Research Page

Nabil Al-Najjar is the John L. and Helen Kellogg Professor of Managerial Economics and Decision Sciences. 

Al-Najjar's research focuses on the development of learning-based models of decision making in markets, games and contracts. His papers have been published in top scholarly journals such the Journal of Economic Theory, Games and Economic Behavior, Journal of Political Economy, Econometrica,  among others.

For his excellence in teaching, Al-Najjar has twice been the recipient of the school's Sidney J. Levy Award, in 1996-97 for his class in microeconomics, and 2006-07 for his class in competitive strategy. He has also received the Chairs' Core Teaching Award for his class in microeconomics, as well as several Certificate of Impact awards.

Al-Najjar received his PhD in Economics from the University of Minnesota. Prior to joining the Kellogg faculty in 1995, he was a faculty member at the University of Quebec in Montreal.

Decision theory, learning, game theory

• PhD, 1989, Economics, University of Minnesota
• MA, 1982, Economics, University of Ottawa
• BA, 1979, Economics, Al-Mustansiriah University

• Professor, University of Quebec, 11/20/1995
• Professor, Managerial Economics & Decision Sciences, Kellogg School of Management, Northwestern University

• Certificate of Impact Teaching Award (DECS 450), Spring 2016
• Certificate of Impact Teaching Award (DECS 450), Fall 2013
• Certificate of Impact Teaching Award (DECS 450), Northwestern University, Decision Making and Modeling, Fall 2012
• Sidney J. Levy Teaching Award, Kellogg School of Business, 2006-2007, 1996-1997
• Chairs Core Course Teaching Award, Kellogg School of Management, 1999-2000
• Sidney J. Levy Teaching Award, Kellogg School of Business and Management, 1997

• Editorial Board, International Journal of Game Theory, 2003-2009
• Editorial Board, Journal of Mathematical Economics, 1996-2010

Microeconomics, macroeconomics, competitive strategy, commodity industries, decision theory

Economic Theory I: Decision Theory (MECS-550-1)
This course focuses on decision theory and formal theories of individual decision making, with emphasis on decision making under risk/uncertainty. We explore utility theory under certainty, then classic expected utility theories. Following is a review a selection expanding on the classical work in various directions, closer to the current research frontier. The selection covered may vary with available time/interests.

Business Strategy, Behavioral Biases & Deep Uncertainty (MECNX-435-0)
All critical decisions in business involve bets on future events about which there is no reliable past data or consensus about their likelihoods. Judgment in these situations of deep uncertainty tends to be clouded by behavioral biases that lead to frequent, often disastrous, forecast failures. This course provides frameworks to identify persistent psychological biases that underlie these failures and shows how tools of cognitive psychology and probability can effectively mitigate them. The course will cover cases on strategic business decisions in contexts that include: • Mergers and acquisitions • Capacity expansions in commodity industries • Incumbents' failure to anticipate disruptive changes • Failures to anticipate tail events and network dynamics.
We use these cases to identify of biases like over-optimism, herd behavior, narrow framing, escalation of commitment, winner's curse, failure to appreciate tail risks, and the sunk cost fallacy. The course's frameworks will help you anticipate these biases and will provide tools to improve your decision making process. These tools include pre-mortem analysis, scenario analysis, decision trees, models of wisdom of the crowd and informational cascades.

Statistical Decision Analysis (MECNX-434-0)


Analytical Approach to Uncertainty (MECNX-433-0)

Data, Models, and Decisions (MECN-451-0)

This course introduces fundamental concepts and modeling tools for decision making under uncertainty. The pedagogical approach combines business cases, conceptual frameworks of probability and risk analysis, and spreadsheet modeling of managerial decision problems. The course will also provide you with training in state-of-the-art Palisade's DecisionTools Suite to perform and interpret Monte Carlo simulation and decision tree models. Among the concepts discussed in depth are the value of information, option value, selection bias, herd behavior, risk aversion, and the "flaw of averages.'' The concepts and tools are illustrated using business applications in the areas of economics, strategy, operations, and finance.

For more information about the course content, recent syllabus, and TCEs, please click here.