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This is the first comprehensive critical evaluation of the use of rational choice explanations in political science. Writing in an accessible and nontechnical style, Donald P. Green and Ian Shapiro assess rational choice theory where it is reputed to be most successful: the study of collective action, the behavior of political parties and politicians, and such phenomena as voting cycles and Prisoner's Dilemmas. In their hard-hitting critique, Green and Shapiro demonstrate that the much-heralded achievements of rational choice theory are in fact deeply suspect and that fundamental rethinking is needed if rational choice theorists are to contribute to the understanding of politics. Green and Shapiro show that empirical tests of rational choice theories are marred by a series of methodological defects. These defects flow from the characteristic rational choice impulse to defend universal theories of politics. As a result, many tests are so poorly conducted as to be irrelevant to evaluating rational choice models. Tests that are properly conducted either tend to undermine rational choice theories or to lend support for propositions that are banal. Green and Shapiro offer numerous suggestions as to how rational choice propositions might be reformulated as parts of testable hypotheses for the study of politics. In a final chapter they anticipate and respond to a variety of rational choice counterarguments, thereby initiating a dialogue that is bound to continue for some time.

A series of recent works may signal an end to the divorce of political philosophy & scripture; eg, books by Stephen J. Brams, Robert Sacks, Michael Walzer, & Aaron Wildavsky treat a variety of themes from the Hebrew scriptures. Here, discussed is why such diverse authors have now turned their attention to the Bible, the methods of interpretation employed, their confrontation with the Bible as a religious book (which raises the issue of God & faith in political philosophy), & the promise of this confrontation. 22 References. Modified AA

Applications of automated text analysis measuring topics, ideology, sentiment or even personality are booming in fields like political science and political psychology. These developments are to be applauded as they bring about novel insights about politics using new sources of (unstructured) data. However, a divide exists between work in both disciplines using text as data. In this paper we argue in favor of more integration across disciplinary boundaries, structuring our case around four key issues in the research process: (i) sampling text; (ii) authorship as meta data; (iii) pre-processing text; (iv) analyzing text. Along the way we demonstrate that an assessment of speaker characteristics may crucially depend on the text sources under study, and that the use of sentiment words correlates with estimates of policy positions, with implications for interpretation of the latter. As such, this paper contributes to a critical discussion about the merits of automated text analysis methods in political psychology and political science, with an eye towards advancing the considerable potential of text as data in the study of politics.

In this era, massive amounts of data are routinely collected and warehoused to be analyzed for scientific and industrial goals. Text data are a major constituent of these data treasure troves. However, with the steep increase in the amount and variety of accessible text data, it has become very difficult for a human to meaningfully analyze textual data without the help of automated text machine learning models. Topic models are one such method. They reduce the cost of analyzing large-scale corpora by identifying, in an unsupervised manner, the underlying thematic structure of the corpus. This thematic structure provides a coarse summary of the documents and allows researchers to quickly explore how topics connect with each other and change over time. The success of automated topical analysis by topic models has led to another interesting area of text analysis: sentiment analysis. Sentiment analysis is the detecting of opinions, feelings, and general sentiments expressed in text. Sentiment analysis gained relevancy through the rise of social media platforms which increased the amount of sentiment-containing text data, such as Yelp reviews, Tweets, and opinion blogs. Efficient and effective sentiment analysis of such corpora will lead to valuable information about political and social discourse. Hence, social scientists have become increasingly interested in identifying and measuring the relationship between topics and associated sentiments to better understand social and political cultures, attitudes, and processes. In Part 1 of this thesis, we propose a statistical model of text which simultaneously detects both topic and sentiment and allows for the inclusion of document metadata. The proposed model improves upon existing topic-sentiment models in two ways: i) the assumption that topics are associated with a range of sentiments and ii) the ability to use document-level covariates for improved estimation and analysis of the relationship between topics and sentiments. By applying the proposed model to two different datasets, i) a collection of political blogposts and ii) Yelp reviews, we demonstrate how detection of both topic and sentiment with the inclusion of document-level covariates can allow for more informative model summaries as compared to current topic and topic-sentiment models. Topic models are easy to use and interpret; therefore, many variants of topic models have been developed to customize them to various research applications. Evaluation of topic models are thus necessary for appropriate model selection. For this reason, in part II of this thesis, we develop three new metrics which improve upon the existing evaluation approaches by identifying the benefits of topic-sentiment models over topic models. Our evaluation metrics are based on three important criteria: sentiment prediction accuracy, feature stability, and computation time. Not only is it important to be able to show that one model achieves higher sentiment prediction accuracy over another, but it is also vital to ensure that the features used to generate a prediction are meaningful and stable, and that the algorithm has reasonable computational speed. We will use these three metrics to compare our proposed topic-sentiment model to topic models using a case study in which we aim to predict the partisanship and tone of political TV ads. Moreover, since these metrics are not specific to topic models, we will also provide a comparison of topic models with word2vec and Concise Comparative Summaries (CCS) which, to the best of our knowledge, has not been done before. We demonstrate that although the proposed topic-sentiment model is able to better predict sentiment than topic models, word2vec had the highest prediction accuracy and CCS identified the most stable features for prediction and both models required less computation time.

Assesses status of the discipline in terms of research and teaching and in institutional and human aspects.

English-speaking authors discuss personal experiences teaching political science to a group of students in Romania. They relate the difficulties encountered with the language barrier, & cultural differences. They discuss their decision to apply the ESL method known as content-based instruction. They conclude the article with suggestions deemed useful in teaching political science to students who are not fluent in English. 9 References. E. Miller

Mode of access: Internet. ; A translation of the earlier issues of Mercure historique et politique that were not translated into English under the title: The present state of Europe.

Can neuroscientific techniques shed light on important questions in political science? The author argues for increased dialogue across disciplines as wide ranging as evolutionary psychology and biology, biological anthropology, behavioral economics, behavior genetics, behavioral ecology, and cognitive neuroscience. These fields find a clear theoretical convergence around evolutionary development models. These paradigms can be applied successfully to political decision making. In turn, political scientists can offer significant contributions to this research agenda by posing critical questions concerning human social and political behavior, including bias against out-groups, the formation and maintenance of coalitions, and the origin of preferences in decision making.