Probabilistic Forecasting: Why Model Imperfection Is a Poison Pill

Roman Frigg ^[1] ; Seamus Bradley ^[1] ; Reason L. Machete ^[2] ; Leonard A. Smith ^[1]
1. [1] London School of Economics and Political Science
  
  London School of Economics and Political Science
  
  Reino Unido
2. [2] University of Reading
  
  University of Reading
  
  Reino Unido
Localización: New challenges to philosophy of science / coord. por Hanne Andersen, Dennis Dieks, Wenceslao J. González Fernández, Thomas Uebel, Gregory Wheeler, 2013, ISBN 978-94-007-5844-5, págs. 479-491
Idioma: inglés
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Resumen
- Foretelling the future is an age-old human desire. Among the methods to pursue this goal mathematical modelling has gained prominence. Many mathematical models promise to make probabilistic forecasts. This raises the question of exactly what these models deliver: can they provide the results as advertised? The aim of this paper is to urge some caution. Using the example of the logistic map, we argue that if a model is non-linear and if there is only the slightest model imperfection, then treating model outputs as decision relevant probabilistic forecasts can be seriously misleading. This casts doubt on the trustworthiness of model results. This is nothing short of a methodological disaster: probabilistic forecasts are used in many places all the time and the realisation that probabilistic forecasts cannot be trusted pulls the rug from underneath many modelling endeavours.