Scan short courses hosted by professional societies, workshops sponsored by centers and institutes, courses at other area institutions, and online learning opportunities.
Short Courses and Digital Materials
AcademyHealth. Online training offerings including: webinars, Google Hangouts, and seminars on topics such as health equity, public & population health, access to care, quality improvement, costs of care and using health data. AcademyHealth resources.
Institute for Operations Research and the Management Sciences (INFORMS). Video library of sessions from past conferences. INFORMS resources.
International Federation of Operations Research Societies (IFORS). Members have access to educational material such as case studies, and interactive web-based tutorial modules on generic OR topics. Access IFORS.
International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Videos, webinars, online training, short courses on pharmacoeconomics, economic evaluation, technology assessment, and health-related quality of life and their use in health care decisions. ISPOR resources.
International Society for Quality of Life Research (ISOQOL). Online webinars in quality of life and outcomes methods and measurement. ISOQOL resources.
Society for Benefit-Cost Analysis (SBCA). Pre-conference professional development workshops, with previous topics having included retrospective Benefit-Cost Analysis, estimating parameter values in BCA, and policy impact on causal analytics for BCA. SBCA events.
Society for Medical Decision Making (SMDM). Short courses on health decision analysis, medical decision making, decision modeling, health-related quality of life, various other methods, tools and applications, as well as educational modules available on-line. SMDM resources.
Workshops and Online Learning
Centre for Health Economics, University of York. The Centre for Health Economics offers summer workshops in health economic evaluation, outcome measurement and valuation for health technology assessment. Three-day ‘outcome’ workshop covers the key principles of outcome measurement and valuation as well as their practical implementation in health technology assessment. CHE workshops.
edX. edX is a non-profit online initiative created by founding partners Harvard and MIT, and is the platform that hosts HarvardX courses. edX offers interactive online classes and MOOCs from top universities, colleges, and organizations on topics including biology, business, chemistry, computer science, economics, finance, engineering, food and nutrition, history, humanities, law, literature, math, medicine, music, philosophy. Access edX.
University for Health Science, Medical Informatics and Technology (UMIT). The Health Technology Assessment & Decision Science Program (HTAD) of UMIT offers three to five-day workshops in health technology assessment, clinical epidemiology, causal inference, and decision-analytic modelling. Access UMIT.
University of Glasgow, Health Economics and Health Technology Assessment. Two to three-day workshops on “decision analytic modelling methods for economic evaluation” as foundational and advanced courses in Glasgow, Scotland and York, England. HEHTA workshops.
Courses at Area Institutions
Courses at MIT
Behavioral Decision Theories and Applications (MIT, Fall) Instructor(s): TBA. Introduces fundamental behavioral theories of human decision making and demonstrates how they impact the design of management strategies and policies. Topics include prospect theory, reference-dependence preferences, loss aversion, ambiguity aversion, regret, inter-temporal preferences, social preferences, cognitive hierarchy, bounded rationality, and adaptive learning. Studies these concepts in a wide range of applications, including pricing, supply chain management, social welfare, marketing, contract design, sustainability, and e-commerce. Discusses experimental methodologies to identify and measure various preferences and phenomena, as well as mathematical models to capture them in decision making. Content updated from year to year to include state-of-the-art research. Prerequisites/Notes: Permission of Instructor. Primarily for graduate students. Course ID: 15.795
Cognitive Science (MIT, Spring) Instructor(s): E. Gibson, P. Sinha, J. Tenenbaum. Intensive survey of cognitive science. Topics include visual perception, language, memory, cognitive architecture, learning, reasoning, decision-making, and cognitive development. Topics covered from behavioral, computational, and neural perspectives. Prerequisites/Notes: Permission of Instructor. Primarily for graduate students. Course ID: 9.012
Decisions, Games and Rational Choice (MIT, Spring) Instructor(s): V. McGee. Foundations and philosophical applications of Bayesian decision theory, game theory and theory of collective choice. Why should degrees of belief be probabilities? Is it always rational to maximize expected utility? If so, why and what is its utility? What is a solution to a game? What does a game-theoretic solution concept such as Nash equilibrium say about how rational players will, or should, act in a game? How are the values and the actions of groups, institutions and societies related to the values and actions of the individuals that constitute them? Prerequisites/Notes: Primarily for undergraduate students. Course ID: 24.222
Engineering Systems Analysis for Design (MIT, Fall) Instructor(s): Richard De Neufville. Practical-oriented subject that builds upon theory and methods and culminates in extended application. Covers methods to identify, value, and implement flexibility in design (real options). Topics include definition of uncertainties, simulation of performance for scenarios, screening models to identify desirable flexibility, decision analysis, and multidimensional economic evaluation. Students demonstrate proficiency through an extended application to a system design of their choice. Complements research or thesis projects. Meets with IDS.333 first half of term. Enrollment limited. Prerequisites/Notes: Permission of instructor. For graduate students. Course ID: 1.146
Individuals, Groups, and Organizations (MIT, Spring) Instructor: J. Curhan. Covers classic and contemporary theories and research related to individuals, groups, and organizations. Designed primarily for doctoral students in the Sloan School of Management who wish to familiarize themselves with research by psychologists, sociologists, and management scholars in the area commonly known as micro organizational behavior. Topics may include motivation, decision making, negotiation, power, influence, group dynamics, and leadership. Prerequisites/Notes: Permission of instructor. Primarily for graduate students. Course ID: 15.341
Introduction to Mathematical Programming (MIT, Fall) Instructor(s): D. Bertsimas. This course introduces linear optimization and its extensions emphasizing both methodology and the underlying mathematical structures and geometrical ideas. It covers classical theory of linear programming as well as some recent advances in the field. Topics include: simplex method; duality theory; sensitivity analysis; network flow problems; decomposition; integer programming; interior point algorithms for linear programming; and introduction to combinatorial optimization and NP-completeness. Prerequisites/Notes: For graduate students. Course ID: 6.25
People, Teams, and Organizations Laboratory (MIT, Fall) Instructor(s): John Stephen Carroll. Surveys individual and social psychology and organization theory interpreted in the context of the managerial environment. Laboratory involves projects of an applied nature in behavioral science. Emphasizes use of behavioral science research methods to test hypotheses concerning decision-making, group behavior, and organizational behavior. Instruction and practice in communication includes report writing, team projects, and oral and visual presentation. 12 units may be applied to the General Institute Laboratory Requirement. Shares lectures with 15.310. Prerequisites/Notes: Primarily for undergraduate students. Course ID: 15.301
IDS 410: Modeling and Assessment for Policy (MIT, Fall) Instructor(s): Noelle E. Selin. Explores how scientific information and quantitative models can be used to inform policy decision-making. Develops an understanding of quantitative modeling techniques and their role in the policy process through case studies and interactive activities. Addresses issues such as analysis of scientific assessment processes, uses of integrated assessment models, public perception of quantitative information, methods for dealing with uncertainties, and design choices in building policy-relevant models. Examples focus on models and information used in Earth system governance. Prerequisites/Notes: For graduate students. Course ID: 12.844
Optimization Methods (MIT, Fall) Instructor(s): P. Jaillet. This course introduces the principal algorithms for linear, network, discrete, robust, nonlinear, and dynamic optimization. It emphasizes methodology and the underlying mathematical structures. Topics include the simplex method, network flow methods, branch and bound and cutting plane methods for discrete optimization, optimality conditions for nonlinear optimization, interior point methods for convex optimization, Newton’s method, heuristic methods, and dynamic programming and optimal control methods. Prerequisites/Notes: Primarily for graduate students. Course ID: 6.255
Optimization Methods in Business Analytics (MIT, Spring) Instructor(s): James B. Orlin. This course introduces optimization methods with a focus on modeling, solution techniques, and analysis. Covers linear programming, network optimization, integer programming, nonlinear programming, and heuristics. Applications to logistics, manufacturing, statistics, machine learning, transportation, game theory, marketing, project management, and finance. Includes a project in which student teams select and solve an optimization problem (possibly a large-scale problem) of practical interest. Prerequisites/Notes: 1.00, 1.000, 6.0001, or permission of instructor. Primarily for undergraduate students. Course ID: 15.053
Principles of Autonomy and Decision Making (MIT, Fall) Instructor(s): Howard E. Shrobe. Surveys decision making methods used to create highly autonomous systems and decision aids. Applies models, principles and algorithms taken from artificial intelligence and operations research. Focuses on planning as state-space search, including uninformed, informed and stochastic search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, and Bayesian filtering. Also emphasizes planning with real-world constraints using constraint programming. Includes methods for satisfiability and optimization of logical, temporal and finite domain constraints, graphical models, and linear and integer programs, as well as methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments. Prerequisites/Notes: 6.0002, 6.01, or permission of instructor. For undergraduate students. Course ID: 16.413
Research Seminar in System Dynamics (MIT, Fall and Spring) Instructor(s): D. Keith. Doctoral level seminar in system dynamics modeling, with a focus on social, economic and technical systems. Covers classic works in dynamic modeling from various disciplines and current research problems and papers. Participants critique the theories and models, often including replication, testing, and improvement of various models, and lead class discussion. Topics vary from year to year. Prerequisites/Notes: For graduate students. Course ID: 15.879
IDS 333: Risk and Decision Analysis (MIT, Fall) Instructor(s): Richard de Neufville. Focuses on design choices and decisions under uncertainty. Topics include identification and description of uncertainties using probability distributions; the calculation of commensurate measures of value, such as expected net present values; Monte Carlo simulation and risk analysis; and the use of decision analysis to explore alternative strategies and identify optimal initial choices. Applied analysis of practical examples from a variety of engineering systems using spreadsheet and decision analysis software. Meets with IDS.332 first half of term. Prerequisites/Notes: Primarily for graduate students. Course ID: IDS.333