The Decision, Risk, and Operations Division is a world leader in research and instruction in quantitative, data-driven decision-making through the use modeling, optimization and the management of uncertainty, and all aspects of the operations and analytics functions in firms.
Application areas in which the division has strong expertise include business analytics; e-commerce; revenue management; logistics, distribution and supply-chain management; resource networks and service systems; healthcare operations; market design; quantitative finance with emphasis on the valuation of derivative securities, modern market microstructure, and risk management; and econometrics.
An important aspect of the mission of the division is to foster collaboration with industry and impact society by solving important practical problems such as helping hospitals care for their patients in a more efficient and cost effective manner; coordination and risk mitigation in global supply chains; design of dynamic and responsive pricing algorithms in a variety of industries; creation of innovative securities trading algorithms; design of frameworks to measure systemic risk; and optimization of the operations of online marketplaces.
The division is actively involved in teaching in the MBA and PhD programs. In the MBA program, the division teaches the core courses on Managerial Statistics, Business Analytics, and Operations Management, and offers a suite of electives in various topics in Operations, Analytics, and Technology.
Carson Family Professor of Business;
Chair of Decision, Risk, and Operations Division
carries out advanced studies in the solution of difficult, large-scale optimization problems, with special focus on state-of-the-art implementation of modern algorithms.
provides an umbrella under which diverse research and educational activities in probability and its applications can be focused and supported.
is the home to Financial Engineering, a multidisciplinary field that requests familiarity with financial theory, the methods of engineering, the tools of mathematics and the practice of programming.
is part of an interdisciplinary field of research where contributions from applied mathematics, economics, operations research, statistics and computer science have given birth to remarkable developments in market practice.
Data assets are increasingly vital in modern economies, yet macroeconomic measurement is not well-adapted to capturing their value. Part of the problem is that data is an intangible asset: investments in data are missed in national accounts, and
Bank balance sheet lending is commonly viewed as the predominant form of lending. We document and study two margins of adjustment that are usually absent from this view using microdata in the $10 trillion U.S. residential mortgage market. We first document the limits of the shadow bank substitution margin: shadow banks substitute for traditional “deposit-taking” banks in loans which are easily sold, but are limited from activities requiring on-balance-sheet financing.
In a monetary union, the risk-free rate cannot respond to country-level fiscal positions, leaving only default spreads and convenience yields to respond. Empirically, we find that convenience yields explain a large share of the variation in Eurozone sovereign bond yields. Eurozone countries earn larger convenience yields when they experience larger surpluses, suggesting convenience yields are important fiscal shock absorbers.
Screening human capital based on signals such as job applications or entrepreneurial pitches is crucial for organizations. Signals are often informative insofar as they require differential knowledge and effort to produce. Generative AI (GAI) complicates screening by lowering the cost of producing impressive signals. We model the informational effects of GAI, showing that applicants' access to GAI can increase—but also decrease—an evaluator's screening mistakes. This result depends on how GAI affects experts' signals compared to non-experts'.
We document new evidence on the capitalization, funding structure, and performance of private credit funds using comprehensive fund-and asset-level data covering most of the industry. Private credit funds are highly capitalized, with equity typically accounting for 65-80% of total assetsmore than six times the capitalization of U.S. banks, where equity represents about 10%. Debt usage is moderate and largely reflects bank credit lines used for liquidity management.
