Motivated by the setting of Airbnb, we consider a game theoretical setup in which each seller on the platform provides a single-unit product and competes on price. We investigate sellers' optimal pricing decisions and the platform's optimal assortment display policy. Additionally, we incorporate constraints to guarantee a certain degree of seller and customer fairness. Using data from Airbnb, we present a case study to illustrate how our model framework can be applied in practice.

We study the relative merits of different dispute resolution mechanisms in two-sided platforms, in the context of disputes involving malicious reviews and blackmail. We develop a game-theoretic model of the strategic interactions between a seller and a (potentially malicious) consumer. Our results suggest that decentralization, when implemented correctly, may represent a more efficient approach to dispute resolution.

We consider an on-demand service platform (e.g., Uber, Lyft, DiDi) that serves two customer classes with distinct willingness to wait and to pay. We formulate this problem as a Markov decision process in which the platform controls customer admission and service provider allocation. Using our model's structural properties, we show that the optimal policy is characterized by two admission thresholds. In a numerical study, we then compare the optimal policy with several simpler policies.

Multi-homing gives platforms access to a larger pool of supply; however, it also changes the nature of competition between platforms in a market. It is not clear whether this works to the advantage of platforms or not. In light of this, we ask a fundamental question: When is it better for two-sided platforms to pool their workers? We answer this question through a game-theoretic study. We identify the key trade-offs associated with pooling decision and highlight the key role of scale.

Gig workers prevalently "multihome'' by dynamically allocating their services in real-time between multiple platforms. As a growing number of platforms access the same pool of workers to complete their gigs, the question of how workers choose between competing platforms has grown in salience. In this work, we study gig workers' multihoming decisions by using machine learning methods to estimate a structural model from ride-hailing proprietary data combined with publicly reported trips data.

Using a large panel dataset with repeated choices of riders for both Uber and Lyft, we estimate a structural “consider-then-choose” model to better understand the trade-offs faced by riders. We find that riders' choices are not fully explained by operational factors such as price and waiting time, indicating that riders view the platforms as differentiated services and not as commodities. We also find that the multi-homing behavior of riders is only observed for a small fraction of the rides.

Gig economy workers make strategic decisions about where and when to work. We empirically measure two types of strategic behavior: multihoming, an online change between platforms, and repositioning, a physical change between locations. Using a structural model, we show that workers are highly heterogenous in their preferences for both multihoming and repositioning. We provide counterfactual estimates on the effects of proposed firm and regulatory policies aimed at multihoming and repositioning.

Taxis’ and Uber’s matching technologies differ: taxis random-walk in search of curbside pick-ups while Uber centrally dispatches drivers. We study how taxis can defend against Uber encroachment. We find that imitating Uber’s centralized dispatch can improve taxi market share but only when Uber drivers are relatively reluctant to drive. Otherwise, imitating Uber can entice more Uber drivers to drive, leading to an unintended reduction in taxi market share.

Tele-driving refers to a novel concept in which drivers can remotely operate vehicles. Because remote drivers can be operated as a shared resource, tele-driving has the potential to reduce the severity of the spatial mismatch between vehicle supply and customer demand that is often experienced in on-demand mobility services. In this paper, we compare a traditional ride-hailing system with one with tele-drivers, and quantify the potential gains that could be realized by tele-driving.

We examine the effect of centralization on platforms' pricing decisions in two-sided matching markets. We develop a stylized model that describes the platforms' dynamic matching and pricing process over a continuum of market participants, which is directly motivated by the design of ridehailing platforms. We provide a comprehensive analytical characterisation of the market equilibrium. Next, we develop a simulation-based framework to compare the social welfare under three operating models.

We consider a truckload transportation marketplace in which a platform serves an intermediary to match shippers, who pay for transportation services, with carriers, who get compensation for transporting the loads. The objective of the platform is to design pricing and allocation mechanisms to maximize its long-run average profit. In this paper, we analyze the performance of posted price, auction, and hybrid mechanisms (which combine posted price and auction mechanisms).

The decision to display information of the freight market's current state impacts revenue due to user abandonment. The implications of such a decision are not well understood in platforms for freight exchanges. We study which information design maximizes expected revenue. Queueing models with balking and reneging are applied, and the steady-state behavior of the underlying Markov chains is analyzed. We find that in large markets, showing state information is preferred.

Crowdsourced delivery platforms have made use of two types of couriers: ad-hoc couriers, who are more flexible, and committed couriers, who are more reliable. In this paper we show that by designing a system that intelligently utilizes order partitioning between the two delivery channels (e.g., makes routing and partitioning decisions jointly), the delivery platform can exploit the benefits of each courier base to improve customer service and reduce the total cost when compared to order pooling.

On two-sided matching platforms such as VolunteerMatch (VM), a sizable fraction of website traffic arrives via an external link, bypassing the platform's recommendation algorithm. We study how platforms can account for this, given the goal of maximizing successful matches. We model the problem as a variant of online matching and introduce an algorithm providing near-optimal guarantees in certain parameter regimes. We also show our algorithm’s strong performance in a case study based on VM data.

We propose a dynamic car relocation policy for a car-sharing network with centralized control and uncertain, unbalanced demand. The policy is derived from a reformulation of the fluid model approximation of the dynamic problem. We project the full-dimensional fluid approximation onto the lower-dimensional space of relocations only. Our policy exploits these gradients to make dynamic car relocation decisions. We close the optimality gap on average by 30% in static and time-varying settings.

Investments in PV solar systems in the residential sector in Egypt are not thriving as expected despite the rapid decrease of the capital cost, electricity tariff reforming, and recent regulations for grid connection. This research targeted the residential sector in Egypt as it consumes 47% from the nation's electricity (IRENA, 2018), and explored the decision-making factors that affect the adoption of solar energy technology by the upscale residential sector.

Many developing countries lack the health-emergency infrastructure of the developed world. In this context, our industry partner Flare (operating in Nairobi, Kenya) coordinates existing ambulance providers by operating a platform. We study the operational challenges for such platforms as they often lack the knowledge about all ambulances' future availability and their location at a tactical level, and typically do not fully control these ambulances.

We will use data from a structured online discussion forum to understand what the key engagement drivers in online discussions are, and how we can leverage these drivers to improve the operations and performance of online discussion platforms. We will present both empirical and theoretical results characterizing strategies to guide discussions optimally - a crucial feature in an age where communications in both business and educational settings are increasingly moving to online settings.

Online dating apps are the most common way for couples to meet. Many of these dating apps use subscription based pricing (SP), where subscriptions to the app are sold at a fixed price. In online dating (SP) is controversial as it misaligns the incentives of the platform and its users. Another strategy is contract pricing (CP), where the dating app is contracted at a one time price. We study the profit and welfare trade-offs associated with either pricing strategy for online dating platforms.

We study the herding and learning effects on the incentives for online reviews. We model the evolution of sales and reviews for a seller by a generalized Polya urn process and evaluate the profit for three incentive policies: incentivize before purchase, after purchase, or only if they write positive, possibly fake, reviews. We obtain conditions that each type of incentive is profitable and optimal. The results imply that platforms can curb fake reviews if allowing pre-purchase incentives.