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Frictionless forecasting is a fiction

  • Hubert L. Dreyfus
  • Stuart E. Dreyfus

Abstract

Observers of the political and economic scene note that almost all decisions involve incremental changes from the status quo1. Slightly mitigating the ills we have has always seemed preferable to flying to others that we know not of. It now appears, however, that advanced information technology and improved theoretical understanding of social and economic phenomena may have put us on the verge of a breakthrough, and the possibility exists of flying directly to radically new solutions that we can predict sufficiently well not only to avoid ills but discontinuously to enhance the good. Arguments for this brave new frictionless society go roughly as follows:
  1. 1.

    With the increase in specialization, experts are available today who are capable of short-term forecasts about almost all aspects of social and economic behavior.

     
  2. 2.

    Concurrently, it is becoming increasingly difficult for any one person to integrate all of this understanding.

     
  3. 3.

    Computer technology now allows one to incorporate this immense body of short-term forecast knowledge in programs that can, through simulation, predict the long-term future impacts of contemplated decisions. These impacts are often surprising and counterintuitive, accounting for the fear, in the past, of anything but small changes2.

     
  4. 4.

    Once we can predict the impact of globally discontinuous decisions, debate can appropriately center on which effects are preferable and not on what the impacts will be.

     

Keywords

Inference Rule Incremental Change Synaptic Modification Advance Information Technology Situational Aspect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Notes

  1. 1.
    Lindblom, Charles E., “The Science of ‘Muddling Through’,” Pub.Adm.Rev., 19,1959, 79–88CrossRefGoogle Scholar
  2. 2.
    Forrgster, Jay W., “Lessons from System Dynamics Modeling,” System Dynamics Review, 3,2, Summer 1987, 136–149CrossRefGoogle Scholar
  3. 3.
    Werbos, Paul J., “A menu of designs for reinforcement learning over time,” in Neural Networks for Control, T. Miller, R. Sutton, and P. Werbos, eds., MIT Press, Cambridge, MA., 1990, 67–95Google Scholar
  4. 4.
    Simon, Herbert A., “The Mind’s Eye in Chess,” (with W.G. Chase), in Models of Thought, Yale University Press, New Haven, CT., 1979, 421. See also his Hitchcock Lecture delivered at the University of California at Berkeley, Feb. 13, 1990.Google Scholar
  5. 5.
    A more detailed exposition of our skill-acqusition model, directed more toward coping skills than predictive skills, may be found in our book: Dreyfus, Hubert L. and Dreyfus, Stuart E., Mind over Machine, paperback edition, Free Press, New York, 1988Google Scholar
  6. 6.
    Dreyfus and Dreyfus, Mind Over Machine, op cit, 36–40, 163–167Google Scholar

Copyright information

© Physica-Verlag Heidelberg 1993

Authors and Affiliations

  • Hubert L. Dreyfus
  • Stuart E. Dreyfus

There are no affiliations available

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