Abstract
Balancing the pros and cons of two options is undoubtedly a very appealing decision procedure, but one that has received scarce scientific attention so far, either formally or empirically. We describe a formal framework for pros and cons decisions, where the arguments under consideration can be of varying importance, but whose importance cannot be precisely quantified. We then define eight heuristics for balancing these pros and cons, and compare the predictions of these to the choices made by 62 human participants on a selection of 33 situations. The Levelwise Tallying heuristic clearly emerges as a winner in this competition. Further refinements of this heuristic are considered in the discussion, as well as its relation to Take the Best and Cumulative Prospect Theory.
Similar content being viewed by others
References
Amgoud L., Bonnefon J.F. and Prade H. (2005). An argumentation-based approach for multiple criteria decision. In: Godo, L. (eds) Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty—Lecture Notes in Artificial Intelligence, pp 269–280. Springer Verlag, Berlin
Benferhat S., Cayrol C., Dubois D., Lang J. and Prade H. (1993). Inconsistency management and prioritized syntax-based entailment. In: Bajcsy, R. (eds) Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, pp 640–645. Morgan Kaufmann, San Mateo, CA
Benferhat S., Dubois D., Kaci S. and Prade H. (2006). Bipolar possibility theory in preference modeling: representation, fusion and optimal solutions. Information Fusion 7(1): 135–150
Bröder A. (2000). Assessing the empirical validity of the “Take-The-Best” heuristic as a model of human probabilistic inference. Journal of Experimental Psychology: Learning, Memory and Cognition 26(5): 1332–1346
Bröder A. and Schiffer S. (2003). “Take The Best” versus simultaneous feature matching: probabilistic inferences from memory and effects of representation format. Journal of Experimental Psychology: General 132(2): 277–293
Cacioppo J.T. and Berntson G.G. (1994). Relationship between attitudes and evaluative space: a critical review, with emphasis on the separability of positive and negative substrates. Psychological Bulletin 115(3): 401–423
Cacioppo J.T., Gardner W.L. and Berntson G.G. (1999). The affect system has parallel and integrative processing components: form follows function. Journal of Personality and Social Psychology 76(5): 839–855
Dawes R.M. and Corrigan B. (1974). Linear models in decision making. Psychological Bulletin 81(2): 95–106
Dubois D. and Fargier H. (2005). On the qualitative comparison of sets of positive and negative affects. In: Godo, L. (eds) Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty—Lecture Notes in Artificial Intelligence, pp 305–316. Springer-Verlag, Berlin
Dubois D. and Fargier H. (2006). Qualitative decision making with bipolar information. In: Doherty, P., Mylopoulous, J. and Welty, C. (eds) Proceedings of the Tenth International Conference on Principles of Knowledge Representation and Reasoning, pp 175–186. AAAI Press, Menlo Park, CA
Dubois D. and Prade H. (2004). Possibilistic logic: a retrospective and prospective view. Fuzzy Sets and Systems 144(1): 3–23
Einhorn H.J. and Hogarth R.M. (1975). Unit weighting schemes for decision making, Organizational Behavior and Human Decision Processes 13(2): 171–192
Fargier H., Dubois D. and Prade H. (1996). Refinements of the maximin approach to decision-making in fuzzy environment. Fuzzy Sets and Systems 81(1): 103–122
Gigerenzer G. and Goldstein D.G. (1996). Reasoning the fast and frugal way: models of bounded rationality. Psychological Review 103(4): 650–669
Hogarth R.M. and Karelaia N. (2005). Simple models for multi-attribute choice with many alternatives: when it does and does not pay to face trade-offs with binary attributes. Management Science 51(12): 1860–1872
Ito T.A. and Cacioppo J.T. (2005). Variations on a human universal: individual differences in positivity offset and negativity bias. Cognition and Emotion 19(1): 1–26
Johnson E.J., Hershey J., Meszaros J. and Kunreuther H. (1993). Framing, probability distortions, and insurance decisions. Journal of Risk and Uncertainty 7(1): 35–51
Kahneman D., Knetsch J.L. and Thaler R.H. (1991). The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives 5(1): 193–206
Kahneman D. and Tversky A. (1979). Prospect theory: an analysis of decision under risk. Econometrica 47(2): 263–291
Katsikopoulos K.V. and Martignon L. (2006). Naïve heuristics for paired comparisons: some results on their relative accuracy. Journal of Mathematical Psychology 50: 488–494
Martignon L. and Hoffrage U. (2002). Fast, frugal and fit: simple heuristics for paired comparison. Theory and Decision 52(1): 29–71
Newell B.R. and Shanks D.R. (2003). Take-the-best or look at the rest? Factors influencing ‘one-reason’ decision making. Journal of Experimental Psychology: Learning. Memory and Cognition 29(1): 53–65
Newell B.R., Weston N.J. and Shanks D.R. (2003). Empirical tests of a fast and frugal heuristic: not everyone “takes-the-best”. Organizational Behavior and Human Decision Processes 91(1): 82–96
Rieskamp, J., Hoffrage, U. (1999), When do people use simple heuristics and how can we tell? in G. Gigerenzer, P.M. Todd, and the ABC group (eds.), Simple Heuristics that Make us Smart, Oxford University Press: Oxford, pp. 141–167.
Tversky A. and Kahneman D. (1992). Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4): 297–323
Wald A. (1971). Statistical Decision Functions. Wiley, New York
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bonnefon, JF., Dubois, D., Fargier, H. et al. Qualitative Heuristics For Balancing the Pros and Cons. Theory Decis 65, 71–95 (2008). https://doi.org/10.1007/s11238-007-9050-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11238-007-9050-6