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Boosting Through Structured Introspection : Exploring Decision-Making in Relation to the COVID-19 Pandemic

This thesis explores boosting to improve decision-making in the context of the COVID-19 pandemic using a structured introspection. Structured introspection is an intervention where individuals are prompted with and are asked to estimate the importance of a set of attributes relevant to the decision in order to limit the prevalence of potential cognitive biases. To test the intervention, 281 participants divided into an intervention and control group answered an online survey with a dilemma about COVID-19. The dilemma was whether Sweden should shut down the economy or keep it open during the COVID-19 pandemic. The intervention group was asked to rate how important the attributes “saving lives”, “saving the economy”, “concern for the health of the elderly and risk groups”, and “concern for the quality of life and well-being of all citizens” should be for their decision. The control group was only prompted with the question and asked to think carefully. All participants were asked a set of control variables such as risk perception for self and others and emotions when thinking about COVID-19. The results did not show a significant influence on choice on decisions based on the intervention. They did however show a significant correlation with choice on risk perception as well as a correlation between choice on the dependent variable and the attributes in the intervention group.             The conclusion of the thesis is that structured introspection may not be suitable on a contemporary issue affecting participants directly, as they may already have strong opinions about the issue. Further and broader research needs to be conducted to determine in which circumstances this boost can be effective.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-166404
Date January 2020
CreatorsCampbell, Christoffer
PublisherLinköpings universitet, Institutionen för datavetenskap
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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