Choice Quality as a Function of Decision Accuracy and Search Cost

  • Reza Rastgoo Sisakht Department of Neuroscience and Addiction Studies. School of Advanced Technologies in Medicine, Tehran University of Medical Sciences
  • Shabnam Mousavi The Johns Hopkins Carey Business School, 1625 Massachusetts Avenue, N.W, Washington, D.C 20036
  • Rahimeh Negarandeh Médecins Sans Frontières (MSF), Southern Tehran Project, Tehran, Iran
  • Hamid Valizadegan Decision Science and Knowledge Engineering, Behsazan Mellat Co, Mojgan, Dibaji, Tehran, Iran.
  • Maryam Noroozian Memory and Behavioral Neurology Division, Department of Psychiatry, Ruzbeh Hospital, Tehran University of Medical Sciences, Tehran, Iran.
  • Mehdi Tehrani-Doost Department of Neuroscience and Addiction Studies. School of Advanced Technologies in Medicine, and Department of Psychiatry, School of Medicine, Research Center for Cognitive and Behavioral Studies, Tehran University of Medical Sciences, Tehran, Iran.
  • Emran Mohammad Razaghi School of Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
Keywords: Cost-Benefit Calculations, Decision Process, Expected Value, Mouselab, Quality of Choice, Utility

Abstract

Objective: A prominent challenge in modeling choice is specification of the underlying cognitive processes. Many cognitive-based models of decision-making draw substantially on algorithmic models of artificial intelligence and thus rely on associated metaphors of this field. In contrast, the current study avoids metaphors and aims at a first-hand identification of the behavioral elements of a process of choice.

Method: We designed a game in Mouselab resembling the real-world procedure of choosing a wife. 17 male subjects were exposed to cost-benefit decision criteria that closely mimic their societal respective conditions.

Results: The quality of choice index was measured with respect to its sensitivity to the final outcomes as well as process tracing of decisions. The correlation between this index and individual components of process tracing are discussed in detail. The choice quality index can be configured as a function of expected value and utility. In our sample the quality of choice with an average of 75.98% (SD: ±12.67) suggests that subjects obtained close to 76% of their expected gains.

Conclusion: The quality of choice index, therefore, may be used for comparison of different conditions where the variables of decision-making are altered. The analysis of results also reveals that the cost of incorrect choice is significantly correlated with expected value (0.596, sig = 0.012) but not with utility. This means that when sub-jects face higher costs prior to making a decision, there exists a corresponding higher expectation of gains, i.e., higher expected value.

