Green visions and democratic constraints: the possibility and design of democratic institutions for environmental decision-making

This thesis addresses a recurrent question of our time – whether democracy can secure environmental sustainability – by drawing on literatures in the normative theory of democracy, social choice theory and environmental politics. I propose a basic, yet substantial organising principle, the ‘dilemma of green democracy’, which maps out the possibility of realising green outcomes under democratic constraints. Interdisciplinary ideas from neighbouring disciplines are also imported for the purpose of studying the design of good environmental-democratic institutions. The analytical framework is an integrated one, comprising formal choice theory and normative democratic theory. The first part of the thesis focuses on the possibility of environmentaldemocratic institutions. Chapter 1 introduces the dilemma of green democracy – a conflict between three plausible desiderata for environmental democracy – and suggests several proposals for avoiding the dilemma. It concludes that, as long as the dilemma is resolved, it is logically possible to construct environmental-democratic institutions. Chapters 2, 3 and 4 assess the desirability of the different proposals in terms of procedure and outcome. The general conclusion is that whether these proposals are desirable depends on a number of conditions and/or contextual factors. The second part of the thesis examines the substantive issues in designing environmental-democratic institutions. Chapter 5 discusses how the discursive dilemma in social choice theory and the normative ends of deliberation constrain the inputs of such institutions. Chapter 6 demonstrates how the concept of distributed cognition, drawn from cognitive/computer science, reconciles the tension between technocracy and democracy. Chapter 7 suggests how the theory of cognitive dissonance, drawn from psychology, challenges the epistemic performance of practicable (environmental-) deliberative-democratic institutions. The overall conclusion is two-fold. First, democracy can, at least in principle, secure environmental sustainability, provided that the dilemma of green democracy is resolved. Second, interdisciplinary ideas are useful for designing good democratic institutions for collective environmental decision-making. This conclusion has implications not only for intellectual enquiry, but also for institutional design in practice

Fairness in Federated Learning via Core-Stability

Federated learning provides an effective paradigm to jointly optimize a model benefited from rich distributed data while protecting data privacy. Nonetheless, the heterogeneity nature of distributed data makes it challenging to define and ensure fairness among local agents. For instance, it is intuitively "unfair" for agents with data of high quality to sacrifice their performance due to other agents with low quality data. Currently popular egalitarian and weighted equity-based fairness measures suffer from the aforementioned pitfall. In this work, we aim to formally represent this problem and address these fairness issues using concepts from co-operative game theory and social choice theory. We model the task of learning a shared predictor in the federated setting as a fair public decision making problem, and then define the notion of core-stable fairness: Given $N$ agents, there is no subset of agents $S$ that can benefit significantly by forming a coalition among themselves based on their utilities $U_N$ and $U_S$ (i.e., $\frac{|S|}{N} U_S \geq U_N$). Core-stable predictors are robust to low quality local data from some agents, and additionally they satisfy Proportionality and Pareto-optimality, two well sought-after fairness and efficiency notions within social choice. We then propose an efficient federated learning protocol CoreFed to optimize a core stable predictor. CoreFed determines a core-stable predictor when the loss functions of the agents are convex. CoreFed also determines approximate core-stable predictors when the loss functions are not convex, like smooth neural networks. We further show the existence of core-stable predictors in more general settings using Kakutani's fixed point theorem. Finally, we empirically validate our analysis on two real-world datasets, and we show that CoreFed achieves higher core-stability fairness than FedAvg while having similar accuracy.Comment: NeurIPS 2022; code: https://openreview.net/attachment?id=lKULHf7oFDo&name=supplementary_materia

A Comparative Study of the Robustness of Voting Systems Under Various Models of Noise

