Cross-Category Retailing Management: Substitution and Complementarity

Problem definition: This paper studies pricing and assortment management for cross-category products, a common practice in brick-and-mortar retailing and e-tailing. Academic/practical relevance: We investigate the complementarity effects between the main products and the secondary products, in addition to the substitution effects for products in the same category. Methodology: In this paper, we develop a multistage sequential choice model, under which a consumer first chooses a main product and then selects a secondary product. The new model can alleviate the restriction of the independence of irrelevant alternatives property and allows more flexible substitution patterns and also takes into account complementarity effects. Results: We characterize the impact of the magnitude of complementarity effects on pricing and assortment management. For the problems that are hard to solve optimally, we propose simple heuristics and establish performance guarantee. In addition, we develop easy-to-implement estimation algorithms to calibrate the proposed sequential choice model by using sales data. Managerial implications: We show that ignoring or mis-specifying complementarity effects may lead to substantial losses. The methodologies on modeling, optimization, and estimation have potential to make an impact on cross-category retailing management.

Probing the decision-making mechanisms underlying choice between drug and nondrug rewards in rats

Delineating the decision-making mechanisms underlying choice between drug and nondrug rewards remains a challenge. This study adopts an original approach to probe these mechanisms by comparing response latencies during sampling versus choice trials. While lengthening of latencies during choice is predicted in a deliberative choice model (DCM), the race-like response competition mechanism postulated by the Sequential choice model (SCM) predicts a shortening of latencies during choice compared to sampling. Here, we tested these predictions by conducting a retrospective analysis of cocaine-versus-saccharin choice experiments conducted in our laboratory. We found that rats engage deliberative decision-making mechanisms after limited training, but adopt a SCM-like response selection mechanism after more extended training, while their behavior is presumably habitual. Thus, the DCM and SCM may not be general models of choice, as initially formulated, but could be dynamically engaged to control choice behavior across early and extended training.

Dynamic Learning of Sequential Choice Bandit Problem under Marketing Fatigue

Motivated by the observation that overexposure to unwanted marketing activities leads to customer dissatisfaction, we consider a setting where a platform offers a sequence of messages to its users and is penalized when users abandon the platform due to marketing fatigue. We propose a novel sequential choice model to capture multiple interactions taking place between the platform and its user: Upon receiving a message, a user decides on one of the three actions: accept the message, skip and receive the next message, or abandon the platform. Based on user feedback, the platform dynamically learns users’ abandonment distribution and their valuations of messages to determine the length of the sequence and the order of the messages, while maximizing the cumulative payoff over a horizon of length T. We refer to this online learning task as the sequential choice bandit problem. For the offline combinatorial optimization problem, we show a polynomialtime algorithm. For the online problem, we propose an algorithm that balances exploration and exploitation, and characterize its regret bound. Lastly, we demonstrate how to extend the model with user contexts to incorporate personalization.

The Valuation Cost Decreases as a Function of Extended Exposure to a Risky-Choice Procedure

Several studies in pigeons and rats have reported a predictable relation between latencies during no-choice trials and the ulterior preference in choice trials. The Sequential Choice Model (SCM) was proposed in 2008 to account for these results, and more importantly to make precise predictions about the correlation between latency and preference. Eight male Wistar rats were exposed to 48 sessions in a risk-sensitive procedure, each session was composed by 10 blocks of trials (2 no-choice and 4 choice trials). We analyzed data taking latencies of response and testing the SCM’s predictions. Our data support partially the SCM’s predictions, but a monotonic decrease to a floor effect in all latencies of response does not allow confirming all predictions. The results are discussed regarding a decrease in the valuation cost as a result of extended exposure, and arguing that diminishing latencies in this particular procedure contributed to increase the whole rate of reinforcement.

A Pseudo-Sequential Choice Model for Valuing Multi-Attribute Environmental Policies or Programs in Contingent Valuation Applications

The assumption of independence of irrelevant alternatives in a sequential contingent valuation format should be questioned. Statistically, most valuation studies treat nonindependence as a consequence of unobserved individual effects. Another approach is to consider an inferential process in which any particular choice is part of a general choosing strategy of a survey respondent. A stochastic model is suggested, consistent with the reflexivity, transitivity, and continuity axioms of utility analysis. An application of this theoretical model to the valuation of watershed ecosystem restoration demonstrates that an empirical model recognizing reflexivity and transitivity, and also allowing for continuity, shows the highest in-sample predictive ability.