Tabula rasa agents display emergent in-group behavior
Bibliographic record
Abstract
Theories on group-bias often posit an internal preparedness to bias one's cognition to favor the in-group (often envisioned as a product of evolution). In contrast, other theories suggest that group-biases can emerge from nonspecialized cognitive processes. These perspectives have historically been difficult to disambiguate given that observed behavior can often be attributed to innate processes, even when groups are experimentally assigned. Here, we use modern techniques from the field of AI that allow us to ask what group biases can be expected from a learning agent that is a pure blank slate without any intrinsic social biases, and whose lifetime of experiences can be tightly controlled. This is possible because deep reinforcement-learning agents learn to convert raw sensory input (i.e. pixels) to reward-driven action, a unique feature among cognitive models. We find that blank slate agents do develop group biases based on arbitrary group differences (i.e. color). We show that the bias develops as a result of familiarity of experience and depends on the visual patterns becoming associated with reward through interaction. The bias artificial agents display is not a static reflection of the bias in their stream of experiences. In this minimal environment, the bias can be overcome given enough positive experiences, although unlearning the bias takes longer than acquiring it. Further, we show how this style of tabula rasa group behavior model can be used to test fine-grained predictions of psychological theories.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".