A Modification of Multiple Discrete‐Continuous (MDC) Choice Model to Consider Nonmonotonic Preference in Episode‐Level Time‐Use Behaviors
Bibliographic record
Abstract
The multiple discrete‐continuous extreme value model with ordered preferences (MDCEV‐OP) has broad prospects in activity‐based modeling (ABM) since it can model episode‐level time‐use decisions and ensure a logical prediction across different episodes of an activity. However, the current MDCEV‐OP framework assumes a monotonically increasing utility function for each episode alternative, which fails to accommodate potential nonmonotonic preference in episode‐level time consumption. In this paper, we modify the traditional MDCEV‐OP model by adding a baseline marginal utility parameter, making the model more flexible to reflect the potential nonmonotonic preference in episode‐level time‐use behaviors, as well as ensuring the logically consistent prediction as in the traditional model. To our knowledge, it is the first time to develop an episode‐level MDCEV model that considers nonmonotonic preference. The new MDCEV‐OP model was applied to analyze the episode‐level time‐use pattern of noncommuters in Shanghai, China. The empirical results show that the new model provides plausible explanations for nonmonotonic preference in episode‐level time‐use behaviors and outperforms the traditional model both in data fitting and forecasting performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".