Confirmation Bias in Decision-making: Implications in Finance, Business, and Digital Media
Bibliographic record
Abstract
Confirmation bias is the tendency to look for supporting evidence for an established belief. Many applications of confirmation bias occur in people's lives unconsciously, resulting in poor decision-making as they become unable to evaluate the situation objectively. It can also perpetuate false beliefs, increase group polarization, and make people more vulnerable to manipulation. Understanding and mitigating this bias is crucial for improving rational decision-making across various fields. This study examines three applications of confirmation bias in the real world by extrapolating from case studies and experiments. In finance, investors and traders are influenced by confirmation bias to make suboptimal investments and trades. In marketing, consumers are influenced by advertisements and brand loyalty to have a product fit their expectations. In digital media, confirmation bias reinforces echo chambers, promoting group polarization. Finally, the study provides policy recommendations to reduce the effect of confirmation bias, such as improving transparency in marketing and promoting partisan dialogue. It is not enough that society is aware of the problems confirmation bias poses, people should act to reduce the impact of the bias in the economy and online.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".