MS&E 20: Discrete Probability Concepts And Models
Concepts and tools for the analysis of problems under uncertainty, focusing on structuring, model building, and analysis. Examples from legal, social, medical, and physical problems. Topics include axioms of probability, probability trees, belief networks, random variables, conditioning, and expectation.
Terms: Sum
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Units: 3
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Grading: Letter (ABCD/NP)
Instructors:
Shachter, R. (PI)
MS&E 22Q: The Flaw of Averages
Uncertain assumptions in business and public policy are often replaced with single ¿best guess¿ or average numbers. This leads to a fallacy as fundamental as the belief that the earth is flat, which I call the Flaw of Averages. It states, in effect, that: plans based on average assumptions are wrong on average. This class will discuss mitigations of the flaw of averages using simulation and other methods from probability management.
Terms: Aut
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Units: 3
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Grading: Satisfactory/No Credit
Instructors:
Savage, S. (PI)
MS&E 41: Financial Literacy
Practical knowledge about personal finance and money management including budgeting, pay checks, credit cards, banking, insurance, taxes, and saving. Class especially appropriate for those soon to be self-supporting. Limited enrollment. Admission by order of enrollment in Axess.
Terms: Win, Spr
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Units: 1
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Grading: Satisfactory/No Credit
Instructors:
Morrison, M. (PI)
MS&E 52: Introduction to Decision Making
Experienced management consultants share lessons and war stories. Case studies, disguised examples from real engagements, and movie clips illustrate theories and concepts of decision analysis. Student teams critique decisions made in actual organizations. Topics include what makes a good decision, how decisions can be made better, framing and structuring techniques, modeling and analysis tools, biases and probability assessment, evaluation and appraisal methods, decision psychology, creativity and organizational leadership, and effective presentation styles. Not intended for MS&E majors.
Terms: Sum
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Units: 3
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Grading: Letter or Credit/No Credit
Instructors:
Robinson, B. (PI)
MS&E 71SI: Entrepreneurship through the Lens of Venture Capital
How successful startups navigate funding, managing, and scaling their new enterprise. Process explored through guest lectures and mentorship from experienced venture capital investors and seasoned entrepreneurs who manage these issues on a daily basis in Silicon Valley. Course themes: customer value equation, board management, market strategy, company culture, and hyper growth. Enrollment is limited to 20 students. Visit
http://www.stanford.edu/dept/MSandE/lensofvc for application and more information.
Terms: Win
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Units: 1-2
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Grading: Satisfactory/No Credit
Instructors:
Kosnik, T. (PI)
MS&E 92Q: International Environmental Policy
Preference to sophomores. Science, economics, and politics of international environmental policy. Current negotiations on global climate change, including actors and potential solutions. Sources include briefing materials used in international negotiations and the U.S. Congress.
Terms: Win
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Units: 3
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Grading: Letter or Credit/No Credit
Instructors:
Weyant, J. (PI)
MS&E 93Q: Nuclear Weapons, Energy, Proliferation, and Terrorism
Preference to sophomores. What are nuclear weapons; what do they do? How are they different from other weapons? What drives proliferation of nuclear weapons? Why do countries want them? Can they be eliminated? What about Iran and North Korea? What role does nuclear energy play? Can it help combat global climate change? What are the risks of nuclear terrorism? Recommended: a course in international relations, engineering, or physical science.
Terms: not given this year
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Units: 3
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UG Reqs: GER:DBEngrAppSci
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Grading: Letter or Credit/No Credit
MS&E 94Q: The Public Use and Misuse of Mathematics: How to Interpret Numbers as Used by Media and Politicians
Preference to sophomores. How to unearth and interpret relevant math to illuminate underlying political and economic issues. How to interpret public budgets, whether jury pool selection is biased, estimate pollution risks, and when to believe poll results and statistical relationships; how to deal with rare but high-consequence eventualities such as terrorism, a nuclear meltdown, or a possible pandemic. How to determine how much to pay to reduce carbon emissions, when a medicine should be withdrawn, and what is a useful forecast.
Terms: not given this year
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Units: 3
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Grading: Letter (ABCD/NP)
MS&E 101: Undergraduate Directed Study
Subject of mutual interest to student and faculty member. Prerequisite: faculty sponsor.
Terms: Aut, Win, Spr, Sum
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Units: 1-15
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Repeatable for credit
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Grading: Letter or Credit/No Credit
Instructors:
Bambos, N. (PI)
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Barley, S. (PI)
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Blank, S. (PI)
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Brandeau, M. (PI)
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Byers, T. (PI)
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Chiu, S. (PI)
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De La Grandville, O. (PI)
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Dunn, D. (PI)
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Eesley, C. (PI)
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Eisenhardt, K. (PI)
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Erhun, F. (PI)
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Giesecke, K. (PI)
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Glynn, P. (PI)
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Goel, A. (PI)
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Hausman, W. (PI)
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Hecker, S. (PI)
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Hinds, P. (PI)
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Holtzman, S. (PI)
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Howard, R. (PI)
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Huntington, H. (PI)
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Infanger, G. (PI)
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Johari, R. (PI)
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Johnson, B. (PI)
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Katila, R. (PI)
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Kieffel, H. (PI)
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Kosnik, T. (PI)
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Luenberger, D. (PI)
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May, M. (PI)
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McGinn, R. (PI)
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Murray, W. (PI)
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Novitsky, D. (PI)
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Pate-Cornell, E. (PI)
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Perry, W. (PI)
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Pietzsch, J. (PI)
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Primbs, J. (PI)
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Rafinejad, D. (PI)
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Robinson, B. (PI)
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Roizen, J. (PI)
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Saberi, A. (PI)
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Saunders, M. (PI)
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Savage, S. (PI)
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Seelig, T. (PI)
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Shachter, R. (PI)
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Sutton, R. (PI)
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Sweeney, J. (PI)
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Tabrizi, B. (PI)
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Tse, E. (PI)
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Van Roy, B. (PI)
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Veinott, A. (PI)
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Weber, T. (PI)
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Weyant, J. (PI)
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Ye, Y. (PI)
MS&E 107: Interactive Management Science (MS&E 207)
Analytical techniques such as linear and integer programming, Monte Carlo simulation, forecasting, decision analysis, and Markov chains in the environment of the spreadsheet. Probability management. Materials include spreadsheet add-ins for implementing these and other techniques. Emphasis is on building intuition through interactive modeling, and extending the applicability of this type of analysis through integration with existing business data structures.
Terms: Aut, Sum
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Units: 3
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UG Reqs: GER:DBEngrAppSci
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Grading: Letter or Credit/No Credit
Instructors:
Savage, S. (PI)