Opening-Up the Objective Function: Choice Behavior and Economic and Non-Economic Variables – Core and Marginal Altruism
Bibliographic record
Abstract
A revised model of the preference function is presented incorporating utility maximizing acts of material self-sacrifice. This model incorporates neoclassical and behavioral arguments, allowing for the stylized fact that economic agents are motivated by both material and non-material incentives. Given such a preference function, choice behavior is modeled as a function of relative opportunity costs (price) and real income. Preferences are determined by a variety of variables inclusive of social capital and education. There is therefore a core preference based upon non-economic variables and a 'marginal' component which is a function of conventional economic variables. The relative importance of these two components in determinating choice behavior is an empirical question. Building upon conventional tools, a demand curve for moral acts is derived and underlying income and substitution effects discussed. Empirical evidence from the tipping literature is used to illustrate the model.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".