References

1. Payne JW, Braunstein ML, Carroll JS. Exploring predecisional behavior: An alternative approach to decision research. Organizational Behavior and Human Performance. 1978;22(1):17-44.
2. Payne JW, Braunstein ML. Risky choice: An examination of information acquisition behavior. Memory & Cognition. 1978;6(5):554-61.
3. Noel X, Brevers D, Bechara A. A neurocognitive approach to understanding the neurobiology of addiction. Curr Opin Neurobiol. 2013;23(4):632-8.
4. Bechara A, Damasio H, Tranel D, Damasio ARJS. Deciding advantageously before knowing the advantageous strategy. 1997;275(5304):1293-5.
5. Dutt V, Gonzalez C. The role of inertia in modeling decisions from experience with instance-based learning. Frontiers in psychology. 2012;3:177.
6. Moreno A, Ruiz-Mirazo K, Barandiaran X, editors. The Impact Of The Paradigm Of Complexity On The Foundational Frameworks Of Biology And Cognitive Science.: Elsevier B.V Press; 2011.
7. Schwenk CR. Cognitive simplification processes in strategic decision‐making. Strategic management journal. 1984;5(2):111-28.
8. Rubinstein A. “Economics and psychology”? The case of hyperbolic discounting. International Economic Review. 2003;44(4):1207-16.
9. Jasper J, Shapiro J. MouseTrace: A better mousetrap for catching decision processes. Behavior Research Methods, Instruments, & Computers. 2002;34(3):364-74.
10. Willemsen MC, Johnson EJ. Visiting the decision factory: Observing cognition with MouselabWEB and other information acquisition methods. A handbook of process tracing methods for decision research. 2011:21-42.
11. Bröder A. Take The Best, Dawes’ rule, and compensatory decision strategies: A method for classifying individual response patterns. 2004.
12. Riedl R, Brandstätter E, Roithmayr F. Identifying decision strategies: A process- and outcome-based classification method. Behavior Research Methods. 2008;40(3):795-807.
13. Harte JM, Koele P. Modelling and describing human judgement processes: The multiattribute evaluation case. Thinking & reasoning. 2001;7(1):29-49.
14. Hoffrage U, Rieskamp J, editors. When Do People Use Simple Heuristics and How Can We Tell?: Oxford University Press; 1999.
15. Bröder A, Schiffer S. Bayesian strategy assessment in multi‐attribute decision making. Journal of Behavioral Decision Making. 2003;16(3):193-213.
16. Lohse GL, Johnson EJ, editors. A comparison of two process tracing methods for choice tasks1996 1996: IEEE.
17. Vakkari P. Task‐based information searching. Annual review of information science and technology. 2003;37(1):413-64.
18. Chu P-C, Spires EE. The joint effects of effort and quality on decision strategy choice with computerized decision aids. Decision Sciences. 2000;31(2):259-92.
19. Coupey E, Narayanan S. Effects of knowledge types on choice quality and perceptions of choice performance. Psychology & Marketing. 1996;13(7):715-38.
20. Johnson EJ, Payne JW. Effort and accuracy in choice. Management science. 1985;31(4):395-414.
21. Zakay D, Wooler S. Time pressure, training and decision effectiveness. Ergonomics. 1984;27(3):273-84.
22. www.mouselabweg.org.
23. Schulte-Mecklenbeck M, Kühberger A. Out of sight–out of mind? Information acquisition patterns in risky choice framing. Polish Psychological Bulletin. 2014;45(1):21-8.
24. Bettman JR, Johnson EJ, Payne JW. A componential analysis of cognitive effort in choice. Organizational behavior and human decision processes. 1990;45(1):111-39.
25. Cubitt R. The Adaptive Decision Maker. JSTOR; 1995.
26. Payne JW, Bettman JR, Johnson EJ. Behavioral decision research: A constructive processing perspective. Annual review of psychology. 1992;43(1):87-131.
27. Rieskamp J. The importance of learning when making inferences. Judgment and Decision Making. 2008;3(3):261.
28. Gabaix X, Laibson D, Moloche G, Weinberg S. Costly information acquisition: Experimental analysis of a boundedly rational model. American Economic Review. 2006;96(4):1043-68.
29. Glicksohn A, Cohen A. The role of Gestalt grouping principles in visual statistical learning. Attention, Perception, & Psychophysics. 2011;73(3):708-13.
30. Huntley J, Bor D, Hampshire A, Owen A, Howard R. Working memory task performance and chunking in early Alzheimer’s disease. The British Journal of Psychiatry. 2011;198(5):398-403.
31. Maybery MT, Parmentier FB, Jones DM. Grouping of list items reflected in the timing of recall: Implications for models of serial verbal memory. Journal of Memory and Language. 2002;47(3):360-85.
32. Miller GA. The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological review. 1956;63(2):81.
33. Sakai K, Kitaguchi K, Hikosaka O. Chunking during human visuomotor sequence learning. Experimental brain research. 2003;152(2):229-42.
34. Gigerenzer G, Gaissmaier W. Heuristic decision making. Annu Rev Psychol. 2011;62:451-82.
35. Payne JW, Bettman JR, Johnson EJ. Adaptive strategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition. 1988;14(3):534.
36. Ashton RH, Ashton AH. Judgment and decision-making research in accounting and auditing: Cambridge University Press; 1995.
37. Ackert LF, Church BK, Tkac PA. An experimental examination of heuristic-based decision-makingin a financial setting. Journal of Behavioral Finance. 2010;11(3):135-49.
38. Creyer EH, Bettman JR, Payne JW. The impact of accuracy and effort feedback and goals on adaptive decision behavior. Journal of Behavioral Decision Making. 1990;3(1):1-16.
39. Dieckmann A, Dippold K, Dietrich H. Compensatory versus noncompensatory models for predicting consumer preferences. 2009.
40. Haran U, Moore DA, Morewedge CK. A simple remedy for overprecision in judgment. Judgment and Decision Making. 2010;5(7):467.
41. Reimer T, Hoffrage U. The Ecological Rationality of Simple Group Heuristics: Effects of Group Member Strategies on Decision Accuracy. Theory and Decision. 2006;60(4):403-38.
42. Skvortsova A, Schulte-Mecklenbeck M, Jellema S, Sanfey A, Witteman C. Deliberative versus intuitive psychodiagnostic decision. 2016.
43. Sokolowska J. Rationality and psychological accuracy of risky choice models based on option-vs. dimension-wise evaluations. 2014.
44. Todd PM, Dieckmann A, editors. Heuristics for ordering cue search in decision making. Advances in neural information processing systems; 2005.
Published
2019-07-28
How to Cite
1.
Rastgoo Sisakht R, Mousavi S, Negarandeh R, Valizadegan H, Noroozian M, Tehrani-Doost M, Razaghi EM. Choice Quality as a Function of Decision Accuracy and Search Cost. Iran J Psychiatry. 14(3):203-210.
Section
Original Article(s)