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Decision Strategies: Something Old, Something New, and Something Borrowed
Stockholm University, Faculty of Social Sciences, Department of Psychology.
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis, some old decision strategies are investigated and a new one that furthers our understanding of how decisions are made is introduced. Three studies are presented. In Study I and II, strategies are investigated in terms of inferences and in Study III, strategies are investigated in terms of preferences. Inferences refer to decisions regarding facts, e.g., whether a patient has a heart disease or not. Preferences refer to decision makers’ personal preferences between different choice alternatives, e.g., which flat out of many to choose. In all three studies, both non-compensatory strategies and compensatory strategies were investigated. In compensatory strategies, a high value in one attribute cannot compensate for a low value in another, while in non-compensatory strategies such compensation is possible. Results from Study I showed that both compensatory (logistic regression) and non-compensatory (fast and frugal) strategies make inferences equally well, but logistic regression strategies are more frugal (i.e., use fewer cues) than the fast and frugal strategies. Study II showed that the results were independent of the degree of expertise. The good inferential ability of both non-compensatory and compensatory strategies suggests there might be room for a strategy that can combine the strengths of the two. Study III introduces such a strategy, the Concordant-ranks (CR) strategy. Results from Study III showed that choices and attractiveness evaluations followed this new strategy. This strategy dictates a choice of an alternative with concordant ranks between attribute values and attribute weights when alternatives are about equally attractive. CR also serves as a proxy for finding the alternative with the shortest distance to an ideal. The CR strategy combines the computational simplicity of non-compensatory strategies with the superior information integration ability of compensatory strategies.

Place, publisher, year, edition, pages
Stockholm: Department of Psychology, Stockholms University , 2011. , 80 p.
Keyword [en]
Decision strategies, inference, preference, compensatory, non-compensatory
National Category
Psychology
Research subject
Psychology
Identifiers
URN: urn:nbn:se:su:diva-57174ISBN: 978-91-7447-294-3OAI: oai:DiVA.org:su-57174DiVA: diva2:414478
Public defence
2011-06-03, David Magnussonsalen (U31), hus 8, Frescati Hagväg 8, Stockholm, 10:00 (English)
Opponent
Supervisors
Note
At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Submitted. Paper 2: Submitted.Available from: 2011-05-12 Created: 2011-05-03 Last updated: 2011-05-09Bibliographically approved
List of papers
1. Judgment Analysis in the Medical Domain: Making a Fair Comparison Between Logistic Regression and Fast & Frugal Models
Open this publication in new window or tab >>Judgment Analysis in the Medical Domain: Making a Fair Comparison Between Logistic Regression and Fast & Frugal Models
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(English)Article in journal (Refereed) Submitted
Abstract [en]

Using participant data from the medical domain, the robustness of logistic regression (LR) with different cue inclusion levels and two fast and frugal (F&F) models in terms of predictive accuracy and frugality were tested. Two data sets based on judgments of verbally described patients were used: Heart failure (66 analysts), and Hyperlipidemia (38 analysts). In both data sets, when the models were cross-validated, there was a significant decrease in predictive accuracy for all models, especially when all cues were used in LR. The other models had about equal predictive accuracy, also when comparisons were made with actual diagnoses, with a slight advantage for LR in the Heart failure study. LR using the 5% inclusion level was more frugal than F&F. These results emphasize the importance of using cross-validation and of choosing the proper significance levels for cue inclusion and when comparing different judgment models.

Keyword
Logistic regression, fast and frugal, cross-validation, fit, prediction, frugality
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:su:diva-57071 (URN)
Available from: 2011-05-02 Created: 2011-05-02 Last updated: 2011-05-09Bibliographically approved
2. Do We Really Need Medical Experts when modelling in Judgment Analysis?: Lack of Difference Between Expert and Non-Expert models in Judgment Analysis
Open this publication in new window or tab >>Do We Really Need Medical Experts when modelling in Judgment Analysis?: Lack of Difference Between Expert and Non-Expert models in Judgment Analysis
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(English)Article in journal (Refereed) Submitted
Abstract [en]

It is assumed that in judgment analysis, experts provide better models than non-experts. In this study we challenge this view by showing that data from non-experts might be equally suitable for building models. We show this by modeling the decisions of 21 medical students, 27 general practitioners, and 22 cardiologists on real patient vignettes regarding diagnosing heart failure. The models used were logistic regression and fast and frugal models. Results showed that there were no differences between any of the expertise groups in terms of fit, prediction, information searched, or percent of actual diagnosis in any of the models. Therefore, it seems, at least for the studied conditions, using models built on decision data from non-experts versus experts might be equally valid in judgment analysis.

Keyword
Judgment analysis, Expertise, Decision making, Judgment
Identifiers
urn:nbn:se:su:diva-57072 (URN)
Available from: 2011-05-02 Created: 2011-05-02 Last updated: 2011-05-09Bibliographically approved
3. Coming close to the ideal alternative: The concordant-ranks strategy
Open this publication in new window or tab >>Coming close to the ideal alternative: The concordant-ranks strategy
2011 (English)In: Judgment and decision making, ISSN 1930-2975, Vol. 6, no 3, 196-210 p.Article in journal (Refereed) Published
Abstract [en]

We present the Concordant-Ranks (CR) strategy that decision makers use to quickly find an alternative that is proximate to an ideal alternative in a multi-attribute decision space. CR implies that decision makers prefer alternatives that exhibit concordant ranks between attribute values and attribute weights. We show that, in situations where the alternatives are equal in multi-attribute utility (MAU), minimization of the weighted Euclidean distance (WED) to an ideal alternative implies the choice of a CR alternative. In two experiments, participants chose among, as well as evaluated, alternatives that were constructed to be equal in MAU. In Experiment 1, four alternatives were designed in such a way that the choice of each alternative would be consistent with one particular choice strategy, one of which was the CR strategy. In Experiment 2, participants were presented with a CR alternative and a number of arbitrary alternatives. In both experiments, participants tended to choose the CR alternative. The CR alternative was on average evaluated as more attractive than other alternatives. In addition, measures of WED, between given alternatives and the ideal alternative, by and large agreed with the preference order for choices and attractiveness evaluations of the different types of alternatives. These findings indicate that both choices and attractiveness evaluations are guided by proximity of alternatives to an ideal alternative.

Keyword
multi-attribute decisions, concordant ranks, strategies, weighted Euclidian distance
National Category
Psychology (excluding Applied Psychology)
Research subject
Psychology
Identifiers
urn:nbn:se:su:diva-57070 (URN)000290025600002 ()
Available from: 2011-05-03 Created: 2011-05-02 Last updated: 2012-02-02Bibliographically approved

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