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SUMMARY:Detecting and Modelling the Decoy Effect in Transportation
DTSTART:20151009T121500
DTEND:20151009T131500
DTSTAMP:20260509T225647Z
UID:78a0088bae760ca82919d30be0f731fe6cb4c23b2b7bb576435e2961
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Angelo Guevara\, Universidad de los Andes\, Santiago\, C
 hile\nEmpirical evidence suggests that\, under some circumstances\, the in
 troduction of a new option in a choice-set can increase the choice probabi
 lity of other alternatives. This result\, known as the decoy effect\, defi
 es the basic regularity assumption\, which is at the root of standard mode
 ls of choice that are based on a compensatory approach under the Random Ut
 ility Maximization (RUM) framework. The goal of this research was threefol
 d. First\, we worked toward the development of a practical probabilistic c
 hoice-model that could account for the decoy effect\, building upon variou
 s types of choice behaviors that that been described in cognitive psycholo
 gy. Then\, we used the proposed choice model to study\, with Monte-Carlo s
 imulation\, the power of different statistical tests for detecting the pre
 sence of this phenomenon. Finally\, we designed and applied a Stated Prefe
 rences (SP) survey to detect and to characterize the decoy effect in route
  choice. Results of this research showed first that all the decoy effect t
 ypes that have been described in the literature\, can be replicated by the
  Random Regret Minimization (RRM) discrete-choice model. Regarding statist
 ical testing for the presence of the decoy effect\, we found that McNemar 
 and Proportions tests showed larger power when the effect size was modeled
  as RRM. Finally\, four conclusions were driven from the application of th
 e SP survey. The first was that the decoy effect was present in route choi
 ce\, but that it was hard to detect it in the context of commuting trips o
 r when alternatives were far from the true trade-off line. The second resu
 lt of the SP experiment was that the magnitude of the average sample effec
 t obtained from it was coherent with a data generation process based on th
 e RRM model. Third\, the SP survey showed that the larger decoys found wer
 e of the compromise type\, and that the more robust ones were those of the
  range type. Finally\, the SP survey indicated that\, although an emergent
 -values Logit model showed slightly better fit\, the RRM had substantially
  superior performance in outer-sample forecasting. This final result sugge
 sts that the RRM does capture\, to some extent\, the underlying behavior t
 hat is causing the decoy effect\, but that this choice-model may still be 
 somehow incomplete for this purpose. Four future steps of this line of res
 earch can be identified. The first is to improve the RRM model. The second
  step corresponds to the design and application of a Revealed Preference (
 RP) experiment to detect the decoy effect in real transportation behavior.
  The next\, is to deepen the analysis of the circumstances under which the
  decoy effect occurs. The final step corresponds to the study of possible 
 transportation public policies that can benefit from the decoy effect\, su
 ch as seated-only buses to favor the use of public transportation or diffe
 rent pricing strategies.\nBio : C. Angelo Guevara is associate professor a
 t Universidad de los Andes in Chile\; research affiliate of the Intelligen
 t Transportation Systems (ITS) laboratory at the Massachusetts Institute o
 f Technology (MIT)\; and external affiliate of the Choice Modelling Centre
  (CMC) at the University of Leeds. He holds an MSc in transportation from 
 Universidad de Chile\, as well as an MSc and a PhD in the same area from M
 IT. He has been awarded the Fulbright and the Martin-Family fellowships\, 
 as well as the honorable mention of IATBR's Eric Pas dissertation prize. H
 is main research interest is in the modeling of choice behavior\, with rec
 ent contributions on endogeneity\, sampling of alternatives\, behavioral e
 conomics.
LOCATION:GC C3 30 http://plan.epfl.ch/?lang=fr&room=GCC330
STATUS:CONFIRMED
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