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
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'.
In evaluative contexts, evaluatees typically seek to present themselves in a favorable light, while evaluators ask penetrating questions to assess these claims. Here we develop a framework to identify curveball questions: ones that are on-topic yet perplexing (i.e., difficult to predict) relative to past discourse. We develop a language-based measure of curveball questions and apply it to a corpus of quarterly earnings calls.
We explore how traders' equity capitalization influences asset prices in a framework that accounts for market power. In our model traders with capital constraints engage in transactions in an imperfectly competitive market. We demonstrate that looser capital constraints elevate both asset prices and price impact, which diminishes market liquidity. Using Canadian Treasury auction data, we illustrate how to apply our model to quantify these effects. We estimate the shadow costs of capital constraints by exploiting a temporary policy exemption during 2020-2021.
This paper studies the implications of central bank credibility for long-run inflation and inflation dynamics. We introduce central bank lack of commitment into a standard non-linear New Keynesian economy with sticky-price monopolistically competitive firms. Inflation is driven by the interaction of lack of commitment and the economic environment. We show that long-run inflation increases following an unanticipated permanent increase in the labor wedge or decrease in the elasticity of substitution across varieties. In the transition, inflation overshoots and then gradually declines.
