Harvard University

APMTH 231: Decision Theory (FAS, Applied Mathematics, Spring) Instructor: Demba Ba. Mathematical analysis of decision making. Bayesian inference and risk. Maximum likelihood and nonparametric methods. Algorithmic methods for decision rules: perceptrons, neural nets, and back propagation. Hidden Markov models, Blum-Welch, principal and independent components. Course ID: 203548

ECON 2059: Decision Theory (FAS, Economics) Instructor: Tomasz Strzalecki. This course prepares students for pure and applied research in axiomatic decision theory. We start 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). We then delve 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 last part of the course explores the recently flourishing literature on stochastic choice (which is related to, but distinct from, discrete choice econometrics). Prerequisites: basic microeconomic theory at the level of Mas Colell, Whinston, Green; being comfortable with abstract models. Course ID: 121331

RDS 284: Decision Theory (HSPH, Fall) Instructor: James Hammitt. 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: 10136

API 302: Analytic Frameworks for Policy (HKS, Fall) Instructor: Christopher Avery, Richard Zeckhauser. This course develops abilities in using 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. Prerequisite/Level: Primarily for graduate students. Course ID: 170053

FRSEMR 70E: Climate Change Economics: Analysis and Decisions (FAS, Spring) Instructor: Martin Weitzman. Climate change is one of the most difficult problems facing humanity. 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? Prerequisite/Level: For undergraduate students. Course ID: 203008

GHP 228: Quantitative Methods in Impact Evaluation (HSPH, Spring) Instructor: Jessica Cohen. The objective of this course is to provide students with a set of theoretical, econometric and reasoning skills to estimate the causal impact of one variable on another. 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 will learn to critically analyze evaluation research and to gauge how convincing the research is in identifying a causal impact. Prerequisite/Level: Econometrics and intermediate micro-economics are required for this course. The course is intended for doctoral students who are finishing their course work and aims to help them transition into independent research. Course ID: 10128

RDS 280: Decision Analysis for Health and Medical Practices (HSPH, Fall) Instructor: 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. Course Note: Introductory economics is recommended but not required. Course Note: Students cannot take RDS 280 if they have already taken RDS 286 (exceptions only allowed with permission of RDS 280 instructor). Pre-requisites: BIO200 or BST201 or BST202&203 or BST206&207 or BST206&208 or BST206&209 (all courses may be taken concurrently) or permission from the instructor. Students who have taken RDS 286 may not take RDS 280. Course ID: 191102

RDS 282: Economic Evaluation of Health Policy & Program Management (HSPH, Spring) Instructors: 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. Course Prerequisites: Students must have taken RDS280 or RDS286. 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) Instructors: Nicolas Menzies. This is an intermediate-level course 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. Course Prerequisites: (BST201 or ID201) and (RDS280 or RDS286). Concurrent enrollment is allowed for RDS 286. Course ID: 191106

ECON 2020B: Microeconomic Theory II (FAS, Spring) Instructors: Christopher Avery and Elon Kohlberg. A continuation of Economics 2020a. 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: 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 will cover 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. Prerequisite/Level: For graduate students. Priority Enrollment: 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) Instructors: Nir Eyal and 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) Instructors: Zhiming Kuang, Lakshiminarayanan Mahadevan. 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. Prerequisite/Level: Must take APMTH105 or APMTH108 or APMTH104 or MATH112 before taking APMTH115. For both graduate and undergraduate students. Course ID: 118021, 156427

APMTH 222: Stochastic Modeling (FAS, Applied Math, Spring) Instructors: Nikolaos Trichakis and 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. Prerequisite/Level: Recommended: Applied Mathematics 121. For both graduate and undergraduate students. Course ID: 109344

E-PSCI 236: Environmental Modeling and Data Analysis (FAS, Earth & Planetary Sciences, Fall) Instructor: Steven Wofsy and Daniel Jacob. This course covers topics that include chemical transport models (principles and numerical methods), inverse models (Bayes’ theorem, optimal estimation, Kalman filter, adjoint methods), and analysis of environmental data (visualization, time series analysis, Monte Carlo methods, statistical assessment). Students prepare projects and presentations. Prerequisite/Level: Primarily for graduate students. Course ID: 120783

