Duncan Watts

Duncan Watts
  • Stevens University Professor
  • Professor of Operations, Information and Decisions
  • Professor of Communication
  • Professor of Computer and Information Science

Contact Information

  • office Address:

    3401 Walnut Street, 459C

Overview

Duncan Watts is the Stevens University Professor and twenty-third Penn Integrates Knowledge Professor at the University of Pennsylvania. In addition to his appointment at Wharton and as the inaugural Rowan Fellow, he holds faculty appointments in the Department of Computer and Information Science in the School of Engineering and Applied Science, and the Annenberg School of Communication.

Before coming to Penn, Watts was a principal researcher and partner at Microsoft and a founding member of the Microsoft Research NYC lab. He was also an AD White Professor at Large at Cornell University. Prior to joining MSR in 2012, he was a professor of Sociology at Columbia University, and then a principal research scientist at Yahoo! Research, where he directed the Human Social Dynamics group.

His research on social networks and collective dynamics has appeared in a wide range of journals, from NatureScience, and Physical Review Letters to the American Journal of Sociology and Harvard Business Review, and has been recognized by the 2009 German Physical Society Young Scientist Award for Socio and Econophysics, the 2013 Lagrange-CRT Foundation Prize for Complexity Science, and the 2014 Everett M. Rogers Award. In 2018, he was named an inaugural fellow of the Network Science Society.

Watts is the author of three books: Six Degrees: The Science of a Connected Age (W.W. Norton 2003), Small Worlds: The Dynamics of Networks between Order and Randomness (Princeton University Press 1999), and Everything is Obvious: Once You Know The Answer (Crown Business 2011).

He holds a B.Sc. in Physics from the Australian Defence Force Academy, from which he also received his officer’s commission in the Royal Australian Navy, and a Ph.D. in Theoretical and Applied Mechanics from Cornell University.

Continue Reading

Teaching

All Courses

  • CIS0099 - Ugrad Resrch/Ind Study

    An opportunity for the student to become closely associated with a professor (1) in a research effort to develop research skills and techniques and/or (2) to develop a program of independent in-depth study in a subject area in which the professor and student have a common interest. The challenge of the task undertaken must be consistent with the student's academic level. To register for this course, the student must submit a detailed proposal, signed by the independent study supervisor, to the SEAS Office of Academic Programs (111 Towne) no later than the end of the "add" period. Prerequisite: A maxium of 2 c.u. of CIS 099 may be applied toward the B.A.S. or B.S.E. degree requirements.

  • CIS7980 - Explaining Explanation

    In the social sciences we often use the word "explanation" as if (a) we know what we mean by it, and (b) we mean the same thing that other people do. In this course we will critically examine these assumptions and their consequences for scientific progress. In part 1 of the course we will examine how, in practice, researchers invoke at least three logically and conceptually distinct meanings of "explanation:" identification of causal mechanisms; ability to predict (account for variance in) some outcome; and ability to make subjective sense of something. In part 2 we will examine how and when these different meanings are invoked across a variety of domains, focusing on social science, history, business, and machine learning, and will explore how conflation of these distinct concepts may have created confusion about the goals of science and how we evaluate its progress. Finally , in part 3 we will discuss some related topics such as null hypothesis testing and the replication crisis. We will also discuss specific practices that could help researchers clarify exactly what they mean when they claim to have "explained" something, and how adoption of such practices may help social science be more useful and relevant to society.

  • CIS8990 - PhD Independent Study

    For doctoral students studying a specific advanced subject area in computer and information science. The Independen t Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 500 or 600 level course. The Independent Study may also be used by doctoral students to explore research options with faculty, prior to determining a thesis topic. Students should discuss with the faculty supervisor the scope of the Independent Study, expectations, work involved, etc. The Independent Study should not be used for ongoing research towards a thesis, for which the CIS 999 designation should be used.

  • CIS9990 - Thesis/Diss Research

    For students pursuing advanced research to fulfill PhD dissertation requirements.

  • COMM7999 - Independent Research

    Proposal written in specified form and approved by both the student's project supervisor and academic advisor or another member of the faculty must be submitted with registration.

