Ph.D. Yale University, 1979 (Psychology);
M.A. University of British Columbia, 1976;
B.A. University of British Columbia, 1975;
2011 -present Leonore Annenberg University Professor, School of Arts and Sciences (Psychology) and Wharton School (Management), University of Pennsylvania;
2002- 2010 Mitchell Endowed Professorship, Haas School of Business, University of California Berkeley;
2005-2006 Russell Sage Scholar;
1996-2001 Harold Burtt Professor of Psychology and Political Science, The Ohio State University;
1993-1994 Fellow, Center for Advanced Study in the Behavioral Sciences, Stanford;
1993-1995 Distinguished Professor, University of California, Berkeley;
1988-1995 Director, Institute of Personality and Social Research, University of California, Berkeley;
1987-1996 Professor, Department of Psychology, University of California, Berkeley;
1984-1987 Associate Professor, Department of Psychology, University of California, Berkeley;
1980-1995 Research Psychologist, Survey Research Center, University of California, Berkeley;
1979-1984 Assistant Professor, Department of Psychology, University of California, Berkeley;
Group Chair, Organizational Behavior and Industrial Relations, Haas School of Business, University of California, Berkeley, 2002-present;
Associate Dean for Academic Affairs, Haas School of Business, University of California, Berkeley, 2003-2004;
Director, Ph.D. programs, Haas School of Business, University of California, Berkeley;
Director, Institute of Personality Assessment and Research (renamed in 1992 as Institute of Personality and Social Research), University of California, Berkeley, 1988-1995.
Philip Tetlock and Dan Gardner, Superforecasting: The Art and Science of Prediction (2015)
Jonathan Baron, Barbara Mellers, Philip Tetlock, Eric Stone, Lyle Ungar (2015), Two Reasons to Make Aggregated Probability Forecasts More Extreme, Decision Analysis.
Barbara Mellers, Eric Stone, Terry Murray, Angela Minster, Nick Rohrbaugh, Michael Bishop, Eva Chen, Joshua Baker, Yuan Hou, Michael Horowitz, Lyle Ungar, Philip Tetlock (2015), Identifying and Cultivating Superforecasters as a Method of Improving Probabilistic Predictions, Perspectives on Psychological Science.
Mandeep Dhami, David Mandel, Barbara Mellers, Philip Tetlock (2015), Improving Intelligence Analysis with Decision Science, Perspectives on Psychological Science.
Edgar Merkle, Mark Steyvers, Barbara Mellers, Philip Tetlock (2015), Item Response Models of Probability Judgments: Application to a Geopolitical Forecasting Tournament, decision.
Ville Satopää, Shane T. Jensen, Barbara Mellers, Philip Tetlock, Lyle H. Ungar (2014), Probability Aggregation in Time-Series: Dynamic Hierarchical Modeling of Sparse Expert Beliefs, The Annals of Statistics, 8 (2), pp. 1256-1280.
Barbara Mellers, Philip Tetlock, Nick Rohrbaugh, Eva Chen (2014), Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate, Current Directions in Psychological Science.
Philip Tetlock and Barbara Mellers (2014), Judging political judgment, PNAS.
Barbara Mellers, Lyle Ungar, Jonathan Baron, Jaime Ramos, Burcu Gurcay, Katrina Fincher, Sydney Scott, Don Moore, Pavel Atanasov, Samuel Swift, Terry Murray, Eric Stone, Philip Tetlock (2014), Psychological Strategies for Winning a Geopolitical Forecasting Tournament, Psychological Science.
This course will explore the diverse ways in which scholars and practitioners have defined "good judgment." And it will introduce students to practical tools for assessing and improving judgment, with special emphasis on probabilistic reasoning. Students will have the opportunity both to fine-tune their personal judgment skills as well as to master and then weave together insights from several bodies of scientific knowledge, including frequentist and Bayesian statistics, psychological work on judgment and choice, group dynamics, organizational behavior and political science (key concepts discussed in Tetlock's (2015) book "Superforecasting"). ,We will focus on bottom-line accuracy in sizing up real world problems. Class work will be primarily exercises, including working as an individual and in teams. You will have opportunities to forecast on a wide range of political, business, and macro-economic questions, which we will use as feedback tools to help you calibrate your judgment. Assessments include a weekly concept test and a final group presentation aimed to help you improve your judgment. The goal is to launch you on the lifelong process of learning how much trust you should place in your judgments of trustworthiness. ,Finally, note this has been approved by the Curriculum Committee effective 11/11/15.
This course, is required of all first-year doctoral students in Management and open to other Penn students with permission, provides an introduction to the psychological and sociological roots of management theory and research. The course is predicated on the belief that to be effective as a contemporary management scholar one needs a background in "the classics." Therefore, we will be reading classics from the fields of psychology and sociology in their original form during this semester.
The purpose of this quarter course is to continue to explore key concepts and research programs in the field of micro-organizational behavior that we began to study in MGMT 951. To do so, we will cover a blend of classic and contemporary literature so that we can appreciate the prevailing theories and findings in various areas of micro-organizational behavior. In addition, for each topic we will then try to go beyond the existing literature. We will work to increase our understanding by re-framing the research variables, altering the perspective, bringing in new theory, and comparing levels of analysis. Building on the topics we examined in MGMT 951, we will explore further organizational behavior topics including identity, fit, extra role behaviors, job design, creativity, status, power and influence.
With some 38,000 U.S. automobile deaths a year, self-driving cars are poised to boost safety considerably. But recent fatal accidents show there is a long way to go.Knowledge @ Wharton - 2018/07/6