Identifying Proposers Behavioral Patterns in Human-AI Economic Interactions
Why this work is in the frame
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Bibliographic record
Abstract
Understanding how humans adapt their decisionmakingin economic interactions with artificial intelligence (AI)is essential for building socially attuned AI agents. In this study,we analysed human proposers’ behavior in the Ultimatum Game(UG) using interpretable behavioural features and supervisedmachine learning models to classify strategic proposer types (Fair,Selfish, Learner, Tit-for-Tat). Using data from human–human andhuman–AI interactions in a UG experiment, we uncover contextsensitivepatterns in proposer behaviour. The analyses revealedthat machine learning models—especially Random Forest (RF)and Neural Network (NN)—can reliably identify behavioral strategytypes with high accuracy across both interaction contexts.Classification was slightly more stable in the human condition,but the strongest models generalized well to AI interactions aswell. In contrast, simpler models such as Logistic Regression(LR) and Support Vector Machine (SVM) showed reducedperformance in the AI condition, indicating greater variabilityin human behavior when interacting with artificial agents.These findings suggest that while strategic behavior remainsrecognizable, collaboration with AI partners introduces greatervariability, potentially due to expectancy violations or ambiguousfairness norms. Outcomes in human–AI interactions appear todepend on whether the context is cooperative (e.g., fair or tit-fortatstrategies) or competitive (e.g., exploitative or self-maximisingbehavior). These insights can inform AI design, particularly whenit comes to developing systems that interact more effectively andadaptively with humans.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it