Subjectivity in Decision Analysis

1. Subjectivity in Decision Analysis David L. Olson James & H.K. Stuart Professor in MIS University of Nebraska Lincoln Human Centered Processes - 2008

2. Rational Choice TheoryK. Morrell, Journal of Business Ethics [2004] • Dominant model for business decision making • Compared with Image Theory • Utilitarian ethics • both consistent • Kantian ethics • Image theory yes, RCT no • Virtue-based ethics • Image theory yes, RCT no Human Centered Processes - 2008

3. Ideal • Objective measures Max Weber • Accurate preference input • “Rational” decision maker Accounting: Jensen - Agency Theory Economics: Williamson - Transaction Cost Analysis Human Centered Processes - 2008

4. Basic Preference Model Can use multiplicative model for interactions Human Centered Processes - 2008

5. Objective Measures • Objective preferred • can measure • past profit, after tax • Subjective • know conceptually, but can’t accurately measure • response to advertising Human Centered Processes - 2008

6. Problems with Objective Approach von Neumann & Morgenstern [1944] Theory of Games– utility is measurable Georgescu-Roegen [1954] The Quarterly Journal of Economics – requires to many assumptions of rationality Lindblom [1965] Public Administration Review– muddling through Morgenstern [1972] Journal of Economic Literature – 13 critical points • uncertainty • ambiguity • disagreement in groups Human Centered Processes - 2008

7. EMPIRICAL EVIDENCE contrary to rational choice models Braybrooke & Lindblom [1969]; Simon [1985] Payne, et al. [1993] • Some problems never reach decision maker • decision makers often have simple maps of real problems • all alternatives not known, so decision makers do not have full, relevant information • individual altruism Tversky [1969] • systematic & predictable economic intransitivities Kahneman, Slovic & Tversky [1982] • people use heuristics rather than follow rational model Human Centered Processes - 2008

8. James G. March Bell Journal of Economics [1978] • Rational choice involves guesses: • About future consequences of current actions • About future preferences of those consequences Administrative Science Quarterly [1996] • Alternatives & their consequences aren’t given, but need to be discovered & estimated • Bases of action aren’t reality, but perceptions of reality • Supplemental exchange theories emphasize the role of institutions in defining terms of rationality Human Centered Processes - 2008

9. Overview • Inputs to preference models involve subjectivity • Weights are function of individual • Scores also valued from perspective of individual • Subjective assessment MAY be more accurate • Purpose of analysis should be to design better alternatives Human Centered Processes - 2008

10. Means to Cope Payne, Bettman & Johnson [1993] • strategy will differ by number of alternatives • few - focus on all relevant information • many - noncompensatory simplifying heuristics • $/lives tradeoff varies by context • Hogarth: many find explicit tradeoffs uncomfortable • PROSPECT THEORY: initial analysis simple, weed out; for selected alternatives, more detailed • As people learn more about problem structure, construct choice strategies Human Centered Processes - 2008

11. Objective/Subjective • OBJECTIVE: what is convenient to model • ideal - eliminate bias, arbitrary judgment • extreme: cost/benefit analysis spanning years of measuring the unmeasurable • SUBJECTIVE: what people do to cope • value is subjective after all anyway • value is what MAUT, MCDA seeks to measure Human Centered Processes - 2008

12. ACCURATE PREFERENCE INPUT • incomplete information • uncertain measures • uncertain preferences • group participation • risk • time pressure: Edwards - how can you calculate expected utility in available time? • change competition complexity Human Centered Processes - 2008

13. RELATIONSHIP TO MCDA • We shouldn’t expect so much theoretical purity • the world has shifted away from appreciation of numerical analysis • Just because assumptions are not met doesn’t mean pure approach better Human Centered Processes - 2008

14. MCDA Methods • Multiattribute utility theory • Analytic hierarchy process • Outranking • ELECTRE, PROMETHEE • Fuzzy, DEA, Verbal Decision Analysis • Image Theory Human Centered Processes - 2008

