A Decision-By-Sampling Account of Decision Under Risk
Lead Research Organisation:
University of Warwick
Department Name: Psychology
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
People |
ORCID iD |
| Neil Stewart (Principal Investigator) |
Publications
Gherzi S
(2014)
The meerkat effect: Personality and market returns affect investors' portfolio monitoring behaviour
in Journal of Economic Behavior & Organization
Reimers S
(2009)
Using SMS text messaging for teaching and data collection in the behavioral sciences.
in Behavior research methods
Reimers S
(2008)
Using Adobe Flash Lite on mobile phones for psychological research: Reaction time measurement reliability and interdevice variability.
in Behavior research methods
Stewart N
(2015)
On the Origin of Utility, Weighting, and Discounting Functions: How They Get Their Shapes and How to Change Their Shapes
in Management Science
Stewart N
(2009)
The cost of anchoring on credit-card minimum repayments.
in Psychological science
Stewart N
(2009)
Relative judgment and knowledge of the category structure.
in Psychonomic bulletin & review
Stewart N
(2009)
Decision by sampling: the role of the decision environment in risky choice.
in Quarterly journal of experimental psychology (2006)
Ungemach C
(2011)
How incidental values from the environment affect decisions about money, risk, and delay.
in Psychological science
Ungemach C
(2009)
Are probabilities overweighted or underweighted when rare outcomes are experienced (rarely)?
in Psychological science
Vlaev I
(2011)
Does the brain calculate value?
in Trends in cognitive sciences
| Description | This research is about how we make risky decisions. In expected utility theory?the normative economic model of how decisions should be made?each outcome is assigned a utility and people choose the option with the highest utility averaged over its possible outcomes. An influential approach to modelling risky decision making is to adapt the expected utility model to incorporate new psychological ideas. In contrast, we ask how people might make decisions given the set of cognitive tools available to them. The resulting model?decision by sampling?assumes that people reach decisions by comparing the risks and outcomes they face with other risks and outcomes that they have experienced before. The option which compares to their past experience most favourably is chosen. A strong prediction from this model is that if the risks and outcomes that people experience are manipulated, then future decisions will be systematically different. The primary part of this project tested and confirmed this hypothesis. Even trivial everyday activities, such as experiencing supermarket prices or thinking about the likelihood of rain, are enough to reverse choices between simple lotteries. Models derived from the expected utility framework (which assume people have stable preferences) cannot predict these findings. The second part of this project was a model comparison tournament, comparing the predictions of a set of models (including expected utility and decision by sampling) across benchmark sets of risky choices. We have expanded the tournament beyond our original plans, and are pleased to report that the competition is near completion. |
| Exploitation Route | This work will contribute to a shift away from the expected utility framework, which has proved very influential in the past 30 years. We are now at the limit of the choice data, as most contending models of risky decision making make similar predictions for all but the most cunningly constructed choices. Further, many of these models are "as-if" models, and deliberately make no claims about cognitive processing. Building models like decision by sampling which make predictions about cognitive processing allows a connection with data from psychology and neuroeconomics, where reaction times, eye movements, event-related potentials, and fMRI BOLD responses are all being collected. We have also been using an insight which comes from the decision-by-sampling model in public policy interventions by providing information in the form of a rank-based social nudge. Decision-by-sampling predicts that the rank of an attribute value within the sample is, effectively, the subjective value of that attribute. Boyce, Brown, and Moore (2010) have shown how rank of income, not income, predicts life satisfaction. Rank information can generate significant behavioural change. Brown has found that telling people about their rank position in terms of alcohol consumption is effective at getting them to reduce consumption. |
| Sectors | Financial Services, and Management Consultancy |
| URL | http://www.researchcatalogue.esrc.ac.uk/grants/RES-062-23-0952/read |