EPI 260: Mathematical Modeling of Infectious Diseases (HSPH, Spring) Instructor: Marc Lipsitch. This course will cover 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 will 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 will 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. This course is designed for students with a basic understanding of mathematical modeling concepts who want to develop models for their own work. Course Note: Previous course in calculus is required. Course Prerequisite(s): EPI501; may be taken concurrently. Course ID: 10284

EPI 501: Dynamics of Infectious Diseases (HSPH, Spring) Instructor: Caroline Buckee. This course covers the basic concepts of infectious disease dynamics within human populations. Focus will be on transmission of infectious agents and the effect of biological, ecological, social, political, economic forces on the spread of infections. We will emphasize 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. A key component of the course is the introduction to the programming language R, which we will use for all mathematical modeling activities and examples. Course Activities: In-class demonstrations and practical sessions, written homework assignments and final class debate. Previous coursework in epidemiology and programming helpful but not required.Students outside of HSPH must request instructor permission to enroll in this course. Course ID: 10172

MGMT S-5070: Decision Support Models and Spreadsheet Analysis (Harvard Summer School) Instructor: 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: 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. Course Note: Introductory economics is recommended but not required. Course Note: Students cannot take RDS 280 if they have already taken RDS 286 (exceptions only allowed with permission of RDS 280 instructor). Pre-requisites: BIO200 or BST201 or BST202&203 or BST206&207 or BST206&208 or BST206&209 (all courses may be taken concurrently) or permission from the instructor. Students who have taken RDS 286 may not take RDS 280. Course ID: 191102

RDS 285: Decision Analysis Methods in Public Health and Medicine (HSPH, Spring) Instructors: Nicolas Menzies. This is an intermediate-level course 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. Course Prerequisites: (BST201 or ID201) and (RDS280 or RDS286). Concurrent enrollment is allowed for RDS 286. Course ID: 191106

API-222: Machine Learning and Big Data Analytics (HKS, Fall) Instructor: 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: Nicole Immorlica. Topics on the design and analysis of algorithms, processes, and systems related to crowds and social networks. Readings in AI, theoretical CS, machine learning, social science theory, economic theory, and operations research. Course ID: 109667

Engineering Systems Analysis for Design (MIT, Fall) Instructor: 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. Prerequisite/Level: For graduate students. Course ID: 1.146

ENG-SCI 121/ MTH 121: Introduction to Optimization: Models and Methods (FAS, Fall) Instructor: Yiling Chen, David Parkes. Introduction to basic mathematical ideas and computational methods for solving deterministic optimization problems. Topics covered: 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, Spring) Instructor: 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 will introduce 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. Prerequisite/Level: Open only to students in Master in Health Care Management Program. Course ID: 10065

MLD 601: Operations Management (HKS, Fall) Instructor: 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. The course is oriented toward the general manager or those interested in an introduction to the field. A Friday recitation provides additional practice with the tools that are taught. Course ID: 170531

Optimization Methods (MIT, Fall) Instructors: D. Bertsimas and P. Parrilo. 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. Prerequisite/Level: 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: James B. Orlin. This course Introduces optimization methods with a focus on modeling, solution techniques, and analysis. Covers linear programming, network optimization, integer programming, and decision trees. Applications to logistics, manufacturing, data analysis, transportation, 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. Prerequisite/Level: Primarily for undergraduate students. Course ID: 15.053

2582: Judgment and Decision-Making (HLS, Spring) Instructor: 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. Prerequisite/Level: Primarily for graduate students.

API 304: Behavioral Economics and Public Policy (HKS, Fall) Instructor: Brigitte Madrian. This course will examine 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 will review 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) Instructors: David Laibson and Andrei Shleifer. 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. Prerequisite/Level:  Knowledge of multivariable calculus and econometrics necessary. Primarily for graduate students but open to undergraduates. Course ID: 119960

ECON 980BB: Behavioral Economics (FAS, Economics, Fall) Instructor: 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 observable in a lab. Students will design experiments to test various theories and also study the types of behavior for which there are not good models yet and try to understand what a good model would look like. Prerequisite/Level: Prior knowledge of behavioral economics will be useful. The course will focus on analytical methods and therefore requires knowledge of calculus. Primarily for undergraduate students. Course ID: 24433

