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Critics of deliberative democracy theory have argued that deliberation should be supplemented with forms of emotional expression to eliminate the inequalities of gender, race, and class which are reproduced in deliberations that privilege rational discussion. This article presents results from a qualitative empirical study on emotion work in deliberations. Emotional expression requires emotion work on the part of the participants. The capacity for such emotion work appears to depend on the individual participant's emotional capital. The results show that, given the participants' varying levels of emotional capital, an emphasis on emotion work tends to reproduce inequalities, rather than to eliminate them.

This paper reports early steps in research that seeks to clarify how publications of scientists interact dynamically with citations and reputation in shaping the evolution of scientific fields. We assume that Lotka's modified law holds for scientific fields. A primary approach to model publication productivity was published by Yablonsky. In contrast to Yablonsky's unfinished mathematical approach, our simulation approach is not predominantly driven by insight into the formal generation mechanisms of certain processes but more theory driven. It considers the evolution of publication and citation distributions over the histories of scientific fields using both simulated and real historical data.

We investigate a selection-mutation model for the dynamics of technological innovation, a special case of reaction-diffusion equations. Although mutations are assumed to increase the variety of technologies, not their average success ("fitness"), they are an essential prerequisite for innovation. Together with a selection of above-average technologies due to imitation behavior, they are the "driving force" for the continuous increase in fitness. We will give analytical solutions for the probability distribution of technologies for special cases and in the limit of large times. The selection dynamics is modeled by a "proportional imitation" of better technologies. However, the assessment of a technology's fitness may be imperfect and, therefore, vary stochastically. We will derive conditions under which a wrong assessment of fitness can accelerate the innovation dynamics, as has been found in some surprising numerical investigations.