Incentive Design for Operations-Marketing Multitasking
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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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".