Constrained Dynamic Optimization and Efficient Decentralized Decision Making in Nature: A Brief Overview
Bibliographic record
Abstract
This chapter suggests that the study of economics as being practised in the economics profession today is needlessly human centered. The direct interactions between individual people and nature (hereafter human–nature interactions) have attracted growing interest. There is evidence that all living things, including humans, are motivated by economic considerations. Their actions are motivated by the limited dynamic optimisation goal, which is that they act logically. Decentralised decision-making is also used by vast groups of organisms to achieve efficiency, such as ant and bee colonies and forest trees. The evidence supporting this claim stems from a variety of observations on the behaviours of numerous plants and animals as well as how their genomes are structured and work. Recent research suggests that the origin of life itself had the underlying motive that was economic in nature, i.e., that life was not a chance occurrence but an inevitable outcome of energy-dissipation-driven organization of the matters behaving so as to maximize the economic efficiency along the evolutionary path of increasing entropy production. It is striking that the motive of economic efficiency underlies the behaviours of all successful physical systems, both organic and inorganic, and that all successful systems, organic or inorganic, can be said to behave rationally, successful in the sense that they have been successful in surviving the rigorous natural selection process. Further, observations on a wide range of natural phenomena, including straight-line path of sunlight, symmetry of snowflakes and crystals, lead us to believe that it is not just living organisms that behave rationally but inorganic matters as well - rationally in the sense that they behave with the objective of constrained dynamic optimization that produces efficient outcome.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".