Why Forecasts Fail: The Limits and Potential of Forecasting in International Relations and Economics

Abstract

A forecast is a prediction based on knowledge of past behavior. The forecaster must consider to what extent past trends will continue in the future. In linear forecasts, the past is prologue, and forecasting amounts to linear extrapolation of the past trend into the future. When conditions are propitious and behavior over time is approximately linear, the linear forecast will fit the data tolerably well. But forecasts ultimately fail because no technique has been developed that allows the forecaster to predict, prior to the event itself, when a nonlinearity will occur. This essay argues that a nonlinearity is a critical point at which expectations (predictions) induced by the prior trend suddenly confront a profound alteration in that trend, indeed, an abrupt inversion. A nonlinearity is a total break from the past trend, a discontinuity. The theory of relative power (systemic structure) dynamics known as power cycle theory provides both a thorough, graphic explanation of this discontinuity in expectations that occurs at critical points in the process, and the reason why nonlinearities are impossible to derive from prior trends. Theoretical and empirical assessment of a process and its dynamics makes possible an explanation of the conditions that give rise to such nonlinearity. Hence such dynamical analysis can predict that such a nonlinearity will occur, but in all but a closed system it still cannot predict when the nonlinearity will occur.

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