Essays in political economy

This thesis is comprised of three chapters. In the first chapter, I examine a voting model where two political parties have fixed positions on a unidimensional policy space but where the implemented policy is the convex combination of the two positions and study the effects of opinion polls on election results and social welfare. Voters are completely agnostic about the distribution of preferences and gain sequential and partial information through series of opinion polls. Voters' behavior is driven in part by regret minimization. The mass of undecided voters decreases monotonically with the number of polls, but may not necessarily disappear. Voters who remain undecided have centrist ideologies. Finally, social welfare is not necessarily increasing in the number of polls: having more polls is not always better. Features of the model are con firmed by empirical evidence. In the second chapter, which is a joint work with Agustin Casas and Guillermo Diaz, we evaluate the effect of an institutional provision designed to increase accountability of local officials, and we show that its implementation can lead to a distribution of power within the legislature which is not consistent with voters' true preferences. The cause of this inconsistency is the ballot design which asymmetrically affects the officials listed on it. We analyze the case of the Lima's 2013 city legislature recall referendum and show that the design of the referendum ballot had adverse and signifficant effects on the composition of the Lima's city legislature. We also show that the election results with more \neutral" ballot designs would have been signifficantly different, and the composition of the legislature would have been more representative of voters' true preferences. More specifically, we use our results to simulate the outcome of the election with a random order of candidates. Even though the voters' fatigue is still present, it affects all parties equally, obtaining a more faithful representation of the voters' preferences. Finally, the third chapter is a joint work with Marco Serena. For small electorates, the probability of casting the pivotal vote drives one's willingness to vote, however the existence of costs of voting incentivizes ones abstention. In two-alternative pivotal-voter models, this trade-off has been extensively studied under private information on the cost of voting. We complement the literature by providing an analysis under complete information, extending the analysis of Palfrey and Rosenthal [1983. A strategic calculus of voting. Public Choice. 41, 7-53]. If the cost of voting is sufficiently high at least for supporters of one of the two alternatives, the equilibrium is unique, and fully characterized. If instead the cost of voting is sufficiently low for everyone, we characterize three classes of equilibria and we find that all equilibria must belong to one of these three classes, regardless of the number of individuals. Furthermore we focus on equilibria which are continuous in the cost of voting. We show that this equilibrium refinement pins down a unique equilibrium. We conclude by discussing an application of our findings to redistribution of wealth.Polling in a Proportional Representation System / Christos Mavridis. -- The last shall be the first: failed accountability due to voters fatigue and ballot design / Christos Mavridis, Agustin Casas and Guillermo Diaz. -- Costly voting under complete information / Christos Mavridis, Marco SerenaPrograma Oficial de Doctorado en EconomíaPresidente: Pablo Amorós González; Secretario: Fracisco Marhuenda Hurtado; Vocal: Orestis Troumpouni

Annealing Optimization for Progressive Learning with Stochastic Approximation

In this work, we introduce a learning model designed to meet the needs of applications in which computational resources are limited, and robustness and interpretability are prioritized. Learning problems can be formulated as constrained stochastic optimization problems, with the constraints originating mainly from model assumptions that define a trade-off between complexity and performance. This trade-off is closely related to over-fitting, generalization capacity, and robustness to noise and adversarial attacks, and depends on both the structure and complexity of the model, as well as the properties of the optimization methods used. We develop an online prototype-based learning algorithm based on annealing optimization that is formulated as an online gradient-free stochastic approximation algorithm. The learning model can be viewed as an interpretable and progressively growing competitive-learning neural network model to be used for supervised, unsupervised, and reinforcement learning. The annealing nature of the algorithm contributes to minimal hyper-parameter tuning requirements, poor local minima prevention, and robustness with respect to the initial conditions. At the same time, it provides online control over the performance-complexity trade-off by progressively increasing the complexity of the learning model as needed, through an intuitive bifurcation phenomenon. Finally, the use of stochastic approximation enables the study of the convergence of the learning algorithm through mathematical tools from dynamical systems and control, and allows for its integration with reinforcement learning algorithms, constructing an adaptive state-action aggregation scheme.Comment: arXiv admin note: text overlap with arXiv:2102.0583

