Meenal Chhabra

Expert-mediated sequential search

This paper studies markets, such as Internet marketplaces for used cars or mortgages, in which consumers engage in sequential search. In particular, we consider the impact of information-brokers (experts) who can, for a fee, provide better information on true values of opportunities. We characterize the optimal search strategy given a price and the terms of service set by the expert, and show how to use this characterization to solve the monopolist expert’s service pricing problem. Our analysis enables the investigation of three common pricing schemes (pay-per-use, unlimited subscription, and package pricing) that can be used by the expert. We demonstrate that in settings characteristic of electronic marketplaces, namely those with lower search costs for consumers and lower costs of production of expert services, unlimited subscription schemes are favored. Finally, we show that the platform that connects consumer and experts can improve social welfare by subsidizing the purchase of expert services. The optimal level of subsidy forces the buyer to exactly fully internalize the marginal cost of provision of expert services. In electronic markets, this cost is minimal, so it may be worthwhile for the platform to make the expert freely available to consumers.

Team Formation in Social Networks

It is now recognized that the performance of an individual in a group depends not only on her own skills but also on her relationship with other members of the group. It may be possible to exploit such synergies by explicitly taking into account social network topology. We analyze team-formation in the context of a large organization that wants to form multiple teams composed of its members. Such organizations could range from intelligence services with many analysts to consulting companies with many consultants, all having different expertise. The organization must divide its members into teams, with each team having a specified list of interrelated tasks to complete, each of which is associated with a different reward. We characterize the skill level of a member for a particular task type by her probability of successfully completing that task. Members who are connected to each other in the social network provide a positive externality: they can help each other out on related tasks, boosting success probabilities. We propose a greedy approximation for the problem of allocating interrelated tasks to teams of members while taking social network structure into account. We demonstrate that the approximation is close to optimal on problems where the optimal allocation can be explicitly computed, and that it provides significant benefits over the optimal allocation that does not take the network structure into account in large networks. We also discuss the types of networks for which the social structure provides the greatest boost to overall performance.

Market mechanisms for Value of Information driven resource allocation in Sensor Networks

This paper examines the possible uses of different market mechanisms for resource allocation at different levels of Wireless Sensor Network (WSN) architecture. The goal is to maximize the Value of Information (VoI) for WSN users. We discuss three different levels of WSN architecture. The lowest level focuses on individual nodes and their basic functions of sensing and routing. We give an example showing how the use of auctions at individual nodes can help to efficiently perform these functions. The middle level focuses on services that are abstractions of applications running on sensors. Complex applications are composed automatically from basic ones. We discuss the use of switch options to address some of the challenges arising in such dynamic service composition. Finally, we consider the highest level - network deployment and sharing - and conjecture that options may be valuable in creating proper incentives for rational deployment and sharing of WSNs.

The Promise and Perils of Myopia in Dynamic Pricing With Censored Information

A seller with unlimited inventory of a digital good interacts with potential buyers with i.i.d. valuations. The seller can adaptively quote prices to each buyer to maximize long-term profits, but does not know the valuation distribution exactly. Under a linear demand model, we consider two information settings: partially censored, where agents who buy reveal their true valuations after the purchase is completed, and completely censored, where agents never reveal their valuations. In the partially censored case, we prove that myopic pricing with a Pareto prior is Bayes optimal and has finite regret. In both settings, we evaluate the myopic strategy against more sophisticated look-aheads using three valuation distributions generated from real data on auctions of physical goods, keyword auctions, and user ratings, where the linear demand assumption is clearly violated. For some datasets, complete censoring actually helps, because the restricted data acts as a “regularizer” on the posterior, preventing it from being affected too much by outliers.