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S ystems Analysis Laboratory Helsinki University of Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen [email protected], [email protected] Systems Analysis Laboratory Aalto University, Finland sal.aalto.fi

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Page 1: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Biases and Path Dependency in the

Even Swaps Method

Raimo P. HämäläinenTuomas J. Lahtinen

[email protected], [email protected]

Systems Analysis LaboratoryAalto University, Finland

sal.aalto.fi

Page 2: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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 effectsBiases 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

Page 3: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Even Swaps

• Smart Choices (1999)

• Even Swaps is part of the PrOACT approach

Page 4: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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 dominatedThere is another alternative, which is equal or better than this in every attribute, and better at least in one attribute

b) Attributes irrelevantEach 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

Page 5: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Office selection problem (Hammond, Keeney, Raiffa 1999)

Dominatedby

Lombard

Practicallydominated

byMontana

(Slightly better in Monthly Cost, but equal or worse in all other attributes)

78

25

An even swap

Commute time removed as irrelevant

Page 6: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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?

Page 7: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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 DM’s perception of the problem

Context dependent preferences (Tversky and Simonson 1992)

• Effects related to the measuring stick attributeTempting to always use money as the measuring stickScale compatibility (Tversky et al. 1988)

Page 8: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Loss aversion in even swaps

Loss aversion gives extra weight for lossesEven 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 oneContradicts preferential equality assumption of even swaps

Page 9: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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 stickThis affects even swaps

Page 10: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Experiment• Students (83) from Aalto University used Even

Swaps with the Smart-Swaps softwareSummer job selection taskApartment selection task

• Subjects carried out both tasks on two or three paths

Pricing path: Money used as the measuring stickHours path: Working hours used as the measuring stickSmart-Swaps path (2 versions): Path suggested by the softwareFixed reference path: All swaps carried out in a single alternative

Page 11: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

TasksApartments

Attributes A B C DSize 25m2 27m2 20m2 32m2

Commuting time 40 min 5 min 15 min 25 minRent 300€ 450€ 350€ 500€Condition 3 1 2 3

  JobsAttributes A B C DSalary 2600€ 1850€ 2800€ 2100€Daily hours 7.5h 9h 8.5h 8hAtmosphere 2 3 1 2Commuting time 60 min 45 min 30 min 35 minFlexibility 1 3 1 2

Page 12: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Experiment leads to six comparisons• Outcomes of the same subject compared on

pairs of paths in each decision task

Task Path 1 Path 2 N

Apartment PRI SS (dominance) 32

Apartment PRI SS (irrelevance) 32

Job PRI SS (dominance) 33

Job PRI SS (irrelevance) 33

Apartment Hours SS (dominance) 45

Job Swaps in B Swaps in D 38

Same subjects in all four comparisons

Same subjects in both comparisons

• Statistical analysis by McNemar’s test with binomial statistic

Page 13: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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

Page 14: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Pricing path vs. Smart-Swaps pathJob selection task High salary job Low 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 attributeThis can be explained by scale compatibility – money is used as the measuring stick on pricing path

Page 15: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Swaps only in one alternative

Job selection task Alternative B Alternative D

Swaps only in B 50% 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 itOne-way p-value: 0.004

Page 16: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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

Page 17: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Reducing trade-off biasesExperiment with 82 subjects, reference group given typical instructionsTreatment group:

Think of trade-off judgment from two reference points orThink 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

Page 18: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

What needs to be done?

• Sensitivity analysis practically infeasible

Focus on the process especially important

Page 19: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Support learningGood 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)

Page 20: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Design the process to cancel out biasesKleinmuntz (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

Page 21: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

Debiasing?• Used in DA by Bleichrodt et al. 2001, Anderson and Hobbs

2002, Jacobi and Hobbs 2007

• Normative use can be problematicCredibility and transparency issues

• Analyst can use the estimates of biases to support DM’s learning

Our Even Swaps experiment: Scale compatibility and loss aversion bias coefficients

Task measure stick attribute worsened attributeApartment 1.21 1.15Job 1.34 1.15

Page 22: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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 stepsDo paths have an impact?

• Path dependency needs attention also in interactive MCO methods

• Learning is essential• Software can provide help

Page 23: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki 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.

Page 24: S ystems Analysis Laboratory Helsinki University of Technology Biases and Path Dependency in the Even Swaps Method Raimo P. Hämäläinen Tuomas J. Lahtinen

S ystemsAnalysis LaboratoryHelsinki University of Technology

ReferencesKleinmuntz, 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.


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