Internalizing cognitive bias: An experiential exercise for teaching and learning the anchoring effect
Bibliographic record
Abstract
The ability to make good decisions is key to personal and professional success for students. In this case study, we outline a set of in-class exercises that we have used for students in business, law, human resources and public policy to help them understand and internalize their own susceptibility to cognitive errors. Specifically, we illustrate an experiential way to teach and learn the anchoring effect: a cognitive bias that causes decision-makers to rely too heavily on initial information when making subsequent judgments. We describe an anchoring exercise that can be easily adapted across various settings, and show the effectiveness of the exercise in achieving the learning outcomes based on aggregated classroom data and our own experiences of student reactions. We show the robustness of the exercise to adaptation and highlight challenges we encountered. We also discuss how the exercise can be used to encourage students to consider anchoring’s ethical implications, as well as strategies to safeguard against being anchored.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".