The wellbeing implications of maximizing: A conceptual framework and meta‐analysis
Bibliographic record
Abstract
Abstract Decision‐making literature establishes that maximizers, who always strive for the best option, paradoxically experience lower wellbeing. The current study aims to discover the conditions that attenuate or exacerbate the detrimental effect of maximization on wellbeing by using a large‐scale meta‐analysis based on 683 effect sizes from 108 papers, spanning 47,245 unique respondents. We develop a conceptual framework for the literature and classify potential moderators of the maximization‐wellbeing relationship along two dimensions: (i) whether they enable the decision‐maker to focus on the choice process or the choice outcome, and (ii) the extent to which they contribute to choice complexity, expecting that process (vs. outcome) focus and less complex choices can assuage maximizers’ wellbeing deficit. Our meta‐analysis supports our expectations for all the choice focus moderators, but not for all the choice complexity moderators. Alongside theoretical and practical implications, we offer a framework to guide future research that should uncover when choice complexity moderators most accurately explain the wellbeing of maximizers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".