Harvard University

APMTH 231: Decision Theory (FAS, Applied Mathematics, Spring) Instructor(s): Demba Ba. This course focuses on statistical inference and estimation from a signal processing perspective. The course emphasizes the entire pipeline from writing a model, estimating its parameters and performing inference utilizing real data. The first part of the course focuses on linear and nonlinear probabilistic generative/regression models (e.g. linear, logistic, Poisson regression), and algorithms for optimization (ML/MAP estimation) in these models. The course plays particular attention to sparsity-induced regression models, that arise for instance in compressed sensing, because of their relation to artificial neural networks, the topic of the second part of the course. The second part of the course introduces students to the nascent and exciting research area of generative models of deep networks called model-based deep learning. At present, we lack a principled way to design artificial neural networks, the workhorses of modern AI systems. Moreover, modern AI systems lack the ability to explain how they reach their decisions. In other words, we cannot yet call AI explainable or interpretable which, as a society, poses important questions as to the responsible use of such technology. Model-based deep learning provides a framework to develop and constrain neural-network architectures in a principled fashion. We will see, for instance, how neural-networks with ReLU nonlinearities arise from sparse probabilistic generative models introduced in the first part of the course. This will form the basis for a rigorous recipe we will teach you to build interpretable deep neural networks, from the ground up. The course invites an exciting line up of speakers. Speakers will suggest papers that a group of students will present at the beginning of lecture, which will build up to a final project/paper that utilizes/on model-based deep learning applied to problems of interest to students. Course ID: 203548

ECON 2059: Decision Theory (FAS, Economics, Fall) Instructor(s): Tomasz Strzalecki. This course prepares students for pure and applied research in axiomatic decision theory. The course starts with a rigorous treatment of the classical topics that are at the heart of all of economics (utility maximization, expected utility, discounted utility, Bayesian updating, dynamic consistency, option value). Then, the course delves into a number of modern topics inspired by the observed violations of the classical models (“exotic preferences” used in macro-finance, ambiguity aversion, temptation and self-control). The final part of the course explores the recently flourishing literature on stochastic choice (which is related to, but distinct from, discrete choice econometrics). Prerequisites/Notes: Basic microeconomic theory at the level of Mas Colell, Whinston, Green; comfort with abstract models. Course ID: 121331

RDS 284: Decision Theory (HSPH, Fall) Instructor(s): James Hammitt. This course introduces the standard model of decision-making under uncertainty, its conceptual foundations, challenges, alternatives, and methodological issues arising from the application of these techniques to health issues. Topics include von Neumann-Morgenstern and multi-attribute utility theory, Bayesian statistical decision theory, stochastic dominance, the value of information, judgment under uncertainty and alternative models of probability and decision making (regret theory, prospect theory, generalized expected utility). Applications are to preferences for health and aggregation of preferences over time and across individuals. Course ID: 191105

API 302 / ECON 1415: Analytic Frameworks for Policy (HKS, Economics, Fall) Instructor(s): Richard Zeckhauser. This course develops student’s ability to use analytic frameworks in the formulation and assessment of public policies. It considers a variety of analytic techniques, particularly those directed toward uncertainty and interactive decision problems. It emphasizes the application of techniques to policy analysis, not formal derivations. Students encounter case studies, methodological readings, modeling of current events, the computer, a final exam, and challenging problem sets. Prerequisites/Notes: An understanding of intermediate-level microeconomic theory and the basics of decision analysis is suggested; API 101, API 102, or equivalent, are sufficient. Open to MPP1 students only if they have exempted from API 101. Course ID: 170053

FRSEMR 70E: Climate Change Economics: Analysis and Decisions (FAS, Spring) Instructor(s): Martin Weitzman. Climate change is one of the most difficult problems facing humanity. This seminar relies on modern economic theory to focus on how an economist frames and views the basic issues. A small sample of questions to be asked and answers attempted in this seminar includes the following: How do we analyze and decide what to “do” about climate change? What are the basic “models” combining economics with climate science, what are these models telling us, and how do we choose among their varying messages? How are risk and uncertainty incorporated? How do we estimate future costs of carbon-light technologies? How do we quantify damages, including ecosystem damages? Who pays for what? Why are discounting and the choice of discount rate so critical to the analysis and what discount rate should we use? What is the “social cost of carbon” and how is it used? Which instruments (prices, quantities, standards, etc.) are available to control greenhouse gas emissions and what are the strengths and weaknesses of each? What is “climate sensitivity” and why is it, and the feedbacks it incorporates, so important? How should the possibility of catastrophic climate change be evaluated and incorporated? What are costs and benefits of geoengineering the planet to counter global warming? Why has climate change been characterized as “the biggest international market failure of all time” and how might the world resolve the associated free-rider problem? Course ID: 203008

GHP 228: Econometric Methods in Impact Evaluation (HSPH, Spring) Instructor(s): Jessica Cohen. This course provides students with a set of theoretical, econometric and reasoning skills to estimate the causal impact of one variable on another. Examples from the readings explore the causal effect of policies, laws, programs and natural experiments. The course goes beyond causal effects estimates to analyze the channels through which the causal impact was likely achieved. This requires that the students are familiar with microeconomic theories of incentives, institutions, social networks, etc. The course will introduce students to a variety of econometric techniques in impact evaluation and a set of reasoning skills intended to help them become both a consumer and producer of applied empirical research. Students learn to critically analyze evaluation research and to gauge how convincing the research is in identifying a causal impact. They will use these skills to develop an evaluation plan for a topic of their own, with the aim of stimulating ideas for dissertation research. Prerequisites/Notes: Students interested in taking this course must request instructor permission. Students outside of HSPH must request instructor permission to enroll. A course in econometrics and a course in intermediate microeconomics are required. While students can get by with just these two subjects, some previous experience with regression analysis and applied economic research will be a huge advantage. Students seeing applied regression analysis for the first time in this course will most likely struggle with the reading. This is a methods class that relies heavily on familiarity with econometrics and microeconomics. These are prerequisites for the course without exception. The course is intended for doctoral students who are finishing their course work and aims to help them transition into independent research. The aim of this course is to prepare doctoral students in the health systems area of specialization of the Global Health and Population department for the dissertation phase of their research and thus they will be given priority in enrollment. The course is also open to other GHP doctoral students and other doctoral and master’s students, conditional on having adequate training in economics and the course having enough space. Course ID: 190392

