ALP 301, This is a team-based course where students will work on a project to improve a product using data and experimentation. We will cover key considerations for designing and executing high-quality research for product innovation to drive business outcomes and social impact. Students will have the opportunity to apply methods from machine learning and causal inference to a real-world scenario provided by a partner organization. Topics include designing research and experiments, data analysis, experimental and non-experimental methods for estimating the impact of product features, as well as management consideration for the delivery of actionable research. The course involves three weekly meetings: two lectures and one lab. Lectures will focus on research methods and will provide examples of research outputs for students to discuss and evaluate. Labs will comprise technical training in data analysis and structured team meetings. Students will work in cross-functional teams of 5-6 with milestones throughout the quarter. The final deliverable will be a presentation that highlights the team's work and delivers actionable recommendations that draw from the team's research. The class will include a mix of students with different backgrounds and skills. Each team will have at least one member with significant experience with data analysis. This course is part of the GSB's new Action Learning Program, in which you will work on real business challenges under the guidance of faculty. In this intensive project-based course, you will: Learn research-validated foundations, tools, and practices, Apply these tools and learnings to a real project for an external organization, Create value for the organization by providing insights and deliverables, Be an ambassador to the organization by exposing them to the talent, values, and expertise of the GSB. You will also have the opportunity to: Gain practical industry experience and exposure to the organization, its industry, and the space in which it operates, Build relationships in the organization and industry, and gain an understanding of related career paths. Prerequisites: Some experience with statistical analysis and the R statistical package. Students with less experience will have an opportunity to catch up through tutorials provided through the course. Non-GSB students are expected to have an advanced understanding of tools and methods from data science and machine learning as well as a strong familiarity with R, Python, SQL, and other similar high-level programming languages.
STRAMGT 529, Marketplaces for Goods and Services. In this class we will analyze the economics and strategy of marketplaces and platforms for goods and services. We will consider the forces that have led to the proliferation of these marketplaces, as well as the economics behind which ones are likely to succeed and become profitable. We will analyze the economic costs and benefits of these marketplaces for society, and consider the regulatory environment and challenges. We will also study the microeconomics of managing these marketplaces: how should matching work, how can marketplace design solve problems of congestion or market thinness, and how a platform should trade off the welfare of the different sides of the market as it enters and grows. Applications include ride-sharing and transportation; room-sharing and vacation rentals; on-demand labor and services such as babysitting, massage, manual labor, and dog-sitting; dating; and organized labor markets.
MGTECON 634, Machine Learning and Causal Inference. Course Outline. This course will cover statistical methods based on the machine learning literature that can be used for causal inference. In economics and the social sciences more broadly, empirical analyses typically estimate the effects of counterfactual policies, such as the effect of implementing a government policy, changing a price, showing advertisements, or introducing new products. Recent advances in supervised and unsupervised machine learning provide systematic approaches to model selection and prediction, methods that are particularly well suited to datasets with many observations and/or many covariates. Spring 2016
MGTECON 513, Platform Competition in Digital Markets. Course Outline. This class will analyze the economics of digital platform markets. The class format will consist of lectures, guest speakers, and student presentations. Concepts will be presented in the context of leading examples of internet and technology platforms such as online advertising, computing technology platforms (e.g. mobile), marketplaces, social networks, cloud computing, and financial technology platforms.
STRAMGT 517, Topics in Digital Business. Course Outline.This class will guide students through in-depth research projects focusing on specific case studies of digital businesses, where students select topics individually or in teams. The research projects must be framed using principles from economics and strategy. Winter 2015
STRAMGT 518, Advertising and Monetization. Course Outline. The Global advertising market is forecast to top $600 Billion in 2016, with advances in advertising technology, such as online publishing, digital and mobile advertising platforms, as well as new ways of consuming video content, driving a rapid evolution of the market. We analyze this evolution from the perspective of three main constituents: 1) Marketers who rely on advertising to launch and sustain product sales, 2) Publishers and media owners for whom advertising often represents the largest source of monetization, and 3) Advertising agencies who design, plan and buy media for advertising campaigns. Spring 2016
STRAMGT 538, Financial Technologies. Course Outline. This class will provide an overview of the rapidly evolving world of financial technologies. New market entrants are promising to change the way we borrow, save, invest, and transact. Incumbents enjoy substantial market power but are struggling to keep up technologically as they wrestle with antiquated core infrastructure. We will analyze the emerging competitive landscape and the strategic dynamics in play. Spring 2016
STRAMGT 588, Leading Organizations. Course Outline. This course studies principles for leading organizations and creating business value from the perspective of a high-level executive. Topics include product development, business models and pricing, people management, time allocation, measurement and accountability, creative destruction, the development of new capabilities, and marketing. Co-Instructor: Steve Ballmer. Fall 2014-15
Prior Years' Courses
Economics 1056 (Harvard), Market Design (Advanced Undergraduate Course). Studies topics in market design, focusing on auctions, auction-based marketplaces and platform markets. Theory and applications.
Economics 2056b (Harvard), Market Design (Graduate Elective). Studies topics in market design, focusing on auctions, auction-based marketplaces and platform markets. Covers methods and results from theory, empirical work, econometrics and experiments, highlighting practical issues in real-world design.
Economics 289, Advanced Topics in Game Theory and Information Economics
Economics 157 (Stanford), Imperfect Competition
Economics 257 (Stanford), The Economics of Industry, Regulation, and Firm Organizations I (with Liran Einav)
Economics 257 (Stanford), The Economics of Industry, Regulation, and Firm Organizations I (with Pat Bajari)
MIT 14.121 Microeconomic Theory I (graduate)
MIT 14.129 Contract Theory (graduate)
MIT 14.03 Intermediate Applied Microeconomics (undergraduate)