  • COMM8980 - Explaining Explanation

    In the social sciences we often use the word "explanation" as if (a) we know what we mean by it, and (b) we mean the same thing that other people do. In this course we will critically examine these assumptions and their consequences for scientific progress. In part 1 of the course we will examine how, in practice, researchers invoke at least three logically and conceptually distinct meanings of "explanation:" identification of causal mechanisms; ability to predict (account for variance in) some outcome; and ability to make subjective sense of something. In part 2 we will examine how and when these different meanings are invoked across a variety of domains, focusing on social science, history, business, and machine learning, and will explore how conflation of these distinct concepts may have created confusion about the goals of science and how we evaluate its progress. Finally , in part 3 we will discuss some related topics such as null hypothesis testing and the replication crisis. We will also discuss specific practices that could help researchers clarify exactly what they mean when they claim to have "explained" something, and how adoption of such practices may help social science be more useful and relevant to society.

  • NETS1120 - Networked Life

    How do infectious diseases spread? Why do some memes spread virally while others do not? Why do some teams or organizations perform better than others? Are we all really connected by six degrees of separation and, if so, how is that are our neighborhoods, workplaces, and social circles are so segregated? The answers to these questions and many more are all part of Network Science, a fascinating subject at the intersection of many disciplines, including computer science, communications, psychology, sociology, mathematics, and economics. This course will provide an introduction to the technical language of network science as well as to a collection of applications such as mathematical epidemiology, social contagion, games of cooperation and coordination, and collective problem solving.

  • OIDD2990 - Judg & Dec Making Res Im

    This class provides a high-level introduction to the field of judgment and decision making (JDM) and in-depth exposure to the process of doing research in this area. Throughout the semester you will gain hands-on experience with several different JDM research projects. You will be paired with a PhD student or faculty mentor who is working on a variety of different research studies. Each week you will be given assignments that are central to one or more of these studies, and you will be given detailed descriptions of the research projects you are contributing to and how your assignments relate to the successful completion of these projects. To complement your hands-on research experience, throughout the semester you will be assigned readings from the book Nudge by Thaler and Sunstein, which summarizes key recent ideas in the JDM literature. You will also meet as a group for an hour once every three weeks with the class's faculty supervisor and all of his or her PhD students to discuss the projects you are working on, to discuss the class readings, and to discuss your own research ideas stimulated by getting involved in various projects. Date and time to be mutually agreed upon by supervising faculty and students. the 1CU version of this course will involve approx. 10 hours of research immersion per week and a 10-page paper. The 0.5 CU version of this course will involve approx 5 hours of research immersion per week and a 5-page final paper. Please contact Professor Joseph Simmons if you are interested in enrolling in the course: jsimmo@wharton.upenn.edu

  • OIDD9530 - Explaining Explanation

    In the social sciences we often use the word "explanation" as if (a) we know what we mean by it, and (b) we mean the same thing that other people do. In this course we will critically examine these assumptions and their consequences for scientific progress. In part 1 of the course we will examine how, in practice, researchers invoke at least three logically and conceptually distinct meanings of "explanation:" identification of causal mechanisms; ability to predict (account for variance in) some outcome; and ability to make subjective sense of something. In part 2 we will examine how and when these different meanings are invoked across a variety of domains, focusing on social science, history, business, and machine learning, and will explore how conflation of these distinct concepts may have created confusion about the goals of science and how we evaluate its progress. Finally , in part 3 we will discuss some related topics such as null hypothesis testing and the replication crisis. We will also discuss specific practices that could help researchers clarify exactly what they mean when they claim to have "explained" something, and how adoption of such practices may help social science be more useful and relevant to society.

In the News

Knowledge at Wharton

Activity

In the News

Are Teams Better Than Individuals at Getting Work Done?

Whether teams or individuals are better at accomplishing tasks depends on the complexity of the work, according to a new study co-authored by Wharton’s Duncan Watts.Read More

Knowledge at Wharton - 10/12/2021
All News