While the study of election theory is not a new field in and of itself, recent research has applied various concepts in computer science to the study of social choice theory, which includes election theory. From a security perspective, it is pertinent to investigate how stable election systems are in the face of noise, disruption, and manipulation. Recently, work related to computational election systems has also been of interest to artificial intelligence researchers, where it is incorporated into the decision-making processes of distributed systems. The quantitative analysis of a voting rule\u27s resistance to noise is the robustness, the probability of how likely the outcome of the election is to change given a certain amount of noise. Prior research has studied the robustness of voting rules under very small amounts of noise, e.g. swapping the ranking of two adjacent candidates in one vote. Our research expands upon this previous work by considering a more disruptive form of noise: an arbitrary reordering of an entire vote. Given k noise disruptions, we determine how likely the election is to remain unchanged (the k-robustness) by relating the k-robustness to the 1-robustness. We can thereby provide upper and/or lower bounds on the robustness of voting rules; specifically, we examine five well-established rules: scoring rules (a general class of rules, containing Borda, plurality, and veto, among others), Copeland, Maximin (also known as Minimax or Simpson-Kramer), Bucklin, and plurality with runoff

(WP 2016-03) Economics, Neuroeconomics, and the Problem of Identity

This paper reviews the debate in economics over neuroeconomics’ contribution to economics. It distinguishes majority and minority views, argues that this debate has been framed by mainstream economics’ conception of itself as an isolated science, and argues that this framing has put off the agenda in economics issues such as individual identity that are increasingly important in connection with the social and historical context of economic explanations in a changing complex world. The paper first discusses how the debate over neuroeconomics has been limited to the question of what information from other sciences might be employed in economics. It then goes on to the individual identity issue, and discusses how economics’ top-down, closed character generates a circular individual identity conception, while bottom-up, open character of psychology and neuroscience, and their continual concern with the changing relation between theory and evidence, has produced four competing individual identity conceptions in neuroeconomic research

Factors Influencing Students’ Choice of an Institution of Higher Education

This study examined the following research question: What factors influence student college selection process? The study sought to fill an existing gap in the literature by examining what role technology and other relevant factors have on students’ decision-making as related to college choice. By identifying influencers of college choice, the study’s findings can add to the body of knowledge that admission counselors might use as they develop an appropriate recruiting mix of strategies best suited for today’s college applicants . As the theoretical framework, this research drew on the previous work of Hamrick & Hossler (1996) which combined constructs of both economic and sociologic perspectives with college choice. Additionally, an adaptation of the Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh, Morris, & Davis, 2003) was created with key constructs such as Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions. In addition, the adapted model incorporated two sets of moderators (University Attributes and Individual Attributes) that were hypothesized to influence university or college choice. Socio-demographic information was also collected to better understand how students are being recruited and what methods they perceive as most effective. A convenience sample of students from the freshman class at a major research university in the Southeast were surveyed. Approximately 750 students were selected to receive the main survey, selected with the help of university advisement personnel and university faculty in identifying possible classes to participate. The survey was distributed by e-mail. Over the course of a two-month period, 427 students responded, with 341 surveys completed. Usable surveys were analyzed using the SPSS 25 statistical package. From the data analyzed via multiple regression, Performance Expectancy and Facilitating Conditions were found to be statistically significant whereas Effort Expectancy and Social Influence were found to be insignificant. Individual Attributes as a moderating factor within the model was found to be insignificant. University attributes as a moderating factor within the model was found to be partially confirmed, as only the relationship between social influence (SI) and school of choice behavior (B) was significant, whereas the other hypothesized paths were insignificant. Socio-demographic information from the survey suggested that students were being recruited via email most often, with mail and brochure usage also noted. Social media platforms such as Instagram and Facebook were found to be highly used by students but were not effective recruiting tools. The results suggest that performance expectancy and facilitating conditions such as classrooms, athletic facilities, and academic reputation have a significant and positive relationship with behavior (school choice). Conversely, effort expectancy and social influence did not have a significant direct relationship with school of choice behavior. As technology continues to evolve and become a more pervasive influence on students, colleges need to explore if social media might be a useful recruitment tool. The data from this study adds to the body of literature on economic and status-based factors related to school of choice by including the role of technology

