Abstract
The study of cognitive biases in decision-making has largely arisen within the context of the subjective expected utility school of decision analysis. Many of the behavioural patterns that have been discovered do seem to be relevant to broader areas of multicriteria decision analysis (MCDA). In this paper, we look specifically at the judgemental inputs required in implementing goal programming (GP) models. The potential relevance of some of the known cognitive biases in this context are identified, and their impact studied by means of simulation experiments. It is found, inter alia, that biases due to anchoring and adjustment and to avoidance of sure loss can lead to substantial degradation in the performance of GP algorithms. Suggestions for practice and recommendations for follow-up research are derived from the simulation results.
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Acknowledgements
This work was partly carried out while the author was visiting professor at the Leeds University Business School, Leeds LS2 9JT, England. The hospitality and financial support from LUBS as well as useful discussions with John Maule and Clare Harries are gratefully acknowledged.
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Appendix A. Assumed form of value function
Appendix A. Assumed form of value function
Let z be a criterion vector with elements z1, z2, …, z m , such that 0⩽z j ⩽1 for all j. For purposes of generating simulated ‘true’ preferences, the value function V(z) is defined as follows:
where w j is the importance weight associated with criterion j, and the corresponding marginal value function is defined by
The parameters may be interpreted in the following manner:
- τ j ::
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The reference level, below which the decision-maker perceives outcomes as losses, and above which the outcomes are perceived as gains.
- λ j ::
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The proportion of the value range which is associated with outcomes perceived as losses.
- α j ::
-
Curvature of the value function below the reference level.
- β j ::
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Curvature of the value function above the reference level; according to prospect theory19 we should expect 0<β j <α j .
For the simulation studies reported in this paper, the above parameters were generated randomly for each run, according to the following processes:
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Weights were initially generated independently from the uniform distribution on [0.2; 1.0], and then re-scaled to sum to 1.
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The other parameters were independently generated, uniformly on the following intervals: 0.2⩽λ j ⩽0.5; 0.2⩽τ j ⩽0.5; 5⩽α j ⩽10 and 2⩽β j ⩽5.
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Stewart, T. Goal programming and cognitive biases in decision-making. J Oper Res Soc 56, 1166–1175 (2005). https://doi.org/10.1057/palgrave.jors.2601948
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DOI: https://doi.org/10.1057/palgrave.jors.2601948