Please note: This PhD defence will take place in DC 3317 and online.
Ben Armstrong, PhD candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Kate Larson
With increasing connectivity between humans and the rise of autonomous agents, group decision-making scenarios are becoming ever more commonplace. Simultaneously, the requirements placed upon decision-making procedures grow increasingly nuanced as social choices are made in more niche settings. To support these demands, a deeper understanding of the behaviour of social choice procedures is needed.
The standard theoretical approach to analyze social choice procedures is limited in the type of question it can answer. Theoretical analyses can be rigid: It may speak to the incompatibility of different properties without also providing a deeper understanding of the properties themselves, or might stop at proving the worst-case outcome of a voting rule without communicating the rule’s typical behaviour.
In this dissertation, we address these limitations by demonstrating that experimental analysis of social choice domains can provide an understanding of social choice which is both complementary and additional to theoretical findings. In particular, experimental approaches can form a middle ground between theory and practice: more practical than theoretical approaches in a setting more controlled than real-world application. We apply this approach to a new form of delegative voting and to a task of learning existing and novel voting rules. In each area we find results of a type and scale which are infeasible to traditional analysis.
We first examine an abstract model of delegative voting — agents use \textit{liquid democracy} to transitively delegate their vote — in a setting where the voters collectively agree on a correct outcome. Through extensive simulations we show the dynamic effects on group accuracy from varying a wide range of parameters that collectively encompass many types of human behaviour. We identify two features of this paradigm which result in improvements to group accuracy and highlight a possible explanation for their effectiveness. Subsequently, we apply this liquid democracy framework to the process of training an ensemble of classifiers. We show that the experimental findings from our simulations are largely maintained on a task involving real-world data and result in further improvements when considering a novel metric of the training cost of ensembles.
Additionally, we demonstrate the creation of a robust framework for axiomatic comparison of arbitrary voting rules. Rather than proving whether individual rules satisfy particular axioms, we establish a framework for showing experimentally the degree to which rules general satisfy sets of axioms. This enables a new type of question — degrees of axiom satisfaction — and provides a clear example of how to compare a wide range of single and multi-winner voting rules. Using this framework, we develop a procedure for training a model to act as a novel voting rule. This results in a trained model which realizes a far lower axiomatic violation rate than most existing rules and demonstrates the possibility for new rules which provide superior axiomatic properties.
To attend this PhD defence in person, please go to DC 3317. You can also attend virtually on Zoom.