Market Design Center

Markets for Sustainability.

Carbon removal is a crucial part of global sustainability efforts. Unfortunately, the current market for high-quality carbon removal is extremely thin. Better incentives for production and investment are sorely needed. Our group is actively investigating market mechanisms that improve the supply of carbon removal credits reliably and efficiently.

One of our projects in this space relates to the development of afforestation contracts. Afforestation (growth of net new trees) pulls carbon from the atmosphere. Trees on farms can also improve agricultural productivity and provide additional income to farmers through the sale of carbon reduction credits. Buyers of such credits, however, want to ensure that the farmer is properly incentivized to safeguard tree growth over time. Using the lens of contract theory, this research shows how to calculate the optimal stream of payments over time to ensure incentive alignment. This research can be applied to enlarge the supply of carbon reduction credits, especially outside the developed world. For more information, see our published paper Contract Design for Afforestation Programs (opens in new tab).

Territory Management and Salesforce Matching.

Microsoft enterprise customers are assigned teams of sales and support professionals to help manage and maintain their contracts and products. Each year, Microsoft must form a matching between its salesforce and customers, spanning many salesperson roles, geographic regions, and account types. This is a complex multi-objective matching problem, which must satisfy multiple constraints and balance different criteria of what makes a successful match.

The Market Design Center has worked with the Behavioral Analytics team to design and implement an algorithm to evaluate and propose assignments of salespeople to sales accounts. Our solution leveraged the theory of simulated annealing and many-to-many matchings to balance exploration and convergence to a high-valued feasible assignment. In addition to direct optimization, this algorithmic framework is used to evaluate counter-factual adjustments to the market, such as estimating the marginal benefit of increasing the number of salespeople in a given role.

Online Advertising in Bing.

The market design center has a long-running engagement with the Bing Advertising team, and we work closely with them to analyze pricing and allocation rules for ads on Bing. One sample engagement centers around pricing rules for so-called Rich ads. Rich ads have varying sizes, and as such, in a poorly-designed system, large ads can poach space from small ones. Our solution to selling Rich ads leverages the theory of core auctions to maximize the value of displayed ads while maintaining good revenue for the platform. For more information, see our published paper Fast Core Pricing for Rich Advertising Auctions (opens in new tab).

Another theme of engagement centers around the design of autobidders. An autobidder employs machine learning techniques to optimizing bidding strategies on behalf of advertisers. To understand the efficacy of an autobidder design, it is not enough to consider the individual guarantees obtained for an advertiser in isolation: one must also consider emergent behavior that arises when autobidders interact with each other. Our work in this space develops guidelines for the development of auction rules and autobidder designs that jointly achieve high-welfare outcomes in the aggregate, avoiding market failures due to unintended algorithmic collusion. For more information, see the following papers: