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Unstable Preferences: A Shift in Valuation or an Effect of the Elicitation Procedure?*

Unstable Preferences: A Shift in Valuation or an Effect of the Elicitation Procedure?* Peter P. Wakker ; joint with S.J.T. Jansen, A.M. Stiggelbout, M.A. Nooij, E.M. Noordijk, & J. Kievit (2000), Medical Decision Making 20, 62–71. Dept. of Economics, Univ. of Amsterdam.

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Unstable Preferences: A Shift in Valuation or an Effect of the Elicitation Procedure?*

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  1. Unstable Preferences: A Shift in Valuation or an Effect of the Elicitation Procedure?* Peter P. Wakker; joint with S.J.T. Jansen, A.M. Stiggelbout, M.A. Nooij, E.M. Noordijk, & J. Kievit (2000), Medical Decision Making 20, 62–71.Dept. of Economics, Univ. of Amsterdam *Grant from the Dutch Cancer Society We examin the well-being (utility/quality of life) of patients during radio-therapy treatment.

  2. p cure no radiotherapy no cure 1-p p+ cure, inconvenience radiotherapy no cure, inconvenience, palliation 1 -p- 2 Clients: post-operative early-stage breast-cancer patients who had completed primary treatment (lumpectomy or mastectomy). Decision problem:

  3. 3 Radiotherapy scenario (“inconvenience”) • Practical: Daily hospital visit for radiotherapy over a period of six weeks; • Physical: skin reactions (warm, red breast and dry skin), general fatigue; • Psychological:feelings of anxiety, worry about one's future health; • Social: limitations to work or other daily activities, restrictions on leisure activities.

  4. 4 Decision analysis approach: Probabilities delivered by epidemiologists. Topic of this lecture: How get utilities? We focus on one aspect:Utility ("well-being") during radio-therapy.

  5. 5 Project led to two theoretical contributions(in 3 papers): • Multiattribute utility: A method for mea-suring utility if attributes strongly interact; • Decision utility versus experienced utility: a design for detecting the causes of differences.

  6. n U(x1,…,xn) = j=1wjuj(xj,x) 6 Problem: U(radio-thpy) depends on prognosis. No time separability; no usual “QALYs.” We face special case of multiattribute utility (xj health state during period j) with uj(xj) depending on other xis. Model too general to measure/identify. No separability/utility-independence. Multivalent and hypercube models (Farquhar & Fishburn 1981) are too general.

  7. ½ ½ (g1,c2,…,cn) (b1,c2,…,cn) ~ (b1,d2,…,dn) (g1,d2,…,dn) ½ ½ 7 We developed “anchor levels.” g1,b1 are anchor levels if

  8. 8 Identify and measure u1(x1,x): • U(x)  U(g1x) and g1x ~ (p,x; 1p,b1x):u1(x1,x) = 1/p  1; • U(g1x)  U(x)  U(b1x) and x ~ (p,g1x; 1p,b1x): 0  u1(x1,x) = p  1; • U(b1x)  U(x) and b1x ~ (p,g1x; 1p,x): u1(x1,x) = -p/(1-p)  0.

  9. 9 Theorem. {bi,gi}, i=1,…,n, are anchor levels if and only if U(x1,...,xn) =wjuj(xj,x)+W(x) whereijwjuj(xj,x)+W(x) is independent of xj, i.e. U depends on xi only through wjuj(xj,x), and everything identifiable ... Is theoretical foundation. In: Wakker, Jansen, & Stiggelbout (2002), "Measuring Attribute Utilities when Attributes Interact," Leiden University Medical Center.

  10. 10 Feasibility: • Jansen, Stiggelbout, Wakker, Vliet Vlieland, Leer, Nooy, & Kievit (1998), “... Utilities for Cancer Treatments: Feasibility of a Chained Procedure ...," Medical Decision Making 18, 391–399. • Feasibility enhanced because • health states can be taken in natural context; • need not be taken as chronic.

  11. 11 So, we can measure U(radio-therapy). We focus on one question, often debated in the medical field:

  12. 12 Whose utilities are “better” (using Kahneman’s terminology): -decision: of patients prior to treatment? or -experienced: of patients during treatment? They face the decision! They know better! A dilemma!

  13. 13 Wouldn’t matter if utilities were to coincide. So, how about that? Common finding in health domain:measurements of decision U<measurements of experienced U. Common conclusion:decisionU<experienced U. Then the dilemma persists!

  14. 14 We: Conclusion of last < is premature! Problems in measurement! E.g., prior descriptions of predicted outcomes may be systematically too negative: Doctors may tend to emphasize downsides of impaired health states. Then decision utility itself need not be lower. Question: How test such an hypothesis?

  15. :control scen. (ch.tpy; dec.uty) 1 .9 .8 U .7 .6 .5 .4 bef.r.tpy bef.r.tpy bef.r.tpy bef.r.tpy bef.r.tpy during r.tpy during r.tpy during r.tpy during r.tpy during r.tpy bef.r.tpy during r.tpy Adapta-tion? 15 :actual state (expnd uty) :radio tpy. scen. (dec. uty) Red:sign (.05) Green:not sign Valuati-on shiftagain. Adapta-tion? Our data:non-cor-respond-ing des-cription. Common-ly found discrep-ancy; adapta-tion? (the com-mon inter-pretation.) Not adap-tation! Non-cor-respon-ding des-cription. Yes! No. Valu-ationshift.

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