Nudging Can Be Sophisticated With Evolutionary Insights: From Plastic Recycling to Energy Conservation [Conference Presentation Abstract]
Bibliographic record
Abstract
Nudging is a term coined in the context of Behavioral Economics to softly motivate people to make better choices without forbidding any options.Although this concept emphasizes people's intuitive decisionmaking relevant to human evolution, there has been no consistent theory for designing nudges, and the applications have been based on trial and error.Evolutionary insights may provide a meta-theory that can help to design interventions more efficiently.To specify this new concept of what we call "Evolutionary Nudging," we developed a messaging method to promote the acceptance of technologies that might potentially be perceived to have risks, using insights obtained from simulation models of altruistic evolution.The messages highlighted the indirect kin support of older generations incurred in establishing these technologies for environmental sustainability.Significant intervention effects were identified in multiple countries (i.e., Japan, Canada, and the US) for topics such as plastic recycling and offshore wind power, suggesting the universality of these new nudging messages.We are currently planning to expand these applications from attitude levels to the promotion of actual energy conservation behaviors, with preliminary results also being discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".