can we use rum and not get drunk?
DESCRIPTION
Invited talk at the Focus Fortnight 8: ""The analysis of discrete choice experiments", organized by the Centre for Bayesian Statistics in Health Economics, University of Sheffield (UK), September, 2007.TRANSCRIPT
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Can we use RUM anddon’ get DRUNK?
Jorge E. ArañaUniversity of Las Palmas de Gran Canaria
Collaborators: Carmelo J. León (ULPGC), W. Michael Hanemann (UC Berkeley)
FF8 FortnightAnalysis of Discrete Choice
DataSheffield, September of 2007
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1. RUM and DC experiments2. Sources of mistakes in Citizens choices3. An Extended Frame: Bayesian Modelling4. Example: Heuristics and DCE
4.1. STUDY 1: Is it really a practical problem? A Verbal Protocol Analysis. 4.2. STUDY 2: A Bayesian Finite Mixture Model in the WTP space. The effects of Complexity and Emotional Load on the use of Heuristics. 4.3. STUDY 3: Heuristics Heterogeneity and Preference Reversals in Choice-Ranking: An Alternative Explanation. 4.4. STUDY 4: Can we use RUM and don’t get DRUNK?. A Monte Carlo Study
5. Discussion and Further Research
Outline
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Valuation of Health = appropriate methodsValuation of Health = appropriate methods
DCE are increasingly used and accepted.
DecisionMakingProcess
IndividualPreferences
CoherentResults forCBA or CEA
DCE and Non-Market Valuation
44
The Underlying Economic Theory The Underlying Economic Theory • Morishima (METRO,59) – Value from characteristics Lancaster (JPE, 66)
1. MEASURING PREFERENCES: (defining P) i) Experienced vs Choice Utility
ii) Absolute vs. relative utility (prospect theory)iii) …
2. LINK CHOICES AND PREFERENCES: f (.)
THE TWO MAIN ISSUESTHE TWO MAIN ISSUES
B= observed/stated choices
P= Preferences (Fundamental Value)
E= Random term (Context)
The Departing PointThe Departing Point
From the Economic theory point of view
• Lancaster (1966) – Value for characteristics
2. LINK CHOICES AND PREFERENCES: f (.)
MAIN ISSUESMAIN ISSUES
1. MEASURING PREFERENCES: (defining P) i) Experienced vs Choice Utility
ii) Direct Utility iii) Absolute vs. relative utility (prospect theory)
iv) happiness vs. utility …
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Traditional Answer:(RUM)
“Individuals have a single set of well-defined goals, and her behavior is driven by the choice of the best way to achieve those goals”.
General
Simple
Intuitive
General
Simple
Intuitive
An accurate explanation of agents choices in a wide range of situations
An accurate explanation of agents choices in a wide range of situations
How can we link Choices and Preferences? f (.)
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However…
Strong and large evidence that citizens don’t choose what make them happy?
Why? Failing Predicting Future Experiences
- Projection bias, Distinction bias, Memory bias, Belief bias,
Impact bias
Failing Following Predictions- Procrastination , Self-control bias, Overconfidence,
Anchoring
Effects, Simplifyng Decision Rules,…
88
However… However… - Preference Reversals (Slovic and Lichtenstein, 1971,1973)
- Framing effects (Tversky and Kahneman, 1981, 1986)
- …
Do f(.) exists? or just B = ε ?
Our belief: YES, f(.) do exists.
The Challenge: Defining f(.) in a way that can
accommodate these deviations.
Research Strategy: Thinking in a Hyper-rationality concept
Context matters… but Fundamental values too
(McFadden, 2001; Grether & Plott, 1979, Slovic, 2002;…)
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Solutions NEED to be …
Multidisciplinary- Economic Theory
- Social Psychology
- Statistics
- Cognitive Psychology
- Neurology
- Political Science,…
We need an Extended Frame that integrate contributions from these different areas.
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Why not Bayesian?
