Abstract
Behaviorally, regret-based choice models implicitly assume that individuals anticipate the amount of attribute-level regret by comparing the attribute levels of a considered choice alternative against the attribute levels of the best or all other choice alternatives. Arguing that the amount of effort depends on attribute variation and number of paired comparisons, we suggest a way of incorporating the effects of these factors into two regret-based choice models. The cognitive effort involved in anticipating the amount of regret in paired comparisons of choice alternatives is incorporated into the scale of the regret function of each alternative. Because more cognitive effort causes higher randomness in the assessment of the amount of regret (i.e. higher variance of error terms), the cognitive effort is expressed as a flexible heteroscedastic scale factor, which is a decreasing function of attribute variation and number of paired comparisons. The models are applied to two different data sets, and compared with a heteroscedastic multinomial logit model. Estimation results of the suggested flexible heteroscedastic random regret models show a significant improvement in predictive performance over their homoscedastic formulations. A similar but smaller improvement is obtained for multinomial logit models. These results imply that the conventional assumption of identically distributed error terms underlying random regret models may not sufficiently reflect the process of anticipating the amount of regret.
| Original language | English |
|---|---|
| Pages (from-to) | 842-863 |
| Number of pages | 22 |
| Journal | Environment and Planning B: Urban Analytics and City Science |
| Volume | 45 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 1 Sep 2018 |
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Keywords
- attribute variation
- cognitive effort
- number of attribute-level regrets
- paired comparison
- Regret-based choice models
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Accounting for cognitive effort in random regret-only models : effect of attribute variation and choice set size. / Jang, Sunghoon; Rasouli, Soora; Timmermans, Harry.
In: Environment and Planning B: Urban Analytics and City Science, Vol. 45, No. 5, 01.09.2018, p. 842-863.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Accounting for cognitive effort in random regret-only models
T2 - effect of attribute variation and choice set size
AU - Jang, Sunghoon
AU - Rasouli, Soora
AU - Timmermans, Harry
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Behaviorally, regret-based choice models implicitly assume that individuals anticipate the amount of attribute-level regret by comparing the attribute levels of a considered choice alternative against the attribute levels of the best or all other choice alternatives. Arguing that the amount of effort depends on attribute variation and number of paired comparisons, we suggest a way of incorporating the effects of these factors into two regret-based choice models. The cognitive effort involved in anticipating the amount of regret in paired comparisons of choice alternatives is incorporated into the scale of the regret function of each alternative. Because more cognitive effort causes higher randomness in the assessment of the amount of regret (i.e. higher variance of error terms), the cognitive effort is expressed as a flexible heteroscedastic scale factor, which is a decreasing function of attribute variation and number of paired comparisons. The models are applied to two different data sets, and compared with a heteroscedastic multinomial logit model. Estimation results of the suggested flexible heteroscedastic random regret models show a significant improvement in predictive performance over their homoscedastic formulations. A similar but smaller improvement is obtained for multinomial logit models. These results imply that the conventional assumption of identically distributed error terms underlying random regret models may not sufficiently reflect the process of anticipating the amount of regret.
AB - Behaviorally, regret-based choice models implicitly assume that individuals anticipate the amount of attribute-level regret by comparing the attribute levels of a considered choice alternative against the attribute levels of the best or all other choice alternatives. Arguing that the amount of effort depends on attribute variation and number of paired comparisons, we suggest a way of incorporating the effects of these factors into two regret-based choice models. The cognitive effort involved in anticipating the amount of regret in paired comparisons of choice alternatives is incorporated into the scale of the regret function of each alternative. Because more cognitive effort causes higher randomness in the assessment of the amount of regret (i.e. higher variance of error terms), the cognitive effort is expressed as a flexible heteroscedastic scale factor, which is a decreasing function of attribute variation and number of paired comparisons. The models are applied to two different data sets, and compared with a heteroscedastic multinomial logit model. Estimation results of the suggested flexible heteroscedastic random regret models show a significant improvement in predictive performance over their homoscedastic formulations. A similar but smaller improvement is obtained for multinomial logit models. These results imply that the conventional assumption of identically distributed error terms underlying random regret models may not sufficiently reflect the process of anticipating the amount of regret.
KW - attribute variation
KW - cognitive effort
KW - number of attribute-level regrets
KW - paired comparison
KW - Regret-based choice models
UR - http://www.scopus.com/inward/record.url?scp=85027363725&partnerID=8YFLogxK
U2 - 10.1177/0265813516688687
DO - 10.1177/0265813516688687
M3 - Article
AN - SCOPUS:85027363725
VL - 45
SP - 842
EP - 863
JO - Environment and Planning B: Urban Analytics and City Science
JF - Environment and Planning B: Urban Analytics and City Science
SN - 2399-8083
IS - 5
ER -