Misreading Islamist Terrorism: The "War Against Terrorism" and Just-War Theory

Abstract: The Bush administration's military war on terrorism is a blunt, ineffective, and unjust response to the threat posed to innocent civilians by terrorism. Decentralized terrorist networks can only be effectively fought by international cooperation among police and intelligence agencies representing diverse nation‐states, including ones with predominantly Islamic populations. The Bush administration's allegations of a global Islamist terrorist threat to the national interests of the United States misread the decentralized and complex nature of Islamist politics. Undoubtedly there exists a “combat fundamentalist” element within Islamism. But the threat posed to U.S. citizens by Islamist terrorism neither necessitates nor justifies as a response massive military invasions of other nations. Not only does the Bush administration's war on alleged “terrorist states” violate the doctrine of just war, but in addition these wars arise from a new, unilateral, imperial foreign‐policy doctrine of “preventive wars.” Such a doctrine will isolate the United States from international institutions and long‐standing allies. The weakening of these institutions and alliances will only weaken the ability of the international community to deter terrorism.     

Reasons for Conflict: Political Implications of a Definition of Terrorism

This essay analyzes the U.S. political situation before the 2003 invasion of Iraq and ties this conflict to the events of 9/11. The guiding thread of the discussion is the definition of “terrorism” that has led to George W. Bush's declared “war on terrorism.” By means of Hegel's dialectic logic, the essay exposes the problem offered by the category of causality involved in the definition of terrorism: Is terrorism the original “cause” of the war declared on it by the United States (as the Bush administration claims) or is terrorism rather the very “consequence” of that war?     

Higher-order latent trait models for cognitive diagnosis

Higher-order latent traits are proposed for specifying the joint distribution of binary attributes in models for cognitive diagnosis. This approach results in a parsimonious model for the joint distribution of a high-dimensional attribute vector that is natural in many situations when specific cognitive information is sought but a less informative item response model would be a reasonable alternative. This approach stems from viewing the attributes as the specific knowledge required for examination performance, and modeling these attributes as arising from a broadly-defined latent trait resembling theϑ of item response models. In this way a relatively simple model for the joint distribution of the attributes results, which is based on a plausible model for the relationship between general aptitude and specific knowledge. Markov chain Monte Carlo algorithms for parameter estimation are given for selected response distributions, and simulation results are presented to examine the performance of the algorithm as well as the sensitivity of classification to model misspecification. An analysis of fraction subtraction data is provided as an example. This research was funded by National Institute of Health grant R01 CA81068. We would like to thank William Stout and Sarah Hartz for many useful discussions, three anonymous reviewers for helpful comments and suggestions, and Kikumi Tatsuoka and Curtis Tatsuoka for generously sharing data.     

The Implications of Prospect Theory for Human Nature and Values

Central to prospect theory are far-reaching claims about what people fear and what gratifies them. Subjective well-being is a topic that social science has been reluctant to discuss in recent years, but it is central to much of our lives. A loss inflicts more harm than a comparable gain produces pleasure; this fact and the related endowment effect are important parts of our psychological makeup. The importance of change rather than absolute value position, and the related significance of the reference point and how it can be altered, can be seen as integral to human nature.     

Framing and the Role of the Second Actor: An Application of Prospect Theory to Bargaining

An actor's frame of reference significantly affects that actor's risk attitude. Although the frame of reference is often taken as a given, earlier work shows it to be the result of an actor's assumptions and beliefs, which can be manipulated by a second actor in a bargaining situation. As modeled here, confidence in selected assumptions can be manipulated by one of two means: changing the confidence of the actor about the future domain, and getting the actor to adopt a particular domain by discounting the utility of a course of action. Both methods force a change in the perceived domain and a shift in risk attitude. In addition to showing manipulation of an actor's frame, the model adds to our understanding of Kahneman and Tversky's original expression of prospect theory.     

Collective Action Meets Prospect Theory: An Application to Coalition Building in Chile, 1973–75

Expected utility theory explains collective action as an attempt by individuals to maximize their gains. In contrast, my application of prospect theory to collective action suggests that people are motivated to participate in collective action by a fear of loss. These alternative rationalities are considered in the context of the successful cooperative effort of four economic groups in Chile during 1973–75, the first years of the Pinochet military regime. In this case, the logic of prospect theory better captures how actors made decisions about whether or not to engage in collective action. Of the four groups that did join the 1973–75 economic coalition, only one (the mineowners) appears to have maximized its net asset level, as expected utility theory predicts. All four groups seem to have been motivated to cooperate because they found themselves in the domain of losses and expected that cooperation with other, even rival, economic groups might help them recoup their recent losses.     

Social Dominance Theory: Its Agenda and Method

The theory has been misconstrued in four primary ways, which are often expressed as the claims of psychological reductionism, conceptual redundancy, biological reductionism, and hierarchy justification. This paper addresses these claims and suggests how social dominance theory builds on and moves beyond social identity theory and system justification theory.     

Affect, Curiosity, and Socialization-Related Learning: A Path Analysis of Antecedents to Job Performance

This study examined the influence of affect, curiosity, and socialization-related learning on job performance, with 233 service industry employees from a diverse variety of occupations completing surveys at their places of work. Both state and trait curiosity and socialization-related learning (learning associated with employee socialization) were hypothesized to mediate the relationship between affect (operationalized as state and trait anxiety and anger) and job performance. Structural equation analyses indicate that the data are consistent with the theoretical models proposed. As expected, anxiety negatively influences curiosity, socialization-related learning, and job performance; conversely, anger positively influences curiosity, socialization-related learning, and job performance. Overall, the findings suggest that affects indeed predict perception of job performance, but through the mediation of curiosity and the learning associated with the socialization process. These results highlight the importance of the complex interplay between affect, curiosity, and learning when thinking about successful employee socialization and best possible job performance.     

Wofür sprechen die Daten?

What Do the Data Tell Us? Justification of scientific theories is a three-place relation between data, theories, and background knowledge. Though this should be a commonplace, many methodologies in science neglect it. The article will elucidate the significance and function of our background knowledge in epistemic justification and their consequences for different scientific methodologies. It is argued that there is no simple and at the same time acceptable statistical algorithm that justifies a given theory merely on the basis of certain data. And even if we think to know the probability of a theory, that does not decide whether we should accept it or not.