Post on 07-Apr-2019
transcript
What can hypothetical choices tell us about unobservable behaviours?
Nicolas KRUCIEN, Verity WATSON, Mandy RYAN
Health Economics Research Unit University of Aberdeen
10th World Congress in Health Economics
Dublin, 13/07-16/07/14
1/26
Context • Growing evidence that participants to a DCE do not process the
information as suggested by the RUM model – ANA: Some attributes are excluded from the utilities
computation and comparison – RRM: Utilities comparison would mainly based on the
direct comparison of some attributes • Accounting for these behavioural phenomenon can lead to
qualitatively different DCE results • Models have been developed to account for these departures
from the standard RUM model, but weak theoretical foundations
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Objective
• To improve modelling of preferences in a DCE by allowing respondents to use more flexible information processing strategies [IPS] (‘as if’ approach).
• Two main propositions: 1) Limited Attention instead of classical non-attendance 2) Co-existence of several IPS within the same choice
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Method: Proposition 1
• Limited attention – Inspired by the Rational Inattention (RI) theory (Sims, 2003)
• Main components of the RI framework: – Agents have limited amount of cognitive resources they can
allocate to the decision problem – They have to decide which pieces of information (attributes)
is worth looking at (Comparison of costs and benefits) – The final choice (2nd stage) is conditional upon information
trade-offs made at the 1st stage – Agents still act rationally (Optimisation under constraint)
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Method: Proposition 1
• “Impossible” to specify a cost function without additional information on attributes' valuation by the respondents
• Respondents simplify the information processing by ‘guessing’ about attributes’ values
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Method: Proposition 1 • Different ‘guessing’ processes are compared: • Look at one alternative and use the observed values to infer
those of the remaining alternative – Probability of similarity: {0%, 10%, 20%}
• Look at one alternative and make ‘simple’ guesses about values of the other alternative – Actual values are replaced by expected values based on the
assumption that levels are equally likely • Look at one alternative and make ‘intelligent’ guesses about
the values of the other alternative – Actual values are replaced by expected values that took into
account the probability of each level to occur so far
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Method: Proposition 2 • Random Regret Minimisation (RRM) (Chorus et al, 2008)
– Close to RUM in terms of GoF but still leads to important differences (predicted probabilities)
– Agents’ decision making is based on attributes comparison (Low order) vs. alternatives comparison (High order)
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Attribute Alt [A] Alt [B] Alt [C]Cost (in £) 10 5 5
Time (in min) 30 20 30
U(A) U(B) U(C)
BR(C)
BR(C)
Method: Proposition 2 • Choice of RUM/RRM as an IPS is made for each attribute
separately depending on several factors (importance; format; uniqueness)
• 16 ‘hybrid’ models are estimated and compared to identify which attribute is best accounted for by either RUM/RRM
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RUM RRM
DCE
• Secondary data analysis • Patients' preferences for
the role of the pharmacist in management of drug therapy • 4 attributes:
– Travel + Waiting time to GP [3 levels] – Travel + Waiting time to pharmacy [3 levels] – Chance of receiving best treatment [3 levels] – Cost [4 levels]
• 16 tasks including 3 alternatives {A, B, SQ} • 204 respondents (3,265 observations)
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Results • Choice proportions • Limited Attention
Remark: Best ANA model “Pharma time” omited: LL= -2811.1
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Utility RegretLOOK [A] INFER [B] (Rate: 0%) -2800.6 -2804.9LOOK [A] INFER [B] (Rate: 10%) -2805.3 -2809.6STANDARD -2811.1 -2813.1LOOK [A] INFER [B] (Rate: 20%) -2821.9 -2825.4LOOK [A] ASSUME DYNAMIC [B] -2834.6 -2836.5
Information processingLog-Likelihood
Table. Comparison of the different 'guessing' processes (Top 5)
A B SQ31.3% 12.0% 56.7%
Results • Hybrid IPS (+ “Look [A] Infer [B] at 0%”)
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GP time Pharma time Chance Cost LogLikR R U R -2793.6R U U R -2793.7U R U R -2795.1U U U R -2795.2R U R R -2797.2
Table. Comparison of the different hybrid RUM-RRM models (Top 5)
Results
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Beta 0.7184 * 0.4807 * 0.6058 * 0.5988 *SE 0.0528 0.0372 0.0522 0.0541
Beta -0.0139 * -0.0087 * -0.0133 * 0.0090 *SE 0.0014 0.0009 0.0014 0.0009
Beta -0.0001 -0.0003 0.0070 * -0.0049 *SE 0.0020 0.0013 0.0024 0.0016
Beta 0.6836 * 0.4684 * 0.6864 * 0.6824 *SE 0.0363 0.0249 0.0388 0.0388
Beta -0.0624 * -0.0399 * -0.0722 * 0.0513 *SE 0.0047 0.0030 0.0052 0.0036
LogLik -2811.1 -2813.1 -2800.6 -2793.6
Pharma time
Chance
Cost
AttributeTable. Comparison of the different discrete choice models
(1): Regret={GP time; Pharma time; Cost} - Utility={Chance}
RUM RRM RUM - LA HYBRID - LA (1)
ASC_SQ
GP time
Conclusion • Evidence that participants to a DCE make some assumptions
about the content of the alternatives – Is ANA generated by the researcher?
• In line with regret theory (Loomes & Sugden, 1982), respondents’ preferences are partially based on the anticipated performance of a considered option + other alternative(s) – Why is it true for only some attributes?
• Application of RI theory to DCM choice modelling is an interesting research avenue (raising several methodological issues) – Use of eye-tracking technology to collect data about respondents'’
“information- trade-offs among the attributes
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Thank you for your attention
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