Scarce Means with Alternative Uses: Robbins’ Definition of Economics and Its Extension to the Behavioral and Neurobiological Study of Animal Decision Making
Bibliographic record
Abstract
Almost 80 years ago, Lionel Robbins proposed a highly influential definition of the subject matter of economics: the allocation of scarce means that have alternative ends. Robbins confined his definition to human behavior, and he strove to separate economics from the natural sciences in general and from psychology in particular. Nonetheless, I extend his definition to the behavior of non-human animals, rooting my account in psychological processes and their neural underpinnings. Some historical developments are reviewed that render such a view more plausible today than would have been the case in Robbins' time. To illustrate a neuroeconomic perspective on decision making in non-human animals, I discuss research on the rewarding effect of electrical brain stimulation. Central to this discussion is an empirically based, functional/computational model of how the subjective intensity of the electrical reward is computed and combined with subjective costs so as to determine the allocation of time to the pursuit of reward. Some successes achieved by applying the model are discussed, along with limitations, and evidence is presented regarding the roles played by several different neural populations in processes posited by the model. I present a rationale for marshaling convergent experimental methods to ground psychological and computational processes in the activity of identified neural populations, and I discuss the strengths, weaknesses, and complementarity of the individual approaches. I then sketch some recent developments that hold great promise for advancing our understanding of structure-function relationships in neuroscience in general and in the neuroeconomic study of decision making in particular.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.029 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".