Beyond Harvard

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.

Institute for Operations Research and the Management Sciences (INFORMS). Video library of sessions from past conferences.

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.

International Health Economics Association (iHEA). Member only content includes webinars, online access to previous webinars, and additional educational and economic resources. iHEA resources.

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.

International Society for Quality of Life Research (ISOQOL). Online webinars in quality of life and outcomes methods and measurement.

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.

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.

Society for Risk Analysis (SRA). Offers members materials including a webinar series and collated teaching resources including syllabi, notes and reading lists on risk-related topics.

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 short courses.

Cochrane. Online, interactive training modules on conducting systematic reviews. Cochrane resources.

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.

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.

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.

Courses at MIT

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. Course ID: 9.012

Decisions, Games and Rational Choice (MIT, Spring) Instructor(s): TBA. 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? Enrollment may be limited; preference to Course 24 majors and minors. Prerequisites/Notes: One philosophy subject or permission of instructor. Course ID: 24.222

System Design and Management for a Changing World: Combined (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. Class is “flipped” to maximize student engagement and learning. Meets with IDS.333 in the first half of term. Enrollment limited. Prerequisites/Notes: Permission of instructor. Course ID: 1.146

Individuals, Groups, and Organizations (MIT, Spring) Instructor: TBA. 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. Course ID: 15.341

Introduction to Mathematical Programming (MIT, Fall) Instructor(s): P. Jaillet. Introduction to linear optimization and its extensions emphasizing both methodology and the underlying mathematical structures and geometrical ideas. Covers classical theory of linear programming as well as some recent advances in the field. Topics: simplex method; duality theory; sensitivity analysis; network flow problems; decomposition; robust optimization; integer programming; interior point algorithms for linear programming; and introduction to combinatorial optimization and NP-completeness. Prerequisites/Notes: 18.06 is a prerequisite. Course ID: 6.25

People, Teams, and Organizations Laboratory (MIT, Fall) Instructor(s): K. Thompson. Develops appreciation for organizational dynamics and competence in navigating social networks, working in a team, demystifying rewards and incentives, leveraging the crowd, understanding change initiatives, and making sound decisions. Laboratory sessions emphasize the importance of the organizational context in influencing which individual styles and skills are effective by employing a wide variety of learning tools, from experiential learning to the more conventional discussion of written cases. Emphasizes use of behavioral science research methods to test hypotheses concerning decision-making, group behavior, and organizational behavior. Laboratory sessions provide instruction and practice in communication including report writing, team projects, and oral and visual presentation. 12 units may be applied to the General Institute Laboratory Requirement. 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. Course ID: 12.844

Optimization Methods (MIT, Fall) Instructor(s): Dimitris Bertsimas, Alexandre Jacquillat. 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 undergraduate students. Expectations and evaluation criteria differ for students taking graduate version. Course ID: 6.255

Optimization Methods in Business Analytics (MIT, Spring) Instructor(s): James B. Orlin, Tom Magnanti. 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. Course ID: 15.053

Principles of Autonomy and Decision Making (MIT, Fall) Instructor(s): A. Bobu. 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 sampling-based search, activity and motion planning, probabilistic and adversarial planning, Markov models and decision processes, Bayesian filtering, reinforcement learning, and machine learning. Includes methods for satisfiability and optimization of logical and finite domain constraints, graphical models, and methods for search, inference, and conflict-learning. Students taking graduate version complete additional assignments. Prerequisites/Notes: 6.100B, 6.1010, 6.9080, or permission of instructor. Course ID: 16.413

Research Seminar in System Dynamics (MIT, Fall and Spring) Instructor(s): C. Yang. Doctoral seminar in system dynamics modeling, with a focus on building advanced modeling and research skills. Topics vary from year to year and may include: classic works in dynamic modeling from various disciplines (e.g., psychology, sociology, behavioral economics) and current research problems and papers; advanced system dynamics models focused on research and practical problems of interest to students; analytic tools and methods for model development, estimation, and analysis (e.g., automating modeling workflow, maximum likelihood, simulated method of moments, dynamical games, dynamic programming); bootcamp for enhancing modeling skills working on multiple problem sets. Prerequisites/Notes: 15.873 and permission of instructor. Course ID: 15.879