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Modeling the Societal Impact of Fatal Accidents


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  • Paul Slovic

    (Decision Research, Inc., 1201 Oak Street, Eugene, Oregon 97401)

  • Sarah Lichtenstein

    (Decision Research, Inc., 1201 Oak Street, Eugene, Oregon 97401)

  • Baruch Fischhoff

    (Decision Research, Inc., 1201 Oak Street, Eugene, Oregon 97401)

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    A number of proposals have been put forth regarding the proper way to model the societal impact of fatal accidents. Most of these proposals are based on some form of utility function asserting that the social cost (or disutility) of N lives lost in a single accident is a function of N \alpha . A common view is that a single large accident is more serious than many small accidents producing the same number of fatalities, hence \alpha > 1. Drawing upon a number of empirical studies, we argue that there is insufficient justification for using any function of N fatalities to model societal impacts. The inadequacy of such models is attributed, in part, to the fact that accidents are signals of future trouble. The societal impact of an accident is determined to an important degree by what it signifies or portends. An accident that causes little direct harm may have immense consequences if it increases the judged probability and seriousness of future accidents. We propose that models based solely on functions of N be abandoned in favor of models that elaborate in detail the significant events and consequences likely to result from an accident.

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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 30 (1984)
    Issue (Month): 4 (April)
    Pages: 464-474

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    Handle: RePEc:inm:ormnsc:v:30:y:1984:i:4:p:464-474

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    Related research

    Keywords: risk assessment; risk management; risk perception; impact assessment; accident analysis;


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    Cited by:
    1. Carlsson, Fredrik & Daruvala, Dinky & Jaldell, Henrik, 2013. "Do administrators have the same priorities for risk reductions as the general public?," Karlstad University Working Papers in Economics 7, Department of Economics, Karlstad University.
    2. Weber, Elke U. & Johnson, Eric J., 2012. "Psychology and behavioral economics lessons for the design of a green growth strategy," Policy Research Working Paper Series 6240, The World Bank.
    3. Colin F. Camerer & Howard Kunreuther, 1989. "Decision processes for low probability events: Policy implications," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 8(4), pages 565-592.
    4. Yetano Roche, MarĂ­a & Mourato, Susana & Fischedick, Manfred & Pietzner, Katja & Viebahn, Peter, 2010. "Public attitudes towards and demand for hydrogen and fuel cell vehicles: A review of the evidence and methodological implications," Energy Policy, Elsevier, vol. 38(10), pages 5301-5310, October.


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