Limited resources or limited luck? Why people perceive an illusory negative correlation between the outcomes of choice options despite unequivocal evidence for independence
Bibliographic record
Abstract
Abstract When people learn of the outcome of an option they did not choose (the alternative outcome) before they know their own outcome, they see an illusory negative correlation between the two outcomes, the Alternative Omen Effect (ALOE). Why does this happen? Here, we tested several alternative explanations and conclude that the ALOE may derive from a pervasive belief that good luck is a limited resource. In Experiment 1, we show that the ALOE is due to people seeing a good alternative outcome as a bad sign regarding their outcome, relative to seeing a neutral alternative, but find no evidence for seeing a bad alternative outcome as a good sign. Experiment 2 confirms that the ALOE replicates across tasks, and that the ALOE cannot be explained by preconceptions regarding outcome distribution, including: 1) the Limited Good Hypothesis (zero-sum bias), according to which people see the world as a zero-sum game, and assume that resources there means fewer resources here, and/or 2) a more specific assumption that laboratory tasks are programmed as zero-sum games. To neutralize these potential beliefs, participants had to draw actual colored beads from two real, distinct bags. The results of Experiment 3 were consistent with a prediction of the Limited Luck Hypothesis: by eliminating the value of the outcomes we eliminated the ALOE. Taken together, our results show that either the limited good belief is so robust that it defies strong situational evidence, or that individuals perceive good luck itself as a limited resource. Such a limited-luck belief might have important consequences in decision making and negotiations.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".