Duncan Watts

• Linnea Gandhi, Anoushka Kiyawat, Colin Camerer, Duncan Watts (Working), Prospects for using hypothetical nudges to approximate real behavior change. Abstract

  Hypothetical scenarios provide an essential alternative to field experiments for scholars interested in nudging behavior change, comprising a substantial proportion of the literature. Yet the conditions under which hypotheticals more or less accurately estimate real-world treatment effects is not well understood. To investigate, we identified five recent field studies of real-world nudges in distinct domains and designed four styles of hypothetical scenarios to approximate each of those five studies. This setup allows clear comparison of old field data with new hypothetical data. Across our 20 pre-registered experiments (N=16,071, n>200 per cell), we find that hypothetical scenarios accurately estimated the direction of treatment effects, but varied widely in estimating the magnitudes of those effects. None of our four designs reliably reduced estimation error. Instead, hypotheticals appeared most calibrated when real-world treatment effects were extremely small, a promising direction for future study.

  Related
  Links

  (Preprint, 2024)
• Linnea Gandhi and Duncan Watts (Work In Progress), Predicting the generalizability of choice architecture interventions.

Current Courses

• CIS8990 - Doctoral Independent Study

  For doctoral students studying a specific advanced subject area in computer and information science. The Independent Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 5000 or 6000 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 9990 designation should be used.

  CIS8990071

• CIS9990 - Thesis/dissertation Research

  For students pursuing advanced research to fulfill PhD dissertation requirements.

  CIS9990071

• COMM3200 - Common Sense Vs. Data Science In Communications Research And Practice

  Policy makers, entrepreneurs, and marketers frequently rely on common sense when planning for the future; yet their predictions are often wrong, and their plans fail for reasons that seem obvious after the fact. In this course you will learn about the nature of common sense, when it should be expected to work effectively, and why we are tempted to use it even when we should not. The course will also introduce a data science perspective on explanation, understanding, and decision making, covering topics such as experiments, predictive analytics, forecasting tournaments, scenario planning, and epistemic humility. The course will be conceptual rather than methodological and so is equally appropriate for students with technical and nontechnical backgrounds.

  COMM3200301 ( Syllabus )

• OIDD3990 - Supervised Study

  This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.

  OIDD3990001 ( Syllabus )

• OIDD9999 - Independent Study

  Independent Study

  OIDD9999006 ( Syllabus )

Past 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 maximum of 2 c.u. of CIS 0099 may be applied toward the B.A.S. or B.S.E. degree requirements.

• CIS5970 - Master's Thesis Research

  For students working on an advanced research leading to the completion of a Master's thesis.

• 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 Independent Study may involve coursework, presentations, and formally gradable work comparable to that in a CIS 5000 or 6000 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 9990 designation should be used.

• CIS9990 - Thesis/Diss Research

  For students pursuing advanced research to fulfill PhD dissertation requirements.

• COMM3200 - Common Sense vs. Data Science

  Policy makers, entrepreneurs, and marketers frequently rely on common sense when planning for the future; yet their predictions are often wrong, and their plans fail for reasons that seem obvious after the fact. In this course you will learn about the nature of common sense, when it should be expected to work effectively, and why we are tempted to use it even when we should not. The course will also introduce a data science perspective on explanation, understanding, and decision making, covering topics such as experiments, predictive analytics, forecasting tournaments, scenario planning, and epistemic humility. The course will be conceptual rather than methodological and so is equally appropriate for students with technical and nontechnical backgrounds.

• COMM3999 - Independent Study

  The independent study offers the self-motivated student an opportunity for a tailored, academically rigorous, semester-long investigation into a topic of the student's choice with faculty supervision. Students must complete and file a designated form, approved and signed by the supervising faculty member and the Associate Dean for Undergraduate Studies. This form must be received by the Undergraduate Office before the end of the first week of classes in the semester in which the independent study will be conducted.

• COMM6990 - Advance Project Medium

  Proposal written in specified form and approved by both the student's project supervisor and academic advisor must be submitted with registration. Open only to graduate degree candidates in communication.

• 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.

• ESE0099 - Independent Study

  An opportunity for the student to become closely associated with a professor in (1) a research effort to develop research skills and technique 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 and professor jointly submit a detailed proposal to the undergraduate curriculum chairman no later than the end of the first week of the term.

• 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

• OIDD3990 - Supervised Study

  This course number is currently used for several course types including independent studies, experimental courses and Management & Technology Freshman Seminar. Instructor permission required to enroll in any independent study. Wharton Undergraduate students must also receive approval from the Undergraduate Division to register for independent studies. Section 002 is the Management and Technology Freshman Seminar; instruction permission is not required for this section and is only open to M&T students. For Fall 2020, Section 004 is a new course titled AI, Business, and Society. The course provides a overview of AI and its role in business transformation. The purpose of this course is to improve understanding of AI, discuss the many ways in which AI is being used in the industry, and provide a strategic framework for how to bring AI to the center of digital transformation efforts. In terms of AI overview, we will go over a brief technical overview for students who are not actively immersed in AI (topic covered include Big Data, data warehousing, data-mining, different forms of machine learning, etc). In terms of business applications, we will consider applications of AI in media, Finance, retail, and other industries. Finally, we will consider how AI can be used as a source of competitive advantage. We will conclude with a discussion of ethical challenges and a governance framework for AI. No prior technical background is assumed but some interest in (and exposure to) technology is helpful. Every effort is made to build most of the lectures from the basics.

• 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.

• OIDD9999 - OIDD 9999

  Independent Study

Knowledge @ Wharton

• The Regulatory Challenges of AI in Finance, Knowledge @ Wharton - 11/25/2024
• Building Stronger Family Offices by Sharing Knowledge and Securing the Future, Knowledge @ Wharton - 11/21/2024
• State of the NBA with Seth Partnow, Knowledge @ Wharton - 11/20/2024
• How Are Companies Really Using AI? A New Report Has Answers, Knowledge @ Wharton - 11/19/2024
• Impact vs. Time: A Leader’s Guide to Slow Productivity, Knowledge @ Wharton - 11/19/2024
• Kevin O’Leary Illustrates the Benefits of Tariffs for U.S. Businesses, Knowledge @ Wharton - 11/19/2024
• Boosting Donations Through Self-expressive Choices | Jonah Berger, Knowledge @ Wharton - 11/19/2024
• Why Are Hospitals So Expensive?, Knowledge @ Wharton - 11/18/2024
• New Phenomena in Behavioral and Social Investing, Knowledge @ Wharton - 11/18/2024
• Bigger Hospitals, Better Care? Wharton Research Finds Benefits Are Only at the Operational Level, Knowledge @ Wharton - 11/14/2024