RDS 202: Decision Science for Public Health (HSPH, Spring) Instructor(s): Sue J. Goldie and Eve Wittenberg. Challenges in public health policy and clinical medicine are marked by complexity, uncertainty, competing priorities and resource constraints. This course is designed to introduce the student to the methods and applications of decision analysis and cost-effectiveness analysis in clinical and public health decision making. The objectives of the course are: (1) to provide a basic introduction to the methods and tools of decision science, and to recognize when, how, and in what context they can provide value in clinical and public health decision making; (2) to equip students with the ability to structure and bound a decision problem logically (articulating the objective, perspective, and time horizon), identify key elements (alternatives, uncertainties, and outcomes) and influential factors (preferences, risk attitudes, values); (3) to provide students with basic skills in revising probabilities given new information, building and analyzing decision trees, conducting cost effectiveness analysis, performing sensitivity analyses, and communicating results; (4) to enable students to thoughtfully and critically evaluate published analyses conducted to evaluate or inform clinical strategies, health technologies, and public health policies in developed and developing countries. Prerequisites/Notes: Preference is given to students in the MPH-EPI and Summer-Only programs. However, all degree students are encouraged to participate. Course ID: 204407

RDS 280: Decision Analysis for Health and Medical Practices (HSPH, Fall) Instructor(s): Ankur Pandya. This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in developed and developing countries. Prerequisites/Notes: Prerequisites include BIO 200 or BST 201 or BST 202 & 203 or BST 206 & 207 or BST 206 & 208 or BST 206 & 209 (all courses may be taken concurrently) or permission from the instructor. Introductory economics is recommended but not required. Students cannot take RDS 280 if they have already taken RDS 286 or RDS 202 (exceptions only allowed with permission of RDS 280 instructor). Course ID: 191102

RDS 282: Economic Evaluation of Health Policy & Program Management (HSPH, Spring) Instructor(s): Stephen Resch. This course features the application of health decision science to policymaking and program management at various levels of the health system. Both developed and developing country contexts will be covered. Topics include: (1) theoretical foundations of cost-effectiveness analysis (CEA) with comparison to other methods of economic evaluation; (2) challenges and critiques of CEA in practice; (3) design and implementation of tools and protocols for measurement and valuation of cost and benefit of health programs; (4) use of evidence of economic value in strategic planning and resource allocation decisions, performance monitoring and program evaluation; (5) the role of evidence of economic value in the context of other stakeholder criteria and political motivations. Prerequisites/Notes: Students must have taken RDS 280 or RDS 286. Concurrent enrollment is allowed. Prior coursework in Microeconomics is recommended. Course ID: 191104

RDS 285: Decision Analysis Methods in Public Health and Medicine (HSPH, Spring) Instructor(s): Nicolas Menzies. This intermediate-level course focuses on methods and health applications of decision analysis modeling techniques. Topics include Markov models, microsimulation models, life expectancy estimation, cost estimation, deterministic and probabilistic sensitivity analysis, value of information analysis, and cost-effectiveness analysis. Course Note: Familiarity with matrix algebra and elementary calculus may be helpful but not required; lab or section times to be announced at first meeting. Prerequisites/Notes: Prerequisites include (BST 201 or ID 201) and (RDS 280 or RDS 286). Concurrent enrollment is allowed for RDS 286. Course ID: 191106

RDS 286: Decision Analysis in Clinical Research (HSPH, Summer) Instructor(s): Uwe Siebert. This course introduces students to systematic methods of decision analysis relevant to clinical decision making, clinical research, comparative effectiveness research and cost-effectiveness analysis. Topics of the sessions include: the use of causal estimands to express efficacy and real-world clinical effectiveness; the use of probability and sensitivity analysis to express and assess uncertainty; Bayes theorem and evaluation of diagnostic procedures; utility theory and its use to express patient preferences for health outcomes; benefit-harm analysis for patient-shared decision making and clinical guideline development; cost-effectiveness analysis and health technology assessment for health policy decision making. Lecture s are accompanied by case problems, review sessions and computer exercises. After this course, students will understand the uses, strengths, limitations and ethical issues of decision analysis and cost effectiveness in clinical decision making and research design. We will discuss case examples from different disease areas including cancer, cardiovascular disease, infectious disease and others. Requires prior knowledge of clinical medicine (through training and/or clinical research experience) and strong quantitative ability/aptitude. Priority for enrollment will be given to students in the Program for Clinical Effectiveness (PCE). Prerequisites/Notes: Prerequisites include BST202 or BST206 (which may be taken concurrently) or BST201. Course restricted to HSPH Degree or PCE students. Any other interested students must request instructor permission to enroll in this course. Course ID: 191107

RDS 290: Experiential Learning and Applied Research in Decision Analysis (HSPH, Spring) Instructor(s): Jane J. Kim. This course is geared towards Masters-level students from any department. Students will undertake semester-long research projects on a clinical or public health decision problem using decision analysis, simulation modeling, and/or cost-effectiveness analysis. Each session will be dedicated to a particular topic of decision analytic methods or student presentations of prospectus, works-in-progress, and final projects. Students may work alone or in pairs, including at least one student who is familiar with the clinical content area of the project. Prerequisites/Notes: Prerequisites include (RDS 280 or RDS 286) and (RDS 285 or RDS 288). Course ID: 206897

RDS 500: Risk Assessment (HSPH, Fall) Instructor(s): John Evans. This course introduces the framework of risk assessment, considers its relationship with cost-benefit, decision analysis and other tools for improving environmental decisions. The scientific foundations for risk assessment (epidemiology, toxicology, and exposure assessment) are discussed. The mathematical sciences involved in developing models of dose-response, fate and transport, and the statistical aspects of parameter estimation and uncertainty analysis are introduced. Case studies are used to illustrate various issues in risk assessment and decision making. Course Activities: Lectures, discussions, case studies. Prerequisites/Notes: Course required for all Exposure, Epidemiology and Risk Program students. Course ID: 191111