One Elegant and Robust way of integrating Multidisciplinary contributions to DC Theory and Data Analysis: Bayesian Econometrics
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Potential Bayesian Contributions to DCE
Can use prior information (there is a lot of prior info available!. previous research, experts, Benefit Transfer, Optimal Designs,…).
Able to tackle more complex/sophisticated models More accurate results (e.g. Exact theory in finite samples)
More informational results (reports full posterior distributions instead of
just one or two moments) Sample means are inefficient and sensitive to outliers (this is
especially important when studying heterogeneity in behaviour. The role of tails have been long ignored)
Bayesian methods can quantify and account for several kinds of components of uncertainty.
More interpretable inferences (probabilities, confidence?,…)
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EXAMPLE: Heterogeneous Decision Rules and DC
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The Heterogeneity in Decision Rules Argument
-Decision Making requires an Information Process Simon (1956) Kahnemann and Tversky (1974)
Individuals have a set of decision strategies h1, h2,…, hH
at their disposal that vary in terms of:
- Effort=EC (how much cognitive work is necessary to make the decision using that strategy)
- Accuracy=EU (the ability of that strategy to produce a good outcome).
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The Adaptive Decision Maker (Payne, Bettman, and Johnson, 1993) • Toolbox of possible choice heuristics in multi-attribute choice
•WADD: Weighted additive rule•EQW: equal weight heuristic•SAT: Satisficing Rule (Simon, 1955)•LEX: Lexicographic Heuristics•EBA: Elimination by Aspects (Tversky, 1972)•ANC: Anchoring Heuristic (Tversky and Kahneman)•MCD: Majority Confirming dimensions (Russo & Dosser, 1983)•ADDIF: Additive difference model (Tversky, 1969)•FRQ: Freq. of good and bad features (Alba and Marmorstein, 1987)•AH: Affect Heuristic. Slovic (2002)•Combined Strategies
Literature on Heuristics
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Choosing How to Choose (CHTC)
TWO STEP PROCESS
STEP 1. Choosing How to Choose. (Choice of the D.
Rule)
STEP 2. Applying the Decision Rule.
Applications:
Manski (1977), Gensch (1987), Chiang et al (1999),
Gilbride and Allenby (2004), Beach and Potter(1992)
Swait and Adamovicz (2001), Amaya and Ryan (2004)
Araña, Hanemann and León (2005)
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The Theoretical Model
For a well-behaved preference map, a general indirect utility functionof individual i, given an alternative j:
if the individual faces a multi attribute discrete choice problem, the researcher will observe that individual i chooses alternative j* if,
such that
Different specifications of I(.) makes the model collapse to alternative decision rules
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Different Heuristics
Model Specification Decision Rule to choose alternative j
M1: Full Compensatory Rule jlVV lj
M2: Complete Ignorance jlVV lj and m =0 m
M3: Conjunctive Rule jl VV lj such that
M
m imijm γXI1
1,
M4: Satisfaction Rule jl VV lj such that
M
m imijm γXI1
1, and m =0 m
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Non regularityNon regularity
Problem 1:The likelihood surface for a heuristic is discontinuous, andtherefore, the global concavity can not be guaranteed.
Solution: Rewriting the probability as the product of a second step of thechoice process and a marginal heuristic probability. That is,
.
By adding the likelihood functions over the different decision rules,resulting in a globally concave likelihood surface,
f(.) is a mixture distribution
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Evaluate an Intractable FunctionEvaluate an Intractable Function
.
