Assistant Professor of Operations Management, HEC Paris
- INFORMS 2019:
- SB14: Oct 20, 11:00am, 14-CC 302
- MD17: Oct 21, 4:30pm, 17-CC 305
Research Interests: operations management in interconnected information-based economies; privacy & information; social networks, platforms & marketplaces;
Teaching Interests: operations & supply chain management; platforms, networks, & data analytics; business model innovation.
Published & Forthcoming Papers
 The Use and Value of Social Network Information in Selective Selling of Exclusive Products - [Read full abstract] - [SSRN Link]
We consider the use and value of social network information in selectively selling goods and services whose value derives from exclusive ownership among network connections or friends. Our stylized model accommodates customers who are heterogeneous in their number of friends (degree)
and their proclivity for social comparisons (conspicuity). Firms with information on either (or both) of these characteristics can use it to make a product selectively available and increase their profits by better managing the exclusivity-sales trade-off. We find that the firm’s best
targets are high-conspicuity customers within intermediate-degree segments – in contrast with the practice of targeting high degree customers. We also find that information about degree is more valuable than information about conspicuity. Surprisingly, strategies informed only by degree
perform no worse than those informed by degree and conspicuity both, yet the opposite is not true. Customer self-selection is a perfect substitute for unavailable information on conspicuity, but there is no such recourse when degree information is unavailable. Examining alternate settings
(conformance social comparisons, functional value heterogeneity) suggests that there are two canonical categories of social information– less valuable information on characteristics where the firm’s preferred customers are also the most interested customers and more valuable information on
characteristics where they are not.
published at Network Science, 2017
Joint with Matt Leduc (Paris School of Economics)
What are incentives of the individuals (or firms) in a generic networked environment (e.g. social platform, supply chain) to invest in costly protection against both intrinsic (e.g. direct hacker attack, natural disaster) and networked (e.g. leakage of one’s sensitive information that exposes his/her social connections, cascading disruptions) risk?
We study the incentives that agents have to invest in costly protection against cascading failures in networked systems. Applications include vaccination, computer security and airport security. Agents are connected through a network and can fail either intrinsically or as a result of the failure of a subset of their neighbors. We characterize the equilibrium based on an agent’s failure probability and derive conditions under which equilibrium strategies are monotone in degree (i.e. in how connected an agent is on the network). We show that different kinds of applications (e.g. vaccination, airport security) lead to very different equilibrium patterns of investments in protection, with important welfare and risk implications. Our equilibrium concept is flexible enough to allow for comparative statics in terms of network properties and we show that it is also robust to the introduction of global externalities (e.g. price feedback, congestion).
Working Papers & Papers under Review
Joint with Itay P. Fainmesser (Johns Hopkins University) and Andrea Galeotti (London Business School), in preparation for submission, 2019
How to design digital businesses when consumers care about privacy? Do advertisement-driven companies store more of consumers' data as compared to transaction-driven companies? How to quantify damage to consumers from revelation of their information?
This paper proposes a framework for studying data policy, information security, and privacy concerns in digital businesses. We offer a full characterization of the optimal design of data storage and data protection policies for a digital company and also of how those policies affect users' activity, privacy, and welfare. Our framework features a taxonomy that distinguishes between advertisement-driven companies (e.g., Facebook and Google) and transaction-driven companies (e.g., Amazon and Uber). This distinction reveals that advertisement-driven businesses store either all or none of the user-generated data whereas transaction-driven businesses exhibit a smoother pattern that may include an intermediate data storage policy. Comparing the amount of user information that these two types of companies store, we find that—contrary to public opinion—advertisement-driven companies do not invariably retain more of their users' data than do transaction-driven companies. Our study establishes that measuring the direct damage inflicted by adversaries on consumers significantly underestimates not only the welfare loss but also the loss of consumer surplus due to adversarial activity. Finally, we identify the conditions under which advertisement-driven businesses generate more consumer surplus than that generated by their transaction-driven counterparts.
 Impact of Workforce Flexibility on Customer Satisfaction: Empirical Framework & Evidence from a Cleaning Services Platform - [Read full abstract] - [SSRN Link]
Joint with Ekaterina Astashkina (University of Michigan) and Marat Salikhov (Yale, INSEAD), in preparation for re-submission, 2019
Should on-demand platforms allow their workers to choose tasks on their own?
Problem definition: Contrary to classic applications of matching theory, in most contemporary on-demand service platforms, matches can not be enforced because workers are flexible – they choose their tasks. Such flexibility makes it difficult to manage workers while keeping customers satisfied. We build a framework to compare platform matching policies with less flexible and more flexible workers, and empirically quantify by how much worker flexibility hurts customer satisfaction and customer equity.
Academic/Practical relevance: In academic literature, there is no established framework that allows for the comparison of matching policies in on-demand platforms. Further, the link between worker flexibility and customer satisfaction is understudied.
Methodology: We propose a tripartite framework for empirical evaluation and comparison of the operational policies with different degrees of worker flexibility. Step 1: Predictive modeling of customer satisfaction based on estimation of individual unobservable characteristics: customer difficulty and worker ability (item-response theory model). Step 2: Evaluation of the effect of matching policy (under a given level of flexibility) on customer satisfaction (bipartite matching). Step 3: Quantification of the associated monetary impact (customer lifetime value model).
Results: We apply our framework to the dataset of one of the world's largest on-demand platforms for residential cleanings. We find that customer difficulty and cleaner ability are good predictors of customer satisfaction. Granting full flexibility to workers reduces customer satisfaction by 3% and customer lifetime revenue by 0.2%. We propose a family of matching policies that provide sufficient flexibility to workers, while alleviating 75% of the detrimental effect of worker flexibility on customer satisfaction.
Managerial implications: Our results suggest that, in platforms with flexible workforce, the presence of worker and customer heterogeneity translates into matching inefficiency – the drop in customer satisfaction. Our empirical framework helps practitioners to decide on the right level of worker flexibility and the means for achieving it.
 Inventory Management for 1% Products - [Read full abstract]
Joint with Elena Belavina (Cornell University), preliminary draft is available upon request, 2018
What is optimal long-term inventory policy when selling products to customers engaged in social comparisons?
Sociological trends have led to consumer's increasing willingness to purchase goods whose value lies partly in the exclusivity of their ownership. Firms selling these goods face a trade-off--while producing more allows for extracting more revenues, it also may compromise the product's reputation for exclusivity. In this study, we develop a dynamic game-theoretic model of reputations--a repeated game with long-lived, short-lived players, incomplete information, adverse selection, Bayesian updating of reputations and strategic memory. Our findings suggest a radical departure from the recommendations of the existing literature. Unlike the over-production equilibrium that is widely touted in the existing literature, our more realistic dynamic model suggests that in equilibrium the firm follows a non-stationary cyclic strategy that alternates scarcity phases with the overproduction phases. While the former is used as an exclusivity reputation building mechanism, the latter represents a reputation exploitation phase.
Work in Progress
 Validating a Business Idea in a Social Network: Who to Ask and What to Ask For?
Joint with Evgeny Kagan (Johns Hopkins University), preparation of the experiment at the incubator for startups Station F (Paris), 2019
- Recipient of The HEC Foundation Research Grant: EUR 22,000
 User Privacy in Queues
Joint with Ming Hu (University of Toronto) and Jianfu Wang (Nanyang Technological University), preliminary results are available upon request, 2019
 The Value of Information in Online Ad Auctions
Joint with Alex Nikulkov (Facebook Research), preliminary results are available upon request, 2019