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EURO Journal on Decision Processes

, Volume 6, Issue 1–2, pp 213–233 | Cite as

Predicting in shock: on the impact of negative, extreme, rare, and short lived events on judgmental forecasts

  • Ian Durbach
  • Gilberto Montibeller
Original Article

Abstract

The occurrence of unexpected events that are extreme in magnitude, rare in frequency, and short-lived in duration poses distinctive challenges to decision makers and planners. In this paper we examine the impact of negative versions of these events, which we term “shocks”, on the judgmental forecasts of subjects experiencing them. A behavioral experiment asking participants to forecast monthly time series in the presence of temporary but extreme decreases in those series is used. Average changes to annual prediction intervals and 1-month ahead forecasts were much smaller than the magnitude of the shock and occurred in proportion to the size of the shock. Changes to prediction intervals were more persistent for moderate than large shocks, and larger for shocks occurring a second time. Our results provide supporting evidence for the view that decision makers underweight rare and extreme events rather than overweight them, consistent with a discounting or forgetting effect. The behavioral findings are relevant to operations researchers involved in expert judgment elicitation and in supporting decision making.

Keywords

Behavioral decision making Judgmental forecasting Prediction intervals Price forecasting Uncertainty Extreme events Rare events 

Mathematics Subject Classification

90B50 

References

  1. Abdi H (2007) Bonferroni and šidák corrections for multiple comparisons. Encycl Meas Stat 3:103–107Google Scholar
  2. Baucells M, Weber M, Welfens F (2011) Reference-point formation and updating. Manag Sci 57(3):506–519CrossRefGoogle Scholar
  3. Becker O, Leitner J, Leopold-Wildburger U (2009) Expectation formation and regime switches. Exp Econ 12(3):350–364CrossRefGoogle Scholar
  4. Bolger F, Harvey N (1993) Context-sensitive heuristics in statistical reasoning. Q J Exp Psychol 46(4):779–811CrossRefGoogle Scholar
  5. Du N, Budescu D (2007) Does past volatility affect investors’ price forecasts and confidence judgements? Int J Forecast 23(3):497–511CrossRefGoogle Scholar
  6. Eggleton I (1982) Intuitive time-series extrapolation. J Account Res 20(1):68–102CrossRefGoogle Scholar
  7. Goodwin P, Gönül M, Önkal D (2013) Antecedents and effects of trust in forecasting advice. Int J Forecast 29(2):354–366CrossRefGoogle Scholar
  8. Goodwin P, Wright G (1993) Improving judgmental time series forecasting: a review of the guidance provided by research. Int J Forecast 9(2):147–161CrossRefGoogle Scholar
  9. Hämäläinen RP, Luoma J, Saarinen E (2013) On the importance of behavioral operational research: the case of understanding and communicating about dynamic systems. Eur J Oper Res 228(3):623–634CrossRefGoogle Scholar
  10. Hertwig R, Barron G, Weber E, Erev I (2004) Decisions from experience and the effect of rare events in risky choice. Psychol Sci 15(8):534–539CrossRefGoogle Scholar
  11. Hertwig R, Erev I (2009) The description–experience gap in risky choice. Trends Cogn Sci 13(12):517–523CrossRefGoogle Scholar
  12. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econom J Econom Soc 47(2):263–291Google Scholar
  13. Kousky C (2010) Learning from extreme events: risk perceptions after the flood. Land Econ 86(3):395–422CrossRefGoogle Scholar
  14. Kunreuther H, Meyer R, Zeckhauser R, Slovic P, Schwartz B, Schade C, Luce M, Lippman S, Krantz D, Kahn B et al (2002) High stakes decision making: normative, descriptive and prescriptive considerations. Mark Lett 13(3):259–268CrossRefGoogle Scholar
  15. Lawrence M, Edmundson R, O’Connor M (1985) An examination of the accuracy of judgmental extrapolation of time series. Int J Forecast 1(1):25–35CrossRefGoogle Scholar
  16. Lawrence M, Goodwin P, O’Connor M, Önkal D (2006) Judgmental forecasting: a review of progress over the last 25 years. Int J Forecast 22(3):493–518CrossRefGoogle Scholar
  17. Lawrence M, Makridakis S (1989) Factors affecting judgmental forecasts and confidence intervals. Organ Behav Hum Decis Process 43(2):172–187CrossRefGoogle Scholar
  18. Lawrence M, O’Connor M (1993) Scale, randomness and the calibration of judgmental confidence intervals. Organ Behav Hum Decis Process 56:441–458CrossRefGoogle Scholar
  19. Lawrence M, O’Connor M (1995) The anchor and adjustment heuristic in time-series forecasting. J Forecast 14(5):443–451CrossRefGoogle Scholar
  20. Leitner J, Leopold-Wildburger U (2011) Experiments on forecasting behavior with several sources of information—a review of the literature. Eur J Oper Res 213(3):459–469CrossRefGoogle Scholar
  21. Meyer R (2012) Failing to learn from experience about catastrophes: the case of hurricane preparedness. J Risk Uncertain 45(1):25–50CrossRefGoogle Scholar
  22. Montibeller G, von Winterfeldt D (2015) Cognitive and motivational biases in decision and risk analysis. Risk Anal 35(7):1230–1251CrossRefGoogle Scholar
  23. O’Connor M, Remus W, Griggs K (1993) Judgemental forecasting in times of change. Int J Forecast 9(2):163–172CrossRefGoogle Scholar
  24. O’Connor M, Remus W, Griggs K (1997) Going up-going down: how good are people at forecasting trends and changes in trends? J Forecast 16(3):165–176CrossRefGoogle Scholar
  25. Petropoulos F, Fildes R, Goodwin P (2016) Do big losses in judgmental adjustments to statistical forecasts affect experts behaviour? Eur J Oper Res 249(3):842–852CrossRefGoogle Scholar
  26. Schoemaker PJH (1993) Multiple scenario development: its conceptual and behavioral foundation. Strateg Manag J 14(3):193–213CrossRefGoogle Scholar
  27. Shafran A (2011) Self-protection against repeated low probability risks. J Risk Uncertain 42(3):263–285CrossRefGoogle Scholar
  28. Tversky A, Kahneman D (1992) Advances in prospect theory: cumulative representation of uncertainty. J Risk Uncertain 5(4):297–323CrossRefGoogle Scholar
  29. Ungemach C, Chater N, Stewart N (2009) Are probabilities overweighted or underweighted when rare outcomes are experienced (rarely)? Psychol Sci 20(4):473–479CrossRefGoogle Scholar
  30. Weber EU, Shafir S, Blais A-R (2004) Predicting risk sensitivity in humans and lower animals: risk as variance or coefficient of variation. Psychol Rev 111(2):430CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg and EURO - The Association of European Operational Research Societies 2017

Authors and Affiliations

  1. 1.Center for Statistics in Ecology, the Environment and Conservation, Department of Statistical SciencesUniversity of Cape TownCape TownSouth Africa
  2. 2.African Institute for Mathematical SciencesCape TownSouth Africa
  3. 3.Management Science and Operations Group, School of Business and EconomicsLoughborough UniversityLoughboroughUK

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