Letter From the Chair
The Quantitative Advantage
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.
Charles E. Exley Professor of Management;
Chair of Decision, Risk, and Operations Division
Affiliated Departments and Research Centers
The Computational Optimization Research Center
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.
Center for Applied Probability
provides an umbrella under which diverse research and educational activities in probability and its applications can be focused and supported.
Industrial Engineering and Operations Research
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.
The Center for Financial Engineering
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.
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Privacy and the Value of Data
How does protecting the privacy of consumers affect the value of their personal data? We model an intermediary that uses consumers' data to influence the price set by a seller. When privacy is protected, consumers choose whether to disclose their data to the intermediary. When privacy is not protected, the intermediary can access consumers' data without their consent. We illustrate that protecting consumers' privacy has complex effects. It can increase the value of some consumers' data while decreasing that of others.
Proximity Bias: Motivated Effects of Spatial Distance on Probability Judgments
Welfare Consequences of Sustainable Finance
We model the welfare consequences of portfolio mandates that restrict investors to hold firms with net-zero carbon emissions. To qualify for these mandates, value-maximizing firms have to accumulate decarbonization capital. Qualification lowers a firm’s required rate of return by its decarbonization investments divided by Tobin’s q, i.e., the dividend yield shareholders forgo to address the global-warming externality.
The Value of Data Records
Many e-commerce platforms use buyers' personal data to intermediate their transactions with sellers. How much value do such intermediaries derive from the data record of each single individual? We characterize this value and find that one of its key components is a novel externality between records, which arises when the intermediary pools some records to withhold the information they contain. Ignoring this can significantly bias the evaluations of data records.
Thompson Sampling with Information Relaxation Penalties
We consider a finite-horizon multi-armed bandit (MAB) problem in a Bayesian setting, for which we propose an information relaxation sampling framework. With this framework, we define an intuitive family of control policies that include Thompson sampling (TS) and the Bayesian optimal policy as endpoints. Analogous to TS, which, at each decision epoch pulls an arm that is best with respect to the randomly sampled parameters, our algorithms sample entire future reward realizations and take the corresponding best action.