ECON 2020B: Microeconomic Theory II (FAS, Spring) Instructor(s): TBA. This course is a continuation of Economics 2020B. Topics include game theory, economics of information, incentive theory, and welfare economics. Course ID: 113615

GHP 230: Introduction to Economics with Applications to Health and Development (HSPH, Fall) Instructor(s): Margaret McConnell. This course provides an overview of the microeconomic theories and concepts most relevant for understanding health and development. Each work of the course covers basic concepts in economics with an application to health. It describes how the markets for health and health services are different from other goods, with a particular emphasis on the role of government and market failure. In addition, it discusses the theoretical and empirical aspects of key health economics issues, including the demand for health and health services, supply side concerns, health insurance, the provision of public goods, and related topics. The course encourages students to fundamentally and rigorously examine the role of the market for the provision of health and health services and how public policy can influence these markets. Prerequisites/Notes: Course is required for GHP-SM2, MPH45-GH and MPH65-GH. Any remaining seats will be available on a first-come first-serve basis. Students outside of HSPH must request instructor permission to enroll in this course. Course ID: 190394

ID 250: Ethical Basis of the Practice of Public Health (HSPH, Fall) Instructor(s): Daniel I. Wikler, Ole Norheim. This course serves as an introduction to ethical issues in the practice of public health. Students will identify a number of key ethical issues and dilemmas arising in efforts to improve and protect population health and will become familiar with the principal arguments and evidence supporting contesting views. The class aims to enhance the students’ capacity for using ethical reasoning in resolving the ethical issues that will arise throughout their careers. Unlike courses in medical ethics, which mainly examine ethical dilemmas facing individual clinicians, the population-level focus of this course directs our attention to questions of ethics and justice that must be addressed at the societal level. Course ID: 190768

APMTH 115/ ENG-SCI 115: Mathematical Modeling (SEAS, Fall; FAS, Spring) Instructor(s): Zhiming Kuang. Abstracting the essential components and mechanisms from a natural system to produce a mathematical model, which can be analyzed with a variety of formal mathematical methods, is perhaps the most important, but least understood, task in applied mathematics. This course approaches a number of problems without the prejudice of trying to apply a particular method of solution. Topics drawn from biology, economics, engineering, physical and social sciences. Course ID: 118021, 156427

APMTH 222: Stochastic Modeling (FAS, Applied Math, Spring) Instructor(s): Nikolaos Trichakis, Joel Goh. The course covers the modeling, analysis, and control of stochastic systems. Topics include Bernoulli and Poisson processes, Markov chains and Markov decision processes, optimization under uncertainty, queuing theory, and simulation. Applications will be presented in healthcare, inventory management, and service systems. Course ID: 109344

E-PSCI 236: Environmental Modeling and Data Analysis (FAS, Earth & Planetary Sciences, Fall) Instructor(s): Steven Wofsy. This course provides an introduction to environmental modeling and data analysis: data visualization, statistical inference, Bayes Theorem, optimal estimation, adjoint methods, Monte Carlo methods, time series analysis, denoising; principles and numerical methods for chemical transport and inverse models. Prerequisites/Notes: For graduate students. Course ID: 120783

EPI 260: Mathematical Modeling of Infectious Diseases (HSPH, Spring) Instructor(s): Marc Lipsitch. This course covers selected topics and techniques in the use of dynamical models to study the transmission dynamics of infectious diseases. Class sessions will primarily consist of lectures and demonstrations of modeling techniques. Techniques include design and construction of appropriate differential equation models, equilibrium and stability analysis, parameter estimation from epidemiological data, determination and interpretation of the basic reprodutive number of an infection, techniques for sensitivity analysis, and critique of model assumptions. Specific topics include the use of age-seroprevalence data, the effects of population heterogeneity on transmission, stochastic models and the use of models for pathogens with multiple strains. Prerequisites/Notes:  EPI 501; may be taken concurrently. Previous course in calculus is required. This course is designed for students with a basic understanding of mathematical modeling concepts who want to develop models for their own work. Course ID: 190321

EPI 501: Dynamics of Infectious Diseases (HSPH, Spring) Instructor(s): Caroline Buckee. This course covers the basic concepts of infectious disease dynamics within human populations. Focus is on transmission of infectious agents and the effect of biological, ecological, social, political, economic forces on the spread of infections. The course emphasizes the impact of vaccination programs and other interventions. The dynamics of host-parasite interaction are illustrated using basic mathematical modeling techniques. A key component of the course is the introduction to the programming mathematical modeling techniques and the introduction to the programming language R, which we will use for all mathematical modeling activities and examples. Course activities include in-class demonstrations and practical sessions, written homework assignments and final class debate. Prerequisites/Notes: Previous coursework in epidemiology and programming helpful but not required. Students outside of HSPH must request instructor permission to enroll. Course ID: 10172

GHP 201: Advanced Modeling for Health System Analysis & Priority Setting (HSPH, Spring) Instructor(s): Stéphane Verguet. This course directly builds on GHP 501, and offers advanced methods for modeling for health system analysis and priority setting in global health. Students will apply a range of techniques to address central topics, including: health disparities; medical impoverishment and financial risk protection; economic evaluations for health policy assessment; health system modeling; health system performance and country performance on health. Through readings, basic programming using R software (www.r-project.org), and research projects, students will develop their research skills around three main areas of application, with an emphasis on low- and middle-income countries: I. Economic evaluation for health policy assessment, II. Health system modeling, III. Efficiency, equity, and performance. Prerequisites/Notes: There will be a required one-hour lab session that meets once per week. The exact day and time of this lab session will be determined during the first week of class. Instructor permission is required for enrollment. Students who wish to enroll must request instructor permission in my.Harvard. Please include the following information in the comment box: name, academic department and degree program, an explanation of how you will benefit from taking this course, and the relevance to individual career path and/or research plans. Course ID: 207842

