Neha Sharma

Neha Sharma
  • Assistant Professor

Contact Information

  • office Address:

    3730 Walnut Street
    550 Jon M. Huntsman Hall
    Philadelphia, PA 19104

Research Interests: Online Marketplaces | Knowledge Sharing Communities | Urban Mobility in Emerging Economies | Energy Markets |

Links: CV, Personal Website, Google Scholar, CV, SSRN, GitHub

Overview

Neha is an Assistant Professor of Operations, Information, and Decisions at the Wharton School, University of Pennsylvania. Her research designs and evaluates pricing, incentive, and governance policies for digital marketplaces and platform-based services using empirical data, stochastic models, and game theory. She studies how platform rules shape participation, service quality, and welfare across sharing and delivery platforms, online knowledge communities, and access-oriented markets, including EV adoption and financing for low-income consumers. Her work has received recognition from INFORMS and IBM competitions. She earned her PhD in Operations Research from Northwestern University (Kellogg) in 2023 and teach OIDD 2200: Operations Management Analytics.

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Research

  • Haosen Ge, Neha Sharma, Hamsa Bastani, Osbert Bastani (Under Revision), Rethinking Algorithmic Fairness for Human-AI Collaboration. Abstract

    Existing approaches to algorithmic fairness aim to ensure equitable outcomes \emph{if} human decision-makers comply perfectly with algorithmic decisions. However, perfect compliance with the algorithm is rarely a reality or even a desirable outcome in human-AI collaboration. Yet, recent studies have shown that selective compliance with fair algorithms can \textit{amplify} discrimination relative to the prior human policy. As a consequence, ensuring equitable outcomes requires fundamentally different algorithmic design principles that ensure robustness to the decision-maker’s (a priori unknown) compliance pattern. We define the notion of \textit{compliance-robustly} fair algorithmic recommendations that are guaranteed to (weakly) improve fairness in decisions, regardless of the human’s compliance pattern. We propose a simple optimization strategy to identify the best performance-improving compliance-robustly fair policy.  However, we show that it may be infeasible to design algorithmic recommendations that are simultaneously fair in isolation, compliance-robustly fair, and more accurate than the human policy; thus, if our goal is to improve the equity and accuracy of human-AI collaboration, it may not be desirable to enforce traditional algorithmic fairness constraints. We illustrate the value of our approach on criminal sentencing data before and after the introduction of an algorithmic risk assessment tool in Virginia.

  • Neha Sharma, Maya Ganesh, Debjit Roy (Under Review), Partial Information in Fork-Join Operations: Evidence from Multi-Brand Cloud Kitchens. 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 toward in-house brands by providing them with greater 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.

  • Vikas Deep, Neha Sharma, Leann Thayaparan (Under Review), Scaling EV Access: Designing Intervention Policies for Asset-Backed Lending. Abstract

    Asset-backed lending can expand access to high-cost productive assets, such as electric vehicles (EVs), for small commercial operators in emerging markets. With volatile borrower incomes and noisy repayment signals, a central operational challenge is how lenders respond when borrowers miss payments: they may send reminders or renegotiate informally, but eventually must decide whether to deploy scarce field capacity for enforcement actions, including repossession. In practice, these decisions are often ad hoc: lenders either wait too long and absorb large losses, threatening the viability of continued lending, or enforce too aggressively, disrupting livelihoods and discouraging future participation. Because vehicle value depreciates quickly, delays in enforcement reduce recoverable value and can foreclose redeployment opportunities in secondary markets. We study this problem using data from an Indian lender financing EVs for small commercial operators. We formulate capacity-constrained enforcement decisions as a budgeted restless bandit problem and develop a tractable dual-based priority policy that ranks accounts by the value of immediate enforcement versus continued engagement. We prove global convergence to a unique steady state under a simple verifiable cutoff condition and establish asymptotic optimality as the portfolio scales. We also show that the resulting dual-priority policy is interpretable, computationally simple, and exhibits monotone structure under realistic conditions. Empirically, our dual-priority policy outperforms commonly used industry benchmarks by more than 15\%. In observational data, it improves on the firm’s current strategy by 1.5\% even under highly conservative evaluation assumptions, while rarely enforcing against borrowers who would have eventually repaid. The gains are driven by two mechanisms: earlier enforcement when recovery is unlikely, which preserves recovery value, and avoidance of reactive enforcement in response to short-term repayment fluctuations. As a result, the policy extends greater flexibility to lower-income, more volatile borrowers. This framework provides lenders with a practical and interpretable tool for enforcement decisions under limited capacity.

