Research

Abstract: Peer-to-peer (P2P) reservation platforms often struggle to manage advance-booking customers. Because supply is self-scheduled, hosts' and customers' incentives may be misaligned. When platforms set high prices, the asset owners (hosts) would commit their assets early—i.e., list in advance; however, it may deter advance-booking customers from reserving. Conversely, setting lower prices would discourage hosts from listing early. Moreover, a host’s decision to list also depends on matching probability, which is governed by the platform’s design and search frictions--- that is,  its matching efficiency. We examine when and why advance-booking customers face a supply shortfall and how the platform's matching efficiency contributes to this outcome. We develop a two-period game-theoretic model in which the platform sets prices dynamically, and hosts choose when to list their assets. The model incorporates endogenous matching probabilities to capture real-world search frictions. To validate our theoretical insights, we analyze data from a major car-sharing platform, focusing on host listing decisions and their impact on service availability. We characterize the hosts’ optimal listing strategies in response to the platform's prices and identify three possible outcomes: no commitment, partial commitment, and full commitment. We show that when the demand imbalance between advance-booking and just-in-time customers exceeds a threshold, the platform prefers to induce a breakdown in the advance-booking market. Counterintuitively, this threshold decreases as the platform’s matching efficiency increases, meaning more efficient platforms are more prone to such breakdowns. Peer-to-peer reservation platforms should be cautious when choosing revenue-maximizing prices during severe demand imbalances, as it can disrupt advance-booking markets. Beyond pricing, a platform designer can use matching efficiency as a strategic lever to shape outcomes. By improving matching for early listers—through visibility boosts or recommendation algorithms—platforms can better align host incentives, reduce breakdowns, and enhance service across customer segments.

Abstract: Large language models (LLMs) are reshaping how people seek and produce technical knowledge. To evaluate the nature of knowledge where human expertise remains essential, we find a natural testbed in the largest technical Question and Answer community, StackOverflow. Each question asked in the community carries tags that mark the knowledge domains it draws upon. We term the unique combinations of these knowledge domains (i.e., unique sets of tags) a "specialization'' that characterizes the skill set needed to answer that question. Following ChatGPT's public release in November 2022, total question volume on StackOverflow fell sharply---a widely noticed trend. Yet beneath this decline, we discovered a notable trend: the proportion of novel questions that require stitching together knowledge domains in unprecedented ways rose significantly. More interestingly, we find that this surge in novelty emerges mainly from new recombinations of existing domains rather than new domains introduced by LLMs. This shift has also resulted in the reorganization of the community's knowledge structure. In particular, modeling the knowledge in the community as a network of co-occurring domains, we find that popular knowledge domains, i.e., historically well-discussed domains, have weakened significantly, as activity migrates toward niche domains. Consequently, the community network becomes more fragmented with a weaker core. Our findings suggest that LLMs substitute for standardized queries, while novel problems that require creative integration of knowledge domains still need human expertise. Such selective substitution of questions has profound implications for the sustainability of online knowledge communities and for the reliability of training data that future AI systems will depend on.

Abstract: Online Q&A communities have transformed customer support by decentralizing knowledge exchange. However, their success may differ, illustrated by Stack Overflow's growth under strict moderation compared to Yahoo Answers' decline despite weaker moderation. This paper investigates how community design, particularly the cost of asking questions, shapes user participation and community survival. We develop a multistage stochastic game model where users with heterogeneous skill levels dynamically decide to join, leave, ask, and answer questions. Our framework explicitly incorporates the ``cost of asking" as the effort users must exert to meet community standards and avoid moderation penalties. We first characterize the unique steady-state equilibrium of user participation. Under this equilibrium, we establish conditions under which empirically observed core-periphery network structures are the only possible configuration, thereby providing a theoretical validation for existing empirical work. Counter to common intuition, our findings reveal that a community's survival critically depends on the joining of low-knowledge users, while it can persist even when high-knowledge users do not join. Finally, our analysis highlights a strategic trade-off for community designers between participation level and user retention. We demonstrate that the total number of active users in the community is non-monotonic in the cost of asking questions, revealing that increasing the cost of asking is not always detrimental and can, in fact, improve user satisfaction.

