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Utility Aggregation in Temporally Extended Experiences: What´s in Representative Moments?

Utility Aggregation in Temporally Extended Experiences: What´s in Representative Moments?. Irina Cojuharenco* Dmitry Ryvkin**. *Department of Economics and Management Universitat Pompeu Fabra irina.cojuharenco@upf.edu **Department of Economics, Florida State University.

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Utility Aggregation in Temporally Extended Experiences: What´s in Representative Moments?

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  1. Utility Aggregation in Temporally Extended Experiences: What´s in Representative Moments? Irina Cojuharenco* Dmitry Ryvkin** *Department of Economics and Management Universitat Pompeu Fabra irina.cojuharenco@upf.edu **Department of Economics, Florida State University

  2. Temporally Extended Experiences • MBA programs • Medical procedures • Meals • Music • Job performance All can be translated into the language of Utility!

  3. Definitions Experienced Utility – measurable states of satisfaction experienced by people (moment utility – experienced utility measured at a particular moment) Total Utility – an objective measure summarizing the utility of all moment within an experience, e.g., average of moment utilities Remembered Utility – subjective report of total utility, found to be the average of Peak and End experienced utility utility time

  4. Experiences 4-5-1-8-9-6-9-4-7-7-2 8-2-1-5-1-0-3-3-2-7-8 … 3-0-2-3-1-1-4-2-8-9-4 End 2 8 … 4 Research Question How different is remembered utility based on Peak-Endversus Average experienced utility? Will experiences be ranked differently: what´s the correlation between Peak-End and Average utility? Peak 7 8 … 9 Peak-End 5 8 … 6 Average 6 4 … 3

  5. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  6. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  7. Random Experiences . . . . . . . . Assumptions: Utility, ut, is derived from hedonic stimuli st Stimuli, st, are drawn from uniform [0,1] ut= st t=1, …, T where T is the length of experience Correlation between Peak-End and Average utility, r(T):

  8. Random Experiences . . . . . . . . r(T) and simulations for N=1000, the number of experiences summarized by Peak-End versus Average experienced utility r(T) T In summarizing long random experiences little correlation can be expected between Peak-End and Average experienced utility.

  9. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  10. The Data (54 Data Sets) . . . . . . . . Data sets 1-34: Baumgartner, Sujan, and Padget (1997), per-second evaluations of advertisements. Data set 44: Ariely and Car- mon (2003), hourly reports of pain in a day-long hospital field study. Data sets 35-38, 39-43, 45-54: our unpublished research, evaluations of images in image-viewing experiments, evaluations of classroom explanations and discussions in classroom field studies, evaluation of life aspects in a month-long life satisfaction study.

  11. The Data . . . . . . . . Correlation between Peak-End and Average experienced utility is high and significant, almost uniformly across data sets (variation in population correlations controlling for sampling error 0.008).

  12. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment (Hogarth & Einhorn, 1992) • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  13. Anchoring-and-Adjustment . . . . . . . . Assumptions: Utility, ut, is derived from hedonic stimuli st and past period utility ut-1. Stimuli, st, are drawn from uniform [0,1]. or t=1, …, T where T is the length of experience. α determines the transmission of “information” from one moment utility to the other.

  14. Anchoring-and-Adjustment . .. . . . . . Correlation between Peak-End and Average utility, r(T): examined in simulations for α=0, 0.1, 0.2,…,1 and t=2, 3, …, 100. r(T) T The variability in Average utility helps explain 25% of variability in Peak-End utility for experiences characterized by α= 0.9 and T = 100.

  15. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility (Frederick & Loewenstein, 1999) • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  16. Adaptation . . . . . . . . Assumptions: Utility, ut, is derived from hedonic stimuli st adjusted by the adaptation level (the level of utility due to previous experience that leaves one hedonically neutral). Stimuli, st , uniform [0,1]. and or or t=1, …, T where T is the length of experience. β determines the transmission of “information” from one moment utility to the other.

  17. Adaptation . . . . . . . . Correlation between Peak-End and Average utility, r(T): examined in simulations for β=0, 0.1, 0.2,…,1 and t=2, 3, …, 100. r(T) T The variability in Average utility helps explain 49% of variability in Peak-End utility for experiences characterized by β= 1 and T = 100.

  18. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  19. Individual Heterogeneity . . . . . . . . Due to: • initial moods (initial condition u0i) • individual-specific effects (ci added to equation for ut) Variance in u0i or ci may make variability in individual-specific means of reported utility (between variability) greater than variability in experience-specific moment utilities (within variability). This may explain high and significant correlation between Peak-End and Average experienced utility.

