Can social media promote civic engagement and collective action? Advocacy organizations think so. Obar, Zube, and Lampe surveyed 169 individuals from 53 advocacy groups of diverse interests and sizes and identified a revealing trend. All groups admitted that they use social media technologies to communicate with citizens almost every day. Respondents also believe that social media enable them to accomplish their advocacy and organizational goals across a range of specified activities. The authors note that the relationship between this and real political and ideological change is still speculative, but suggest that future studies can build on their research.
{"title":"Advocacy 2.0: An Analysis of How Advocacy Groups in the United States Perceive and Use Social Media as Tools for Facilitating Civic Engagement and Collective Action","authors":"Jonathan A. Obar, Paul Zube, Cliff Lampe","doi":"10.2139/SSRN.1956352","DOIUrl":"https://doi.org/10.2139/SSRN.1956352","url":null,"abstract":"\u0000 Can social media promote civic engagement and collective action? Advocacy organizations think so. Obar, Zube, and Lampe surveyed 169 individuals from 53 advocacy groups of diverse interests and sizes and identified a revealing trend. All groups admitted that they use social media technologies to communicate with citizens almost every day. Respondents also believe that social media enable them to accomplish their advocacy and organizational goals across a range of specified activities. The authors note that the relationship between this and real political and ideological change is still speculative, but suggest that future studies can build on their research.","PeriodicalId":117077,"journal":{"name":"Political Methods: Computational eJournal","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114878845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Under what conditions do states maintain trade cooperation? We explore this question using models of imperfect monitoring. Most formal models of international cooperation rely on signaling games, in which actors' actions are perfectly observable. Here we examine conditions for cooperation when the actions of states are not perfectly observable. We argue that our modeling strategy is a more accurate reflection of the problems that arise in international trade. The paper examines variants of a repeated prisoners' dilemma with imperfect monitoring and offers a novel theoretical finding – free trade will be difficult to sustain when one trading partner is fully committed to free trade.
{"title":"Imperfect Monitoring in International Trade Cooperation","authors":"J. Gray, Rene Lindstaedt, Jonathan B. Slapin","doi":"10.2139/ssrn.1763876","DOIUrl":"https://doi.org/10.2139/ssrn.1763876","url":null,"abstract":"Under what conditions do states maintain trade cooperation? We explore this question using models of imperfect monitoring. Most formal models of international cooperation rely on signaling games, in which actors' actions are perfectly observable. Here we examine conditions for cooperation when the actions of states are not perfectly observable. We argue that our modeling strategy is a more accurate reflection of the problems that arise in international trade. The paper examines variants of a repeated prisoners' dilemma with imperfect monitoring and offers a novel theoretical finding – free trade will be difficult to sustain when one trading partner is fully committed to free trade.","PeriodicalId":117077,"journal":{"name":"Political Methods: Computational eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134628827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study of state formation often focuses on building state capacity. The formation and subsequent bolstering of state capacity, however, are distinctly different activities. While the study of state capacity building has provided considerable insight into the role of formal institutions in maintaining stable governance, the implicit assumption in this work is the existence of a state upon which to build capacity. The collective decision to formalize institutions into a state - a necessary prior condition for building state capacity - is rarely addressed. In the following paper the role of informal institutions; specifically, social networks as informal institutions, in the process of state germination is explored. Using Afghanistan as a framework for the discussion, the research presented below attempts to illustrate the importance of the initial structural conditions of these networks, and the actors therein, in this process. The paper begins with a brief description of the role of informal institution and social networks in Afghanistan. Next, a provision point public goods game is presented as a basic model of the collective action problem inherent in state formation. Then, a network variant of this game is presented, which is implemented as a computational model. In the final sections the results of simulations from the computational model are presented, with a discussion and conclusions.
