A sheep in wolf’s clothing: levels of deceit and detection in the evolution of cue-mimicry
Bibliographic record
Abstract
In an evolutionary context, trusted signals or cues provide individuals with the opportunity to manipulate them to their advantage by deceiving others. The deceived can then respond to the deception by either ignoring the signals or cues or evolving means of deception-detection. If the latter happens, it can result in an arms race between deception and detection. Here, we formally analyse these possibilities in the context of cue-mimicry in prey-predator interactions. We demonstrate that two extrinsic parameters control whether and for how long an arms race continues: the benefits of deception, and the cost of ignoring signals and cues and having an indiscriminate response. As long as the cost of new forms of deception is less than its benefits and the cost of new forms of detection is less than the cost of an indiscriminate response, an arms race results in the perpetual evolution of better forms of detection and deception. When novel forms of deception or detection become too costly to evolve, the population settles on a polymorphic equilibrium involving multiple strategies of deception and honesty, and multiple strategies of detection and trust.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".