From Bayes’ theorem,
Problem 2: The posterior distribution is intractable and difficult to evaluate
Solution:Here we deal with that complication by employing MCMC methodsas is proposed in discrete choice by Albert and Chib (1993) by combining…
GS Algorithm (Geman and Geman, 1984)
DA Technique (Tanner and Wong, 1987)
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Prior DistributionsPrior Distributions
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MCMC AlgorithmMCMC Algorithm
Model 1. Linear Compensatory rule
i) ijWTP from equation (A2.1)
ii) i from equation (A2.2)
iii) i from equation (A2.3)
iv)
from equation (A2.4)
v) from equation (A2.5)
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MCMC AlgorithmMCMC Algorithm
Model 3. Elimination by aspects
i) ijWTP from equation (A2.6)
ii) im from equation (A2.7)
iii) i from equation (A2.2)
iv) i from equation (A2.3)
v)
from equation (A2.4)
vi) from equation (A2.5)
vii) m from equation (A2.8)
viii) from equation (A2.9)
ix) from equation (A2.10)
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MCMC AlgorithmMCMC Algorithm
Model 4. Satisfaction Rule
i) ijWTP from equation (A2.6)
ii) im from equation (A2.7)
iii) from equation (A2.5)
iv) m from equation (A2.8)
v) from equation (A2.9)
vi) from equation (A2.10)
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Different Studies that have been discussed during FF8
Study 1: Determinants of Choosing Decision Rules (task
complexity, emotional load,…)
Study 2: Heuristics and Preference Reversals in Ranking vs Choice.
Study 3: Testing the Validity of the Model to screen out Heuristics
Study 4: Monte Carlo Simulation Study
Study 5: Verbal Protocol and Emotional Load
STUDY 1: The DataSTUDY 1: The Data
Programmes
Sample Size
link
550 Individuals
Survey Design
Survey Process
(From Jun-2004
To Ap-2005)
- 2 Focus Groups
- 3 Pre-Test Questionnaires
- Final Questionnaire
Good to be valued Valuation of a set of programs designed to
improve health care conditions for the elderly in
the island of Gran Canaria.
• D-optimal design method (Huber & Zwerina,96)
• Elicitation Technique: Choice Experiment
• Scenario were successfully tested in prior research
Testing Complexity effects on CHTCTesting Complexity effects on CHTC
TWO SPLIT SAMPLES SAMPLE I
2 pairs of alternatives + status quo
SAMPLE II
4 pairs of alternatives + status quo
Individuals emotional intensity Scale (EIS)
MEASURING EMOTIONS - Content (what we remember)
- Process (how we reason)
Emotional Intensity -------- mood experience ----- individual decision making
Def. Emotion: “ Stable individual differences in the strenght with which
individuals experience their emotions” (Larsen and Diener, 1987)
EIS-R (Geuens and Pelsmacker, 2002)
Testing Emotional load effects on CHTCTesting Emotional load effects on CHTC
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
Results & Discussion Results & Discussion
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST I: COMPLEXITY AND VALUATION RESULTS
Table 3. Welfare Estimation Results for M1 (€)Table 3. Welfare Estimation Results for M1 (€)
Programs 2 alter. + SQ 4 alter. + SQ
DRUGS43.45
(32.45, 54.44)
38.34
(31.65, 45.02)
DAY CARE19.51
(11.02, 27.99)
9.54
(3.24, 15.83)
HOSPITAL51.28
(39.10, 63.45)
67.88
(61.56, 74.19)
RESULT 1:RESULT 1: Complexity seems to affects absolute values of WelfareEstimations, BUT DO NOT affect programs ranking.
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST I: COMPLEXITY AND VALUATION RESULTS
Table 3. Welfare Estimation Results for M1 (€)Table 3. Welfare Estimation Results for M1 (€)
Programs 2 alter. + SQ 4 alter. + SQ
DRUGS43.45
(32.45, 54.44)
38.34
(31.65, 45.02)
DAY CARE19.51
(15.52, 24.49)
9.54
(4.24, 14.83)
HOSPITAL51.28
(39.10, 63.45)
67.88
(61.56, 74.19)
RESULT 2:RESULT 2: Complexity makes people focus on the most appreciate
attributes,what leads to higher valuations for most valued prog.
(HOSPITAL)and lower valuations for less valued prog. (DAY CARE).
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Complexity and Choosing how to Choose
Decision Rule 2 alter + SQ 4 alter + SQ
Full Compensatory
44.36 28.33
Complete Ignorance
6.21 11.19
EBA (Conjunctive)
31.13 36.11
Satisfaction 14.63 19.45
Disjunctive 3.66 4.92
RESULT 3:RESULT 3: The proportion of people responding in a totally randomway is low.
