The Dynamic Relationship Between Freedom of Speech and Equality

This Article examines the dynamic intersection between freedom of speech and equal protection, with a particular focus on the race and LGBT equality movements. Unlike other works on expression and/or equality, the Article emphasizes the relational and bi-directional connections between freedom of speech and equal protection. Freedom of speech has played a critical role in terms of advancing constitutional equality. However, with regard to both race and LGBT equality, free speech rights also failed in important respects to facilitate equality claims and movements. Advocacy and agitation on behalf of equality rights have also left indelible positive and negative marks on free speech doctrines, principles, and rights. The free speech-equality relationship underscores several important lessons regarding reliance on speech rights to advance constitutional equality. Moreover, through a comparative analysis, the Article demonstrates that freedom of speech intersects in distinctive ways with different types of equalities. The Article’s general lessons and comparative observations carry important implications for future equality movements, including the current campaign for transgender equality

Veblen in the (Inner) City: On the Normality of Looting

Drawing on Veblen's concept of 'pecuniary prowess' I will argue that the August riots can be understood not so much in terms of protest but as an appropriation of the underlying acquisitive logic of capitalism. The violent realisation of that logic across class divides has become more likely due to an erosion in plausibility of discourses of meritocratic legitimacy. Recent denigrating discourses around \"chavs\" as dangerous and undeserving poor can be understood as attempts to reinstate meritocratic legitimacy rhetorically, but in an increasingly unequal society this becomes an ever more difficult enterprise. On the other hand, the assertion of the order of property through an effective police response may have eased the pressure by providing evidence that anxieties about a full scale insurgence are unfounded.August 2011 Riots; Thorstein Veblen; Inequality; Capitalism; Violence

Axiomatic Characterization of Data-Driven Influence Measures for Classification

We study the following problem: given a labeled dataset and a specific datapoint x, how did the i-th feature influence the classification for x? We identify a family of numerical influence measures - functions that, given a datapoint x, assign a numeric value phi_i(x) to every feature i, corresponding to how altering i's value would influence the outcome for x. This family, which we term monotone influence measures (MIM), is uniquely derived from a set of desirable properties, or axioms. The MIM family constitutes a provably sound methodology for measuring feature influence in classification domains; the values generated by MIM are based on the dataset alone, and do not make any queries to the classifier. While this requirement naturally limits the scope of our framework, we demonstrate its effectiveness on data

Taxation and stability in cooperative games

Cooperative games are a useful framework for modeling multi-agent behavior in environments where agents must collaborate in order to complete tasks. Having jointly completed a task and generated revenue, agents need to agree on some reasonable method of sharing their profits. One particularly appealing family of payoff divisions is the core, which consists of all coalitionally rational (or, stable) payoff divisions. Unfortunately, it is often the case that the core of a game is empty, i.e. there is no payoff scheme guaranteeing each group of agents a total payoff higher than what they can get on their own. As stability is a highly attractive property, there have been various methods of achieving it proposed in the literature. One natural way of stabilizing a game is via taxation, i.e. reducing the value of some coalitions in order to decrease their bargaining power. Existing taxation methods include the ε-core, the least-core and several others. However, taxing coalitions is in general undesirable: one would not wish to overly tamper with a given coalitional game, or overly tax the agents. Thus, in this work we study minimal taxation policies, i.e. those minimizing the amount of tax required in order to stabilize a given game. We show that games that minimize the total tax are to some extent a linear approximation of the original games, and explore their properties. We demonstrate connections between the minimal tax and the cost of stability, and characterize the types of games for which it is possible to obtain a tax-minimizing policy using variants of notion of the ε-core, as well as those for which it is possible to do so using reliability extensions. Copyright © 2013, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved

Forming Probably Stable Communities with Limited Interactions

A community needs to be partitioned into disjoint groups; each community member has an underlying preference over the groups that they would want to be a member of. We are interested in finding a stable community structure: one where no subset of members $S$ wants to deviate from the current structure. We model this setting as a hedonic game, where players are connected by an underlying interaction network, and can only consider joining groups that are connected subgraphs of the underlying graph. We analyze the relation between network structure, and one's capability to infer statistically stable (also known as PAC stable) player partitions from data. We show that when the interaction network is a forest, one can efficiently infer PAC stable coalition structures. Furthermore, when the underlying interaction graph is not a forest, efficient PAC stabilizability is no longer achievable. Thus, our results completely characterize when one can leverage the underlying graph structure in order to compute PAC stable outcomes for hedonic games. Finally, given an unknown underlying interaction network, we show that it is NP-hard to decide whether there exists a forest consistent with data samples from the network.Comment: 11 pages, full version of accepted AAAI-19 pape