MLD 304: Science of Behavior Change (HKS, Spring) Instructor: Todd Rogers. This course is devoted to understanding the nature, causes, implications and applications of how people’s decisions deviate from optimal choices as well as the consequences of such deviations. This course focuses on how these judgment, decision-making and behavior tendencies can inform the design and development of welfare-enhancing interventions with the object to improve students’ abilities to design policies and interventions that improve societal well-being. Prerequisite/Level: Primarily for graduate students. Course ID: 170483

PSY 1584: Leadership Decision Making (FAS, Spring) Instructor: Jennifer S. Lerner. This course is devoted to understanding the nature, causes, implications and applications of these limitations. This course focuses on how these judgment, decision-making and behavior tendencies can inform the design and development of welfare-enhancing interventions. The Science of Behavior Change (MLD 304) has one central objective: 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: 205646

PSY 2650/HBS 4420: Behavioral Approaches to Decision Making and Negotiation (FAS/HBS, Fall) Instructor: Francesca Gino. Research overview of behavioral decision making and decision analytic perspectives to negotiation. Explores bounded rationality, decision biases, human decision making. Develops a behavioral decision perspective to negotiation, and examines how the field is currently evolving. Prerequisite/Level: Primarily for graduate students, but open to juniors and seniors in psychology and economics who are writing, or plan to write, a senior thesis. Course ID: 115060

PSY 2670a: Decision Making and the Psychology of Possibility (FAS, Fall) Instructor: Ellen Langer. Topics in 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. Prerequisite/Level: Primarily for graduate students, but open to qualified undergraduates. Course ID: 131189

PSY 2670b: Decision Making and the Psychology of Possibility II (FAS, Spring) Instructor: Ellen Langer. 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 and Fall) Instructor: 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 will also explore how people actually behave in strategic settings through a series of participatory demonstrations. Prerequisite/Level: API-101 or an equivalent intermediate microeconomics background. Primarily for graduate students. Course ID: 170054

ECON 1050: Strategy, Conflict, and Cooperation (FAS, Economics, Spring) Instructor: 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. In this course, we will explore 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 will 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. We will 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, we will 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 objective: 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. No special mathematical preparation is required. Course ID: 123893

ECON 2052: Game Theory I: Equilibrium Theory (FAS, Economics, Spring) Instructor: Shengwu Li.  This course covers equilibrium analysis and its applications. Topics vary, but typically include equilibrium refinements (sequential equilibrium), the equilibria of various classes of games (repeated games, auctions, signaling games) and the definition and application of common knowledge. Prerequisite/Level: Economics 2010a or permission of the instructor. Primarily for graduate students. Course ID: 113349

HPM 252: Negotiation (HSPH, Spring) Instructor: Linda Kaboolian. This course will present 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, you will 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 your experience as well as your outcomes will help you make adjustments in your negotiating practice that better reflect your intentions and preferences. I hope that in addition to developing a better understanding of strategy, you will learn a great deal about yourself in this course. You will have repeated exposure to situations that involve a shifting mix of opportunities for cooperation and competition as well as important ethical choices. The main pedagogical perspective is to improve your own repertoire of action practice and by reflecting on your practice. As a result, your negotiating effectiveness should increase significantly. Overall, I expect that you will finish the course as an analytically savvy, flexible, efficacious negotiator. Registration Note: Priority goes to DrPH students. Course ID: 190570

MLD 222M: Negotiation Analysis (HKS, Fall) Instructor: 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 will 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. Prerequisite/Level: For graduate students. Course ID: 170474

MLD 224B: Behavioral Science of Negotiation (HKS, Fall) Instructor: Julia Minson. We negotiate every day. We negotiate with co-workers, bosses, subordinates, clients, salespeople, romantic partners, and many others. MLD-224 is designed to build your understanding, skill, and confidence so that you achieve better outcomes in all your negotiations — large and small. In this course you will 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 yourself as you 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: 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 class is designed to look under the hood at the cognitive processes at play when we make decisions and negotiate with others. We will discuss original research articles and use simulated class exercises to gain a better understanding of how the human mind makes choices and persuades others. Prerequisite/Level: Science of Living Systems 20 (or equivalent) and PSY 15. For undergraduate students. Course ID: 160692

