PJ Lamberson

Senior Lecturer of Management & Organizations

Print Overview
PJ Lamberson is a Senior Lecturer in the Management and Organizations Department and a Senior Research Associate at the Northwestern Institute on Complex Systems (NICO). Before coming to Northwestern, PJ was a Senior Lecturer in the System Dynamics group at MIT Sloan and a Research Fellow at the Center for the Study of Complex Systems at the University of Michigan. He received his PhD in Mathematics from Columbia University in 2006. PJ’s research uses modeling to understand the theory and applications of social dynamics and networks. His research addresses questions such as how do information, products, and behaviors spread through networks, how should members be selected for a forecasting team, and why do people turn out to vote. His work has appeared in Management Science, Economics Letters, Connections, Transactions of the American Mathematics Society, and Ecological Modeling. He designed and teaches the course Social Dynamics and Networks.

Areas of Expertise
Behavioral Economics
Computational Economics
Consumer Decision-Making
Economic Models
Economic Theory
Group Decision-Making
Information Economics
Print Vita
Ph.D., 2006, Mathematics, Columbia University
M.Phil, 2005, Mathematics, Columbia University
M.A., 2003, Mathematics, Columbia University
B.A, 2001, Mathematics, University of Chicago, Honors in the College and Honors in Mathematics

Academic Positions
Senior Lecturer of Management & Organizations, Kellogg School of Management, Northwestern University , 2011-present
Senior Lecturer, Sloan School of Management, MIT, 2010-2011
Visiting Assistant Professor, Sloan School of Management, MIT , 2008-2010
Postdoctoral Research Fellow, Center for the Study of Complex Systems, University of Michigan, 2006-2008

Grants and Awards
Faculty Impact Award, 2013
Chair's Core Course Teaching Award, 2011-2012

Print Research
Research Interests
Social dynamics, social influence, complex systems, networks, diffusion

Lamberson, PJ and Scott E. Page. 2012. The Effect of Feedback Variability on Success in Markets with Positive Feedbacks. Economics Letters. 114: 259-261.
Lamberson, PJ and Scott E. Page. 2012. Optimal Forecasting Groups. Management Science. 58: 791-804.
Shoham, DA, L Tong, PJ Lamberson, AH Auchincloss, J Zhang, LR Dugas, JS Kaufman and R. S. Cooper. 2012. An Actor-based Model of Adolescent Body Size, Screen Time, and Physical Activity. PLOS One. 7(6): e39795.
Lamberson, PJ and Scott E. Page. 2012. Tipping Points. Quarterly Journal of Political Science. 7: 175-208.
Lamberson, PJ. 2011. Linking Network Structure and Diffusion through Stochastic Dominance. Connections. 31(1)
Lamberson, PJ. 2010. A Typology of Mechanisms that Generate Clustering in Networks. American Journal of Epidemiology.(171): Suppl 11.
Lamberson, PJ. 2010. Social Learning in Social Networks. B. E. Journal of Theoretical Economics. 10(1): Article 36.
Lamberson, PJ. 2009. The Milnor Fiber Conjecture and Iterated Branched Cyclic Covers. American Mathematical Society. 9(361): 4653—4681.
Lamberson, PJ. 2008. The Diffusion of Hybrid Electric Vehicles.
Wildhaber, Mark L. and PJ Lamberson. 2004. Importance of the Habitat Choice Behavior Assumed when Modeling the Effects of Food and Temperature on Fish Populations. Ecological Modelling. 4(175): 395—409.
Wildhaber, Mark L., PJ Lamberson and David L. Galat. 2003. A Comparison of Measures of Riverbed Form for Evaluating Distributions of Benthic Fishes. North American Journal of Fisheries Management. 23: 543—557.
Lamberson, William R., PJ Lamberson and Laura L. Melton. 2002. A Relationship-based Algorithm for Identifying Genetically Diverse Subpopulations. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production. 28: 24-26.
Wildhaber, Mark L., JoAnne E. Whitaker, Ann L. Allert, Daniel W. Mulhern, PJ Lamberson and Kenneth L. Powell. 2000. Ictalurid Populations in Relation to the Presence of a Main-stem Reservoir in a Midwestern Warmwater Stream with Emphasis on the Threatened Neosho Madtom. Transactions of the American Fisheries Society. 129: 1264—1280.
Working Papers
Lamberson, PJ. 2014. Superstars and Long Tails: An Agent Based Model of Consumer Choice.
Lamberson, PJ. 2013. Two-Way Contagion.
Lamberson, PJ. 2013. Network Games with Local Correlation and Clustering.
Lamberson, PJ and Georgia Kernell. 2012. Empirical Implications of Including Social Influence in the Calculus of Voting.

Print Teaching
Teaching Interests
Social dynamics, networks, modeling, system dynamics, complexity, decision making
Full-Time / Part-Time MBA
Business Analytics I (DECS-430-A)
Analytics is the discovery and communication of meaningful patterns in data. This course will provide students with an analytics toolkit, reinforcing basic probability and statistics while throughout emphasizing the value and pitfalls of reasoning with data. Applications will focus on connections among analytical tools, data, and business decision-making

Decision Making Under Uncertainty (DECS-433-0)

This course counts toward the following majors: Decision Sciences.

Provides frameworks for reasoning about decisions in uncertain environments. Case studies and experiments are used to motivate the importance of probabilistic reasoning to avoid the systematic biases that cloud managers' decision making. Formal probabilistic tools are introduced and their relevance to modern business issues is conveyed via cases, exercises, and class experiments. Some of the applications include: inventory management with uncertain demand, principal-agent models, herd behavior, selection bias, rare events, real options and risk. The course is self-contained, and should be of value to all students, including those with prior exposure to formal probability models.

Social Dynamics and Network Analytics (MORS-945-0)

This course counts toward the following majors: Management and Organizations, Entrepreneurship and Innovation, Media Management

Today’s business leaders face unparalleled levels of connectedness and complexity. To help students meet these challenges, Social Dynamics and Networks Analytics provides an in-depth introduction to the emerging fields of social dynamics and network science including social networks, social media, tipping points, contagion, the wisdom of crowds, prediction markets, and social capital. Using simple yet powerful hands-on interactive models and exercises, the course covers both theory and applications of social dynamics for organizational growth, leadership, and competitiveness. The course was developed jointly with Professor Uzzi and complements the MORS 430 leadership and organizations course

Executive MBA
Social Dynamics & Networks (MORSX-945-0)
The results of a recent IBM survey of over 1500 CEOs worldwide identified complexity as the most pressing challenge facing today's business leaders. To provide Kellogg EMBA students with the tools and skills necessary to confront this accelerating change and increasing interconnection, Social Dynamics and Networks explores cutting edge research on social networks, social media, tipping points, contagion, herd behavior, the wisdom of crowds, and prediction markets. The course employs simple yet powerful interactive models and hands-on exercises to develop understanding of both the theory and applications of social dynamics and network science.