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Complexity and Choosing how to Choose
Decision Rule 2 alter + SQ 4 alter + SQ
Full Compensatory
44.36 28.33
Complete Ignorance
6.21 11.19
EBA (Conjunctive)
31.13 36.11
Satisfaction 14.63 19.45
Disjunctive 3.66 4.92
RESULT 4:RESULT 4: Deviations from M1 are extended in the sample (55%),although M1 has the larger proportion.
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Complexity and Choosing how to Choose
Decision Rule 2 alter + SQ 4 alter + SQ
Full Compensatory
44.36 28.33
Complete Ignorance
6.21 11.19
EBA (Conjunctive)
31.13 36.11
Satisfaction 14.63 19.45
Disjunctive 3.66 4.92
RESULT 5:RESULT 5: Complexity does increase the likelihood that Individuals follow non compensatory decision rules.
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Complexity and Choosing how to Choose
Decision Rule 2 alter + SQ 4 alter + SQ
Full Compensatory
44.36 28.33
Complete Ignorance
6.21 11.19
EBA (Conjunctive)
31.13 36.11
Satisfaction 14.63 19.45
Disjunctive 3.66 4.92
RESULT 5:RESULT 5: Complexity does increase the likelihood that Individuals follow non compensatory decision rules.
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
Emotional Level 2 alter + SQ 4 alter + SQ
Low EIS 58.32 59.30
Avg. EIS 42.38 35.70
High EIS 71.15 77.45
RESULT 6:RESULT 6: Emotional Sensitivity does affect the use of Alternative decision rules
TEST III: Emotional Intensity and Choosing how to choose
Table 5. Individuals assigned to non-compensatory rulesTable 5. Individuals assigned to non-compensatory rules
According to the degree of EIS (%)According to the degree of EIS (%)
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
Emotional Level 2 alter + SQ 4 alter + SQ
Low EIS 58.30 59.30
Avg. EIS 42.38 35.70
High EIS 71.15 77.45
TEST III: Emotional Intensity and Choosing how to choose
Table 5. Individuals assigned to non-compensatory rulesTable 5. Individuals assigned to non-compensatory rules
According to the degree of EIS (%)According to the degree of EIS (%)
RESULT 7:RESULT 7: Extreme EIS (high or low) induces a larger departurefrom M1 than average EIS.
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Summary of ResultsSummary of Results
Shows that Decision Rules are different in Shows that Decision Rules are different in Choice and in Ranking. When we take responses Choice and in Ranking. When we take responses to ranking that are worse than status quo out of to ranking that are worse than status quo out of the sample, decision rules and mean WTP are the sample, decision rules and mean WTP are very similar (although variances are lower in RK very similar (although variances are lower in RK since it uses more information)since it uses more information)
STUDY 3: RK-Choice Preference Reversals
The DataThe Data
Population
Sample Size
Gran Canaria Island Population
540 Individuals
Survey Design •D-optimal design method (Huber and Zwerina, 1996).
•Elicitation Techniques: Choice and Ranking.
•Scenario (verbal and photos) were tested in prior research..
Survey Proccess
(14 months in total)- 3 Focus Group
- Pre-Test Questionnaire
- 1 Focus Group
- Final Questionnaire
Good to be valued Valuation of a set of environmental actions in a
vast rural park in the island of Gran Canaria called
“The Guiniguada valley”.
ResultsResultsTable 3. Welfare Estimations from M1(RUM) for Choice and Ranking
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Table 4. Proportion of individuals assigned to each decision rule in each model
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Table 4. Proportion of individuals assigned to each decision rule in each model
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Table 4. Proportion of individuals assigned to each decision rule in each model
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ResultsResultsTable 5. Welfare Estimations from Aggregated Model forChoice and Ranking
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ConclusionsConclusions
In this application, the EBA is the most predominantIn this application, the EBA is the most predominant
heuristic (over the FLC)heuristic (over the FLC)
A small % of subjects follows the A small % of subjects follows the Completely Random Heuristic.Completely Random Heuristic.
