COMPUTATIONAL SOCIAL SCIENCE SEMINAR – Ages and Lifetimes of U.S. Firms – Axtell

September 29, 2017 @ 3:00 pm – 4:30 pm
Center for Social Complexity Suite 3rd Floor, Research Hall, Fairfax Campus
Karen Underwood

Computational Social Science

Robert Axtell, PhD
Computational Social Science Program, Department of Computational and Data Sciences,
College of Science
Department of Economics, College of Humanities and Social Sciences
Krasnow Institute for Advanced Study
George Mason University

Computational Public Policy Lab
Krasnow Institute for Advanced Study and Schar School of Policy and Government

External Professor, Santa Fe Institute (
External Faculty, Northwestern Institute on Complex Systems (
Scientific Advisory Council, Waterloo Institute for Complexity and Innovation (

Getting Younger by Growing Older: U.S. Firms Gain Longevity as they Age

Friday,  September 29, 3:00 p.m.
Center for Social Complexity Suite
3rd Floor, Research Hall


Abstract:  Using data on the entire population of American firms I will first show that the distribution of firm ages is approximately stationary, with small ‘defects’ arising at the start of last decade’s Financial Crisis now propagating through the distribution. From these data I will derive the distribution of U.S. firm lifetimes and demonstrate that it has a specific structure that conforms to economists’ intuitions about new (and small) firms having higher failure probabilities than older (and larger) firms. I will then demonstrate that the large body of statistical theory on ‘survival analysis’ is directly applicable to firms. Specifically, I will focus on firm hazard functions and empirically show that for the U.S. this is a power law over a wide range of ages, ostensibly a new finding, declining with age. This permits computation of the expected remaining lifetime of firms as a function of their age, an INCREASING function, implying that American firms gain longevity as they get older, a very non-biological type of aging. Conditioning on firm size produces further results. Specifically, using the Cox ‘proportional hazards’ specification, the reduction in failure probability associated with larger size is quantified. At the end I will demonstrate that an ABM of firm dynamics can be calibrated to reproduce all of these features of U.S. firms.

Rob Axtell earned an interdisciplinary Ph.D. degree at Carnegie Mellon University, where he studied computing, social science, and public policy. His teaching and research involves computational and mathematical modeling of social and economic processes. Specifically, he works at the intersection of multi-agent systems computer science and the social sciences, building so-called agent-based models for a variety of market and non-market phenomena.

His research has been published in the leading scientific journals, including Science and the Proceedings of the National Academy of Sciences, USA, and reprised in Nature, and has appeared in top disciplinary journals (e.g., American Economic Review, Computational and Mathematical Organization Theory, Economic Journal), in general interest journals (e.g., PLOS One) and in specialty journals (e.g., Journal of Regulatory Economics, Technology Forecasting and Social Change.) His research has been supported by American philanthropies (e.g., John D. and Catherine T. MacArthur Foundation, Institute for New Economic Thinking) and government organizations (e.g., National Science Foundation, Department of Defense, Small Business Administration, Office of Naval Research, Environmental Protection Agency). Stories about his research have appeared in major magazines (e.g., Economist, Atlantic Monthly, Scientific American, New Yorker, Discover, Wired, New Scientist, Technology Review, Forbes, Harvard Business Review, Science News, Chronicle of Higher Education, Byte, Le Temps Strategique) and newspapers (e.g., Wall St. Journal, Washington Post, Los Angeles Times, Boston Globe, Detroit Free Press, Financial Times). He is co-author of Growing Artificial Societies: Social Science from the Bottom Up (MIT Press) with J.M. Epstein, widely cited as an example of how to apply modern computing to the analysis of social and economic phenomena.