API 201: Quantitative Analysis and Empirical Methods (HKS, Fall). Instructors: Jonathan Borck, Dan Levy, Theodore Syoronos, Maya Sen, and David Deming. 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: 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, Spring) Instructor: Robert Glynn, David Wypij. Topics include 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 will provide 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: Andrea Bellavia. Topics will include types of censoring, hazard, survivor, and cumulative hazard functions, Kaplan-Meier and actuarial estimation of the survival distribution, comparison of survival using log rank and other tests, regression models including the Cox proportional hazards model and the accelerated failure time model, adjustment for time-varying covariates, and the use of parametric distributions (exponential, Weibull) in survival analysis. Methods for recurrent survival outcomes and competing risks will also be discussed, as well as design of studies with survival outcomes. Class material will include presentation of statistical methods for estimation and testing along with current software (SAS, Stata) for implementing analyses of survival data. Applications to real data will be emphasized. Course Prerequisite(s): BST210 or BST213 or BST 232 or BST 260 or PHS2000AFormerly BIO223. Course ID: 190040

EDU S052: Applied Data Analysis (GSE, Spring) Instructor: 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. S-052 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. S-052 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: 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. See the syllabus at the instructor’s website, https://scholar.harvard.edu/andrewho/classes, for more details. Course ID: 180866

STAT 110: Introduction to Probability (FAS, Statistics, Fall) Instructor: Joseph Blitzstein. 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, Fall) Instructor: Lorenzo Trippa. 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: Models for Causal Inference (HSPH, Spring) Instructor: Miguel Hernan. Causal Inference is a fundamental component of epidemiologic research. EPI289 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.EPI289 is designed to be taken after EPI201/EPI202 and before EPI204 and EPI207. Epidemiologic concepts and methods studied in EPI201/202 will be reformulated within a modeling framework in EPI289. This is the first course in the sequence of EPI core courses on modeling (EPI289, EPI204, EPI207). EPI289 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 EPI204, and time-varying exposures in EPI207. Familiarity with either SAS or R language is strongly recommended. Course Prerequisite(s): EPI201 and EPI202; may not be taken concurrently. Course ID: 190332

PSY 2030: Bayesian Data Analysis (FAS, Psychology, Spring 2019) Instructor: Patrick Mair. This course covers basic and advanced topics of Bayesian statistics in a very applied way with a strong focus on applications in Psychology (and Social Sciences in general). The first part of the course introduces students to the Bayesian paradigm of inferential statistics (as opposed to the frequentist approach everyone is familiar with). It elaborates on Bayes’ seminal theorem and introduces the core components of Bayesian inference: prior distributions, posterior distributions, and Bayes factors. Subsequently, we will learn about simulation based approaches for sampling posterior distributions, Bayesian inference and testing, including simple statistical tests and models such as t-test, ANOVA, and regression, computed in a Bayesian way, and then extends modeling approach to generalized linear models (GLM) and a hierarchical (aka multilevel or mixed-effects) models. Finally, the course covers psychometric Bayesian methods such as multidimensional/multilevel item response theory (IRT), Bayesian latent variable models, latent Dirichlet allocations and Bayesian networks. Prerequisite/Level: Psych1950 (or an equivalent course), or permission of instructor. This course is primarily for graduate students, and intended for doctoral students. Course ID: 160667

STAT 220: Bayesian Data Analysis (FAS, Statistics, Spring) Instructor: Jun Liu. 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: Maciej Kotowski. 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 the instructor’s approval. Course ID: 170008

ECON 1011A: Microeconomic Theory (FAS, Economics, Fall) Instructor: Edward Glaeser. Economics 1011a is similar to Economics 1010a, but 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 for this course include Mathematics 21a or permission of the instructor. Course ID: 120711

ECON 2120: Principles of Econometrics (FAS, Economics, Fall) Instructor: Gary Chamberlain. 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: Victoria Fan. This is a course in applied econometrics for doctoral and advanced master level students. The course 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 Note: 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. Course Activities: Optional review and computer lab sessions will be held. Course Prerequisites: BST210 or BST213; or equivalent course taken at Harvard Chan or HGSE with instructor permission. Course ID: 190440