Multi-agent decision-making dynamics inspired by honeybees

When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological models of decision-making. Using tools of nonlinear dynamics we show how the designed agent-based dynamics recover the high performing value-sensitive decision-making of the honeybees and rigorously connect investigation of mechanisms of animal group decision-making to systematic, bio-inspired control of multi-agent network systems. We further present a distributed adaptive bifurcation control law and prove how it enhances the network decision-making performance beyond that observed in swarms

Algorithmic and complexity aspects of simple coalitional games

Simple coalitional games are a fundamental class of cooperative games and voting games which are used to model coalition formation, resource allocation and decision making in computer science, artificial intelligence and multiagent systems. Although simple coalitional games are well studied in the domain of game theory and social choice, their algorithmic and computational complexity aspects have received less attention till recently. The computational aspects of simple coalitional games are of increased importance as these games are used by computer scientists to model distributed settings. This thesis fits in the wider setting of the interplay between economics and computer science which has led to the development of algorithmic game theory and computational social choice. A unified view of the computational aspects of simple coalitional games is presented here for the first time. Certain complexity results also apply to other coalitional games such as skill games and matching games. The following issues are given special consideration: influence of players, limit and complexity of manipulations in the coalitional games and complexity of resource allocation on networks. The complexity of comparison of influence between players in simple games is characterized. The simple games considered are represented by winning coalitions, minimal winning coalitions, weighted voting games or multiple weighted voting games. A comprehensive classification of weighted voting games which can be solved in polynomial time is presented. An efficient algorithm which uses generating functions and interpolation to compute an integer weight vector for target power indices is proposed. Voting theory, especially the Penrose Square Root Law, is used to investigate the fairness of a real life voting model. Computational complexity of manipulation in social choice protocols can determine whether manipulation is computationally feasible or not. The computational complexity and bounds of manipulation are considered from various angles including control, false-name manipulation and bribery. Moreover, the computational complexity of computing various cooperative game solutions of simple games in dierent representations is studied. Certain structural results regarding least core payos extend to the general monotone cooperative game. The thesis also studies a coalitional game called the spanning connectivity game. It is proved that whereas computing the Banzhaf values and Shapley-Shubik indices of such games is #P-complete, there is a polynomial time combinatorial algorithm to compute the nucleolus. The results have interesting significance for optimal strategies for the wiretapping game which is a noncooperative game defined on a network

Beliefs in Decision-Making Cascades

This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an $N$-agent binary hypothesis test in which each agent sequentially makes a decision based not only on a private observation, but also on preceding agents' decisions. In addition, the agents have their own beliefs instead of the true prior, and have nonidentical noise variances in the private signal. We focus on the Bayes risk of the last agent, where preceding agents are selfish. We first derive the optimal decision rule by recursive belief update and conclude, counterintuitively, that beliefs deviating from the true prior could be optimal in this setting. The effect of nonidentical noise levels in the two-agent case is also considered and analytical properties of the optimal belief curves are given. Next, we consider a predecessor selection problem wherein the subsequent agent of a certain belief chooses a predecessor from a set of candidates with varying beliefs. We characterize the decision region for choosing such a predecessor and argue that a subsequent agent with beliefs varying from the true prior often ends up selecting a suboptimal predecessor, indicating the need for a social planner. Lastly, we discuss an augmented intelligence design problem that uses a model of human behavior from cumulative prospect theory and investigate its near-optimality and suboptimality.Comment: final version, to appear in IEEE Transactions on Signal Processin

Decision-Making: A Neuroeconomic Perspective

This article introduces and discusses from a philosophical point of view the nascent field of neuroeconomics, which is the study of neural mechanisms involved in decision-making and their economic significance. Following a survey of the ways in which decision-making is usually construed in philosophy, economics and psychology, I review many important findings in neuroeconomics to show that they suggest a revised picture of decision-making and ourselves as choosing agents. Finally, I outline a neuroeconomic account of irrationality