Multiproduct Dynamic Pricing With Popularity Bias

The decision of sourcing channels is a critical strategy for online retail platforms (ORs) when they encroach into the product market of the third-party retailers (3PRs). Whether the products of the OR are provided by the common supplier or the one different from that of the 3PR leads to different supply chain structures. This paper focuses on the sourcing channel decision of an encroaching OR given the sourcing channel of a 3PR. The OR may source either from the common supplier, i.e., the same supplier as that of the 3PR, or from a different supplier, i.e., one different from that of the 3PR. This paper develops two game-theoretical models to examine the sourcing channel decision of an OR in quantity competition with a 3PR on the platform. The results show that the OR’s preference for the sourcing channel is significantly affected by the production cost, product substitutability and commission rate. By comparing the performances of the OR under these two sourcing channels, we find that when the production cost of the different supplier is very low, or when the product substitutability of the different supplier is low, or when the commission rate is either very low or very high, the OR prefers to source from the different supplier. Otherwise, the OR tends to source from the common supplier. We also find that the OR’s encroachment into the sales market of a 3PR by sourcing from the common supplier will always hurt the 3PR, but the platform encroachment by sourcing from the different supplier will not necessarily hurt, especially when the supplier selected by the OR has a significant disadvantage in production cost compared to those of the 3PR. Furthermore, when the OR encroaches by sourcing from the common supplier, it becomes the largest retailer in the market; otherwise, the 3PR is the largest retailer in that market. Our results also indicate that platform encroachment by different sourcing can not only expand the sales market, but also weaken the negative effects of platform encroachment on the profit of the 3PR.

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

The most commonly used variant of conjoint analysis is choice-based conjoint (CBC). Here, hierarchical Bayesian (HB) multinomial logit (MNL) models are widely used for preference estimation at the individual respondent level. A new and very flexible approach to address multimodal and skewed preference heterogeneity in the context of CBC is the Dirichlet Process Mixture (DPM) MNL model. The number and masses of components do not have to be predisposed like in the latent class (LC) MNL model or in the mixture-of-normals (MoN) MNL model. The aim of this Monte Carlo study is to evaluate the performance of Bayesian choice models (basic MNL, HB-MNL, MoN-MNL, LC-MNL and DPM-MNL models) under varying data conditions (especially under multimodal heterogeneity structures) using statistical criteria for parameter recovery, goodness-of-fit and predictive accuracy. The core finding from this Monte Carlo study is that the standard HB-MNL model appears to be highly robust in multimodal preference settings.

Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt

Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.

ABSTRACTThe use of neuro-physiological data in models of consumer choice is gaining popularity. This article presents some of the benefits of using psycho-physiological data in analyzing consumer valuation and choice. Eye-tracking, facial expressions, and electroencephalography (EEG) data were used to construct three non-conventional choice models, namely, eye-tracking, emotion and brain model. The predictive performance of the non-conventional models was compared to a baseline model, which was based entirely on conventional data. While the emotion and brain models proved to be as good as conventional data in explaining and predicting consumer choice, the eye-tracking model generated superior predictions. Moreover, we document a significant increase in predictive power when biometric data from different sources were combined into a mixed model. Finally, we utilize a machine learning technique to sparse the data and enhance out-of-sample prediction, thus showcasing the compatibility of biometric data with well-established statistical and econometric methods.

Statisticians along with other scientists have made significant computational advances that enable the estimation of formerly complex statistical models. The Bayesian inference framework combined with Markov chain Monte Carlo estimation methods such as the Gibbs sampler enable the estimation of discrete choice models such as the multinomial logit (MNL) model. MNL models are frequently applied in transportation research to model choice outcomes such as mode, destination, or route choices or to model categorical outcomes such as crash outcomes. Recent developments allow for the modification of the potentially limiting assumptions of MNL such as the independence from irrelevant alternatives (IIA) property. However, relatively little transportation-related research has focused on Bayesian MNL models, the tractability of which is of great value to researchers and practitioners alike. This paper addresses MNL model specification issues in the Bayesian framework, such as the value of including prior information on parameters, allowing for nonlinear covariate effects, and extensions to random parameter models, so changing the usual limiting IIA assumption. This paper also provides an example that demonstrates, using route-choice data, the considerable potential of the Bayesian MNL approach with many transportation applications. This paper then concludes with a discussion of the pros and cons of this Bayesian approach and identifies when its application is worthwhile.

