Managing large-scale, geographically distributed, and long-term risks arising from diverse underlying causes – ranging from poverty to underinvestment in protecting against natural hazards or failures of sociotechnical, economic, and financial systems – poses formidable challenges for any theory of effective social decision-making. Participants may have different and rapidly evolving local information and goals, perceive different opportunities and urgencies for actions, and be differently aware of how their actions affect each other through side effects and externalities. Six decades ago, political economist Charles Lindblom viewed “rational-comprehensive decision-making” as utterly impracticable for such realistically complex situations. Instead, he advocated incremental learning and improvement, or “muddling through,” as both a positive and a normative theory of bureaucratic decision-making when costs and benefits are highly uncertain. But sparse, delayed, uncertain, and incomplete feedback undermines the effectiveness of collective learning while muddling through, even if all participant incentives are aligned; it is no panacea. We consider how recent insights from machine learning – especially, deep multiagent reinforcement learning – formalize aspects of muddling through and suggest principles for improving human organizational decision-making. Deep learning principles adapted for human use can not only help participants in different levels of government or control hierarchies manage some large-scale distributed risks, but also show how rational-comprehensive decision analysis and incremental learning and improvement can be reconciled and synthesized.

This research presents risk factors for abuse and neglect among older adults who accessed health and social service organizations in London, Ontario, 1991. The paper reviews four theoretical perspectives on elder abuse and neglect: the situational model, social exchange theory, symbolic interactionism and a feminist model. Risk factors for physical abuse, chronic verbal abuse, material abuse and neglect were identified for each theoretical model. Each set of risk factors was regressed on each type of abuse in order to determine the efficacy of the theoretical models. The data suggest that the risk factors associated with the situational and feminist model(s) provide the best fit in terms of physical abuse; the symbolic interaction model explains the most variance in chronic verbal abuse; both the symbolic interaction and feminist models explain about the same amount of variance in neglect; and none of the theoretical models adequately explains the variance in material abuse.

In this paper we present an analysis of the partial differential equations thatdescribe the Chemical Vapor Infiltration (CVI) processes. The mathematical modelrequires at least two partial differential equations, one describing thegas phase and one corresponding to the solid phase.A key difficulty in the process is the long processing times that are typicallyrequired. We address here the issue of optimization and show that we can chooseappropriate pressure and temperature to minimize these processing times.