Michael Morris

Abstract: In this paper I consider the significant but generally overlooked role that the French Revolution played in the development of German Idealism. Specifically, I argue that Reinhold and Fichte's engagement in revolutionary political debates directly shaped their interpretation of Kant's philosophy, leading them (a) to overlook his reliance upon common sense, (b) to misconstrue his conception of the relationship between philosophical theory and received cognitive practice, (c) to fail to appreciate the fundamentally regressive nature of his transcendental argumentative strategy, and, ultimately, (d) to seek to deduce his philosophy from a single first-principle, one grounded in the immediate awareness of the subject's mental life

Ideology critique generally seeks to undermine selected theories and beliefs by demonstrating their partisan origins and their insidious social functions. This approach rightly reveals the socially implicated nature of much purported knowledge, but also brackets or bypasses its cognitive properties. In contrast, Michael Morris argues that it is possible to integrate the social and epistemic dimensions of belief in a way that preserves the cognitive and adjudicatory capacities of reason, while acknowledging that reason itself is inevitably social, historical, and interested. Drawing upon insights from Hegel, Lukács, Mannheim, and Habermas, he interprets and reconstructs Marx's critique of ideology as a positive theory of knowledge, one that reconciles the inherently interested and inextricably situated nature of thought with more traditional conceptions of rational adjudication, normativity, and truth. His wide-ranging examination of the social and epistemic dimensions of ideology will interest readers in political philosophy and political theory.

Exponential-family random graph models represent the processes that govern the formation of links in networks through the terms selected by the user. The terms specify network statistics that are sufficient to represent the probability distribution over the space of networks of that size. Many classes of statistics can be used. In this article we describe the classes of statistics that are currently available in the ergm package. We also describe means for controlling the Markov chain Monte Carlo algorithm that the package uses for estimation. These controls affect either the proposal distribution on the sample space used by the underlying Metropolis-Hastings algorithm or the constraints on the sample space itself. Finally, we describe various other arguments to core functions of the ergm package.