Bayesian Calibration for Logit Model Microsimulations: Case for PECAS SD in San Diego
Bibliographic record
Abstract
Bayesian inference is a versatile method for incorporating new information into a model while still respecting existing knowledge. One application of Bayesian inference is the calibration of models that are controlled by a large number of parameters, but where the data usable for calibration is incomplete or unreliable. Microsimulation models of urban development fit both of these criteria, but calibrating them is further complicated by their non-determinism. I investigated a calibration method called Bayesian Expected Value Calibration, which is designed to overcome non-determinism while incorporating existing knowledge, in the context of the PECAS Space Development model of the San Diego area. The test consisted of creating synthetic data using known behavioural parameters, calibrating the Space Development model to targets derived from the synthetic data and with priors reflecting imperfect existing knowledge, and assessing how closely the calibrated parameters matched the true values. I found that BEVC was generally effective at converging towards the true values of the parameters, and often received meaningful contributions from both the prior knowledge and the new observations under a range of plausible conditions. As would be expected from Bayesian theory, increasing the number of observations or the amount of useful prior knowledge improved the accuracy of the calibration. The method was robust under reasonable levels of human fallibility in creating the priors, and only suffered from significant loss of accuracy under extreme assumptions. However, more sophisticated methods of objectively determining the weights to assign to the data sources did not significantly improve calibration accuracy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".