Government & Politics Research Works

Permanent URI for this collectionhttp://hdl.handle.net/1903/1642

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End date: 2019

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    An Experimental Analysis of Asymmetric Power in Conflict Bargaining
    (MDPI, 2013-08-02) Sieberg, Katri; Clark, David; Holt, Charles A.; Nordstrom, Timothy; Reed, William
    Demands and concessions in a multi-stage bargaining process are shaped by the probabilities that each side will prevail in an impasse. Standard game-theoretic predictions are quite sharp: demands are pushed to the precipice with nothing left on the table, but there is no conflict regardless of the degree of power asymmetry. Indeed, there is no delay in reaching an agreement that incorporates the (unrealized) costs of delay and conflict. A laboratory experiment has been used to investigate the effects of power asymmetries on conflict rates in a two-stage bargaining game that is (if necessary) followed by conflict with a random outcome. Observed demands at each stage are significantly correlated with power, as measured by the probability of winning in the event of disagreement. Demand patterns, however, are flatter than theoretical predictions, and conflict occurs in a significant proportion of the interactions, regardless of the degree of the power asymmetry. To address these deviations from the standard game-theoretic predictions, we also estimated a logit quantal response model, which generated the qualitative patterns that are observed in the data. This one-parameter generalization of the Nash equilibrium permits a deconstruction of the strategic incentives that cause demands to be less responsive to power asymmetries than Nash predictions.
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    Using Risk to Assess the Legal Violence of Mandatory Detention
    (MDPI, 2016-07-05) Koulish, Robert
    Immigration mandatory detention is a particularly harsh example of the structural violence embedded in immigration enforcement. It deprives liberty without bond for immigrants with prior crimes, and assigns many individuals to the harsh conditions associated with unnecessary and even wrongful detention. Mandatory detention has been justified on the grounds that mandatory detainees are a danger to public safety. This article puts to the test this presumption of dangerousness among mandatory detainees, and finds, to the contrary, that immigrants with prior charges or convictions are no more dangerous than any other category of individuals in Immigration and Customs Enforcement (ICE) custody. Using the risk classification assessment (RCA) tool, which the author is the first to obtain through the Freedom of Information Act, the article contributes to the growing criticism of mandatory detention, providing evidence that many of those in mandatory detention should probably have never been detained.
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    Where in the world is my tweet: Detecting irregular removal patterns on Twitter
    (PLoS, 2018-09-20) Timoneda, Joan C.
    Twitter data are becoming an important part of modern political science research, but key aspects of the inner workings of Twitter streams as well as self-censorship on the platform require further research. A particularly important research agenda is to understand removal rates of politically charged tweets. In this article, I provide a strategy to understand removal rates on Twitter, particularly on politically charged topics. First, the technical properties of Twitter's API that may distort the analyses of removal rates are tested. Results show that the forward stream does not capture every possible tweet -between 2 and 5 percent of tweets are lost on average, even when the volume of tweets is low and the firehose not needed. Second, data from Twitter's streams are collected on contentious topics such as terrorism or political leaders and non-contentious topics such as types of food. The statistical technique used to detect uncommon removal rate patterns is multilevel analysis. Results show significant differences in the removal of tweets between different topic groups. This article provides the first systematic comparison of information loss and removal on Twitter as well as a strategy to collect valid removal samples of tweets.