1. S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen raimo.hamalainen@aalto.fi, tuomas.j.lahtinen@aalto.fi Systems Analysis Laboratory Aalto University, Finland sal.aalto.fi

2. S ystems Analysis Laboratory Helsinki University of Technology Path dependency needs attention Decision support processes often carried out in a sequence of steps Behavioral biases along the path lead to dynamic effects Biases affect the path and the path affects which biases are likely to take place Even Swaps method based on sequence of trade-offs Interactive processes in multicriteria optimization also consist of sequential steps

3. S ystems Analysis Laboratory Helsinki University of Technology Even Swaps Smart Choices (1999) Even Swaps is part of the PrOACT approach

4. S ystems Analysis Laboratory Helsinki University of Technology Even Swaps elimination process Even swap: Alternative swapped to preferentially equivalent one that differs in two attributes Carry out even swaps that make a)Alternatives dominated There is another alternative, which is equal or better than this in every attribute, and better at least in one attribute b)Attributes irrelevant Each alternative has the same value on this attribute »These can be eliminated A sequence of swaps is carried out until the most preferred alternative remains

5. S ystems Analysis Laboratory Helsinki University of Technology Office selection problem (Hammond, Keeney, Raiffa 1999) Dominated by Lombard Practically dominated by Montana (Slightly better in Monthly Cost, but equal or worse in all other attributes) 78 25 An even swap Commute time removed as irrelevant

6. S ystems Analysis Laboratory Helsinki University of Technology Different paths can be followed Paths consist of different sequences of trade-off judgments DM can experience the paths differently Each path should lead to the same choice - does this happen?

7. S ystems Analysis Laboratory Helsinki University of Technology Phenomena related to paths Anchoring to initial comparison tasks and judgments Reference point changes along the path Loss aversion (Tversky and Kaheman 1991) Elimination of alternatives and attributes changes the DMs perception of the problem Context dependent preferences (Tversky and Simonson 1992) Effects related to the measuring stick attribute Tempting to always use money as the measuring stick Scale compatibility (Tversky et al. 1988)

8. S ystems Analysis Laboratory Helsinki University of Technology Loss aversion in even swaps Loss aversion gives extra weight for losses Even swap: a reference change in one attribute is compensated by a change in another attribute If reference change is a loss – compensatory gain overstated If reference change is a gain – compensatory loss understated Modified alternative becomes more attractive than the preceding one Contradicts preferential equality assumption of even swaps

9. S ystems Analysis Laboratory Helsinki University of Technology Scale compatibility bias Trade-off question: How much should you pay to compensate for saving 30 minutes of commuting time? How much should you save in commuting time to compensate for payment of 10 euros? Response: 10 (10 equals 30 min) 20 min (10 equals 20 min) Attribute used as the measuring stick gets extra weight in trade-offs (Slovic 1990, Delquie 1993) The weight of commuting time is higher when it is used as the measuring stick This affects even swaps

10. S ystems Analysis Laboratory Helsinki University of Technology Experiment Students (83) from Aalto University used Even Swaps with the Smart-Swaps software Summer job selection task Apartment selection task Subjects carried out both tasks on two or three paths Pricing path: Money used as the measuring stick Hours path: Working hours used as the measuring stick Smart-Swaps path (2 versions): Path suggested by the software Fixed reference path: All swaps carried out in a single alternative

11. S ystems Analysis Laboratory Helsinki University of Technology Tasks Apartments AttributesABCD Size 25m 2 27m 2 20m 2 32m 2 Commuting time40 min5 min15 min25 min Rent300450350500 Condition3123 Jobs AttributesABCD Salary2600185028002100 Daily hours7.5h9h8.5h8h Atmosphere2312 Commuting time60 min45 min30 min35 min Flexibility1312

12. S ystems Analysis Laboratory Helsinki University of Technology Experiment leads to six comparisons Outcomes of the same subject compared on pairs of paths in each decision task TaskPath 1Path 2N ApartmentPRISS (dominance)32 ApartmentPRISS (irrelevance)32 JobPRISS (dominance)33 JobPRISS (irrelevance)33 ApartmentHoursSS (dominance)45 JobSwaps in BSwaps in D38 Same subjects in all four comparisons Same subjects in both comparisons Statistical analysis by McNemars test with binomial statistic

13. S ystems Analysis Laboratory Helsinki University of Technology Results On every pair of paths over 50% of subjects ended up with different outcomes Not only due to random inconsistencies: Path dependency exists Results can be explained by scale compatibility and loss aversion Here we present some of the results

14. S ystems Analysis Laboratory Helsinki University of Technology Pricing path vs. Smart-Swaps path Job selection task High salary jobLow salary job Pricing path 64%36% Smart-Swaps path (dominance) 30%70% More subjects select a high salary job on the pricing path (one-way p: 0.002) Apartment selection task Low rent apartment High rent apartment Pricing path 72%28% Smart-Swaps path (dominance) 53%47% More subjects select a low rent apartment on the pricing path (one-way p: 0.09) Pricing path favors alternatives that are best in the money related attribute This can be explained by scale compatibility – money is used as the measuring stick on pricing path

