Assortment Planning with Satisficing Customers
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
Limited information, time, or capacity may prevent customers from acting as utility maximizers when making purchase decisions. Rather, they would settle for a good enough option; that is, they stop searching and make a purchase as soon as they find an acceptable alternative. We incorporate this behavior in an assortment-optimization problem. Whereas different approaches to modeling customer choice are adopted in assortment planning, all assume customers are utility maximizers. Our work bridges the research streams of assortment planning and bounded rationality, particularly satisficing behavior. In addition, we define a limit for the search budget of customers, in which customers leave without purchase after examining a certain number of items. This assumption brings a new perspective to the assortment-planning literature, enabling us to capture the choice-overload effect. We prove that the firm’s problem of finding the optimal assortment is NP-hard. We further establish certain structural properties of the optimal decision, which allows us to reformulate the model as a mixed-integer program. We analytically derive a tight upper bound on the percentage loss in the firm’s expected profit for small instances when it assumes incorrectly that customers are utility maximizers. For larger instances, we take a numerical approach to determine the loss. Our results indicate that firms offering low-involvement products, among those dealing with satisficing customers, are more likely to face substantial profit loss if they ignore this behavior. Supplemental Material: The e-companion is available at https://doi.org/10.1287/deca.2022.0063 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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