In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the ‘training’ scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models. This research aims to test these two hypotheses empirically by modelling vehicle ownership choices using household survey data from Dhaka, Bangladesh collected in 2004, 2010 and 2019. The performances of CM (multinomial logit) and ML techniques (neural networks and gradient boosting trees) have been compared using log-likelihood and mean absolute percentage error of market shares. The results indicate that the multinomial logit model (MNL) with a piecewise linear transformation of the household income, has the best performance in terms of log-likelihood and mean absolute percentage error of market shares. This is followed by Neural Networks (NN) and Gradient Boosting Trees (GBT). The results thus provide empirical evidence that the ML techniques do not consistently outperform CM. Moreover, the difference in the performance of the models further increases if the prediction scenario is substantially different. This reinforces the hypothesis that CMs, with their behavioural underpinning, are better suited for long-term forecasting than data-driven ML approaches, especially if the population and network attributes are expected to change substantially. These findings will be useful for planners and policy makers in the selection of the appropriate tool for forecasting travel demand.

We consider assortment optimization problems under the multinomial logit model, where the parameters of the choice model are random. The randomness in the choice model parameters is motivated by the fact that there are multiple customer segments, each with different preferences for the products, and the segment of each customer is unknown to the firm when the customer makes a purchase. This choice model is also called the mixture‐of‐logits model. The goal of the firm is to choose an assortment of products to offer that maximizes the expected revenue per customer, across all customer segments. We establish that the problem is NP complete even when there are just two customer segments. Motivated by this complexity result, we focus on assortments consisting of products with the highest revenues, which we refer to as revenue‐ordered assortments. We identify specially structured cases of the problem where revenue‐ordered assortments are optimal. When the randomness in the choice model parameters does not follow a special structure, we derive tight approximation guarantees for revenue‐ordered assortments. We extend our model to the multi‐period capacity allocation problem, and prove that, when restricted to the revenue‐ordered assortments, the mixture‐of‐logits model possesses the nesting‐by‐fare‐order property. This result implies that revenue‐ordered assortments can be incorporated into existing revenue management systems through nested protection levels. Numerical experiments show that revenue‐ordered assortments perform remarkably well, generally yielding profits that are within a fraction of a percent of the optimal.

Choice models with mixtures: An application to a cocktail experiment

The Exponomial Choice Model

We consider a dynamic combinatorial optimization problem where at each time step, the decision maker selects a subset of cardinality K from N possible items and observes a feedback in the form of the index of one of the items in the said subset or none. Each of the N items is ascribed a certain value (reward), which is collected if the item is chosen. This problem is motivated by that of assortment selection in online retail, where items are products. Akin to that literature, it is assumed that the choice of the item given the subset is governed by a multinomial logit (MNL) choice model whose parameters are a priori unknown. The objective of the decision maker is to maximize the expected cumulative rewards over a finite horizon T or alternatively, minimize the regret relative to an oracle that knows the MNL choice model parameters. We formulate this problem as a multiarmed bandit problem that we refer to as the MNL-bandit problem. We present a Thompson sampling-based algorithm for this problem and show that it achieves near-optimal regret as well as attractive empirical performance. Funding: S. Agrawal is supported in part by the Division of Civil, Mechanical and Manufacturing Innovation [NSF Grant 1846792]. V. Goyal is supported in part by the Division of Civil, Mechanical and Manufacturing Innovation [NSF Grants 1351838 and 1636046]. A. Zeevi is supported in part by the Division of Computer and Network Systems [NSF Grant 0964170] and the United States-Israel Binational Science Foundation [Grant 2010466].

The standard random utility model assumes a fully rational and compensatory choice behaviour. However, various research studies have proven that non-compensatory /semi-compensatory choice behaviour is more realistic. This paper proposes a semi-compensatory framework for the discrete choice model that combines probabilistic choice set formation along with implicit choice constraints in choice making. It combines the Independent Availability Logit (IAL) with Implicit Constrained Multinomial Logit model (CMNL). The model is applied to investigate the mode choice behaviour of the Ottawa-Gatineau regions of Canada’s capital by using the household travel survey data. The explanatory power and elasticity measures of the proposed model are compared with IAL and MNL models. It is found that the IAL-CMNL model outperforms both IAL and MNL models and can reproduce the choice set formation process more effectively. The empirical investigation shows that the IAL-CMNL model results in relatively higher tolerance and softer constraints for cut-off violations compared to the IAL model. Elasticity calculations and outcomes of this research highlight the importance of capturing choice set formation and constrained choice behaviour in the choice modelling.

Choices made by individuals have widespread impacts—for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase—moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Traditional methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.

In this study, we develop a game‐theoretic model to study the encroachment and information sharing decisions in a supply chain with a manufacturer selling through an online retail platform. In the base model, the manufacturer decides whether to encroach or not by selling through an agency channel, in addition to an existing reselling channel, at the same platform, who decides whether or not to share information with the manufacturer. We fully characterize the equilibrium decisions and show how they depend on the commission rate of the agency channel, channel substitutability, and information accuracy. Our analysis unfolds a novel wholesale price effect that extends related results in the literature. We also show that encroachment and information sharing are complementary, and therefore managers should not ignore the impact of one decision on the other decision even when the latter is not a primary motivation of the former. We study several model extensions to obtain additional insights and show that the major findings of the base model are robust and remain mostly valid when some modeling assumptions are changed.