The last shall be the first : failed accountability due to voters fatigue and ballot design

We show how an institutional provision designed to increase accountability of local legislature officials can lead to a distribution of power within the legislature which is not consistent with voters' true preferences. The cause of this inconsistency is the ballot design which asymmetrically affects the officials listed on it. We analyze the case of the Lima's 2013 city legislature recall referendum and show that, controlling for the legislators' individual characteristics, the design of the referendum ballot had adverse and significant effects on the composition of the Lima's city legislature, and examine the counterfactuals of different ballot designs. We show that the election results with more "neutral" ballot designs would have been significantly different, and the composition of the new council would have been more representative of voters' preferences

Deciding for Others: Local Public Good Contributions with Intermediaries

Given the prevalence of local public goods, whose broader use is often limited by distance and borders, we propose a potential solution to the free-riding problem by having each participant/beneficiary delegate the public good contribution decision to a non-local intermediary who neither puts in own endowment into the public good nor benefits from it. Intermediaries make decisions under two compensation mechanisms where the incentives for the intermediary are either non-aligned (fixed) or aligned (variable) with those of the beneficiary. We find that the use of intermediaries, regardless of whether their compensation is aligned or not with that of the beneficiary, significantly increases contributions to the provision of the public good. We conclude that individuals behave differently when they (formally) make decisions for someone else even if their incentive structures are identical

Risk-Sensitive Reinforcement Learning with Exponential Criteria

While reinforcement learning has shown experimental success in a number of applications, it is known to be sensitive to noise and perturbations in the parameters of the system, leading to high variance in the total reward amongst different episodes on slightly different environments. To introduce robustness, as well as sample efficiency, risk-sensitive reinforcement learning methods are being thoroughly studied. In this work, we provide a definition of robust reinforcement learning policies and formulate a risk-sensitive reinforcement learning problem to approximate them, by solving an optimization problem with respect to a modified objective based on exponential criteria. In particular, we study a model-free risk-sensitive variation of the widely-used Monte Carlo Policy Gradient algorithm, and introduce a novel risk-sensitive online Actor-Critic algorithm based on solving a multiplicative Bellman equation using stochastic approximation updates. Analytical results suggest that the use of exponential criteria generalizes commonly used ad-hoc regularization approaches, improves sample efficiency, and introduces robustness with respect to perturbations in the model parameters and the environment. The implementation, performance, and robustness properties of the proposed methods are evaluated in simulated experiments

Weak Solutions to an Euler Alignment System in a Bounded Domain

Euler alignment systems appear as hydrodynamic limits of interacting self-propelled particle systems such as the (generalized) Cucker-Smale model. In this work, we study weak solutions to an Euler alignment system on smooth, bounded domains. This particular Euler alignment system includes singular alignment, attraction, and repulsion interaction kernels which correspond to a Yukawa potential. We also include a confinement potential and self-propulsion. We embed the problem into an abstract Euler system to conclude that infinitely many weak solutions exist. We further show that we can construct solutions satisfying bounds on an energy quantity, and that the solutions satisfy a weak-strong uniqueness principle. Finally, we present an addition of leader-agents governed by controlled ODEs, and modification of the interactions to be Bessel potentials of fractional order $s > 2$

Cooperative Bidirectional Mixed-Traffic Overtaking

Safe overtaking, especially in a bidirectional mixed-traffic setting, remains a key challenge for Connected Autonomous Vehicles (CAVs). The presence of human-driven vehicles (HDVs), behavior unpredictability, and blind spots resulting from sensor occlusion make this a challenging control problem. To overcome these difficulties, we propose a cooperative communication-based approach that utilizes the information shared between CAVs to reduce the effects of sensor occlusion while benefiting from the local velocity prediction based on past tracking data. Our control framework aims to perform overtaking maneuvers with the objective of maximizing velocity while prioritizing safety and passenger comfort. Our method is also capable of reactively adjusting its plan to dynamic changes in the environment. The performance of the proposed approach is verified using realistic traffic simulations.Comment: Published in: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC