Short Programs
Decision-Making, Design, and Strategy Under Uncertainty
Date: July 8-12, 2013 | Tuition: $3,750 | Continuing Education Units (CEUs): 2.8
*This course has limited enrollment. Apply early to guarantee your spot.
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Course Summary | Learning Objectives | Who Should Attend | Program Outline | Schedule | Lecturers | Location | Updates
Course Summary
An all-too-common practice in industrial or policy planning is to use a best-guess forecast, and optimize the design, strategy, operation, or policy for that forecast. Unfortunately, this practice systematically leads to inefficient and generally undesirable outcomes because it does not explicitly consider uncertainty during the design/planning stage.
This course is designed to achieve two critical objectives:
1. To increase your awareness and appreciation for WHY uncertainty matters.
2. To give you the tools to characterize uncertainty and to design flexible strategies that will be robust to uncertainty.
The primary focus of this course is on the concepts and the intuition. Using numerous real-world examples, we will demonstrate the consequences of ignoring uncertainty and what can be done instead. Through hands-on exercises, we will introduce you to existing software tools that can aid you in designing effective strategies, including Decision Analysis, Lattice Models, and Monte Carlo simulation.
Fundamentals: Core concepts, understandings and tools (60%)
Latest Developments: Recent advances and future trends (15%)
Industry Applications: Linking theory and real-world (25%)
Lecture: Delivery of material in a lecture format (40%)
Discussion or Groupwork: Participatory learning (30%)
Labs: Demonstrations, experiments, simulations (30%)
Introductory: Appropriate for a general audience (50%)
Specialized: Assumes experience in practice area or field (30%)
Advanced: In-depth explorations at the graduate level (20%)
Learning Objectives
The participants of this course will be able to:
- Be aware of the pervasiveness of uncertainty and its consequences for decision-making.
- Understand how to use Monte Carlo simulation and Risk Analysis tools to characterize uncertainty.
- Use Decision Trees to structure a design or decision problem, and how to use it to identify potential flexibility.
- Use Lattice Models for problems with more decision points.
- Understand a framework for thinking about design, strategy, or decision under uncertainty.
Who Should Attend
This course is aimed at all professionals, and is applicable to any industry or government sector in which design, planning, strategy, or policy decisions are made under conditions of uncertainty. The focus on key concepts will be useful to high-level managers, and the quantitative tools introduced are appropriate for analysis staff or engineers/designers. Relevant industry sectors include Energy, Biotech, Aerospace, Manufacturing, and Services.
Program Outline
The course will consist of five one-day blocks, and each day will be divided between lecture/discussion to introduce the concepts and tools, and time for hands-on exercises to learn to use the tools. The topics for each day are:
Day 1: Introduction and Uncertainty
Session 1--1.25 hours: Introduction to course, decision-making under uncertainty, Flaw of Averages
Break
Session 2--1.5 hours: Uncertainty: Concepts, Examples
Lunch
Session 3--1.5 hours: Uncertainty Tools: Probability Distributions and Monte Carlo Simulation
Break
Session 4--1.25 hours: Exercise 1: Uncertainty and Monte Carlo
Day 2: Decision under Uncertainty
Session 5--1.75 hours: Structuring Decisions, Decision Trees
Break
Session 6--1.5 hours: Solving Decision Trees, Sensitivity Analysis
Lunch
Session 7--1.5 hours: Value of Information, Risk Preferences
Break
Session 8--1.25 hours: Exercise 2: Decision Trees
Day 3: Lattices
Session 9--1.75 hours: Binomial Lattices
Break
Session 10--1.5 hours: When to use a lattice
Lunch
Session 11--1.5 hours: How to use a lattice
Break
Session 12--1.25 hours: Exercice 3: Lattice
Day 4: Real Options and Advanced Topics
Session 13--1.75 hours: Time value of Money, Discounting
Break
Session 14--1.5 hours: Real Options
Lunch
Session 15--1.5 hours: Simulation of decision-rules
Break
Session 16--1.25 hours: Exercise 4: Real options and Simulation
Day 5: Advanced Topics and Wrap-up
Session 17--1.75 hours: Large-scale problems: Stochastic Programming and Dynamic Programming
Break
Session 18--1.5 hours: Approximate Dynamic Programming and Example Applications
Lunch (provided)
Session 19--1.5 hours: Course Debrief and Feedback
Course schedule and registration times
Class runs 9:30 am - 5:00 pm on Monday and 9:00 am - 5:00pm the rest of the week except for Friday when it ends at 3:00 pm.
Registration is on Monday morning from 8:45 - 9:15 am.
Please note that laptops are required for this course.
About The Lecturers
Mort D. Webster
Prof. Webster is an Assistant Professor of Engineering Systems, with a focus on energy and environmental systems. Prof. Webster specializes in risk analysis, uncertainty analysis, and decision-making under uncertainty. He has published numerous peer-reviewed articles in energy and environmental science, economics, and policy, and has served on several national and international panels, including the US Climate Change Science Program. Current research projects include risk tradeoffs in long-term climate targets, modeling technological change as a stochastic process, evaluation of cost-containment provisions for climate policy under uncertainty, and integrated economic/energy/chemistry modeling for regional air quality policy design. Prof. Webster is active in several research centers at MIT, including the Center for Energy and Environmental Policy Research (CEEPR), the Joint Program on the Science and Policy of Global Change, and the MIT Energy Initiative. He received a Ph.D. (2000) in Engineering Systems and a M.S. (1996) in Technology and Policy from MIT, and a B.S.E. (1988) in Computer Science and Engineering from the University of Pennsylvania. Prior to returning to MIT, Prof. Webster was an assistant professor of public policy in the Department of Public Policy at the University of North Carolina at Chapel Hill.
For more information on the MIT Engineering Systems Division (ESD), please visit http://esd.mit.edu/; for information on CEEPR, please visit http://web.mit.edu/ceepr/www/; for information on the Joint Program, please visit http://globalchange.mit.edu/ and for information on the MIT Energy Initiative, please visit http://globalchange.mit.edu/.
Location
This course takes place on the MIT campus in Cambridge, Massachusetts. We can also offer this course for groups of employees at your location. Please contact the Short Programs office for further details.
