Bibliographic record
Abstract
This research is an exploration of the phenomenon of "intuition" in the context of artificial intelligence (AI). In this work, intuition was considered as the human capacity to make decisions under situations in which the available knowledge was usually low in quality: inconsistent and of varying levels of certainty. The objectives of this study were to characterize some of the aspects of human intuitive thought and to model these aspects in a computational approach. This project entailed the development of a conceptual framework and a conceptual model, and, based on these, a computer system with three general parts: (1) a rule induction module for establishing the knowledge base for the reasoner; (2) the intuitive reasoner that was essentially a rule-based inference engine; (3) two learning approaches that could update the knowledge base over time for the reasoner to make better predictions. A reference reasoner based on established data analysis methods was also constructed, as a bench-mark for evaluating the intuitive reasoner. The input and the rules drawn by the reasoner were allowed to be fuzzy, multi-valued, and of varying levels of certainty. A measure of the certainty level, Strength of Belief, was attached to each input as well as each rule. Rules for the intuitive reasoner were induced from only about 10% of the data available for the reference reasoner. Solutions were formulated through iterations of consolidating intermediate reasoning results, during which the Strength of Belief of corroborating intermediate results was combined. The intuitive and the reference reasoners were tested to predict the value (class) of 12 target variables chosen by the author, of which six were continuous variables and the other six were discrete variables. The intuitive reasoner developed in this study matched the performance of the reference reasoner for three of six continuous target variables and achieved at least 70% of the accuracy of the reference reasoner for all six discrete target variables. The results showed that the intuitive reasoner was able to induce rules from a sparse database and use those rules to make accurate predictions. This suggested that by consolidating numerous outputs from low-certainty rules, an "intuitive" reasoner can effectively perform prediction, or other computational tasks, on the basis of incomplete information of varying quality.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".