15. Spectrum MAUT with strictly objective measures MAUT with constructed measures Likert scales SMART - swing weighting rather than lottery tradeoffs AHP - ratios of subjective scale Human Centered Processes - 2008

16. PROMETHEE Spectrum Class I: ordinal Class II: step advantage Class III: proportional advantage (in range) Class IV: three step Class V: proportional with indifference range Class VI: normal distribution Human Centered Processes - 2008

17. MAUT Hierarchy Overall Cost $billions Lives lost Expected value Risk Probability of major catastrophe Civic improvement Families with upgraded housing Human Centered Processes - 2008

18. Objective Measures Human Centered Processes - 2008

19. Swing Weighting Human Centered Processes - 2008

20. SMART with swing weighting Human Centered Processes - 2008

21. Logical Decision • Hierarchy of criteria • Single-attribute Utility Functions • Worst imaginable utility = 0 • Best imaginable utility = 1 • Assess 0.5 level of either value or utility • Tradeoffs • Pairwise comparisons • Select preferred extreme • Improve other until equal Human Centered Processes - 2008

22. SUF for Lives LostPROBLEM: sensitive to limits set – may warp values more than intended Human Centered Processes - 2008

23. Weight TradeoffsPROBLEM: weights reflect both choices, scale – again hard to control • Cost <Lives Lost [0,1000]>[500,0] [0,1000]=[5,1000] • Cost <Risk [0,1]>[500,0] [0,0.35]=[500,0] • Cost > Civic Improvement [0,100000]>[500,0] [0,100000]=[350,0] • Weights (including scale): • Risk 5.029 • Cost 1.577 • Lives Lost 29.337 • Civic Improvement 64.058

24. Tradeoff: Cost vs. Civic Improvement Human Centered Processes - 2008

25. Result Human Centered Processes - 2008

26. Contributions by Criteria Human Centered Processes - 2008

27. Rock Springs vs. Newark Human Centered Processes - 2008

28. Subjective Ratings Human Centered Processes - 2008

29. Subjective SMART Human Centered Processes - 2008

30. Output Comparisons Human Centered Processes - 2008

31. Comparison with SMART • Simpler allows decision maker to see exactly what ratings are • MAUT • Distrusts human – masks tradeoffs in effort to make “objective” • “Objective” here means have no idea • Theoretically, preferences will be identical • Does allow for nonlinear interaction, but severe impact • My contention: • DIRECT IS BETTER THAN MACHINE Human Centered Processes - 2008

32. Image Theory Human Centered Processes - 2008

33. Image Theory process • Frame decision • Desired states • Actions needed to attain desired states • MORE CRITERIA • Helpful to MCDA in structuring • Context • Elicit participation of as many views as possible • Identify alternatives • Design an ideal rather than settle for existing • MORE ALTERNATIVES Human Centered Processes - 2008

34. Verbal Decision Analysis • Controlled pairwise comparisons of tradeoffs • Focus on critical criteria • Don’t use falsely precise measures • Fuzzify – categorical ratings • Screen alternatives • Preemptive • Focus on critical tradeoffs Human Centered Processes - 2008

35. VDA Process • Eliminate very high lives lost • Newark eliminated • Eliminated risk high or worse • Nome eliminated • Rock Springs now dominates Duquesne • FOCUS ON • Rock Springs • Gary • TRADEOFFS • Rock Springs– a little lower cost, improved lives lost • Gary – civic improvement slightly better Human Centered Processes - 2008

36. Inferences • Objective can’t capture all the complexity of real decisions • OBJECTIVES ARE ALWAYS LEFT OUT • Conventional wisdom – at most 7 matter • BUT THERE IS NO PARETO OPTIMAL unless all considered • When Groups are involved, THERE IS NO ONE BEST DECISION • Ward Edwards – never saw a group pick an option that was first choice of one subgroup • NEGOTIATION Human Centered Processes - 2008

37. Conclusion • Measures of alternative future performance, preference for that performance both subjective • Objective measures not always better • Focus should be on: • Learning (changing preference) • Design of better alternatives (Image Theory)