15. S ystems Analysis Laboratory Helsinki University of Technology Swaps only in one alternative Job selection taskAlternative BAlternative D Swaps only in B 50% Swaps only in D 21%79% Task: two jobs, B and D When swaps are carried out in B, 50% of the subjects select it. When swaps are carried out in D, 21% of the subjects select B. Alternative is favored when all swaps are carried out in it One-way p-value: 0.004

16. S ystems Analysis Laboratory Helsinki University of Technology Explanations Loss aversion causes an alternative to become more attractive in each swap Misunderstanding trade-offs (Keeney 2002) : People can feel that they should benefit from the trade-off I am willing to trade-off vs. I am indifferent between the two alternatives Alternative is favored when all swaps are carried out in it

17. S ystems Analysis Laboratory Helsinki University of Technology Reducing trade-off biases Experiment with 82 subjects, reference group given typical instructions Treatment group: Think of trade-off judgment from two reference points or Think of trade-off judgment with two measuring sticks Results: Loss aversion bias reduced in treatment group Scale compatibility bias not reduced Too much weight for the attribute that was first used as the measuring stick

18. S ystems Analysis Laboratory Helsinki University of Technology What needs to be done? Sensitivity analysis practically infeasible Focus on the process especially important

19. S ystems Analysis Laboratory Helsinki University of Technology Support learning Good practise in preference modeling (Payne et al. 1999, Anderson and Clemen 2013) Carry out the process on multiple paths to identify path dependency – Discuss with the DM Present trade-off questions in multiple ways Converging sequence of preference statements to decide the trade-off (Keeney 2002)

20. S ystems Analysis Laboratory Helsinki University of Technology Design the process to cancel out biases Kleinmuntz (1990) Reducing scale compatibility bias: Select measuring stick attribute in which alternatives are initially close to each other Alternatives become more attractive in each swap: Carry out the same number of swaps in all the alternatives

21. S ystems Analysis Laboratory Helsinki University of Technology Debiasing? Used in DA by Bleichrodt et al. 2001, Anderson and Hobbs 2002, Jacobi and Hobbs 2007 Normative use can be problematic Credibility and transparency issues Analyst can use the estimates of biases to support DMs learning Our Even Swaps experiment: Scale compatibility and loss aversion bias coefficients Taskmeasure stick attributeworsened attribute Apartment1.211.15 Job1.341.15

22. S ystems Analysis Laboratory Helsinki University of Technology Conclusions Path dependency is a real phenomenon DM constructs preferences during the DA process (Slovic 1995) Challenge to design processes which alleviate path dependency Any DA process consists of steps Do paths have an impact? Path dependency needs attention also in interactive MCO methods Learning is essential Software can provide help

23. S ystems Analysis Laboratory Helsinki University of Technology References Anderson, R. M., Clemen, R. 2013. Toward an Improved Methodology to Construct and Reconcile Decision Analytic Preference Judgments, Decision Analysis, 10(2), 121-134. Anderson, R. M., Hobbs, B. F. 2002. Using a Bayesian Approach to Quantify Scale Compatibility Bias. Management Science, 48(12), 1555-1568. Bleichrodt, H. J., Pinto, J. L., Wakker, P. 2001. Making descriptive use of prospect theory to improve the prescriptive use of expected utility. Management Science, 47(11), 1498-1514. Delquié, P. 1993. Inconsistent Trade-offs between Attributes: New Evidence in Preference Assessment Biases. Management Science, 39(11), 1382-1395. Hammond, J.S., Keeney, R.L., Raiffa, H., 1999. Smart Choices: A practical guide to making better decisions. Harvard Business School Press, Boston, MA. Jacobi, S. K., Hobbs, B. F. 2007. Quantifying and mitigating the splitting bias and other value tree-induced weighting biases, Decision Analysis, 4(4), 194-210. Keeney, R. 2002. Common mistakes in making value trade-offs. Operations research, 50, 935-945.

24. S ystems Analysis Laboratory Helsinki University of Technology References Kleinmuntz, D. K. 1990. Decomposition and control of error in decision- analytical model. Insights in decision making: A tribute to Hillel J. Einhorn, 107-126. Payne, J. W., Bettman, J. R. Schkade, D. A. 1999. Measuring constructed preferences: Towards a building code, Journal of Risk and Uncertainty, 19(1-3), 243-270. Slovic, P. 1995. The construction of preference. American Psychologist, 50(5), 364. Slovic, P., Griffin, D., Tversky, A. 1990. Compatibility effects in judgment and choice. Insights in decision making: A tribute to Hillel J. Einhorn, 5-27. Tversky, A., Sattath, S., Slovic, P. 1988. Contingent Weighting in Judgment and Choice. Psychological Review, 94(3), 371-384. Tversky, A., Kahneman, D. 1991. Loss Aversion in Riskless Choice: A Reference-Dependent Model. Quarterly Journal of Economics, 106(4), 1039-1061. Tversky, A., Simonson, I. 1993. Context-dependent preferences. Management Science, 39(10), 1179-1189.