A Computational Model of Worker Protest

This paper presents an agent-based model of worker protest. Workers have varying degrees of grievance depending on the difference between their wage and the average of their neighbors. They protest with probabilities proportional to grievance, but are inhibited by the risk of being arrested – which is determined by the ratio of coercive agents to probable rebels in the local area. We explore the effect of similarity perception on the dynamics of collective behavior. If workers are surrounded by more in-group members, they are more risk-taking; if surrounded by more out-group members, more risk-averse. Individual interest and group membership jointly affect patterns of workers protest: rhythm, frequency, strength, and duration of protest outbreaks. Results indicate that when wages are more unequally distributed, the previous outburst tends to suppress the next one, protests occur more frequently, and they become more intensive and persistent. Group identification does not seriously influence the frequency of local uprisings. Both their strength and duration, however, are negatively affected by the ingroup-outgroup assessment. The overall findings are valid when workers distinguish \'us\' from \'them\' through simple binary categorization, as well as when they perceive degrees of similarity and difference from their neighbors.Workers Protest, Tags, Group Identity, Trust, Netlogo

Social media censorship in times of political unrest: a social simulation experiment with the UK riots

Following the 2011 wave of political unrest, extending from the Arab Spring to the UK riots, the formation of a large consensus around Internet censorship is underway. The present paper adopts a social simulation approach to show that the decision to “regulate”, filter or censor social media in situations of unrest changes the pattern of civil protest and ultimately results in higher levels of violence. Building on Epstein's (2002) agent-based model, several alternative scenarios are generated. The systemic optimum, represented by complete absence of censorship, not only corresponds to lower levels of violence over time, but allows for significant periods of social peace after each outburst

Explicit diversification of event aspects for temporal summarization

During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness

Equilibrium Wage Arrears: A theoretical and empirical analysis of institutional lock-in

We present a model of managerial choice of wage delays that implies a possibility of multiple equilibria in the level of arrears. Positive feedback arises because each employer's wage arrears choice has externalities for other employers by affecting worker quit, effort and protest behavior and the probability of legal penalties. We study the case of three equilibria, distinguishing two that are stable - the "punctual payment equilibrium" and the "late payment equilibrium" - and one unstable "critical mass equilibrium," a threshold of arrears in the local labor market beyond which even profitable firms may adopt the practice. Our econometric analysis of linked employer-employee data for Russia provides evidence that workers' responses to wage delays are attenuated by local labor market arrears, that the wage arrears reaction function exhibits positive feedback, and that the theoretical conditions for multiple equilibria under symmetric local labor market competition are satisfied empirically in 1995 and 1998. Simulation results imply clustering of regions around two stable levels of arrears, with the late payment equilibrium characterized by six months overdue wages for a typical worker in 1995 and nine months in 1998.http://deepblue.lib.umich.edu/bitstream/2027.42/39705/2/wp321.pd

Agent-based modeling of social conflict, civil violence and revolution: State-of-the-art-review and further prospects

In this paper, we present a state-of-the-art review of Agent-based models (ABM) for simulation of social conflict phenomena, such as peaceful or violent street protests, civil violence and revolution. First, a simplified characterization of social conflict phenomena as emergent properties of a complex system is presented, together with a description of their macro and micro levels and the scales of the emergent properties. Then, existing ABM for simulation of crowd dynamics, civil violence and revolution are analyzed and compared, using a framework that considers their purpose/scope, environment representation, agent types and their architecture, the scales of the emergent properties, the qualitative and quantitative understanding of the phenomena provided by the results obtained from the models. We discuss the strengths and limitations of the existing models, as well as the promising lines of research for filling the gaps between the state-of-the-art models and real phenomena. This review is part of a work in progress on the assembling and dynamics of protests and civil violence, involving both simulation of the assembling process and the protest dynamics, as well as data collection in real protest events, and provides hints and guidelines for future developments.info:eu-repo/semantics/publishedVersio

Equilibrium Wage Arrears: Institutional Lock-In of Contractual Failure in Russia

We present a model of managerial choice of wage delays that implies a possibility of multiple equilibria in the level of arrears. Positive feedback arises because each employer's wage arrears choice has externalities for other employers by affecting worker quit, effort and protest behavior and the probability of legal penalties. We study the case of three equilibria, distinguishing two that are stable - the "punctual payment equilibrium" and the "late payment equilibrium" - and one unstable "critical mass equilibrium," a threshold of arrears in the local labor market beyond which even profitable firms may adopt the practice. Our econometric analysis of linked employer-employee data for Russia provides evidence that workers' responses to wage delays are attenuated by local labor market arrears, that the wage arrears reaction function exhibits positive feedback, and that the theoretical conditions for multiple equilibria under symmetric local labor market competition are satisfied empirically in 1995 and 1998. Simulation results imply clustering of regions around two stable levels of arrears, with the late payment equilibrium characterized by six months overdue wages for a typical worker in 1995 and nine months in 1998.

SMAN : Stacked Multi-Modal Attention Network for cross-modal image-text retrieval

This article focuses on tackling the task of the cross-modal image-text retrieval which has been an interdisciplinary topic in both computer vision and natural language processing communities. Existing global representation alignment-based methods fail to pinpoint the semantically meaningful portion of images and texts, while the local representation alignment schemes suffer from the huge computational burden for aggregating the similarity of visual fragments and textual words exhaustively. In this article, we propose a stacked multimodal attention network (SMAN) that makes use of the stacked multimodal attention mechanism to exploit the fine-grained interdependencies between image and text, thereby mapping the aggregation of attentive fragments into a common space for measuring cross-modal similarity. Specifically, we sequentially employ intramodal information and multimodal information as guidance to perform multiple-step attention reasoning so that the fine-grained correlation between image and text can be modeled. As a consequence, we are capable of discovering the semantically meaningful visual regions or words in a sentence which contributes to measuring the cross-modal similarity in a more precise manner. Moreover, we present a novel bidirectional ranking loss that enforces the distance among pairwise multimodal instances to be closer. Doing so allows us to make full use of pairwise supervised information to preserve the manifold structure of heterogeneous pairwise data. Extensive experiments on two benchmark datasets demonstrate that our SMAN consistently yields competitive performance compared to state-of-the-art methods