I argue that a theory’s assumptions always are and ought to be unrealistic. Further, we should attempt to make them more unrealistic in order to increase a theory’s fruitfulness. Many sociologists believe that a theory’s assumptions ought to be empirically realistic. I contend that this criticism

The most common argument against the use of rational choice models outside economics is that they make unrealistic assumptions about individual behav-ior. We argue that whether the falsity of assumptions matters in a given model depends on which factors are explanatorily relevant. Since

The most common argument against the use of rational choice models outside economics is that they make unrealistic assumptions about individual behavior. We argue that whether the falsity of assumptions matters in a given model depends on which factors are explanatorily relevant. Since

This article proposes a solution of the Lambertian Shape From Shading (SFS) problem by designing a new mathematical framework based on the notion of viscosity solutions. The power of our approach is twofolds: 1) it defines a notion of weak solutions (in the viscosity sense) which does not necessarily require boundary data. Note that, in the previous SFS work of Rouy et al. [23, 15], Falcone et al.

Although studies about climate change impacts on plant species are often published on prestigious journals, in particular when they deal with broad areas and numerous species, in this manuscript I advance my doubts on their methodological robustness and, as a consequence, on their results. In particular I focus my attention on two major drawbacks, i.e. the need for a) nonlinear community-based models instead of species-based ones, and b) for the replacement of the potential niche with the future niche in predictive models.

undermining this redundancy. One approach to preventing these "Sybil attacks" is to have a trusted agency certify identities. This paper shows that, without a logically centralized authority, Sybil attacks are always possible except under extreme and unrealistic assumptions of resource parity

Are Americans more individualistic and less collectivistic than members of other groups? The authors summarize plausible psychological implications of individualism–collectivism (IND-COL), meta-analyze cross-national and within-United States IND-COL differences, and review evidence for effects of IND-COL on self-concept, well-being, cognition, and relationality. European Americans were found to be both more individualistic—valuing personal independence more—and less collectivistic—feeling duty to in-groups less—than others. However, European Americans were not more individualistic than African Americans, or Latinos, and not less collectivistic than Japanese or Koreans. Among Asians, only Chinese showed large effects, being both less individualistic and more collectivistic. Moderate IND-COL effects were found on self-concept and relationality, and large effects were found on attribution and cognitive style. To contemporary Americans, being an individualist is not only a good thing; it is a quintessentially American thing. However, the term individualism itself appears to have its roots outside of the North American continent, namely in the French Revolution. It appears that individualism was first used to describe the negative

Many prominent theorists have argued that accurate perceptions of the self, the world, and the future are essential for mental health. Yet considerable research evidence suggests that overly positive selfevaluations, exaggerated perceptions of control or mastery, and unrealistic optimism

We provide an overview of the extensive recent results on the Shannon capacity of single-user and multiuser multiple-input multiple-output (MIMO) channels. Although enormous capacity gains have been predicted for such channels, these predictions are based on somewhat unrealistic assumptions about

, and probabilistically labels the unlabeled documents. It then trains a new classifier using the labels for all the documents, and iterates to convergence. This basic EM procedure works well when the data conform to the generative assumptions of the model. However these assumptions are often violated in practice