Edited by Frank Whelon Wayman, Paul R. Williamson, Bruce Bueno de Mesquita and Solomon Polachek
Chapter 17: Innovations in forecasting the future that one can learn from: predicting the future in science, economics, and politics
Forecasting is a standard undergraduate and master’s degree course in many economics and statistics departments. This course mostly teaches students econometric packages so they can fit time-series data to predict the future from past information, usually via statistical regression analysis of one kind or another. Unfortunately, the approach employed in these disciplines is often too simple to yield reliable and informative predictions for a host of phenomena that policy-makers find important. Predicting the Future in Science, Economics, and Politics (hereafter Prediction) is an important breakthrough pushing forecasting well past its current analytical and conceptual frontiers. By taking account of new theory spanning various academic disciplines, what Edward Wilson (Chapter 3 in this volume) calls “consilience,” and by utilizing sophisticated mathematics and statistics, the authors of this volume map out techniques that can be used to foretell important issues regarding human well-being that forecasters now do not study because these issues are currently far too complex. The innovations outlined in Prediction include a call to expand the topics currently forecast to include predictions on civil and international war severity, environmental catastrophe, and evolutionary biological transformation. Prediction also calls for expanding the forecasting techniques now used. These innovations are to incorporate discounting, boundary value behaviors, complex adaptive and neural systems, social field theory, game theory, and computational dynamics. In what follows, I divide my comments into four parts. First, I describe current forecasting techniques. Second, I explain many of the limitations of current forecasting models.
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