Search for: All records

We introduce Flexible Representative Democracy (FRD), a novel hybrid of Representative Democracy and Direct Democracy in which voters can alter the issue-dependent weights of a set of elected representatives. In line with the literature on Interactive Democracy, our model allows the voters to actively determine the degree to which the system is direct versus representative. However, unlike Liquid Democracy, Flexible Representative Democracy uses strictly non-transitive delegations, making delegation cycles impossible, and maintains a fixed set of accountable, elected representatives. We present Flexible Representative Democracy and analyze it using a computational approach with issues that are binary and symmetric. We compare the outcomes of various voting systems using Direct Democracy with majority voting as an ideal baseline. First, we demonstrate the shortcomings of Representative Democracy in our model. We provide NP-Hardness results for electing an ideal set of representatives, discuss pathologies, and demonstrate empirically that common multi-winner election rules for selecting representatives do not perform well in expectation. To analyze the effects of adding delegation to representative voting systems, we begin by providing theoretical results on how issue-specific delegations determine outcomes. Finally, we provide empirical results comparing the outcomes of various voting systems: Representative Democracy, Proxy Voting, and FRD with issue-specific delegations. Our results show that variants of Proxy Voting yield no discernible benefit over unweighted representatives and reveal the potential for Flexible Representative Democracy to improve outcomes as voter participation increases.

 

We investigate the problem of determining a binary ground truth using advice from a group of independent reviewers (experts) who express their guess about a ground truth correctly with some independent probability (competence) p_i. In this setting, when all reviewers are competent with p >= 0.5, the Condorcet Jury Theorem tells us that adding more reviewers increases the overall accuracy, and if all p_i's are known, then there exists an optimal weighting of the reviewers. However, in practical settings, reviewers may be noisy or incompetent, i.e., p_i < 0.5, and the number of experts may be small, so the asymptotic Condorcet Jury Theorem is not practically relevant. In such cases we explore appointing one or more chairs (judges) who determine the weight of each reviewer for aggregation, creating multiple levels. However, these chairs may be unable to correctly identify the competence of the reviewers they oversee, and therefore unable to compute the optimal weighting. We give conditions when a set of chairs is able to weight the reviewers optimally, and depending on the competence distribution of the agents, give results about when it is better to have more chairs or more reviewers. Through numerical simulations we show that in some cases it is better to have more chairs, but in many cases it is better to have more reviewers. 

Sometimes agents care not only about the outcomes of collective decisions but also about how decisions are made. Both the outcome and the procedure affect whether agents see a decision as legitimate or acceptable. We focus on incorporating agents’ preferences over decision-making processes into the process itself. Taking whole decisions, including decision rules and outcomes, to be the object of agent preferences rather than only decision outcomes, we (1) identify natural, plausible preference structures and key properties, (2) develop general mechanisms for aggregating these preferences to maximize the acceptability of decisions, and (3) analyze the performance of our acceptance-maximizing mechanisms. We apply our general approach to the setting of dichotomous choice, and compare the worst-case rates of acceptance achievable among populations of agents of different types. We include the special case of rule selection, or amendment, and show that amendment procedures proposed by Abramowitz et al. [2] achieve universal acceptance with certain agent types. 

When it comes to collective decisions, we have to deal with the fact that agents have preferences over both decision outcomes and how decisions are made. If we create rules for aggregating preferences over rules, and rules for preferences over rules for preferences over rules, and so on, it would appear that we run into infinite regress with preferences and rules at successively higher “levels.” The starting point of our analysis is the claim that such regress should not be a problem in practice, as any such preferences will necessarily be bounded in complexity and structured coherently in accordance with some (possibly latent) normative principles. Our core contributions are (1) the identification of simple, intuitive preference structures at low levels that can be generalized to form the building blocks of preferences at higher levels, and (2) the de- velopment of algorithms for maximizing the number of agents with such low-level preferences who will “accept” a decision. We analyze algorithms for acceptance maximization in two different domains: asymmetric dichotomous choice and constitutional amendment. In both settings we study the worst-case performance of the appro- priate algorithms, and reveal circumstances under which universal acceptance is possible. In particular, we show that constitutional amendment procedures proposed recently by Abramowitz et al. [2] can achieve universal acceptance. 

In representative democracies, regular election cycles are supposed to prevent misbehavior by elected officials, hold them accountable, and subject them to the “will of the people." Pandering, or dishonest preference reporting by candidates campaigning for election, undermines this democratic idea. Much of the work on Computational Social Choice to date has investigated strategic actions in only a single election. We introduce a novel formal model of pandering and examine the resilience of two voting systems, Representative Democracy (RD) and Flexible Representative Democracy (FRD), to pandering within a single election and across multiple rounds of elections. For both voting systems, our analysis centers on the types of strategies candidates employ and how voters update their views of candidates based on how the candidates have pandered in the past. We provide theoretical results on the complexity of pandering in our setting for a single election, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by single candidates and groups of candidates over many rounds. 

Aggregating signals from a collection of noisy sources is a fundamental problem in many domains including crowd-sourcing, multi-agent planning, sensor networks, signal processing, voting, ensemble learning, and federated learning. The core question is how to aggregate signals from multiple sources (e.g. experts) in order to reveal an underlying ground truth. While a full answer depends on the type of signal, correlation of signals, and desired output, a problem common to all of these applications is that of differentiating sources based on their quality and weighting them accordingly. It is often assumed that this differentiation and aggregation is done by a single, accurate central mechanism or agent (e.g. judge). We complicate this model in two ways. First, we investigate the setting with both a single judge, and one with multiple judges. Second, given this multi-agent interaction of judges, we investigate various constraints on the judges’ reporting space. We build on known results for the optimal weighting of experts and prove that an ensemble of sub-optimal mechanisms can perform optimally under certain conditions. We then show empirically that the ensemble approximates the performance of the optimal mechanism under a broader range of conditions.