1,721,053 research outputs found
Markets Versus Governments: Political Economy of Mechanisms
Full text linkWe study the optimal Mirrlees taxation problem in a dynamic economy with idiosyncratic (productivity or preference) shocks. In contrast to the standard approach, which implicitly assumes that the mechanism is operated by a benevolent planner with full commitment power, we assume that any centralized mechanism can only be operated by a self-interested ruler/government without commitment power, who can therefore misuse the resources and the information it collects. An important result of our analysis is that there will be truthful revelation along the equilibrium path (for all positive discount factors), which shows that truth-telling mechanisms can be used despite the commitment problems and the different interests of the government. Using this tool, we show that if the government is as patient as the agents, the best sustainable mechanism leads to an asymptotic allocation where the aggregate distortions arising from political economy disappear. In contrast, when the government is less patient than the citizens, there are positive aggregate distortions and positive aggregate capital taxes even asymptotically. Under some additional assumptions on preferences, these results generalize to the case when the government is benevolent but unable to commit to future tax policies. We conclude by providing a brief comparison of centralized mechanisms operated by self-interested rulers to anonymous markets.
Subgradient methods for convex minimization
Full text linkThesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2002.Includes bibliographical references (p. 169-174).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Many optimization problems arising in various applications require minimization of an objective cost function that is convex but not differentiable. Such a minimization arises, for example, in model construction, system identification, neural networks, pattern classification, and various assignment, scheduling, and allocation problems. To solve convex but not differentiable problems, we have to employ special methods that can work in the absence of differentiability, while taking the advantage of convexity and possibly other special structures that our minimization problem may possess. In this thesis, we propose and analyze some new methods that can solve convex (not necessarily differentiable) problems. In particular, we consider two classes of methods: incremental and variable metric.by Angelia NediÄ.Ph.D
On the rate of convergence of distributed subgradient methods for multi-agent optimization
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Distributed Gradient Methods for Convex Machine Learning Problems in Networks: Distributed Optimization
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Single timescale regularized stochastic approximation schemes for monotone nash games under uncertainty
Full text linkAbstract—In this paper, we consider the distributed compu-tation of equilibria arising in monotone stochastic Nash games over continuous strategy sets. Such games arise in settings when the gradient map of the player objectives is a monotone mapping over the cartesian product of strategy sets, leading to a monotone stochastic variational inequality. We consider the application of projection-based stochastic approximation schemes. However, such techniques are characterized by a key shortcoming: they can accommodate strongly monotone mappings only. In fact, standard extensions of stochastic ap-proximation schemes for merely monotone mappings require the solution of a sequence of related strongly monotone prob-lems, a natively two-timescale scheme. Accordingly, we consider the development of single timescale techniques for computing equilibria when the associated gradient map does not admit strong monotonicity. We first show that, under suitable assump-tions, standard projection schemes can indeed be extended to allow for strict, rather than strong monotonicity. Furthermore, we introduce a class of regularized stochastic approximation schemes, in which the regularization parameter is updated at every step, leading to a single timescale method. The scheme is a stochastic extension of an iterative Tikhonov regularization method and its global convergence is established. To aid in networked implementations, we consider an extension to this result where players are allowed to choose their steplengths independently and show if the deviation across their choices is suitably constrained, then the convergence of the scheme may be claimed. I
Distributed multiuser optimization: Algorithms and error analysis
Full text linkAbstract—We consider a class of multiuser optimization problems in which user interactions are seen through congestion cost functions or coupling constraints. Our primary emphasis lies on the convergence and error analysis of distributed algorithms in which users communicate through aggregate user information. Traditional implementations are reliant on strong convexity assumptions, require coordination across users in terms of consistent stepsizes, and often rule out early termination by a group of users. We consider how some of these assumptions can be weakened in the context of projection methods motivated by fixed-point formulations of the problem. Specifically, we focus on (approximate) primal and primal-dual projection algorithms. We analyze the convergence behavior of the methods and provide error bounds in settings with limited coordination across users and regimes where a group of users may prematurely terminate affecting the convergence point. I
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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