Politicians, the Representativeness Heuristic and Decision-Making Biases
Bibliographic record
Abstract
Abstract Do politicians use the representativeness heuristic when making judgements, that is, when they appraise the likelihood or frequency of an outcome that is unknown or unknowable? Heuristics are cognitive shortcuts that facilitate judgements and decision making. Oftentimes, heuristics are useful, but they may also lead to systematic biases that can be detrimental for decision making in a representative democracy. Thus far, we lack experimental evidence on whether politicians use the representativeness heuristic. To contribute to and extend the existing literature, we develop and conduct a survey experiment with as main participants Dutch elected local politicians from the larger municipalities (n = 211). This survey experiment examines whether politician participants display two decision-making biases related to the representativeness heuristic: the conjunction error and scope neglect. We also run the experiment with a student sample (n = 260), mainly to validate the experimental design. Our findings show that politician participants neglect scope in one scenario and that they display the conjunction error in two of three scenarios. These results suggest that politician participants use the representativeness heuristic. Conversely, our third conjunction error scenario does not find evidence for politician participants displaying this bias. As we discuss in the article, the latter may be an artifact of our experimental design. Overall, our findings contribute fundamentally to our understanding of how politicians process information and how this influences their judgements and decision making.
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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 | 0.022 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".