Assortment Optimization in the Presence of Focal Effect: Operational Insights and Efficient Algorithms
Bibliographic record
Abstract
The assortment provided by the seller can influence customers’ evaluation of item utility. A possible consequence is that certain items in an assortment become “focal items” to customers, and customers over-evaluate their utilities. We refer to this phenomenon as the focal effect . Kovach and Tserenjigmid (2022) recently propose a focal Luce model (FLM) to describe customers’ choices in the presence of the focal effect. The merit of the FLM lies in its flexibility to model different consumer psychology, which leads to varying choice behaviors. In this paper, we use the FLM to capture several scenarios where the focal effect occurs and consider the associated assortment optimization problems. In the first scenario, the focal effect arises from item ranking, and customers prefer items that appear at certain positions in the ranking. This scenario captures the case where customers have a preference for the cheapest items in the assortment, as well as the compromise effect. The second scenario describes the case where the focal effect exists on some predetermined items. An example of this scenario is choice overload, where customers are more likely to choose the no-purchase option when the assortment size is larger. We characterize the optimal assortment structure under each scenario and give the underlying operational insights. We find that the assortment optimization problems under these two scenarios can be solved in polynomial time with some practical assumptions. The polynomial-solvability extends even to the more challenging joint assortment and pricing optimization problems. Finally, we conduct numerical experiments to evaluate the FLM’s performance using both synthetic and real datasets. The results show that the FLM performs well in predicting customer choice behavior and the corresponding optimal assortment generates higher profit than benchmark assortments when the focal effect exists.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".