  • Neha Sharma and Simin Li (Working), What remains after LLMs: technical knowledge moves from hubs to niches. 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.

    Description
    Will LLMs Replace Coders? Not Entirely.
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  • Neha Sharma, Sumanta Singha, Milind Sohoni, Achal Bassamboo (Under Revision), The Empty Promise: How Strategic Suppliers Could Undermine Reservation Systems. 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.

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  • Neha Sharma, Gad Allon, Achal Bassamboo (Under Review), Structuring Online Communities. Abstract

    Online Question and Answer communities were started to supplement customer support services. In contrast to conventional customer support, users in online communities can post questions, and other users with more experience or knowledge can answer these questions. Generally, questions answered get rewards and visibility in the community, while the askers gain knowledge if their questions get answered. We study how users decide to join, leave, and participate in these communities. We link the user participation decisions to the underlying network structure of the community. Finally, we explore the levers a community designer can use to balance user participation level and the community’s efficiency in providing answers to users’ questions. We model the community as a multistage stochastic game where all the users have different skill levels. We find the stationary equilibrium of this game and theoretically show that only a core-periphery network structure can emerge in such communities. This network structure has been empirically observed in most online communities. Furthermore, we find that increasing the cost of asking questions in the community improves the proportion of askers that get answers to their questions. This results in higher user satisfaction. However, a higher asking cost lowers the participation level in the community. This trade-off between participation and community efficiency results in non-monotonicity in the number of users in the community with the participation cost. The paper explores the cost of asking a question as a lever that can be used by communities to control the number and knowledge type of users in the community. The communities typically operationalize higher asking costs by either directly penalizing question asking activity or setting up stricter guidelines for questions to be answered. We find that increasing the cost of asking is not always bad for the community. In fact, a higher asking cost improves user satisfaction which can lead to an increase in the number of users in the community despite higher asking cost. We also discuss how the existence of low knowledge users in the community (and not necessarily the high knowledge users) is essential to the survival of such communities.

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  • Neha Sharma, Sripad Devalkar, Milind Sohoni (2020), Payment for Results: Funding Non-Profit Operations, Production and Operations Management. https://onlinelibrary.wiley.com/doi/abs/10.1111/poms.13336 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.

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Teaching

Instructor – OIDD 2200: Operations Management Analytics

Term: Spring
This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.

 

Guest Lecturer – OIDD 9010: PhD Seminar

Term: Spring

Current Courses

  • OIDD2200 - Introduction To Operations Management

    This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.

    OIDD2200001 ( Syllabus )

    OIDD2200002 ( Syllabus )

Past Courses

  • OIDD2200 - Operations Management Analytic

    This course introduces basic concepts of operations management and application of the same in business practice today. We will examine the theoretical foundations of operations management and how these principles or models can be employed in both tactical and strategic decision making. Topics covered in detail are forecasting techniques, planning under deterministic and uncertain demand, operations planning and scheduling, queuing theory, service operations management, newsvendor models, risk pooling strategies in firms, capacity and revenue management, and supply chain coordination. We will conclude by discussing how supply chains evolve under technological change.

Awards And Honors

In the News

Knowledge @ Wharton

Activity

Latest Research

Haosen Ge, Neha Sharma, Hamsa Bastani, Osbert Bastani (Under Revision), Rethinking Algorithmic Fairness for Human-AI Collaboration.
All Research

In the News

The Fed’s Payment Rails and Fintech Access

David Zaring, Wharton professor of legal studies and business ethics, discusses the Fed’s proposal to grant limited payment system access to fintech and crypto firms.…Read More

Knowledge @ Wharton - 2026/03/18
All News

Awards and Honors

Finalist IBM Best Student Paper 2023
All Awards