GHP 501: Modeling for Health System Analysis & Priority Setting (HSPH, Spring) Instructor(s) Stéphane Verguet. This course offers an introduction to modeling for health system analysis and priority setting in global health, and its key quantitative methods. Students will learn to use a range of tools to address central concerns and topics, including: health disparities; medical impoverishment and financial risk protection; economic evaluations for health policy assessment; health system performance and country performance on health. Modeling for health system analysis – and therefore this course – draws from the disciplines of global public health, health services research, epidemiology, economics and applied mathematics. Through readings, homework, basic programming using R software (www.r-project.org), and a research assignment, students will gain solid quantitative knowledge of the field. The course is designed around three main areas of inquiry and application, with an emphasis on low- and middle-income countries: I. Economic evaluation for health policy assessment, II. Health system modeling, III. Efficiency, equity, and performance. Course ID: 204258

MGMT S-5070: Decision Support Models and Spreadsheet Analysis (Harvard Summer School) Instructor(s): Philip Anthony Vaccaro. This course introduces nonmathematical managers to the major quantitative models designed for effective decision making in today’s complex and increasingly uncertain operating environment. The course is relevant to manufacturing, service, institutional, and government sectors as well as marketing, finance, and management. Topics include goal programming, simulation, decision trees, probability, optimization, and scenario analysis. Emphasis is placed on a general understanding of theory, mechanics, application potential, and user-friendly software packages. Course ID: 33848

RDS 280: Decision Analysis for Health and Medical Practices (HSPH, Fall) Instructor(s): Ankur Pandya. This course is designed to introduce the student to the methods and growing range of applications of decision analysis and cost-effectiveness analysis in health technology assessment, medical and public health decision making, and health resource allocation. The objectives of the course are: (1) to provide a basic technical understanding of the methods used, (2) to give the student an appreciation of the practical problems in applying these methods to the evaluation of clinical interventions and public health policies, and (3) to give the student an appreciation of the uses and limitations of these methods in decision making at the individual, organizational, and policy level both in developed and developing countries. Prerequisites/Notes: Prerequisites include BIO 200 or BST 201 or BST 202 & 203 or BST 206 & 207 or BST 206 & 208 or BST 206 & 209 (all courses may be taken concurrently) or permission from the instructor. Introductory economics is recommended but not required. Students cannot take RDS 280 if they have already taken RDS 286 or RDS 202 (exceptions only allowed with permission of RDS 280 instructor). Course ID: 191102

RDS 285: Decision Analysis Methods in Public Health and Medicine (HSPH, Spring) Instructor(s): Nicolas Menzies. This intermediate-level course focuses on methods and health applications of decision analysis modeling techniques. Topics include Markov models, microsimulation models, life expectancy estimation, cost estimation, deterministic and probabilistic sensitivity analysis, value of information analysis, and cost-effectiveness analysis. Lab or section times to be announced at first meeting. Prerequisites/Notes: (BST 201 or ID 201) and (RDS 280 or RDS 286). Concurrent enrollment is allowed for RDS 286. Familiarity with matrix algebra and elementary calculus may be helpful but not required. Course ID: 191106

RDS 288: Methods for Decision Making in Medicine (HSPH, Summer) Instructor(s): Myriam Hunink. This course deals with intermediate-level topics in the field of medical decision making. Topics that will be addressed include decision models, evaluation of diagnostic tests, utility assessments, multi-attribute utility theory, Markov cohort models, microsimulation state-transition models, calibration and validation of models, probabilistic sensitivity analysis, value of information analysis, and behavioral decision making. The course will focus on the practical application of techniques and will include published examples and computer practicums. During the course you will have the opportunity to work on a decision problem which you select yourself, which could lead to a publishable paper. Prerequisites/Notes: An introductory course in Decision Analysis (RDS 280 or RDS 286 or RDS 202 or faculty approval of an equivalent course) and knowledge of probability and statistics. Concurrent enrollment allowed with RDS 286. Restricted to HSPH degree or PCE students. Course ID: 191109

RDS 290: Experiential Learning and Applied Research in Decision Analysis (HSPH, Spring) Instructor(s): Jane J. Kim. This course is geared towards Masters-level students from any department. Students will undertake semester-long research projects on a clinical or public health decision problem using decision analysis, simulation modeling, and/or cost-effectiveness analysis. Each session will be dedicated to a particular topic of decision analytic methods or student presentations of prospectus, works-in-progress, and final projects. Students may work alone or in pairs, including at least one student who is familiar with the clinical content area of the project. Prerequisites/Notes: Prerequisites include (RDS 280 or RDS 286) and (RDS 285 or RDS 288). Course ID: 206897

API 222: Machine Learning and Big Data Analytics (HKS, Fall) Instructor(s): Soroush Saghafian. In the last couple of decades, the amount of data available to organizations has significantly increased. Individuals who can use this data together with appropriate analytical techniques can discover new facts and provide new solutions to various existing problems. This course provides an introduction to the theory and applications of some of the most popular machine learning techniques. It is designed for students interested in using machine learning and related analytical techniques to make better decisions in order to solve policy and societal level problems. We will cover various recent techniques and their applications from supervised, unsupervised, and reinforcement learning. In addition, students will get the chance to work with some data sets using software and apply their knowledge to a variety of examples from a broad array of industries and policy domains. Some of the intended course topics (time permitting) include: K-Nearest Neighbors, Naive Bayes, Logistic Regression, Linear and Quadratic Discriminant Analysis, Model Selection (Cross Validation, Bootstrapping), Support Vector Machines, Smoothing Splines, Generalized Additive Models, Shrinkage Methods (Lasso, Ridge), Dimension Reduction Methods (Principal Component Regression, Partial Least Squares), Decision Trees, Bagging, Boosting, Random Forest, K-Means Clustering, Hierarchical Clustering, Neural Networks, Deep Learning, and Reinforcement Learning. Course ID: 208037

COMPSCI 234R: Topics on Computation in Networks and Crowds (SEAS, Fall and Spring) Instructor(s): Nicole Immorlica. This course focuses on the design and analysis of algorithms, processes, and systems related to crowds and social networks. Readings are in AI, theoretical CS, machine learning, social science theory, economic theory, and operations research. Course ID: 109667

TR10.30-12: Engineering Systems Analysis for Design (MIT, Fall) Instructor(s): Richard De Neufville. This course covers theory and methods to identify, value, and implement flexibility in design, also known as “real options.” Topics include definition of uncertainties, simulation of performance for scenarios, screening models to identify desirable flexibility, decision and lattice analysis, and multidimensional economic evaluation. Students demonstrate proficiency through an extended application to a systems design of their choice. Prerequisites/Notes: For graduate students. Course ID: 1.146

ENG-SCI 121/MTH 121: Introduction to Optimization: Models and Methods (FAS, Fall) Instructor(s): Margo Levine. This course is an introduction to basic mathematical ideas and computational methods for solving deterministic optimization problems. Topics covered include linear programming, integer programming, branch-and-bound, branch-and-cut. Emphasis on modeling. Examples from business, society, engineering, sports, e-commerce. Exercises in AMPL, complemented by Mathematica or Matlab. Course ID: 156288

HPM 732: Operations Management in Service Delivery Organizations (HSPH, Summer) Instructor(s): Joseph Pliskin. This course introduces concepts of operations management in service delivery organizations: operations management is concerned with evaluating the performance of operating units, understanding why they perform as they do, designing new or improved operating procedures and systems for competitive advantage, making short-run and long-run decisions that affect operations, and managing the work force. To understand the role of operations in any organization, a manager must understand process analysis, capacity analysis, types of processes, productivity analysis, development and use of quality standards, and the role of operating strategy in corporate strategy. The course introduces students to these concepts and will also present the focused management approach which can help an organization achieve more with existing resources. Similar to HPM 232 – adapted for the non-residential program. Prerequisites/Notes: Open only to students in Master in Health Care Management Program. Course ID: 10065

MLD 601: Operations Management (HKS, Fall) Instructor(s): Mark Fagan. This course is an introduction to operations management which entails creating public value by efficiently delivering quality services. The course provides students with the tools to identify opportunities for improvement, diagnose problems and barriers, and design efficient and effective solutions. The course uses the case method of instruction, drawing examples primarily from the public and nonprofit sectors with some private sector cases. The course roadmap is: creating value, delivering quality services, delivering efficient services, managing performance, utilizing technology, and addressing unique challenges. Throughout the course, tools will be introduced including process mapping and reengineering, capacity and root-cause analysis, and total quality management. The course capstone is a client project in which student teams help local agencies solve actual operational problems. A Friday recitation provides additional practice with the tools that are taught. Prerequisites/Notes: The course is oriented toward the general manager or those interested in an introduction to the field. Course ID: 170531

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 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. 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

2582: Judgment and Decision-Making (HLS, Spring) Instructor(s): Bruce Hay. This course examines human judgment and decision making, with emphasis on the ways in which people depart from rational and/or ethical standards, particularly in groups and organizational settings. The course combines insights from multiple disciplines, including cognitive psychology, behavioral economics, and negotiation theory. Prerequisites/Notes: Primarily for graduate students.

API 304: Behavioral Economics and Public Policy (HKS, Fall) Instructor(s): Brigitte Madrian. This course examines the relationship between behavioral economics and public policy. Individuals frequently make decisions that systematically depart from the predictions of standard economic models. Behavioral economics attempts to understand these departures by integrating an understanding of the psychology of human behavior into economic analysis. The course reviews the major themes of behavioral economics and address the implications for public policy in a wide variety of domains, including: retirement savings, labor markets, credit scores, education, affirmative action, organ donation, and health care. Course ID: 203412

ECON 2030: Psychology and Economics (FAS, Economics, Spring) Instructor(s): David Laibson. This course explores economic and psychological models of human behavior. Topics include bounded rationality, intertemporal choice, decision making under uncertainty, inference, choice heuristics, and social preferences. Economic applications include asset pricing, corporate finance, macroeconomics, labor, development, and industrial organization. Course ID: 119960

ECON 980BB: Behavioral Economics (FAS, Economics, Spring) Instructor(s): Tomasz Strzalecki. This course focuses on theoretical and experimental issues in behavioral economics. Students study the relationships between the mathematical models of individual behavior (both utility maximization and psychologically motivated models) and the kinds of behavior we can observe in the lab. Students design experiments to test various theories and also study the types of behavior for which we don’t have good models yet and try to understand what a good model would look like. Prerequisites/Notes: This is a junior tutorial. Course ID: 24433

MLD 304: Science of Behavior Change (HKS, Fall) Instructor(s): Todd Rogers. This course aims to improve students’ abilities to design policies and interventions that improve societal well-being. It accomplishes this by focusing on how to leverage insights about human decision making to develop interventions (“nudges”). This will be accomplished by building on the toolbox that standard economics provides for influencing behavior (namely, incentives and information) with the insights from behavioral science. Course ID: 170483

PSY 1584: Leadership Decision Making (FAS, Spring) Instructor(s): Jennifer S. Lerner. Organizational leaders make decisions involving risk and uncertainty every day. Whom should our organization hire? Should we choose the gamble or the sure thing? How should we structure accountability systems? How do we avoid operating out of fear? But a leader’s impact only goes so far unless s/he takes steps to engineer optimal decision environments for the organization as a whole. By gaining an understanding of fundamental mind-brain-behavior relationships in judgment and decision making, you will become better able to design decision environments that make everyone smarter – i.e., less susceptible to common errors and biases. Taking this course will not tell you what to choose but it will give you frameworks that reveal how to choose and how to structure optimal decision environments. Specifically, course topics will include (1) fundamental mental processes in perception, memory and context dependence; (2) how questions affect answers; (3) models of decision making; (4) heuristics and biases; (5) social and group influences; (6) common traps; and (7) debiasing techniques. We will also discuss emotional influences on decision making. The lectures and discussions will be coordinated to complement weekly readings, which draw from psychology, behavioral economics, and neuroscience. Throughout the course, the overarching goals are to: (1) Learn about the academic field of behavioral decision making, its major theories, results, and debates. (2) Become a critical consumer of research findings, learning methodological standards for evaluating the soundness of empirical studies. (3) Develop the ability to effectively write and speak about behavioral science theories, results, and debates. (4) Acquire practical skills for improving your own judgments and decisions. (5) Acquire knowledge of which biases individuals can fix with training/knowledge and which biases individuals cannot fix unless leaders engage in institutional design (e.g., nudges). (6) Develop a capstone project in which you apply the material in a way that will improve professional decision making processes. Possible applications to legal process, government institutions, medical settings, public health, education, finance and other domains abound. Course ID: 205646

PSY 2650/HBS 4420: Behavioral Approaches to Decision Making and Negotiation (FAS and HBS, Fall) Instructor(s): Brian Hall. This course provides an overview of the field of behavioral decision making. The focus of the course is the individual as a less-than-perfect decision maker in individual and competitive contexts. These major contexts include: negotiations, marketing, motivation and incentive design, and individual judgment and decision making (JDM) more broadly. The course starts with March and Simon’s (1958) seminal work on bounded rationality, work through the groundbreaking research of Kahneman and Tversky, and update these lines of inquiry through the current decade. Then, students examine the implications of imperfect behavior for theoretical development, as well as help individuals to make wiser decisions and organizations design better systems. This course involves students in an intensive, thorough survey of the intersection of analytic and behavioral perspectives on behavior and decision making. In each class we will cover an area in depth, explicate some major perspectives in the field, review a select set of readings, and discuss some of the critical issues that have been raised with regard to theory and experimentation. At least half of the classes will include guest lectures by top leading professors who will describe the main insights of the topic while also presenting their contributions to the literature. Course ID: 115060

PSY 2670A: Decision Making and the Psychology of Possibility (FAS, Fall) Instructor(s): Ellen Langer. This course focuses on decision making such as rationality, risk-taking, helplessness, and health are examined through the lens of mindfulness theory. Special emphasis given to the psychology of possibility in applied settings. Course ID: 131189

PSY 2670B: Decision Making and the Psychology of Possibility II (FAS, Spring) Instructor(s): Ellen Langer. This course provides a deeper exploration into the theoretical and experimental issues pertaining to decision making and the psychology of possibility, raised in Psychology 2670A. Course ID: 132599

API 303: Game Theory and Strategic Decisions (HKS, Spring) Instructor(s): Pinar Dogan. This course uses game theory to study strategic behavior in real-world situations. It develops theoretical concepts, such as incentives, strategies, threats and promises, and signaling, with application to a range of policy issues. Examples will be drawn from a wide variety of areas, such as competition, bargaining, auction design, and voting behavior. This course also explores how people actually behave in strategic settings through a series of participatory demonstrations. Course ID: 170054

ECON 1050: Strategy, Conflict, and Cooperation (FAS, Economics, Spring) Instructor(s): Robert Neugeboren. Game theory is the study of interdependent decision-making. In the early days of the cold war, game theory was used to analyze an emerging nuclear arms race; today, it has applications in economics, psychology, politics, the law and other fields. This course explores the “strategic way of thinking” as developed by game theorists over the past sixty years. Special attention will be paid to the move from zero-sum to nonzero-sum game theory. Students learn the basic solution concepts of game theory — including minimax and Nash equilibrium — by playing and analyzing games in class, and then we will take up some game-theoretic applications in negotiation settings: the strategic use of threats, bluffs and promises. Students also study the repeated prisoner’s dilemma and investigate how cooperative behavior may emerge in a population of rational egoists. This problematic — “the evolution of cooperation” — extends from economics and political science to biology and artificial intelligence, and it presents a host of interesting challenges for both theoretical and applied research. Finally, students consider the changing context for the development of game theory today, in particular, the need to achieve international cooperation on economic and environmental issues. The course has two main objectives: to introduce students to the fundamental problems and solution concepts of noncooperative game theory; and to provide an historical perspective on its development, from the analysis of military conflicts to contemporary applications in economics and other fields. Prerequisites/Notes: No special mathematical preparation is required. Course ID: 123893

ECON 2052: Game Theory I: Equilibrium Theory (FAS, Economics, Fall) Instructor(s): Shengwu Li. This is an advanced topics course in game theory. This iteration of the course focuses on foundational papers regarding beliefs and learning, and more recent papers in information acquisition and design. Course ID: 113349

HPM 252: Negotiation (HSPH, Spring) Instructor(s): Linda Kaboolian. This course presents conceptual frameworks that will help you analyze negotiations in general and prepare more comprehensively for future negotiations in which you may be involved. In class analysis of case studies and readings from applied game theory, social psychology, political theory and behavioral economics, we will draw out lessons from ongoing, real-world negotiations. Through participation in negotiation simulations, students have the opportunity to exercise your powers of communication and persuasion, and to experiment with a variety of negotiating strategies and tactics. The simulation exercises draw from a wide variety of contexts and their aim is to illustrate concepts and tools that apply to a variety of negotiations settings. In-class debriefs of student experiences as well as their outcomes help make adjustments in negotiating practices that better reflect intentions and preferences. Prerequisites/Notes: Priority goes to DrPH students. Course ID: 190570

MLD 222M: Negotiation Analysis (HKS, Fall) Instructor(s): Kessely Hong. This course introduces students to the theory and practice of negotiation by emphasizing both analytical and interpersonal skills. Analysis is important because negotiators cannot develop promising strategies without a deep understanding of the context of the situation, and the incentives, interests and alternatives of the other parties. Interpersonal skills are important because negotiation is essentially a process of communication, trust building (or breaking), and mutual persuasion. Through case discussions, students develop a set of conceptual frameworks to help students diagnose barriers to agreement and develop creative strategies to address them. Through participation in negotiation simulations, students will have the opportunity to learn how to prepare effectively, to practice communication and persuasion, and to experiment with a variety of negotiation tactics and strategies to both create and claim value. The goal of the course is to help students be better equipped to anticipate challenges in advance, to expand their conception of “what is possible” in order to develop creative and wise strategies, to build sustainable coalitions to support their goals, and to gain confidence in advocating for themselves and others. Course ID: 170474

MLD 224: Behavioral Science of Negotiations (HKS, Fall) Instructor(s): Julia Minson. We negotiate every day. We negotiate with co-workers, bosses, subordinates, clients, salespeople, romantic partners, and many others. This course is designed to build understanding, skill, and confidence so that students achieve better outcomes in all their negotiations — large and small. In this course, students learn how to increase the quality of the agreements you negotiate so as to maximize potential value, and also how to claim as much of that value for themselves as they can. A basic premise of the course is that great negotiators are not born, but made through thoughtful, evidence-based skill building. Thus, the course is structured around three types of activities: (1) Applying analytical skills to gain a strategic understanding of negotiation contexts; (2) Learning empirically validated techniques for advancing your interests; (3) Practice, practice, and more practice. Course ID: 170476

PSY 1500: Decision Making and Negotiation (FAS, Psychology, Spring) Instructor(s): Christine Looser. People make decisions and negotiate with others from a young age; however, most know little about the strategy and the psychology that lie beneath these sophisticated behaviors. This course is designed to look under the hood at the cognitive processes at play when we make decisions and negotiate with others. Original research articles are discussed and use simulated class exercises to gain a better understanding of how the human mind makes choices and persuades others. Course ID: 160692

API 201: Quantitative Analysis and Empirical Methods (HKS, Fall). Instructor(s): Jonathan Borck, Dan Levy, Theodore Svoronos, Maya Sen, David Deming. This course introduces students to concepts and techniques essential to the analysis of public policy issues. Provides an introduction to probability, statistics, and decision analysis emphasizing the ways in which these tools are applied to practical policy questions. Topics include: descriptive statistics; basic probability; conditional probability; Bayes’ rule; decision making under uncertainty; statistical inference; hypothesis testing; and bivariate regression analysis. The course also provides students an opportunity to become proficient in the use of computer software widely used in analyzing quantitative data. Course ID: 170029

API 222: Machine Learning and Big Data Analytics (HKS, Fall) Instructor(s): Soroush Saghafian. In the last couple of decades, the amount of data available to organizations has significantly increased. Individuals who can use this data together with appropriate analytical techniques can discover new facts and provide new solutions to various existing problems. This course provides an introduction to the theory and applications of some of the most popular machine learning techniques. It is designed for students interested in using machine learning and related analytical techniques to make better decisions in order to solve policy and societal level problems. We will cover various recent techniques and their applications from supervised, unsupervised, and reinforcement learning. In addition, students will get the chance to work with some data sets using software and apply their knowledge to a variety of examples from a broad array of industries and policy domains. Some of the intended course topics (time permitting) include: K-Nearest Neighbors, Naive Bayes, Logistic Regression, Linear and Quadratic Discriminant Analysis, Model Selection (Cross Validation, Bootstrapping), Support Vector Machines, Smoothing Splines, Generalized Additive Models, Shrinkage Methods (Lasso, Ridge), Dimension Reduction Methods (Principal Component Regression, Partial Least Squares), Decision Trees, Bagging, Boosting, Random Forest, K-Means Clustering, Hierarchical Clustering, Neural Networks, Deep Learning, and Reinforcement Learning. Course ID: 208037

BST 210: Applied Regression Analysis (HSPH, Biostats, Fall and Spring) Instructor(s): Robert Glynn, Erin Lake. This course focuses on model interpretation, model building, and model assessment for linear regression with continuous outcomes, logistic regression with binary outcomes, and proportional hazards regression with survival time outcomes. Specific topics include regression diagnostics, confounding and effect modification, goodness of fit, data transformations, splines and additive models, ordinal, multinomial, and conditional logistic regression, generalized linear models, overdispersion, Poisson regression for rate outcomes, hazard functions, and missing data. The course provides students with the skills necessary to perform regression analyses and to critically interpret statistical issues related to regression applications in the public health literature. Course ID: 190025

BST 223: Applied Survival Analysis (HSPH, Biostats, Spring) Instructor(s): Sebastien Heneuse. This course focuses on survival analysis, or more generally time-to-event analysis, with the primary audience being graduate students pursuing a Master’s degree in biostatistics or a PhD in one of the other departments at the Harvard Chan School. Covered in the course will be: an introduction to various types of censoring and truncation that commonly arise; the mathematical representations of time-to-event distributions, such as via the hazard and survivor functions; nonparametric methods such as Kaplan-Meier estimation of the survivor function and log-rank test for hypothesis testing; semi-parametric and parametric regression modeling techniques, such as the Cox model, the accelerated failure time model, the additive hazards model and cure fraction models; survival analysis within the causal inference paradigm; the analysis of competing and semi-competing risks; outcome-dependent sampling schemes, such as nested case-control and case-cohort designs; and, power/sample size calculations for studies with time-to-event endpoints. Throughout, equal emphasis will be given to the theoretical/technical underpinnings of survival analysis and to the use of real world data examples. Course ID: 190040

EDU S052: Applied Data Analysis (GSE, Spring) Instructor(s): Andrew Ho. This course is designed for those who want to extend their data analytic skills beyond a basic knowledge of multiple regression analysis and who want to communicate their findings clearly to audiences of researchers, practitioners, and policymakers. The course contributes directly to the diverse data analytic toolkit that the well-equipped empirical researcher must possess in order to perform sensible analyses of complex educational, psychological, and social data. The course begins with general linear models and continues with generalized linear models, survival analysis, multilevel models, multivariate methods, causal inference, and measurement. Specific methods exemplifying each of these topics include regression, discrete-time survival analysis, fixed- and random-effects models, principal components analysis, instrumental variables, and reliability, respectively. This is an applied course. It offers conceptual explanations of statistical techniques and provides many opportunities to examine, implement, and practice these techniques using real data. Students will learn to produce readable and sensible code to enable others to replicate and extend their analyses. Attendance at weekly sections is required.  Prerequisites/Notes: Successful completion of S-040 (B+ or better allowed, A- or A recommended) or an equivalent course or courses that include 12 or more full hours of class time on multiple regression and its direct extensions. Students who have not passed S-40 must discuss their previous training before or at the first class meeting. Students who do not meet the prerequisite should consider S-030. Course ID: 180866

STAT 110: Introduction to Probability (FAS, Statistics, Fall) Instructor(s): Joseph Blitzstein. This course is a comprehensive introduction to probability. Basics: sample spaces and events, conditional probability, and Bayes’ Theorem. Univariate distributions: density functions, expectation and variance, Normal, t, Binomial, Negative Binomial, Poisson, Beta, and Gamma distributions. Multivariate distributions: joint and conditional distributions, independence, transformations, and Multivariate Normal. Limit laws: law of large numbers, central limit theorem. Markov chains: transition probabilities, stationary distributions, convergence. Course ID: 110766