Heuristics Heterogeneity is different Heuristics Heterogeneity is different between Choice and Ranking (in particular between Choice and Ranking (in particular between RK below SQ).between RK below SQ).
When the Heuristics Heterogeneity is incorporated inWhen the Heuristics Heterogeneity is incorporated in
the model the gap between Choice and Ranking isthe model the gap between Choice and Ranking is
drastically reduced.drastically reduced.
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GENERAL DISCUSSION AN FURTHER RESEARCHGENERAL DISCUSSION AN FURTHER RESEARCH
1.1. The model seems to do a good job detecting people The model seems to do a good job detecting people that use these heuristics (average efficiency 85% MC that use these heuristics (average efficiency 85% MC study)study)
2.2. It can be used as a test to further explore the validity It can be used as a test to further explore the validity of a specific DCE are good enough to be used in of a specific DCE are good enough to be used in PUBLIC POLICY (friendly code will be available very PUBLIC POLICY (friendly code will be available very soon).soon).
3.3. Results from these studies can also help to decide Results from these studies can also help to decide several aspects of the DCE design: number of several aspects of the DCE design: number of attributes, levels,…)attributes, levels,…)
4.4. First further research would be to use this information First further research would be to use this information in the DCE design using a Bayesian approach so we in the DCE design using a Bayesian approach so we can improve the accuracy of the results (respondent can improve the accuracy of the results (respondent eficiency vs statistical efficiency).eficiency vs statistical efficiency).
5.5. Results also have implications for Benefit Transfer. It Results also have implications for Benefit Transfer. It is possible to reduce the cost of these studies by is possible to reduce the cost of these studies by transferring results from previous studies to new transferring results from previous studies to new ones. The Bayesian framework seem to be the most ones. The Bayesian framework seem to be the most adequate approach to do so. adequate approach to do so.
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Thanks !!!!!
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STUDY 3: Testing the Validity of the Model to screen out Heuristics
I. Five different treatments were assigned to the samples: Treatment 1: All the simulated respondents follow the FLC rule Treatment 2: All the simulated respondents follow the EBA rule Treatment 3: All the simulated respondents follow the Completely Ignorance
Rule Treatment 4: All the simulated respondents follow the satisfactory rule. Treatment 5: 25 % of the simulated respondents follow the FLC rule, other
25% follow the EBA rule; other 25 % follow the satisfactory Rule and other 25% follow the completely ignorance rule.
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STUDY 3: Testing the Validity of the Model to screen out Heuristics
I. The true utility function was defined with values as close as possible to the ones estimated in the current application. That is, B(DRUGS) =3; B(COST)=-0.01; B(HOSPITAL)=3.5, B(DAY CARE) =1.5. For Treatments 2, 4 and 5 we randomly assigned the cut-off values for each split sample.
II. In order to simulate responses to the “Monte Carlo survey”, we employed the same
experimental designs that were used in the field data experiment. Then, 100 samples were simulated for each treatment and for each condition (e.g. Condition A: 2 options +SQ; and Condition B: 4 options +SQ). In total 1000 samples were simulated (100 samples for 5 treatments in the 2 conditions).
III. After the final responses were collected for each sample, the proposed Bayesian
mixture model was estimated for each one of them, and therefore the probability that each individual follows each decision rule. Results on the average proportion of individuals correctly assigned to each decision rule among the samples are presented in the Table R1 in this reply.
