Speed Limits: Out of Time in Organization Studies

A screaming comes across the sky. Others glare with a vacant intensity. Solaris studies at the very same time that the world appears to be becoming one vast recording studio. Our cameras are in the process of dissolution and decay. This paper hurtles headlong into the Green Burning Car that is the crash of organization studies today. On the cusp of a promised new mode of study in organizational analysis we write on speed, attracting found objects, jump cuts, wierd juxtapositions, and chance encounters in a ‘pataphysical’ dérive. As an exercise in sympathetic magic, or orgiastic ritual, we are able to exorcise here a number of ghosts in organization theory. Speed limits provide an occasion for shame: shame for its bombast and juvenility, its masculinity and narcissism; the end(s) of organization studies intrudes as event, a sacrifice for dreams of what might come.     

Exact Bayesian Inference and Model Selection for Stochastic Models of Epidemics Among a Community of Households

Abstract.  Much recent methodological progress in the analysis of infectious disease data has been due to Markov chain Monte Carlo (MCMC) methodology. In this paper, it is illustrated that rejection sampling can also be applied to a family of inference problems in the context of epidemic models, avoiding the issues of convergence associated with MCMC methods. Specifically, we consider models for epidemic data arising from a population divided into households. The models allow individuals to be potentially infected both from outside and from within the household. We develop methodology for selection between competing models via the computation of Bayes factors. We also demonstrate how an initial sample can be used to adjust the algorithm and improve efficiency. The data are assumed to consist of the final numbers ultimately infected within a sample of households in some community. The methods are applied to data taken from outbreaks of influenza.