BST 249: Bayesian Methodology in Biostatistics (HSPH, Spring) Instructor(s): Jeffrey Miller. This course focuses on general principles of the Bayesian approach, prior distributions, hierarchial models and modeling techniques, approximate inference, Markov chain Monte Carlo methods, model assessment and comparison. Bayesian approaches to GLMMs, multiple testing, nonparametrics, clinical trails, survival analysis. Formerly BIO249. Course ID: 190064

EPI 289:  Epidemiologic Methods III: Models for Causal Inference (HSPH, Spring) Instructor(s): Barbra Dickerman. Causal Inference is a fundamental component of epidemiologic research. This course describes models for causal inference, their application to epidemiologic data, and the assumptions required to endow the parameter estimates with a causal interpretation. The course introduces outcome regression, propensity score methods, the parametric g-formula, inverse probability weighting of marginal structural models, g-estimation of nested structural models, and instrumental variable methods. Each week students are asked to analyze the same data using a different method.  Prerequisites/Notes: This course is designed to be taken after EPI 201/EPI 202 and before EPI 204 and EPI 207. Epidemiologic concepts and methods studied in EPI 201/202 will be reformulated within a modeling framework in EPI 289. This is the first course in the sequence of EPI core courses on modeling (EPI 289, EPI 204, EPI 207). This course focuses on time-fixed dichotomous exposures and time-fixed dichotomous and continuous outcomes. Continuous exposures and failure time outcomes (survival analysis) will be discussed in EPI 204, and time-varying exposures in EPI 207. Familiarity with either SAS or R language is strongly recommended. Course ID: 190332

PSY 2030: Bayesian Data Analysis (FAS, Psychology, Fall) Instructor(s): Patrick Mair. This course covers basic and advanced topics of Bayesian statistics with a strong focus on applications in Psychology. Formulas and technical details are kept on a minimum – it is all about how to integrate Bayesian concepts into your everyday research. The first part of the course introduces students to the Bayesian paradigm of inferential statistics (as opposed to the frequentist approach everyone should be familiar with). To have a good understanding of this idea, we need to elaborate on various concepts of probability theory (e.g., Bayes’ theorem) and statistical distributions. We then introduce the key components of Bayesian inference (prior, likelihood, posterior), and discuss Bayesian hypothesis testing as well as Bayes factors. Subsequent units focus on Bayesian regression (including model checks and model comparison) and mixed-effects models which, within a Bayesian context, belong to the family of Bayesian hierarchical models. There we also elaborate on modern approaches like integrated nested Laplace approximation (INLA) that allow us to efficiently estimate complex nonlinear, spatio-temporal models. After midterm we look at what’s going on under the hood: Markov chain Monte Carlo (MCMC). We introduce Stan, a probabilistic programming language for full Bayesian inference, which interfaces with R. We will then use Stan for some tasks related to Bayesian cognitive modeling. The last three units focus on the following special topics: Gaussian process regression and latent Dirichlet allocation (LDA), selected methods from Bayesian psychometrics, and Bayesian networks. All topics covered will be supported by corresponding computations and illustrations in R. Course ID: 160667

STAT 220: Bayesian Data Analysis (FAS, Statistics, Spring) Instructor(s): Jun Liu. This course focuses on basic Bayesian models, followed by more complicated hierarchical and mixture models with nonstandard solutions. Includes methods for monitoring adequacy of models and examining sensitivity of models. Course ID: 118016

API 111: Microeconomic Theory I (HKS, Fall) Instructor(s): Rahul Deb. This is a comprehensive course in economic theory designed for doctoral students in all parts of the university. Topics include consumption, production, choice under risk and uncertainty, markets, and general equilibrium theory. Topics will be motivated by appealing to related recent theoretical and applied economics research. Undergraduates with appropriate background are welcome, subject to instructor approval. Course ID: 170008

ECON 1011A: Intermediate Microeconomic: Advanced (FAS, Economics, Fall) Instructor(s): Edward Glaeser. This course is similar to Economics 1010A, but includes more mathematical and covers more material. The course teaches the basic tools of economics and to apply them to a wide range of human behavior. Prerequisites/Notes: Mathematics 21A or instructor permission. Course ID: 120711

ECON 2120: Principles of Econometrics (FAS, Economics, Fall) Instructor(s): Elie Tamer. Linear predictor as approximation to conditional expectation function. Least-squares projection as sample counterpart. Splines. Omitted variable bias and panel data. Bayesian inference for parameters defined by moment conditions. Finite sample frequentist inference for the normal linear model. Statistical decision theory and dominating least squares with many predictor variables; applications to estimating fixed effects (teacher effects, place effects) using panel data. Asymptotic inference in the generalized method of moments framework. Likelihood inference using information measures to define best approximations within parametric models. Instrumental variable models and the role of random assignment; applications include models of demand and supply and the evaluation of treatment effects. Course ID: 115026

GHP 525: Econometrics for Health Policy (HSPH, Fall) Instructor(s): Sebastian Bauhoff. This is a course focuses on applied econometrics and has two primary objectives: (1) to develop skills in linking economic behavioral models and quantitative analysis, in a way that students can use in their own research; (2) to develop students’ abilities to understand and evaluate critically other peoples’ econometric studies. The course focuses on developing the theoretical basis and practical application of the most common empirical models used in health policy research. In particular, it pays special attention to a class of models identifying causal effects in observational data, including instrumental variable estimation, simultaneous equations and two-stage-least-squares, quasi-experiments and difference-in-difference method, sample selection, treatment effect models and propensity score methods. Lectures will be complemented with computer exercises building on public domain data sets commonly used in health research. The statistical package recommended for the exercises is Stata. Course Activities: Optional review and computer lab sessions will be held. Prerequisites/Notes: BST 210 or BST 213; or equivalent course taken at Harvard Chan or HGSE with instructor permission. Students are expected to be familiar with probability theory (density and distribution functions) as well as the concepts underlying basic ordinary least square (OLS) estimation. Intended for doctoral and advanced master level students. Course ID: 190440