  20. Between and Within Variability in the Data . . . . . . . . Data sets Standard Deviations in Utility Reports Between and Within Inividuals

  21. Simulations . . . . . . . . We examine individual heterogeneity distributed normally and uniformly with variance large (between>>within), medium (between=within) and small (between<<within).

  22. . . . . . . . .

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  26. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  27. Estimating Dynamics and Individual Heterogeneity in the Data . . . .. . . . Population model: Describes the strength of dynamics: anch.-a-adj. adapt. Stands for unobserved hedonic stimuli for anch.-a-adj. and for adaptation. Individual-specific unobserved effect. Error-term satisfying sequential exogeneity conditional on unobserved effect.

  28. Estimation Strategy . . . .. . . . Step 1. Time differencing to exclude the unobserved effect Step 2. Time dummies Step 3. Instrumental variables Obtain on time Step 4. Regress new variable and individual dummies to obtain and

  29. . . . .. . . . Strength of Dynamics

  30. Estimation Results . . . .. . . .

  31. Estimation Results . . . .. . . .

  32. . . . .. . . . Hedonic Stimuli

  33. Hedonic Stimuli . . . .. . . . are informative about the distribution of in case of anchoring-and-adjustment and in case of adaptation. Sample distribution in 3 first and 3 last data sets: For later purposes, we assume normality and characterize hedonic stimuli in terms of sample mean and standard deviation of .

  34. . . . .. . . . Individual Heterogeneity

  35. Individual Heterogeneity . . . .. . . . are informative about the distribution of individual heterogeneity, in 34 of 51 data sets we cannot reject normality. Sample distribution in 3 first and 3 last data sets: For later purposes, we characterize individual heterogeneity as normally distributed with sample mean and standard deviation of .

  36. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  37. Predicting Correlation between Peak-End and Average Experienced Utility . . . .. . . . Correlation observed in the data r(data) Correlation predicted based on anchoring-and-adjustment given α and individual heterogeneity as estimated r(anch.and adj.) Correlation predicted based on adaptation given β and individual heterogeneity as estimated r(adapt.) Correlation predicted based on individual heterogeneity alone (assuming the underlying model is anchoring-and-adjustment) r(alpha=0) Correlation predicted based on individual heterogeneity alone (assuming the underlying model is adaptation) r(beta=0)

  38. Predicting Correlation between Peak-End and Average experienced utility . . . .. . . . Data sets for which was statistically significant

  39. Predicting Correlation between Peak-End and Average Experienced Utility . . . .. . . . Mean absolute deviation 0.09, correlation with actual r(data) 0.57*** (Spearman rank-order). r(anch.and adj.) Mean absolute deviation 0.09, correlation with actual r(data) 0.42*** (Spearman rank-order). r(adapt.) Mean absolute deviation 0.10, correlation with actual r(data) 0.19 (Spearman rank-order). r(alpha=0) Mean absolute deviation 0.11, correlation with actual r(data) -0.03 (Spearman rank-order). r(beta=0)

  40. Presentation Plan • The case of random experiences • What experimental and field data tells • When experienced utility evolves by anchoring-and-adjustment • When adaptation underlies experienced utility • Individual heterogeneity in the reports of utility • Estimation of dynamics and individual heterogeneity • Predicting correlation between Peak-End and Average experienced utility given estimation results • Conclusion

  41. Conclusion . . . .. .. . • We have helped quantify the similarity/dissimilarity due to selective versus comprehensive aggregation of utility in the comparison of equal-length experiences. • Even few representative moments can potentially rank experiences similarly to average experienced utility. • The high and significant correlation between Peak-End and Average experienced utility can be due to: • Dynamics of experienced utility • Individual heterogeneity in utility reports

  42. Conclusion . . . .. .. . • We have contributed to the studies of unit weighting schemes for decision-making (Einhorn & Hogarth, 1975). Simple one-parameter dynamic processes have been shown to induce a particular structure of intercorrelation between “components” of a composite variable. The value of the parameter has been related to the similarity between the selective and the comprehensive aggregation of “components”. • Even if experienced utility does not evolve by anchoring-and-adjustment or adaptation, correlation between Peak-End and Average experienced utility can be “built into” the data on experiences by the experimenter if he follows the “scripts” of anchoring-and-adjustment or adaptation in the choice of hedonic stimuli.

  43. . . . .. .. . . . . Thank You for Your Attention! For any questions regarding this work, please, contact Irina Cojuharenco at irina.cojuharenco@upf.edu

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