Predicting the Future in Science, Economics, and Politics
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Predicting the Future in Science, Economics, and Politics

Edited by Frank Whelon Wayman, Paul R. Williamson, Bruce Bueno de Mesquita and Solomon Polachek

It is a puzzle that while academic research has increased in specialization, the important and complex problems facing humans urgently require a synthesis of understanding. This unique collaboration attempts to address such a problem by bringing together a host of prominent scholars from across the sciences to offer new insights into predicting the future. They demonstrate that long-term trends and short-term incentives need to be understood in order to adopt effective policies, or even to comprehend where we currently stand and the sort of future that awaits us.
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Chapter 15: Computational dynamic modelling of the global state space

Paul R. Williamson


While nominally distinct, the above two ideas – computational dynamic modeling and state space of a global system – are fundamentally connected. In this introduction I start with the former, global modeling, and briefly identify the idea of state spaces. Then, in subsequent sections I develop a particular global modeling scheme. Finally, drawing on the prior discussion, I use the state space framework to draw several conclusions and suggestions about global modeling and related issues. As used here, the term “global” refers to a scope that includes the entire Earth, in both human and non-human aspects, contemplated (if not yet realized) as a unified subject of study. The term “dynamic” reflects that time is to be explicitly a variable; the intent is to reflect not only what happens, but when it happens. These calculations, of what and when, are to be done to whatever degree of inferred or presumed accuracy is appropriate, in each individual predictive circumstance. Some global modeling is already dynamic in this time-specific sense, particularly econometric models; and there are a few more general dynamic models (e.g. Hughes 1999; Hughes and Hillebrand 2006; Meadows et al. 1972). The further idea being expressed here is that dynamic modeling be inclusive of more variables to the maximally feasible extent, and that it utilize a full repertoire of methods, including some discussed below.

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