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STUDY 3: Testing the Validity of the Model to screen out Heuristics
Average efficiency: 85%
Notes: No prior info and no respondent efficient design have been applied
Table R1. Proportions of individuals correctly assigned to their decision rule by using the Bayesian Mixture Model
Condition A
2 options + SQ Condition B
4 options + SQ
Treatment 1: FLC 92 % 95 %
Treatment 2: EBA 69 % 74 %
Treatment 3: Completely Ignorance
58 % 64 %
Treatment 4: Satisfaction 70 % 76 %
Treatment 5: Mixture Model
82 % 85 %
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• A conventional Conditional Logit model and a A conventional Conditional Logit model and a Hierarchical Bayes Model are estimated in 900 Hierarchical Bayes Model are estimated in 900 samples following same idea that study 2.samples following same idea that study 2.
• Samples differ in terms of the % of citizens Samples differ in terms of the % of citizens following each decision rule (e.g. 10, 20, 30, 40, following each decision rule (e.g. 10, 20, 30, 40, 50, 60, 70, 80, 90%).50, 60, 70, 80, 90%).
STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?
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STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?
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STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?
•It is found that for the most predominant heuristics (EBA, Satisficing),It is found that for the most predominant heuristics (EBA, Satisficing),
the % of individuals that would generate a significant bias in welfarethe % of individuals that would generate a significant bias in welfare
results (10%) is 70% or higher (what is unusual in practice).results (10%) is 70% or higher (what is unusual in practice).
•However, a 20 % of people following the COMPLETELY IGNORANCEHowever, a 20 % of people following the COMPLETELY IGNORANCE
heuristic is enough to seriously bias the results.heuristic is enough to seriously bias the results.
•When we use a Hierarchical Bayes Model, we get smaller bias for any When we use a Hierarchical Bayes Model, we get smaller bias for any
% of people following alternative heuristics. % of people following alternative heuristics.
STUDY 5:EXP. I: Valuation of ExternalitiesSTUDY 5:EXP. I: Valuation of Externalities
Population
Sample Size
8000 individuals (total surrounding population)
288 Individuals (very familiar with the externalities)
Survey Design
Survey Process- 2 Focus Groups
- 2 Pre-Test Questionnaires
- Final Questionnaire
Good to be valued Valuation of a set of policy proposals to ameliorate
externalities of a Stone Mining Facility in the
suburbs of Las Palmas de Gran Canaria (Gran
Canaria).
• D-optimal design method (Huber & Zwerina,96)
• Elicitation Technique: Choice Experiment
• Scenario (verbal and photos) where tested in prior
research
EXPERIMENT I: Valuation of ExternalitiesEXPERIMENT I: Valuation of Externalities
Responses were recorded, transcribed and
evaluated by 2 judges who where unaware of our
hypotheses. (3rd judge for disagreements)
Evaluation Process
Concurrent Protocol Approach:
“Respondents are asked to verbalize their
thoughts and explain how they arrive at the final
choice while they are completing the task”.
MEASURING HEURISTICS Verbal Protocol (Ericsson and Simon, 1980)
-DCCV (Hanemann, 92, Schkade and Payne, 93)
EXPERIMENT I: Valuation of ExternalitiesEXPERIMENT I: Valuation of Externalities
Individuals emotional intensity Scale (EIS)
MEASURING EMOTIONS - Content (what we remember)
- Process (how we reason)
Emotional Intensity -------- mood experience ----- individual decision making
Def. Emotion: “ Stable individual differences in the strenght with which
individuals experience their emotions” (Larsen and Diener, 1987)
EIS-R (Geuens and Pelsmacker, 2002)
EXPERIMENT I: Valuation of ExternalitiesEXPERIMENT I: Valuation of Externalities
Attribute Negative Emotional Load Scale (ANEL)
This scale indicates the amount of affect involved in making trade-offs between
an specific attribute and money.
The ANEL scale is generated as a confirmatory analysis of the following
measures adapted from Lazarus (1991):
1. Severity of the worst potential consequence (scale 0 to 100)
2. Likelihood of negative outcomes (scale 0 to 100)
3. Degree of Threat (scale 0 to 100)
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
ResultsResults
TEST I: Effects of the Verbal Protocol approach
Swait and Louviere (1993) Swait and Louviere (1993)
EQUAL PARAMETER TEST:
-2 [312.8172-148.5683-160.4279] = 7.642 X8 .
