Small data-set EKF-based parameter estimation for a behavior-modification model
Bibliographic record
Abstract
In this work we investigate the use of Extended Kalman Filter (EKF) methods to identify parameters in a nonlinear model. The model, derived in earlier work, describes how a person behaves when he or she is offered a sequence of rewards to carry out a task for which his or her initial attitude is negative. A main conclusion of the paper is that EKF methods can be used to effectively estimate a single parameter, but due to observability problems, estimation of multiple parameters is ineffective. Monte Carlo simulations are used to thoroughly study the performance of the EKF-based estimator to estimate a parameter (related to cognitive dissonance that is experienced by the person making the decision) in cases where only 5, 10, or 20 data samples are available. In addition, preliminary experimental results, based on experiments with 10 data samples, support the validity of the underlying model and demonstrate feasibility of the EKF approach for estimation of the cognitive-dissonance parameter, despite the small size of the data set.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".