{"title":"Networks, Collective Action, and State Formation","authors":"D. Conway","doi":"10.2139/ssrn.1726041","DOIUrl":"https://doi.org/10.2139/ssrn.1726041","url":null,"abstract":"The study of state formation often focuses on building state capacity. The formation and subsequent bolstering of state capacity, however, are distinctly different activities. While the study of state capacity building has provided considerable insight into the role of formal institutions in maintaining stable governance, the implicit assumption in this work is the existence of a state upon which to build capacity. The collective decision to formalize institutions into a state - a necessary prior condition for building state capacity - is rarely addressed. In the following paper the role of informal institutions; specifically, social networks as informal institutions, in the process of state germination is explored. Using Afghanistan as a framework for the discussion, the research presented below attempts to illustrate the importance of the initial structural conditions of these networks, and the actors therein, in this process. The paper begins with a brief description of the role of informal institution and social networks in Afghanistan. Next, a provision point public goods game is presented as a basic model of the collective action problem inherent in state formation. Then, a network variant of this game is presented, which is implemented as a computational model. In the final sections the results of simulations from the computational model are presented, with a discussion and conclusions.","PeriodicalId":117077,"journal":{"name":"Political Methods: Computational eJournal","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133645490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a naive Bayes classifier to detect electoral fraud using digit patterns in vote counts with authentic and synthetic data. The procedure is the following: (1) we create 10,000 simulated electoral contests between two parties using Monte Carlo methods. This training set is composed of two disjoint subsets: one containing electoral returns that follow a Benford distribution, and another where the vote counts are purposively "manipulated" by electoral tampering – a percentage of votes are taken away from one party and given to the other; (2) we calibrate membership values of the simulated elections (i.e. clean or fraudulent) using logistic regression; (3) we recover class-conditional densities using the relative frequencies from the training set; (4) we apply Bayes' rule to class-conditional probabilities and class priors to establish the membership probabilities of authentic observations. To illustrate our technique, we examine elections in the province of Buenos Aires (Argentina) between 1932 and 1942, a period with a checkered history of fraud. Our analysis allows us to successfully classify electoral contests according to their degree of fraud. More generally, our findings indicate that Benford's Law is an effective tool for identifying fraud, even when minimal information (i.e. electoral returns) is available.
{"title":"A Supervised Machine Learning Procedure to Detect Electoral Fraud Using Digital Analysis","authors":"Francisco Cantú, Sebastián Saiegh","doi":"10.2139/ssrn.1594406","DOIUrl":"https://doi.org/10.2139/ssrn.1594406","url":null,"abstract":"This paper introduces a naive Bayes classifier to detect electoral fraud using digit patterns in vote counts with authentic and synthetic data. The procedure is the following: (1) we create 10,000 simulated electoral contests between two parties using Monte Carlo methods. This training set is composed of two disjoint subsets: one containing electoral returns that follow a Benford distribution, and another where the vote counts are purposively \"manipulated\" by electoral tampering – a percentage of votes are taken away from one party and given to the other; (2) we calibrate membership values of the simulated elections (i.e. clean or fraudulent) using logistic regression; (3) we recover class-conditional densities using the relative frequencies from the training set; (4) we apply Bayes' rule to class-conditional probabilities and class priors to establish the membership probabilities of authentic observations. To illustrate our technique, we examine elections in the province of Buenos Aires (Argentina) between 1932 and 1942, a period with a checkered history of fraud. Our analysis allows us to successfully classify electoral contests according to their degree of fraud. More generally, our findings indicate that Benford's Law is an effective tool for identifying fraud, even when minimal information (i.e. electoral returns) is available.","PeriodicalId":117077,"journal":{"name":"Political Methods: Computational eJournal","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127999805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression and propensity score matching are model dependent and often fail to replicate the results of randomized controlled trials. We demonstrate a new non-parametric matching method, Genetic Matching, which is a generalization of propensity score and Mahalanobis distance matching (Sekhon forthcoming), using two contrasting case studies. In the first, an economic evaluation of a clinical intervention (Pulmonary Artery Catheterization), applying Genetic Matching to observational data replicates the substantive results of a corresponding randomized controlled trial unlike the extant literature. And in the second case study evaluating capitation versus fee-for-service, Genetic Matching radically improves balance on baseline covariates and overturns previous conclusions based on traditional methods.
{"title":"A New Non-Parametric Matching Method for Bias Adjustment with Applications to Economic Evaluations","authors":"J. Sekhon, R. Grieve","doi":"10.2139/ssrn.1138926","DOIUrl":"https://doi.org/10.2139/ssrn.1138926","url":null,"abstract":"In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression and propensity score matching are model dependent and often fail to replicate the results of randomized controlled trials. We demonstrate a new non-parametric matching method, Genetic Matching, which is a generalization of propensity score and Mahalanobis distance matching (Sekhon forthcoming), using two contrasting case studies. In the first, an economic evaluation of a clinical intervention (Pulmonary Artery Catheterization), applying Genetic Matching to observational data replicates the substantive results of a corresponding randomized controlled trial unlike the extant literature. And in the second case study evaluating capitation versus fee-for-service, Genetic Matching radically improves balance on baseline covariates and overturns previous conclusions based on traditional methods.","PeriodicalId":117077,"journal":{"name":"Political Methods: Computational eJournal","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128077692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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