EQUAL SCALE TEST:
-2 [312.5553 - 312.8172] = 0.5238 X1
RESULT 1:RESULT 1: The use of verbal protocol in this context seems thatwould not affect individuals’ behaviour.
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Explaining the use of Compensatory D. Rules
Table 3. Results of the Probit modelTable 3. Results of the Probit model
Covariates
Estimations
Coefficient(s. e.)
p-value
Constant -0.1623 (0.2455) 0.5084
Income 0.0297 (0.0284) .2960
Age 0.1489 (0.0375) 0.0875
Gender 0.0392 (0.0421) 0.3514
Education -0.0703 (0.0137) 0.0000
EIS 0.5291 (0.1094) 0.0000
EIS^2 -0.1791 (0.0040) 0.0000
ANEL -0.6491 (0.1094) 0.0000
Log-likel. -2554.651
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Explaining the use of Compensatory D. Rules
Table 3. Results of the Probit modelTable 3. Results of the Probit model
Covariates
Estimations
Coefficient(s. e.)
p-value
Constant -0.1623 (0.2455) 0.5084
Income 0.0297 (0.0284) .2960
Age 0.1489 (0.0375) 0.0875
Gender 0.0392 (0.0421) 0.3514
Education -0.0703 (0.0137) 0.0000
EIS 0.5291 (0.1094) 0.0000
EIS^2 -0.1791 (0.0040) 0.0000
ANEL -0.6491 (0.1094) 0.0000
Log-likel. -2554.651
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Explaining the use of Compensatory D. Rules
Covariates
Estimations
Coefficient(s. e.)
p-value
Constant -0.1623 (0.2455) 0.5084
Income 0.0297 (0.0284) .2960
Age 0.1489 (0.0375) 0.0875
Gender 0.0392 (0.0421) 0.3514
Education - 0.0703 (0.0137) 0.0000
EIS 0.5291 (0.1094) 0.0000
EIS^2 -0.1791 (0.0040) 0.0000
ANEL -0.6491 (0.1094) 0.0000
Log-likel. -2554.651
RESULT 2:RESULT 2: Educated people are more likely to use non
compensatory decision rules (which raise doubts about the cognitive ability explanation: Swait and Adamowicz, 2001)
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Explaining the use of Compensatory D. Rules
Covariates
Estimations
Coefficient(s. e.)
p-value
Constant -0.1623 (0.2455) 0.5084
Income 0.0297 (0.0284) .2960
Age 0.1489 (0.0375) 0.0875
Gender 0.0392 (0.0421) 0.3514
Education -0.0703 (0.0137) 0.0000
EIS 0.5291 (0.1094) 0.0000
EIS^2 -0.1791 (0.0040) 0.0000
ANEL -0.6491 (0.1094) 0.0000
Log-likel. -2554.651
RESULT 3: RESULT 3: Extreme bounds of EIS are less likely to the choice of
compensatory decision rules (related with the evidence that EIS has on task performance – ”Yerkes-Dodson Law”, 1908)
The ModelThe Model
IntroductionIntroduction
The MC The MC ExperimentExperiment
ResultsResults
ApplicationApplication
ConclusionConclusion
TEST II: Explaining the use of Compensatory D. Rules
Covariates
Estimations
Coefficient(s. e.)
p-value
Constant -0.1623 (0.2455) 0.5084
Income 0.0297 (0.0284) .2960
Age 0.1489 (0.0375) 0.0875
Gender 0.0392 (0.0421) 0.3514
Education -0.0703 (0.0137) 0.0000
EIS 0.5291 (0.1094) 0.0000
EIS^2 -0.1791 (0.0040) 0.0000
ANEL -0.6491 (0.1094) 0.0000
Log-likel. -2554.651
RESULT 4: RESULT 4: Individuals are more likely to avoid trade-offs when
negative emotional load is high among the task attributes (exploring levels of trade-offs and ANEL levels)
Table 4. Valuation functions for compensatory and non compensatory heuristics
Covariates
Compensatory heuristic
Non-compensatory heuristics
Pooled
Coefficient(s. e.)
