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.

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Teaching

Current Courses

  • NETS112 - Networked Life

    What kind of science is appropriate for understanding the Facebook? How does Google find what you're looking for... and exactly how do they make money doing so? What properties might we expect any social network to reliably have, and are there simple explanations for them? How does your position in an economic network (dis)advantage you? How are individual and collective behavior related in complex networks? What might we mean by the economics of spam? What do game theory and the Paris subway have to do with Internet routing? Networked Life looks at how our world is connected -- socially, economically, strategically and technologically -- and why it matters.

    NETS112001

Past Courses

  • CIS 798 - 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.

  • CIS 899 - 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.

  • COMM898 - 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.

  • NETS112 - NETWORKED LIFE

    What kind of science is appropriate for understanding the Facebook? How does Google find what you're looking for... and exactly how do they make money doing so? What properties might we expect any social network to reliably have, and are there simple explanations for them? How does your position in an economic network (dis)advantage you? How are individual and collective behavior related in complex networks? What might we mean by the economics of spam? What do game theory and the Paris subway have to do with Internet routing? Networked Life looks at how our world is connected -- socially, economically, strategically and technologically -- and why it matters.

  • OIDD953 - 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

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In the News

How Misinformation Hurts Democracy

While overtly fake news generated by bogus websites can be dangerous, there’s greater harm in subtle misinformation that spreads through mainstream media, according to new research.

Knowledge @ Wharton - 11/3/2020
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