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The department’s engineering research strength is integrated with its educational program at the undergraduate, master’s, and doctoral levels: graduates of the program are trained as engineers and future leaders in technology, policy, and industry. How to organize the decision conversation, the role of the decision analysis cycle and the model sequence, assessing the quality of decisions, framing decisions, the decision hierarchy, strategy tables for alternative development, creating spare and effective decision diagrams, biases in assessment, knowledge maps, uncertainty about probability. Research and teaching activities are complemented by an outreach program that encourages the transfer of ideas to the environment of Silicon Valley and beyond. Successive approximation, policy improvement, and linear programming methods. Examples from inventory, overbooking, options, investment, queues, reliability, quality, capacity, transportation. Prerequisites: MATH 113, 115; Markov chains; linear programming. Decision Analysis II: Professional Decision Analysis. Sensitivity analysis, approximations, value of revelation, joint information, options, flexibility, bidding, assessing and using corporate risk attitude, risk sharing and scaling, and decisions involving health and safety. The program builds on the foundational courses for engineering, including calculus, mathematical modeling, probability, statistics, engineering fundamentals, and physics or chemistry. Emphasis on managing high-growth, early-stage enterprises, especially those with innovation-based products and services.

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MS&E’s programs also provide a basis for contributing to other areas such as biotechnology, defense policy, environmental policy, information systems, and telecommunications. Modern computational and statistical methods offer the promise of greater efficiency, equity, and transparency, but their use also raises complex legal, social, and ethical questions. Advanced stochastic modeling and control of systems involving queueing and scheduling operations. Key results on single queues and queueing networks. Dynamic routing and scheduling in processing networks. We will review recent research that aims to both understand and design such markets. Prerequisites: Mathematical maturity; 300-level background in optimization and probability; prior exposure to game theory. Multiname modeling: index and tranche swaps and options, collateralized debt obligations. Decision trees, utility, two-stage and multi-stage decision problems, approaches to stochastic programming, model formulation; large-scale systems, Benders and Dantzig-Wolfe decomposition, Monte Carlo sampling and variance reduction techniques, risk management, portfolio optimization, asset-liability management, mortgage finance. Prerequisites: 220, 226 or STATS 200, 221 or STATS 217, 245A, or equivalents.

The major prepares students for a variety of career paths, including investment banking, management consulting, facilities and process management, or for graduate school in industrial engineering, operations research, business The department expects undergraduate majors in the program to be able to demonstrate the following learning outcomes. degree, a dual master’s degree in cooperation with each of the other departments in the School of Engineering, and joint master's degrees with the School of Law and the Public Policy Program. Ito integral, existence and uniqueness of solutions of stochastic differential equations (SDEs), diffusion approximations, numerical solutions of SDEs, controlled diffusions and the Hamilton-Jacobi-Bellman equation, and statistical inference of SDEs. Enrollment is limited, and project teams will be selected during the first week of class. Prerequisites: CS 261 or equivalent; understanding of basic game theory.

These learning outcomes are used in evaluating students and the department's undergraduate program. For University coterminal degree program rules and University application forms, see the Registrar's coterminal degrees web site.

Management Science and Engineering (MS&E) provides programs of education and research by integrating three basic strengths: The analytical and conceptual foundations include decision and risk analysis, dynamic systems, economics, optimization, organizational science, and stochastic systems. Team decisions and stochastic programs; quadratic costs and certainty equivalents.

The functional areas of application include entrepreneurship, finance, information, marketing, organizational behavior, policy, production, and strategy. The last decade has witnessed a meteoric rise in the number of online markets and platforms competing with traditional mechanisms of trade. Credit risk modeling, valuation, and hedging emphasizing underlying economic, probabilistic, and statistical concepts. Structural, incomplete information and reduced form approaches. Markov population decision chains in discrete and continuous time.

The Department of Management Science and Engineering leads at the interface of engineering, business, and public policy.

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