Bhatia, Sudeep
(2017).
Decision making in environments with non-independent dimensions, experimental data.
[Data Collection]. Colchester, Essex:
UK Data Archive.
10.5255/UKDA-SN-852830
This network project brings together economists, psychologists, computer and complexity scientists from three leading centres for behavioural social science at Nottingham, Warwick and UEA. This group will lead a research programme with two broad objectives: to develop and test cross-disciplinary models of human behaviour and behaviour change; to draw out their implications for the formulation and evaluation of public policy.
Foundational research will focus on three inter-related themes: understanding individual behaviour and behaviour change; understanding social and interactive behaviour; rethinking the foundations of policy analysis.
The project will explore implications of the basic science for policy via a series of applied projects connecting naturally with the three themes. These will include: the determinants of consumer credit behaviour; the formation of social values; strategies for evaluation of policies affecting health and safety.
The research will integrate theoretical perspectives from multiple disciplines and utilise a wide range of complementary methodologies including: theoretical modeling of individuals, groups and complex systems; conceptual analysis; lab and field experiments; analysis of large data sets.
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Data description (abstract)
This paper tests whether the dimensions involved in preferential choice tasks are evaluated independently from one another. Common decision heuristics satisfy dimensional independence, and multi-strategy models that assume that decision makers use a repertoire of these heuristics predict that they are unable to represent and respond to dimensional dependencies in the decision environment. In contrast, some single-strategy models are able to violate dimensional independence, and subsequently adapt to environments that feature interacting dimensions. Across five experiments, this paper documents systematic violations of the assumption of dimensional independence. This suggests that decision makers are able to modify their behavior to respond to dimensional dependencies in their environment, and in turn those models that are unable to do this do not provide a full account of human strategy selection and behavior change. This paper ends with a discussion of ways in which some existing models can be modified to incorporate violations of dimensional independence.
Data creators: |
Creator Name |
Affiliation |
ORCID (as URL) |
Bhatia Sudeep |
University of Pennsylvania |
|
|
Sponsors: |
Economic and Social Research Council
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Grant reference: |
ES/K002201/1
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Topic classification: |
Economics Psychology
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Keywords: |
multi-attribute choice, decision making, independence, common-consequence effect
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Project title: |
Network for Integrated Behavioural Science
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Grant holders: |
Chris Starmer, Nick Chater, Daniel John Zizzo, Gordon Brown, Anders Poulsen, Martin Sefton, Neil Stewart, Uwe Aickelin, Robert Sugden, John Gathergood, Simon Gaechter, Abigail Barr, Shaun Hargreaves-Heap, Enrique Fatas, Robert MacKay, Daniel Read, Graham Loomes, Theodore Turocy, Robin Cubitt
|
Project dates: |
From | To |
---|
31 December 2012 | 30 September 2017 |
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Date published: |
07 Dec 2017 14:11
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Last modified: |
07 Dec 2017 14:12
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Collection period: |
Date from: | Date to: |
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31 December 2012 | 30 September 2017 |
|
Country: |
United Kingdom |
Data collection method: |
Experimental data. In this paper, we test for violations of independence in choices between bundles composed of different objects (Studies 1 and 2), and real and artificial objects composed of different attributes (Studies 3–5). If the dimensional values of these alternatives do not alter how other dimensions are processed, then changing values on a dimension that is common across all alternatives should not affect choice. In Studies 1, 2, and 3, we use this insight to design binary choice problems in which two bundles contain the same amount of some object, or two objects contain the same amount of some attribute. We vary this common object or attribute across choice problems and find that this affects choice proportions, violating dimensional independence. In Studies 4 and 5, we test for violations of independence with artificial choice alternatives, for which non-independent attribute–reward relationships are learnt through experience. |
Observation unit: |
Individual |
Kind of data: |
Numeric |
Type of data: |
Experimental data
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Resource language: |
English |
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Rights owners: |
Name |
Affiliation |
ORCID (as URL) |
Bhatia Sudeep |
University of Pennsylvania |
|
|
Contact: |
Name | Email | Affiliation | ORCID (as URL) |
---|
Bhatia, Sudeep | bhatiasu@sas.upenn.edu | University of Pennsylvania | Unspecified |
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Notes on access: |
The Data Collection is available to any user without the requirement for registration for download/access.
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Publisher: |
UK Data Archive
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Last modified: |
07 Dec 2017 14:12
|
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