Botcrafting and Botbreeding : A Simplified Economics of Evolutionary Algorithms
Bibliographic record
Abstract
Let us consider the industrial and economic impact of genetic programming and its generic availability to the masses. Is the interaction between genetic programming and the so-called “cognitariat” the beginning of a “genetic revolution” in the industrial sense ? Can we theorise a genetic marketplace, based on the dynamic of bids and asks for genetic algorithms to be made-to-order and leverage any daily routine, thus trading value ? Can genetic algorithms be crossbred to achieve a phenomenon similar to animal domestication, with a transition from wild to domestic type and heterosis, and can such a pressure for domestication percolate to macroeconomic value ? Which economic marketplace would allow such an economic phenomenon to happen and percolate least impeded ? Here I consider the economic implication of evolving genetic algorithms to address any repetitive human task, within a grand genetic marketplace composed of bidders and askers that would be using, evolving and breeding genetic programs (“bots”) and manipulating them as “bot breeders” to leverage their own economic output. I define a cryptocurrency on the buyer’s side, conceived to represent a unit of genetic purchasing power, which I call a “botcoin”.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".