1. Potty Parity: Process Flexibility via Unisex Restroom
Setareh Farajollahzadeh, Ming Hu
Status: Major Revision at Management Science
Abstract: We study the problem of inequitable access to public restrooms by women and the LGBTQ+ community. Individuals enter a restroom based on their gender identity and the expected (or observed) wait time. We consider two measures of potty parity: first, the conventional wait-time parity, and second, our proposed utility parity, which encompasses both wait time and gender identity to estimate users' utility for using a restroom. We show the benefits of unisex restrooms analytically and from various angles: (a) reducing the wait time for the women's restroom; (b) enhancing the potty parity of wait times and users' utility; (c) increasing users' feelings of safety; and (d) shrinking the wait-time disparity when arrival rates fluctuate. Moreover, we provide insights into both renovating existing buildings and designing restrooms from scratch. In particular, we show the following: (i) The process flexibility of having a one-unit unisex restroom, either by converting a unit of the men's restroom or building an additional one, goes a long way toward improving wait time or user utility, and reducing their disparities. (ii) Building the women's room and the unisex restroom next to each other (such that users can jockey lines) improves potty parity. (iii) Even though an all-unisex restroom leads to complete parity of wait times, surprisingly, it does not improve utility potty parity, but reverses the ranking of users' utility in the population. (iv) Providing an all-unisex room plus urinal(s) can increase efficiency still more. Lastly, we also provide a numerical study with empirically calibrated parameters to show the magnitude of the impact of unisex rooms in a stadium.
2. Sharing Newsboys
Setareh Farajollahzadeh, Ming Hu
Status: Major Revision at Operations Research
Keywords: Network games
Abstract: We consider a network of socially connected newsvendors facing random demand for a product who need to commit to a stocking level before demand realizes. A newsvendor can share her ex post excess stock to fulfill the unsatisfied demand of a connected newsvendor. The amount of shared supply that a newsvendor anticipates receiving from her network is affected by two factors: sharing magnitude and tie strength. Sharing magnitude (resp., tie strength) measures the portion of excess stock that a newsvendor will share (resp., the likelihood that a newsvendor will share her excess supply) with a neighboring newsvendor. We adopt a Bayesian game framework with incomplete information about the network structure, where a newsvendor has private information about the number of connections she has (as her type) but does not know her neighbors' types, which she believes are consistent with a network's known degree distribution. First, we demonstrate that with more sharing activity (i.e., greater sharing magnitude or stronger social ties) within a fixed network, all newsvendors decrease their stocking levels regardless of their types, which implies that the total consumption level drops. Second, we show that when tied with the number of connections a newsvendor has, the sharing magnitude has a first-order effect on the mean of the shared supply, while the social tie has a second-order effect on the variability of the shared supply. As the degree distribution of the network increases in the sense of usual stochastic dominance, we show that the two factors may have opposite effects on the equilibrium stocking levels. The effect of sharing magnitude is to increase the equilibrium stocking levels. But the effect of tie strength is such that for a high-fractile product, the population's expected consumption level increases, while it is the other way around for a low-fractile product. Lastly, we extend the supply-sharing base model to complete network information under specific networks and to demand sharing, where unsatisfied demand at one newsvendor can be referred to a neighbor in her network.
3. Learning Customer Preferences from Bundle Sales Data
Ningyuan Chen, Setareh Farajollahzadeh, Guan Wang
Status: To be submitted to Manufacturing and Service Operations Management
Keywords: EM algorithm, Censored demand, Clustering
Abstract: Product bundling is a common selling mechanism used in online retailing. To set profitable bundle prices, the seller needs to learn consumer preferences from the transaction data. When customers purchase bundles or multiple products, classical methods such as discrete choice models cannot be used to estimate customers' valuations. In this paper, we propose an approach to learn the distribution of consumers' valuations toward the products using bundle sales data. The approach reduces it to an estimation problem where the samples are censored by polyhedral regions. Using the EM algorithm and Monte Carlo simulation, our approach can recover the distribution of consumers' valuations. The framework allows for unobserved no-purchases and clustered market segments. We provide theoretical results on the identifiability of the probability model and the convergence of the EM algorithm. The performance of the approach is also demonstrated numerically.
4. Simultaneous vs. Sequential Product Release
Hojat Abdollahnejad, Ningyuan Chen, Setareh Farajollahzadeh, Ming Hu
Status: To be submitted to Management Science
Keywords: Learning, Reviews, Buzz economy
Abstract: We study the profitability of a seller of two products under simultaneous vs. sequential releases. The seller and heterogenous customers have a prior belief of products' popularity before their debut. The sequential release allows customer engagement and learning about the product for an extended period. Customers learn about the attraction from their direct experience (private), reading critics' opinions (public), or reading other customers' reviews (social). Under private learning, the simultaneous release is more profitable than the sequential release for a price-taker seller. However, we show that the result reverses when the seller allows for public and social learning or when the seller can price products intertemporally.
Work in Progress
5. Data-driven Competitive Pricing
Setareh Farajollahzadeh, Ming Hu
Keywords: Data-driven algorithm, Competitive pricing
6. Market Segmentation: Asymmetric Equilibrium among Symmetric Platforms in Ride-hailing Markets
Setareh Farajollahzadeh, Philipp Afeche, Azarakhsh Malekian
Keywords: Queuing games, Two-sided markets, Autonomous vehicles
Abstract: We study duopoly competition between symmetric platforms in the ride-hailing market, who serve customers with heterogeneous delay cost. platforms choose their service fee and driver capacity while customers choose the utility-maximizing service. We demonstrate that whenever the number of customers with low delay cost is either low or large, the market reaches a symmetric equilibrium in which both platforms set the same price and capacity, thus serving the same number of customers. However, if the number of customers with low delay cost is moderate, there exists an asymmetric equilibrium in which the market will be segmented between the two types of customers.