The Cognitive Problem of the Behavioral Decision Theory Through Game Theory: Biases and Heuristics
Bibliographic record
Abstract
This paper examines an alternative approach to the analysis of decision-making under the lens of Game Theory. To do so, this text seeks to demonstrate through the inductive method that this bias is capable of providing the necessary instruments for a better understanding of judicial decisions, distancing itself from the usual once the approach is modified. It deals with the problem of decision theory regarding the lack of explanation about how decisions are produced in order to stimulate the dialogue and the application of multidisciplinary content, reflecting the theoretical-practical concern in indicating the correct form of procedural rationality. Faced with literature review, this paper highlights the evidence that the introduction of game theory to the process is able to improve the procedural reading in an uncertain environment. In this context, the understanding of heuristics and biases through game theory makes it possible to realistically establish the structure of human interactions mediated by the process. The relevance of this issue is the repercussion of judicial decisions on social and procedural relations as a whole.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".