Abstract: Asset-backed lending can improve access to high-cost assets for small businesses in emerging markets. However, managing these loans is challenging due to volatile borrower incomes and asset depreciation. While lenders first use low-intensity interventions, such as reminders and informal renegotiations, they often resort to repossession as a last option. However, repossession is costly for lenders, involving substantial field effort with potential reputation risk, and significant welfare losses for borrowers. We study this problem using data from an Indian lender that finances electric vehicles for small commercial operators. We formulate the lender's problem using a restless bandit framework with budget constraints. We develop a tractable dual-based priority policy that orders borrowers by the value of immediate repossession versus waiting. For our policy, we prove global convergence to a unique steady state and establish asymptotic optimality as the portfolio scales. We demonstrate that the resulting dual-priority policy is interpretable, computationally simple, and exhibits monotone structure under realistic conditions. Our dual-priority policy empirically outperforms commonly used industry benchmarks by more than 15%. We also compare our policy in the observational data, and even under highly conservative evaluations, our policy improves over the firm’s current strategy by 1.5%. Gains in our policy are driven by two main forces. First, our policy prioritizes repossessing from borrowers early in the loan, and second, it offers greater flexibility to borrowers with lower and more volatile income, rather than reacting to fluctuations in repayments. Additionally, our policy rarely repossesses from borrowers who would have eventually repaid. 

Abstract: Multi-brand cloud kitchens offer variety by co-locating multiple restaurant brands, allowing customers to order from multiple brands in a single order. To simplify ordering, platforms also offer pre-designed dish combinations, created either as platform bundles or by brands listing one brand's dish in another brand's menu. Our dataset of 6.24 million orders from a multi-brand operator in Asia (68 kitchens) suggests a significant operational penalty for multi-brand orders, as they are 48% more likely to be late than single-brand orders. Our field visits and interviews suggest that order fulfillment in a multi-brand cloud kitchens is a fork–join queuing process with information asymmetries. Estimates from a reduced-form regression indicate that orders requiring central synchronization across brands, but with information asymmetry for the chefs, have 63\% higher odds of being late compared to single-brand orders. While congestion at cooking stations amplifies coordination costs for all orders, these orders are disproportionately affected. Furthermore, given that the platform hosts both its own and external brands in its kitchens, we find that external brands have roughly half the odds of being late due to multiple contributing factors: simpler menus, better process, and lower congestion. The latter is potentially due to the platform steering demand towards in-house brands by providing them more visibility. Our counterfactual analysis reveals that information sharing and prioritization are complements, as prioritizing multi-brand orders enhances performance only when chefs have complete information. Under partial information, FIFO may yield lower delays and better quality outcomes.

Abstract: Payment for results (PfR) funding approach, where donors reimburse the non-profit organization (NPO) based on outcomes, is being increasingly adopted in the non-profit sector. However, there is also concern expressed by many voluntary organizations that such a funding approach puts an undue financial burden on small NPOs and could actually be detrimental to social welfare. In this study, we build a theoretical framework to analyze PfR funding mechanisms. We use a sequential game to model the interaction between the donor and the NPO, with the donor as the first mover. This model captures how PfR funding is typically implemented in practice using social impact bonds (SIB), wherein social investors provide the upfront funding needed by the NPO to implement the project. The donor provides funding, based on the actual benefit delivered, at the end of the project and the investors are paid back using these funds. We find that higher targets set by the donor do not necessarily translate to higher expected utility or expected benefit delivered under PfR. When comparing the performance of PfR and traditional funding (TF) mechanisms, we find that the donor typically has a higher expected utility under the PfR mechanism when the probability of a negative outcome shock is either high or low, and is better off using the TF approach otherwise. When the donor’s opportunity cost of funding the project is high, the donor is better off using a PfR mechanism when her belief about the NPO having low efficiency is sufficiently high. Interestingly, we find that for a large range of parameter values there is a mismatch between the approach that gives a higher expected utility to the donor and the approach that maximizes the expected social benefit delivered. Our model and analysis suggest that the optimal funding approach, and the optimal target set under PfR, depend on the NPO’s financing cost from social investors and project outcome uncertainty.

Abstract: In a shared economy, users list their assets on the platform for extra earnings. However, in emerging markets, there may not be enough individuals who are willing to share assets. Some platforms nowadays offer asset financing options to ensure enough supply, wherein individuals can own the asset temporarily or permanently by committing to a recurring fee. These users can then list the asset on the platform and earn a share in the revenue. We study such contracts using a sequential game-theoretic model where the platform chooses the recurring fixed fee and revenue share. The individuals then decide to subscribe or not using rational expectations on their future payoffs. The platform can choose a contract with a high subscription fee and high revenue share or one with a low subscription fee and low revenue share. We study conditions under which the platform prefers to choose the former contract over the latter. Specifically, we study the effect of platform prices, the asset's market price, and the demand for the shared asset on the contract design. Our main result is the existence of a budget limit above which the platform will not exhaust its budget to get more subscribers. Interestingly, depending on the market conditions, this budget limit can be lower than that required to get subscribers to satisfy low demand levels. We further find that it is optimal for the platform to have subscribers who list their assets infrequently, even when the platform is supply-constrained. We use the data from a car rental platform in India that offers such subscription contracts to validate our results and support our modeling assumptions. Finally, we also compare the profits of a platform offering subscription contracts against a centralized platform and study if such contracts are always preferable to scale.