Attention, Consideration then Selection Choice Model

Gallego, Guillermo; Li, Anran

doi:10.2139/ssrn.2926942

Cited by 13 publications

(8 citation statements)

References 62 publications

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“…Pan et al (2018) considers a dynamic assortment planning problem in which customers' preferences are unknown beforehand. The assortment-based choice model is the basis of some ranking-based models, such as the random consideration set model (Gallego and Li, 2017). However, the ranking problem suffers more serious consequences of the high-dimensional decision variables than the assortment optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Under this choice model, first, the patients randomly form a consideration set consisting of items at the top of the list, and subsequently choose one product with the highest utility. Gallego and Li (2017) expanded the consideration set model to allow several market segments and tie preferences and provided efficient algorithms to find a performance guaranteed assortment. Derakhshan et al (2018) examined the same consideration set based choice model, which is referred to as the two-stage pandora's box.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, it is beneficial to incorporate rank-based patient choices and optimization in the case of online healthcare platforms. In this regard, Gallego and Li (2017) claimed that the rank-based choice model, specifically the random consideration set model, outperforms the mixture of multi-nominal logit model in assortment planning and provides a better fit to 67% of the market.…”

Section: Introductionmentioning

confidence: 99%

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Physician ranking optimization based on patients' browse behaviors and resource capacities

Pan

Wen

Wang

et al. 2021

INTR

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PurposeDigital healthcare has become one of the most important Internet applications in the recent years, and digital platforms have been acting as interfaces between the patients and physicians. Although these technologies enhance patient convenience, they create new challenges in platform management. For instance, on physician rating websites, information overload negatively influences patients' decision-making in relation to selecting a physician. This scenario calls for an automated mechanism to provide real-time rankings of physicians. Motivated by an online healthcare platform, this study develops a method to deliver physician ranking on platforms by considering patients' browse behaviors and the capacities of service resources.Design/methodology/approachThe authors use a probabilistic model for explicitly capturing the browse behaviors of patients. Since the large volume of information in digital systems makes it intractable to solve the dynamic ranking problem, we design a ranking with value approximation algorithm that combines a greedy ranking policy and the value function approximation methods.FindingsThe authors found that the approximation methods are quite effective in dealing with the ranking optimization on the digital healthcare system, and it is mainly because the authors incorporate the patient behaviors and patient availability in the model.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies to present solutions to the dynamic physician ranking problem. The ranking algorithms can also help platforms improve system and operational performance.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Physician ranking optimization based on patients' browse behaviors and resource capacities

Pan

Wen

Wang

et al. 2021

INTR

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“…The random variables B i , i ∈ N are assumed to be independent in the random consideration set (RCS) model [Manzini and Mariotti, 2014], where the last-choice probabilities correspond to the so-called attention probabilities. The assortment optimization problem for the RCS model was first considered by Gallego and Li [2017], who proved that revenue-ordered heuristic has a 1/2 performance guarantee. By Theorem 2 the revenue-ordered heuristic yields at least half of the expected profits of the clairvoyant firm strengthening their result.…”

Section: Assortment Optimization and The Basic Prophet Inequalitymentioning

confidence: 99%

Bounds, Heuristics, and Prophet Inequalities for Assortment Optimization

Gallego¹,

Berbeglia²

2021

Preprint

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Consider a clairvoyant firm that knows the products' valuations of each arriving consumer and offers them only the most profitable product they are willing to buy. How much more can such a firm make relative to a firm that offers all consumers the assortment that maximizes expected revenues? We show that for general discrete choice models, the ratio can be exponential in the number of products, but at most equal to the number of products for random utility models.We show that the ratio is at most 2 for the α-shaken multinomial logit (α-MNL) which includes the MNL and the general attraction model (GAM) as special cases. We also provide sufficient conditions for the ratio of at most 2 to hold for the latent class MNL, and in fact show that in the limit as the coefficient of variation of the utilities goes to infinity the bound is at most 1.5. For all of these cases the revenue-ordered heuristic yields the stated guarantees relevant to the clairvoyant firm.

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“…Echenique, Saito, and Tserenjigmid (2018) showed that the PALM and the model by Manzini and Mariotti are in fact disjoint. The assortment optimization problem under the Manzini and Mariotti model has been recently studied by Gallego and Li (2017), where they show that revenue-ordered assortments strategy is optimal. Another choice model studied is the negative exponential distribution (NED) model (Daganzo, 1979), also known as the Exponomial model (Alptekinoglu and Semple, 2016) in which customer utilities follow negatively skewed distribution.…”

Section: Relevant Literaturementioning

confidence: 99%

Assortment optimization under the Sequential Multinomial Logit Model

Flores

Berbeglia

Hentenryck

2019

European Journal of Operational Research

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We study the assortment optimization problem under the Sequential Multinomial Logit (SML), a discrete choice model that generalizes the multinomial logit (MNL). Under the SML model, products are partitioned into two levels, to capture differences in attractiveness, brand awareness and, or visibility of the products in the market. When a consumer is presented with an assortment of products, she first considers products in the first level and, if none of them is purchased, products in the second level are considered. This model is a special case of the Perception-Adjusted Luce Model (PALM) recently proposed by Echenique, Saito, and Tserenjigmid (2018). It can explain many behavioral phenomena such as the attraction, compromise, similarity effects and choice overload which cannot be explained by the MNL model or any discrete choice model based on random utility. In particular, the SML model allows violations to regularity which states that the probability of choosing a product cannot increase if the offer set is enlarged.This paper shows that the seminal concept of revenue-ordered assortment sets, which contain an optimal assortment under the MNL model, can be generalized to the SML model. More precisely, the paper proves that all optimal assortments under the SML are revenue-ordered by level, a natural generalization of revenue-ordered assortments that contains, at most, a quadratic number of assortments. As a corollary, assortment optimization under the SML is polynomialtime solvable. This result is particularly interesting given that the SML model does not satisfy the regularity condition and, therefore, it can explain choice behaviours that cannot be explained by any choice model based on random utility.

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Attention, Consideration then Selection Choice Model

Cited by 13 publications

References 62 publications

Physician ranking optimization based on patients' browse behaviors and resource capacities

Physician ranking optimization based on patients' browse behaviors and resource capacities

Bounds, Heuristics, and Prophet Inequalities for Assortment Optimization

Assortment optimization under the Sequential Multinomial Logit Model

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