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Psychiatry faces a number of challenges due largely to the complexity of the relationship between mind and brain. Starting from the now well-justified assumption that the mind is instantiated in the physical substrate of the brain, understanding this relationship is going to be critical to any understanding of function and dysfunction. Key to that translation from physical substrate to mental function and dysfunction is the computational perspective: it provides a way of translating knowledge and understanding between levels of analysis (Churchland and Sejnowski 1994). Importantly, the computational perspective enables translation to both identify emergent properties (e.g., how a molecular change in a receptor affects behavior) and consequential properties (e.g., how an external sociological trauma can lead to circuit changes in neural processing). Given that psychiatry is about treating harmful dysfunction interacting across many levels (from subcellular to sociological), this chapter argues that the computational perspective is fundamental to understanding the relationship between mind and brain, and thus offers a new perspective on psychiatry.

This chapter combines all ‘by-product’ topics that are worth discussing. First, it is argued that complex projects should be adaptive systems. Decentralized short-term flexible planning is encouraged as opposed to centralized detailed scheduling with a primary purpose—to avoid work constraints. Second, a concept of a single failure mode is developed. It is demonstrated that the single failure mode is represented by a two-peak (bi-modal) probabilistic distribution due to existence of a distinctively sensitive risk. Third, a two-well model is drawn from quantum physics to explain a mechanism of the single-mode realization. Fourth, using project system dynamics lessons learned, practical recommendations on organization of project quality assurance (QA) and quality control (QC) are put forward. Fifth, the origin and nature of general uncertainties and internal risk amplifications are uncovered. Sixth, ‘a reverse engineering’ approach to handle risk addressing is contemplated along with a possibility to use stretched targets.

In the opening chapters of this volume, we outlined a series of challenges facing psychiatry, as well as a description of its various promises, and suggested that taking a computational perspective could potentially illuminate a way forward. In this concluding chapter, we revisit these challenges and promises, in the context of what transpired at this Ernst Strüngmann Forum, to highlight the connections between the various themes raised. In particular, we will bring out the points of agreement and disagreement between the discussion groups and the chapters that arose from those discussions. We conclude with a description of the efforts, current and ongoing, to bring the potential synergy between psychiatry and computational neuroscience emphasized in this volume to a reality in the scientific and clinical arenas.

Risk models provide the roadmaps that guide the analyst throughout the journey of risk assessment, if the adage ‘To manage risk, one must measure it’ constitutes the compass for risk management. The process of risk assessment and management may be viewed through many lenses, depending on the perspective, vision, values, and circumstances. This chapter addresses the complex problem of coping with catastrophic risks by taking a systems engineering perspective. Systems engineering is a multidisciplinary approach distinguished by a practical philosophy that advocates holism in cognition and decision making. The ultimate purposes of systems engineering are to (1) build an understanding of the system’s nature, functional behaviour, and interaction with its environment, (2) improve the decision-making process (e.g., in planning, design, development, operation, and management), and (3) identify, quantify, and evaluate risks, uncertainties, and variability within the decision-making process. Engineering systems are almost always designed, constructed, and operated under unavoidable conditions of risk and uncertainty and are often expected to achieve multiple and conflicting objectives. The overall process of identifying, quantifying, evaluating, and trading-off risks, benefits, and costs should be neither a separate, cosmetic afterthought nor a gratuitous add-on technical analysis. Rather, it should constitute an integral and explicit component of the overall managerial decision-making process. In risk assessment, the analyst often attempts to answer the following set of three questions (Kaplan and Garrick, 1981): ‘What can go wrong?’, ‘What is the likelihood that it would go wrong?’, and ‘What are the consequences?’ Answers to these questions help risk analysts identify, measure, quantify, and evaluate risks and their consequences and impacts. Risk management builds on the risk assessment process by seeking answers to a second set of three questions (Haimes, 1991): ‘What can be done and what options are available?’, ‘What are their associated trade-offs in terms of all costs, benefits, and risks?’, and ‘What are the impacts of current management decisions on future options?’ Note that the last question is the most critical one for any managerial decision-making.