Game theory, simulated interaction, and unaided judgement for forecasting decisions in conflicts: Further evidence

Green, Kesten C.

doi:10.1016/j.ijforecast.2005.02.006

Cited by 37 publications

(24 citation statements)

References 12 publications

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“…Simulated interactions allow for a realistic representation. Relative to the current forecasting method (expert judgment) simulated interactions reduced forecast errors by 57% in the eight situations tested to date (Green 2002(Green , 2005. The gains were achieved even though the roles were played by university students who had little knowledge of the types of conflict situations being used.…”

Section: Simulated Interactionmentioning

confidence: 93%

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Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses tests should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory. KeywordsBox-Jenkins, causal forces, causal models, combining forecasts, complex series, conjoint analysis, contrary series, damped seasonality, damped trend, data mining, Delphi, diffusion, game theory, judgmental decomposition, multiple hypotheses, neural nets, prediction markets, rule-based forecasting, segmentation, simulated interaction, structured analogies AbstractEmpirical comparisons of reasonable approaches provide evidence on the best forecasting

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Section: Simulated Interactionmentioning

confidence: 93%

“…In a related study, however, Green (2005) asked game theorists to use game theory to make predictions for eight conflict situations. The game theorists were also expected to benefit from their long experience with conflict situations as well as by their ability to use game theory.…”

Section: Game Theorymentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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“…While it is difficult to accurately forecast decisions in conflict situations, we have shown in Green (2005) and that it is possible to obtain substantially better forecasts. Green (2005) found that simulated interaction, a type of role playing for forecasting behavior in conflicts, reduced error by 47 percent when compared with game-theory experts' forecasts.…”

Section: Discussionmentioning

confidence: 84%

“…Green (2005) found that simulated interaction, a type of role playing for forecasting behavior in conflicts, reduced error by 47 percent when compared with game-theory experts' forecasts. (Role players were mostly undergraduate students.)…”

Section: Discussionmentioning

confidence: 99%

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The Ombudsman: Value of Expertise for Forecasting Decisions in Conflicts

Green

Armstrong

2007

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In important conflicts such as wars and labor-management disputes, people typically rely on the judgment of experts to predict the decisions that will be made. We compared the accuracy of 106 forecasts by experts and 169 forecasts by novices about eight real conflicts. The forecasts of experts who used their unaided judgment were little better than those of novices. Moreover, neither group's forecasts were much more accurate than simply guessing. The forecasts of experienced experts were no more accurate than the forecasts of those with less experience. The experts were nevertheless confident in the accuracy of their forecasts. Speculating that consideration of the relative frequency of decisions across similar conflicts might improve accuracy, we obtained 89 sets of frequencies from novices instructed to assume there were 100 similar situations. Forecasts based on the frequencies were no more accurate than 96 forecasts from novices asked to pick the single most likely decision. We conclude that expert judgment should not be used for predicting decisions that people will make in conflicts. When decision makers ask experts for their opinions, they are likely to overlook other, more useful, approaches. In important conflicts such as wars and labor-management disputes, people typically rely on the judgment of experts to predict the decisions that will be made. We compared the accuracy of 106 forecasts by experts and 169 forecasts by novices about eight real conflicts. The forecasts of experts who used their unaided judgment were little better than those of novices. Moreover, neither group's forecasts were much more accurate than simply guessing. The forecasts of experienced experts were no more accurate than the forecasts of those with less experience. The experts were nevertheless confident in the accuracy of their forecasts. Speculating that consideration of the relative frequency of decisions across similar conflicts might improve accuracy, we obtained 89 sets of frequencies from novices instructed to assume there were 100 similar situations. Forecasts based on the frequencies were no more accurate than 96 forecasts from novices asked to pick the single most likely decision.We conclude that expert judgment should not be used for predicting decisions that people will make in conflicts. When decision makers ask experts for their opinions, they are likely to overlook other, more useful, approaches.

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