Bayesian Dynamical Systems Modelling in the Social Sciences

Data arising from social systems is often highly complex, involving non-linear relationships between the macro-level variables that characterize these systems. We present a method for analyzing this type of longitudinal or panel data using differential equations. We identify the best non-linear functions that capture interactions between variables, employing Bayes factor to decide how many interaction terms should be included in the model. This method punishes overly complicated models and identifies models with the most explanatory power. We illustrate our approach on the classic example of relating democracy and economic growth, identifying non-linear relationships between these two variables. We show how multiple variables and variable lags can be accounted for and provide a toolbox in R to implement our approach

To Enhance Social Equity Through Urban Planning: The Potential for Innovation

This chapter explores the innovation potential of one of the most challenging and unruly goals of planning. Namely, how planning can support the development of more socially equal communities, and consider innovation needs in terms of the need for development of new ideas, practices and instruments of planning. Over the past twenty years, one has observed a significant increase in the attention, expertise, tools and actual experience of integrating public health into planning. However, while reduction of social inequalities is part of the public health agenda, the awareness and concrete experience with this concern is lacking in today’s planning practice. Based on the ideas and experiences of a Delphi Panel, as well as relevant research results, this chapter identifies and discuss promising steps for strengthening the awareness to social inequality as a goal for local development and planning, and specific instruments capable of lifting this concern into the core of planning

Sleep Health: Reciprocal Regulation of Sleep and Innate Immunity

Sleep disturbances including insomnia independently contribute to risk of inflammatory disorders and major depressive disorder. This review and overview provides an integrated understanding of the reciprocal relationships between sleep and the innate immune system and considers the role of sleep in the nocturnal regulation of the inflammatory biology dynamics; the impact of insomnia complaints, extremes of sleep duration, and experimental sleep deprivation on genomic, cellular, and systemic markers of inflammation; and the influence of sleep complaints and insomnia on inflammaging and molecular processes of cellular aging. Clinical implications of this research include discussion of the contribution of sleep disturbance to depression and especially inflammation-related depressive symptoms. Reciprocal action of inflammatory mediators on the homeostatic regulation of sleep continuity and sleep macrostructure, and the potential of interventions that target insomnia to reverse inflammation, are also reviewed. Together, interactions between sleep and inflammatory biology mechanisms underscore the implications of sleep disturbance for inflammatory disease risk, and provide a map to guide the development of treatments that modulate inflammation, improve sleep, and promote sleep health