Coefficient(s. e.)
Coefficient(s. e.)
Explosions0.8084***
(0.1527)0.3077*
(0.1699)0.4744***
(0.1068)
Noise1.1555***
(0.1153)0.0822
(0.1219)0.5874***
(0.0747)
Airdust1.3352***
(0.1354)0.5138*** (0.1247)
0.7871***
(0.0825)
Smokes0.5775***
(0.1137) 0.2987**
(0.1227)0.2911***
(0.0767)
Odours1.2385***
(0.1252) 0.4775***
(0.1134)0.7327***
(0.0752)
Cost-0.0135***
(0.0022)-0.0006 (0.0027)
-0.0066***
(0.0016)
Log-likel. -558.4755 -394.9577 -997.5038
% of individuals
68 32 100
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Welfare Estimates for compensatory and non compensatory heuristics
Pooled Compensatory Non
Compensatory
Attribute Mean WTP Mean WTP Mean WTP
Explosions 71.2448 59.4739 512.83
Noise 88.214 85.0077 137.00
Airdust 118.189 98.2298 856.33
Smokes 43.7179 42.4851 497.83
Odours 110.02 91.1173 795.83
RESULT 5: RESULT 5: The validity of SPM results for guiding public policy is
affected by the proportions of individuals using non compensatory decision rules. (Therefore affected by the levels of EIS and ANEL)
6565
Welfare Estimates for compensatory and non compensatory heuristics
Pooled Compensatory Non
Compensatory
Attribute Mean WTP Mean WTP Mean WTP
Explosions 71.2448 59.4739 512.83
Noise 88.214 85.0077 137.00
Airdust 118.189 98.2298 256.33
Smokes 43.7179 42.4851 49.83
Odours 110.02 91.1173 195.83
6666
EXPERIMENT II: EMOTIONS MANIPULATIONEXPERIMENT II: EMOTIONS MANIPULATION
TREATMENTS
Sample Size
Lerner, Small and Loewestein (2004; Psych. Science)
-Sadness
-Disgust
-Neutral
129 Participants randomly assigned to treatments
Overall Experiment Details
Why a 2nd experiment? -Check results out in a more controlled setting.
-Testing effects of alternative emotional states.
2 unrelated studies with 2 different researchers.
STUDY 1 “imagination study” by a psychologist
STUDY 2 “Externalities Valuation study” by an
economist.
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Sample Size 129 Participants randomly assigned to treatments
Experiment Details
PROCEDURE 1. Welcome and Introduction by researcher in Psycho.
2. Signing Consent Form for STUDY 1.
3. Asking EIS questions
4. Watching a film clip (Lerner et al, 2004)
SAD – “The Champ”
DISGUST – “Trainspotting”
NEUTRAL – “National Geographic”
5. Writing down how they would feel in the clip situation
6. Collecting materials and going to another room
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7. Welcome by the researcher in economics.
8. Signing the Consent form for STUDY 2.
9. Replicating experiment I.
10. Emotion Manipulation check (10 affective states)
11. What do you think is the aim of the study?
12. Subjects get paid (≈15€ for ≈ 45-50 minutes)
EXPERIMENT II: EMOTIONS MANIPULATIONEXPERIMENT II: EMOTIONS MANIPULATION
6868
Figure 3. Self-reported emotion in the three emotion conditions
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EXPERIMENT II: EMOTIONS MANIPULATIONEXPERIMENT II: EMOTIONS MANIPULATION
EXPERIMENT II: EMOTIONS MANIPULATIONEXPERIMENT II: EMOTIONS MANIPULATION
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Neutral Sadness Disgust
Choice Decision Rules under the Alternative Emotion Induction
Conpensatory Non-Compensatory
Decision Rule Neutral Sadness Disgust
% % %
Conpensatory 63.98 58.23 74.17
Non-Compensatory 36.02 41.77 25.83