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Role thinking: Standing in other people’s shoes to forecast decisions in conflicts

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  • Green, Kesten C.
  • Armstrong, J. Scott

Abstract

Better forecasts of decisions in conflict situations, such as occur in business, politics, and war, can help protagonists achieve better outcomes. It is common advice to “stand in the other person’s shoes” when involved in a conflict, a procedure we refer to as “role thinking.” We tested this advice in order to assess the extent to which it can improve accuracy. Improvement in accuracy is important because prior research found that unaided judgment produced forecasts that were little better than guessing. We obtained 101 role-thinking forecasts from 27 Naval postgraduate students (experts) and 107 role-thinking forecasts from 103 second-year organizational behavior students (novices) of the decisions that would be made in nine diverse conflicts. The accuracy of the forecasts from the novices was 33% and of those from the experts 31%. The accuracy of the role-thinking forecasts was little different from chance, which was 28%. In contrast, when we asked groups of participants to each act as if they were in the shoes one of the protagonists, accuracy was 60%.

Suggested Citation

  • Green, Kesten C. & Armstrong, J. Scott, 2009. "Role thinking: Standing in other people’s shoes to forecast decisions in conflicts," MPRA Paper 16422, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:16422
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    References listed on IDEAS

    as
    1. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    2. Green, Kesten C., 2002. "Forecasting decisions in conflict situations: a comparison of game theory, role-playing, and unaided judgement," International Journal of Forecasting, Elsevier, vol. 18(3), pages 321-344.
    3. Kesten C. Green & J. Scott Armstrong, 2004. "Value of Expertise For Forecasting Decisions in Conflicts," Monash Econometrics and Business Statistics Working Papers 27/04, Monash University, Department of Econometrics and Business Statistics.
    4. Green, Kesten C. & Armstrong, J. Scott, 2007. "Structured analogies for forecasting," International Journal of Forecasting, Elsevier, vol. 23(3), pages 365-376.
    5. Richard M. Cyert & James G. March & William H. Starbuck, 1961. "Two Experiments on Bias and Conflict in Organizational Estimation," Management Science, INFORMS, vol. 7(3), pages 254-264, April.
    6. Babcock, Linda, et al, 1995. "Biased Judgments of Fairness in Bargaining," American Economic Review, American Economic Association, vol. 85(5), pages 1337-1343, December.
    7. Green, Kesten C., 2005. "Game theory, simulated interaction, and unaided judgement for forecasting decisions in conflicts: Further evidence," International Journal of Forecasting, Elsevier, vol. 21(3), pages 463-472.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. George Cairns & George Wright, 2019. "Making scenario interventions matter: Exploring issues of power and rationality," Futures & Foresight Science, John Wiley & Sons, vol. 1(1), March.
    2. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    3. Mike Metcalfe & Saras Sastrowardoyo, 2016. "Sense-making Innovative Systems: Prestigious MOOCs," Systems Research and Behavioral Science, Wiley Blackwell, vol. 33(3), pages 437-451, May.
    4. Bradfield, Ron & Cairns, George & Wright, George, 2015. "Teaching scenario analysis—An action learning pedagogy," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 44-52.
    5. de Villiers, Rouxelle & Woodside, Arch G. & Marshall, Roger, 2016. "Making tough decisions competently: Assessing the value of product portfolio planning methods, devil’s advocacy, group discussion, weighting priorities, and evidenced-based information," Journal of Business Research, Elsevier, vol. 69(8), pages 2849-2862.
    6. Wright, George & Rowe, Gene, 2011. "Group-based judgmental forecasting: An integration of extant knowledge and the development of priorities for a new research agenda," International Journal of Forecasting, Elsevier, vol. 27(1), pages 1-13, January.
    7. repec:eee:intfor:v:27:y:2011:i:1:p:1-13 is not listed on IDEAS
    8. Önkal, Dilek & Zeynep Sayım, K. & Lawrence, Michael, 2012. "Wisdom of group forecasts: Does role-playing play a role?," Omega, Elsevier, vol. 40(6), pages 693-702.
    9. Winkler, Jens & Moser, Roger, 2016. "Biases in future-oriented Delphi studies: A cognitive perspective," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 63-76.

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    More about this item

    Keywords

    combining; group decision-making; simulated interaction; unaided judgment;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D7 - Microeconomics - - Analysis of Collective Decision-Making
    • Q34 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Natural Resources and Domestic and International Conflicts
    • F51 - International Economics - - International Relations, National Security, and International Political Economy - - - International Conflicts; Negotiations; Sanctions
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions

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