NDLI: Decisions from experience: How models of aggregate choices explain individual choices?

Content Provider	IEEE Xplore Digital Library
Author	Sharma, N. Dutt, V.
Copyright Year	2014
Description	Author affiliation: Sch. of Comput. & Electr. Eng., Indian Inst. of Technol., Mandi, Mandi, India (Sharma, N.; Dutt, V.)
Abstract	Decisions from Experience (DFE) involve situations where decision makers sample information before making a final choice. Trying clothes before choosing a garment and enquiring about jobs before opting for one are some examples involving such situations. In DFE research, conventionally, the final choice that is made after sampling information is aggregated over all participants and problems in a given dataset. However, this aggregation does not explain the individual choices made by participants. In this paper, we test the ability of computational models of aggregate choice to explain choices at the individual level. Top three DFE models of aggregate choices are evaluated on how these models account for individual choices. A Primed-Sampler (PS) model, a Natural-Mean Heuristic (NMH) model, and an Instance-Based Learning (IBL) model are calibrated to explain individual choices in the Technion Prediction Tournament (the largest publically available DFE dataset). Our results reveal that all the three DFE models of aggregate choices perform well to explain individual choices. Although the PS and NMH models perform slightly better than the IBL model; the IBL model is able to account for all individuals in the dataset compared to the PS and NMH models. We conclude by drawing implications for computational cognitive models in explaining individual choices in DFE research.
Sponsorship	IEEE India Counc. C Chapt.
Starting Page	1221
Ending Page	1226
File Size	182243
Page Count	6
File Format	PDF
e-ISBN	9781479925728
DOI	10.1109/IAdCC.2014.6779501
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-21
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	computational models Computational modeling Conferences Noise sampling Predictive models individual choice experience error ratio Aggregate choice Aggregates Sociology Data models
Content Type	Text
Resource Type	Article

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