

Ben Armstrong
Hrishi Kabra
Nicholas Mattei

We present a website and Python package, optimal_voting, that provides tools for finding the positional scoring rule (PSR) that optimizes for a user-selected target function over a (set of) profiles. The web interface provides an intuitive interface for creating profiles, analyzing them, and exploring various PSRs; while the library provides extensive functionality for researchers and system implementers. By formulating finding the optimal PSR within a larger optimization system, the target of the optimization can be any of a number of widely studied functions including classical social welfare functions (utilitarian, egalitarian, Nash), distortion, or axiom violation rate. By restricting optimization to positional scoring rules we ensure that our results are intuitively explainable. Our library has already been used to generate significant results for two papers and can be directly applied to several further settings.

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AAAI Conference 2026 Conference Paper

What Voting Rules Actually Do: A Data-Driven Analysis of Multi-Winner Voting

Joshua Caiata
Ben Armstrong
Kate Larson

Committee-selection problems arise in many contexts and applications, and there has been increasing interest within the social choice research community on identifying which properties are satisfied by different multi-winner voting rules. In this work, we propose a data-driven framework to evaluate how frequently voting rules violate axioms across diverse preference distributions in practice, shifting away from the binary perspective of axiom satisfaction given by worst-case analysis. Using this framework, we analyze the relationship between multi-winner voting rules and their axiomatic performance under several preference distributions, and propose a methodology for systematically minimizing axioms violations. Our results suggest that data-driven approaches to social choice can inform the design of new voting systems and support the continuation of data-driven research in social choice.

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AAMAS Conference 2024 Conference Paper

Liquid Democracy for Low-Cost Ensemble Pruning

Ben Armstrong
Kate Larson

We show that there is a strong connection between ensemble learning and a delegative voting paradigm, liquid democracy, which can be leveraged to reduce ensemble training costs. We present an incremental training procedure that removes redundant classifiers from an ensemble via delegation. By carefully selecting the underlying delegation mechanism weight-centralization among classifiers is avoided, leading to higher accuracy than some boosting methods with a significantly lower cost than training a full ensemble. This work serves as an exemplar of how ideas from computational social choice can be applied to problems in nontraditional domains.

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IJCAI Conference 2024 Conference Paper

Optimizing Viscous Democracy

Ben Armstrong
Shiri Alouf-Heffetz
Nimrod Talmon

Viscous democracy is a generalization of liquid democracy, a social choice framework in which voters may transitively delegate their votes. In viscous democracy, a "viscosity" factor decreases the weight of a delegation the further it travels, reducing the chance of excessive weight flowing between ideologically misaligned voters. We demonstrate that viscous democracy often significantly improves the quality of group decision-making over liquid democracy. We first show that finding optimal delegations within a viscous setting is NP-hard. However, simulations allow us to explore the practical effects of viscosity. Across social network structures, competence distributions, and delegation mechanisms we find high viscosity reduces the chance of ``super-voters'' attaining large amounts of weight and increases the number of voters that are able to affect the outcome of elections. This, in turn, improves group accuracy as a whole. As a result, we argue that viscosity should be considered a core component of liquid democracy.

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IJCAI Conference 2022 Conference Paper

How Should We Vote? A Comparison of Voting Systems within Social Networks

Shiri Alouf-Heffetz
Ben Armstrong
Kate Larson
Nimrod Talmon

Voting is a crucial methodology for eliciting and combining agents' preferences and information across many applications. Just as there are numerous voting rules exhibiting different properties, we also see many different voting systems. In this paper we investigate how different voting systems perform as a function of the characteristics of the underlying voting population and social network. In particular, we compare direct democracy, liquid democracy, and sortition in a ground truth voting context. Through simulations -- using both real and artificially generated social networks -- we illustrate how voter competency distributions and levels of direct participation affect group accuracy differently in each voting mechanism. Our results can be used to guide the selection of a suitable voting system based on the characteristics of a particular voting setting.

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AAMAS Conference 2021 Conference Paper

Exploring the Relationship Between Social Choice and Machine Learning

Ben Armstrong

My thesis will study the intersection of social choice and machine learning, with a focus on recent or under-explored social choice paradigms, such as liquid democracy, and how social choice and ML can benefit each other. My initial results show the idea of using ML and social choice to understand the other holds promise. An early project of mine uses deep learning to enhance social choice by creating a neural network that acts as a voting rule, able to be trained to select a winner satisfying customizable sets of axioms. More recently, I have explored the idea of using liquid democracy as a framework for ensembles for classification problems. I am particularly interested in improving the real-world applicability of existing social choice methods and understanding how they can more beneficially impact the world. Going forward, my primary tools in these goals are simulation and provable axiomatic or performance guarantees.

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