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Record W7132945037

Looking for bias in all the right places: Incentive-driven optimism and pessimism

2024· dissertation· W7132945037 on OpenAlexaff
Nathan Eldred Wheeler

Bibliographic record

VenueTSpace · 2024
Typedissertation
Language
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPessimismPreferenceIncentiveOptimismModerationNegative informationIndependence (probability theory)Empirical evidence
DOInot available

Abstract

fetched live from OpenAlex

What motivates people to choose biased evidence over more accurate sources? Psychological models of motivated information search have typically explained this by appealing to specific directional motives such as the desire to preserve self-esteem (Beauregard & Dunning, 1998) or signal ingroup membership (Flynn et al., 2017). Although this proliferation of directional accounts helps highlight specific incentives (e.g., self-enhancement, social pressure) that may motivate individuals to prefer biased information, what’s missing is a more general understanding of how any large possible incentive might lead people to prefer biased information in the first place. My thesis offers a parsimonious model of how the mere possibility of any large reward or loss might lead people to prefer biased information through three general processes– a “rational” account in which people prefer biased information based on the increased reward or mitigated losses that can be gained from acting on that information; a trust-based account in which people prefer biased information because trusting it leads to better consequences, and a congeniality bias account in which people prefer biased information because it justifies their belief in how they should act. In chapter 2, I formalize each of these processes and highlight how each predicts that the possibility of large incentives should moderate preference for biased information, such that the possibility of large rewards for making correct decisions would increase preference for optimistic information, while the possibility of large losses for making incorrect decisions would increase preference for pessimistic information. In chapter 3, I provide empirical demonstration of this moderation in three studies. Finally, in chapter 4, I show in a final study how all three of these processes can significantly account for individual differences in biased information search. Together, this work demonstrates that the mere presence of large incentives can lead to preference for biased information through three general motivational processes. Thus, even in lieu of more specific directional accounts of an individual’s motivations, biased information search can be understood through knowledge of the consequences an individual stands to face by acting on that information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0040.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.172
GPT teacher head0.464
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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