Parameter recovery for decision modeling using choice data

Decision

Volumn 1, Issue 4, 2014, Pages 252-274

  Broomell, Stephen B ^a   Bhatia, Sudeep ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Choice data; Decision modeling; Experimental design; Parameter estimation

Indexed keywords

EID: 84986896691     PISSN: 23259965     EISSN: 23259973     Source Type: Journal    
DOI: 10.1037/dec0000020     Document Type: Article

References (55)

1. Baird, J. C., & Noma, E. (1978). Fundamentals of Scaling and Psychophysics. New York, NY: Wiley and Sons
2. Birnbaum, M. H. (2008). New paradoxes of risky decision making. Psychological Review, 115, 463-501
3. Broomell, S. B., Budescu, D. V., & Por, H. H. (2011). Pair-wise comparisons of multiple models. Judgment and Decision Making, 6, 821-831
4. Busemeyer, J. R., & Townsend, J. T. (1993). Decision field theory: A dynamic-cognitive approach to decision making in an uncertain environment. Psychological review, 100, 432-459
5. Cavagnaro, D. R., Gonzalez, R., Myung, J. I., & Pitt, M. A. (2012). Optimal decision stimuli for risky choice experiments: An adaptive approach. Management Science, 59, 358-375. doi:10.1287/mnsc.1120.1558
6. Cavagnaro, D. R., Pitt, M. A., & Myung, J. I. (2011). Model discrimination through adaptive experimentation. Psychonomic Bulletin Review, 18, 204-210
7. Chang, H. H., & Ying, Z. (1996). A global information approach to computerized adaptive testing. Applied Psychological Measurement, 20, 213-229
8. nd ed.). Hillsdale, NJ: Erlbaum.
9. Cover, T., & Thomas, J. (1991). Elements of information theory. New York, NY: Wiley
10. Donkers, B., Melenberg, B., & Van Soest, A. (2001). Estimating risk attitudes using lotteries: A large sample approach. The Journal of Risk and Uncertainty, 22, 165-195
11. Erev, I., Ert, E., Roth, A. E., Haruvy, E., Herzog, S. M., Hau, R., Lebiere, C. (2010). A choice prediction competition: Choices from experience and from description. Journal of Behavioral Decision Making, 23, 15-47
12. Erev, I., Glozman, I., & Hertwig, R. (2008). What impacts the impact of rare events. Journal of Risk and Uncertainty, 36, 153-177
13. Erev, I., Roth, A. E., Slonim, R. L., & Barron, G. (2002). Combining a theoretical prediction with experimental evidence. Retrieved from http://ssrn.com/abstract=1111712. doi:10.2139/ssrn.1111712
14. Ert, E., & Erev, I. (2013). On the descriptive value of loss aversion in decisions under risk: Six clarifications. Judgment and Decision Making, 8, 214-235
15. Fehr, E., & Schmidt, K. M. (1999). A theory of fairness, competition, and cooperation. The Quarterly Journal of Economics, 114, 817-868
16. Fischhoff, B. (1991). Value elicitation: Is there anything in there? American Psychologist, 46, 835-847
17. Fischhoff, B. (2005). Cognitive processes in stated preference methods. In K.-G. Maler & J. Vincent (Eds.), Handbook of environmental economics (pp. 937-968). Amsterdam, The Netherlands: Elsevier
18. Fox, C., & Hadar, L. (2006). Decisions from experience = sampling error + prospect theory: Reconsidering Hertwig, Barron, Weber, & Erev (2004). Judgment and Decision Making, 1, 159-161
19. Frederick, S., Loewenstein, G., & O'Donoghue, T. (2002). Time discounting and time preference: A critical review. Journal of Economic Literature, 40, 351-401
20. Gachter, S., Johnson, E., & Herrmann, A. (2007). Individual-level loss aversion in riskless and risky choices. Retrieved from http://ssrn.com/abstract= 1010597
21. Glöckner, A., & Pachur, T. (2012). Cognitive models of risky choice: Parameter stability and predictive accuracy of prospect theory. Cognition, 123, 21-32. doi:10.1016/j.cognition.2011.12.002
22. Gonzalez, R., & Wu, G. (1999). On the shape of the probability weighting function. Cognitive Psychology, 38, 129-166
23. Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychphysics. New York, NY: Wiley
24. Harrison, G. W., Humphrey, S. J., & Verschoor, A. (2009). Choice under uncertainty: Evidence from Ethiopia, India and Uganda. The Economic Journal, 120, 80-104
25. Hau, R., Pleskac, T. J., Kiefer, J., & Hertwig, R. (2008). The description-experience gap in risky choice. The role of sample size and experienced probabilities. Journal of Behavioral Decision Making, 21, 493-518
