Incentive Design for Operations-Marketing Multitasking
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A firm hires an agent (e.g., a store manager) to undertake both operational and marketing tasks. Marketing tasks boost demand, but for demand to translate into sales, operational effort is required to maintain adequate inventory. The firm designs a compensation plan to induce the agent to put effort into both marketing and operations while facing demand censoring (i.e., demand in excess of available inventory is unobservable). We formulate this incentive-design problem in a principal-agent framework with a multitasking agent subject to a censored signal. We develop a bang-bang optimal control approach, with a general optimality structure applicable to a broad class of incentive-design problems. Using this approach, we characterize the optimal compensation plan, with a bonus region resembling a “mast” and “sail” such that a bonus is paid when either all inventory above a threshold is sold or the sales quantity meets an inventory-dependent target. The optimal mast-and-sail compensation plan implies nonmonotonicity, where the agent can be less likely to receive a bonus for achieving a better outcome. This gives rise to an ex post moral hazard issue where the agent may “hide” inventory to earn a bonus. We show that this ex post moral hazard issue is a result of demand censoring. If available information includes a waiting list (or other noisy signals) to gauge unsatisfied demand, no ex post moral hazard issues remain. This paper was accepted by Vishal Gaur, operations management.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it