The ends of empathy: Constructing empathy from value-based choice
Bibliographic record
Abstract
Empathy, or the ability to understand and resonate with the experiences of others, has long been considered by philosophers and scientists to be an important part of human morality. We present a new framework that explains empathy as resulting from motivated decisions. Drawing on models of cybernetic control, value-based choice, and constructionism, we suggest that empathy shifts depending on how people value and prioritize conflicting goals. We generate novel predictions about the nature of empathy from the science of goal pursuit, and address its apparent limitations. Empathy appears less sensitive to suffering of large numbers and out-groups, leading some to suggest that empathy is an unreliable ethical guide. Whereas these arguments assume that empathy is a limited-capacity resource, we suggest that apparent limits of empathy reflect byproducts of domain-general goal pursuit. Arguments against empathy reflect a misguided essentialism: they mistake our own choices to avoid empathy for intrinsic features of empathy, treating empathy as a capricious emotion in conflict with reason. We suggest that empathy results from a rational decision, even if its rationality is bounded, as in many decisions in everyday life. Empathy may only be limited if we choose to avoid pursuing empathic goals.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".