3730 Walnut Street
558 Jon M. Huntsman Hall
Philadelphia, PA 19104
Prasanna (Sonny) Tambe is an Associate Professor of Operations, Information and Decisions at the Wharton School at the University of Pennsylvania. His research focuses on the economics of technology and labor. Recent research projects focus on 1) understanding how firms compete for software developers, 2) how software engineers choose technologies in which to specialize, and 3) how AI is transforming HR management.
Much of this research has uses Internet-scale data sources to measure labor market activity at novel levels of granularity. His published papers have analyzed data from online job sites and other labor market intermediaries that generate databases of fine-grained information on workers’ skills and career paths or on employers’ job requirements. He is a co-author of “The Talent Equation: Big Data Lessons for Navigating the Skills Gap and Building a Competitive Workforce,” published by McGraw Hill in 2013.
His research has been published or is forthcoming in a number of academic journals including Management Science, Information Systems Research, MIS Quarterly, The Review of Financial Studies, Industrial and Labor Relations Review, Communications of the ACM, and Information Economics and Policy and it has been supported by the Alfred P. Sloan Foundation. His research has also won a number of awards, including the Best Published Paper in Information Systems Research and two papers have been nominees for the Best Published IS Paper in Management Science. He currently serves on the editorial board of Management Science and in the past, has served on the editorial board of Information Systems Research.
Professor Tambe received his S.B. and M.Eng. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and his Ph.D. in Managerial Science and Applied Economics from the Wharton School of the University of Pennsylvania.
Abstract: General purpose technologies like information technology typically require complementary firm-specific human and organizational investments to create value. These complementary investments produce a form of capital, which we call Information technology-related intangible capital ("ITIC''). An understanding of how the accumulation of ITIC contributes to economic growth and differences among firms has been hindered by the lack of measures of the stock of ITIC. We use a new, extended firm-level panel on IT labor investments along with Hall’s Quantity Revelation Theorem to construct measures of both the prices and quantities of ITIC over the last thirty years. We find that 1) prices vary significantly for ITIC, 2) significant quantities of ITIC have been accumulating since the 1990s, with ITIC accounting for at least 25% of firms’ assets by the end of our panel, 3) that it has disproportionately accumulated in small subset of high-value, superstar firms, and 4) that the accumulation of ITIC predicts future productivity.
Students who take this course will engage with the world of data science using tools such as Tableau and R that are becoming increasingly popular in industry. The first half of the course is designed for students with limited experience with data projects, and while familiarity with R, via courses such as STAT 405 or STAT 470, will be ideal preparation, students with other programming exposure can pick up the required skills via review sessions and self-instruction. The second half of the course extends students' experience to industry applications of text mining and machine learning and requires students to work with more unstructured data. Each week of the course will be devoted to analysis of a data set from a particular industry (e.g. HR, sports, fashion, real estate, music, education, politics, restaurants, non-profit work), which we will use to answer business questions by applying analytic techniques. The course is very hands-on, and students will be expected to become proficient at applying data to business decisions and at effectively analyzing large data sets to inform decisions about business problems.
The progression of AI-based technologies promises to transform many aspects of business, labor, and even society. The goal of this course is to provide students with an understanding of the capabilities of modern AI technologies, with an emphasis on being able to critically assess where they can provide societal value, and where they may create new challenges. The course is not intended to provide a deep-dive into the workings of these technologies in the same way as a computer science course might. Rather, business and policy decision-makers will be confronted with a number of important issues as AI becomes integrated into the social decision-making fabric. This course is intended to provide a framework for people who may have to confront these legal, ethical, and economic challenges. In doing so, an objective is to ensure that students who complete the course are comfortable enough in the inner-workings of these technologies to think critically across many AI contexts as well as different domains ranging from public policy, to criminal justice, to health inspections, HR, and marketing. The 0.5 CU course is oriented around hands-on critical written assessments, labs, exams, and a presentation. Broadly, data rich firms in finance, tech, management, marketing, and other industries are increasingly adopting AI as a tool to accelerate and improve decision-making. It is important for modern managers to understand the opportunities and challenges introduced by data and AI so that they can credibly communicate about these issues with others. We will cover many of these issues, so that you will be able to think about the opportunities and challenges that arise when firms try to use AI to solve business problems. Labs will reinforce your learning of how AI works, and how it is being used to solve business problems. During labs, we will focus on gaining experience with introductory machine learning concepts. Students will spend time inside and outside of the classroom combining data and code to provide a foundation for understanding the deep challenges that this will bring to organizations.
OIDD255002 ( Syllabus )
OIDD255004 ( Syllabus )
New research from Wharton's Peter Cappelli and Prasanna Tambe points out the promise and pitfalls of using artificial intelligence in human resources, where algorithms can’t easily replace human decision-making.Knowledge @ Wharton - 8/30/2019