26. Hertwig, R., Barron, G., Weber, E. U., & Erev, I. (2004). Decisions from experience and the effect of rare events in risky choice. Psychological Science, 15, 534-539
27. Hertwig, R., & Pleskac, T. J. (2008). How small samples render choice simpler. In N. Chater & M. Oaksford (Eds.), The probabilistic mind: Prospects for Bayesian cognitive science (pp. 209-235). Oxford, England: Oxford University Press
28. Hertwig, R., & Pleskac, T. J. (2010). Decisions from experience: Why small samples? Cognition, 115, 225-237
29. Holt, C. A., & Laury, S. K. (2002). Risk aversion and incentive effects. The American Economic Review, 92, 1644-1655
30. Kachelmeier, S. J., & Shehata, M. (1992). Examining risk preferences under high monetary incentives: Experimental evidence from the People's Republic of China. The American Economic Review, 82, 1120-1141
31. Kahneman, D., & Tversky, A. (1979). Prospect theory. Econometrica, 47, 263-291
32. Kiefer, J. (1959). Optimum experimental designs. Journal of the Royal Statistical Society: Statistical Methodology, 21, 272-319
33. Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79-86
34. Lichtenstein, S., & Slovic, P. (1971). Reversals of preference between bids and choices in gambling decisions. Journal of Experimental Psychology, 89, 46-55
35. Luce, R. D. (1992). A theory of certainty equivalents for uncertain alternatives. Journal of Behavioral Decision Making, 5, 201-216
36. Merkle, E. C., & Steyvers, M. (2013). Choosing a strictly proper scoring rule. Decision Analysis, 10, 292-304
37. Myung, J. I., Cavagnaro, D. R., & Pitt, M. A. (2013). A tutorial on adaptive design optimization. Journal of mathematical psychology, 57, 53-67
38. Nilsson, H., Rieskamp, J., & Wagenmakers, E. (2011). Hierarchical Bayesian parameter estima-tion for cumulative prospect theory. Journal of Mathematical Psychology, 55, 84-93
39. Pachur, T., Hanoch, Y., & Gummerum, M. (2010). Prospects behind bars: Analyzing decisions under risk in a prison population. Psychonomic Bulletin and Review, 17, 630-636
40. Por, H. H., & Budescu, D. V. (2013). Revisiting the gain-loss separability assumption in prospect theory. Journal of Behavioral Decision Making, 26, 385-396. doi:10.1002/bdm.1765
41. Prelec, D. (1998). The probability weighting function. Econometrica, 66, 497-527
42. Rakow, T., Demes, K. A., & Newell, B. R. (2008). Biased samples not mode of presentation: Reexamining the apparent underweighting of rare events in experience-based choice. Organizational Behavior and Human Decision Processes, 106, 168-179
43. Rieskamp, J. (2008). The probabilistic nature of preferential choice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 34, 1146-1465
44. Selten, R. (1998). Axiomatic characterization of the quadratic scoring rule. Experimental Economics, 1, 43-62
45. Stott, H. P. (2006). Cumulative prospect theory's functional menagerie. Journal of Risk and Uncertainty, 32, 101-130
46. Tversky, A., & Fox, C. R. (1995). Weighting risk and uncertainty. Psychological Review, 102, 269-283
47. Tversky, A., & Kahneman, D. (1992). Cumulative prospect theory: An analysis of decision under uncertainty. Journal of Risk and Uncertainty, 5, 297-323
48. Tversky, A., Slovic, P., & Kahneman, D. (1990). The causes of preference reversal. The American Economic Review, 80, 204-217
49. Tversky, A., & Thaler, R. H. (1990). Preference reversals. The Journal of Economic Perspectives, 4, 201-211
50. Ungemach, C., Chater, N., & Stewart, N. (2009). Are probabilities overweighted or underweighted, when rare outcomes are experienced (rarely)? Psychological Science, 20, 473-479
51. Veldkamp, B. P., & van der Linden, W. J. (2002). Multidimensional adaptive testing with constraints on test content. Psychometrika, 67, 575-588
52. Von Winterfeldt, D., & Edwards, W. (1982). Costs and payoffs in perceptual research. Psychological Bulletin, 91, 609-622
53. Wang, C., & Chang, H. (2011). Item selection in multidimensionsal computerized adaptive testinggaining information from different angles. Psychometrika, 76, 363-384
54. Weber, E. U., Shafir, S., & Blais, A. R. (2004). Predicting risk sensitivity in humans and lower animals: Risk as variance or coefficient of variation. Psychological Review, 111, 430-445
55. Wu, G., & Markle, A. B. (2008). An empirical test of gain-loss separability in prospect theory. Management Science, 54, 1322-1335