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Dr. Robert Axtell to present on 2/15

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The Computational Social Science Research Colloquium /Colloquium in Computational and Data Sciences speaker for Friday, February 15, 2019, will be Robert Axtell, Professor, Computational Social Science Program, Department of Computational and Data Sciences, College of Science/Department of Economics, College of Humanities and Social Sciences, George Mason University. Dr. Axtell’s talk entitled “Lifetime/Survival/Reliability/Duration Analysis for Computational Models” will begin at 3:00 in the Center for Social Complexity Suite located on the 3rd floor of Research Hall. The talk will be followed by a Q&A session along with light refreshments.

This session will be live-streamed on the YouTube channel:

For announcements regarding this and future streams, please join the CSS/CDS student and alumni Facebook group:

For a list of upcoming and previous seminars, please visit:

Abstract: In a variety of computational models, structures arise, evolve, then disappear, perhaps replaced by other, comparable structures. For example, in some economic models firms form from the interactions of agents, operate for some period of time, and then exit. In housing models, households hold mortgages for finite periods of time before replacing them either due to refinancing or moving to a new house. In political (marketing) models the interests of parties (businesses) are aligned with certain segments of voters (consumers) for a period of time, until competition leads to realignment (brand switching). In environmental policy models specific polluting technologies have finite lifetimes and are eventually replaced by cleaner technologies. In disease models people are infected for varying lengths of time based on their health status, policies, etc. Traffic jams and conflicts have finite duration.

In this talk I will review the mathematical and statistical formalisms of lifetime analysis, also known as survival analysis in biostatistics and reliability analysis in engineering, focusing on the concepts most useful for computational models. Specifically while the former field has concerned itself with censored data (e.g., short clinical trials during which not all patient health outcomes can be observed), and the latter has focused on schemes to manage unreliable equipment, in computational modeling we often need to better understand both age and lifetime distributions of objects in our models, typically have large amounts of quasi-exhaustive ‘data,’ normally know some covariates, and usually work in discrete time.