Keynotes & Titans

Track Coordinators: Charles M. Macal, Argonne National Laboratory & Manuel D. Rossetti, University of Arkansas

Keynote Speaker

Joshua Epstein is Professor of Emergency Medicine at Johns Hopkins University, director of JHU’s Center for Advanced Modeling and co-director of its Systems Institute. He holds joint appointments in applied mathematics, civil engineering, economics, environmental health sciences, biostatistics, international health, and is External Professor at the Santa Fe Institute. A pioneer in agent-based modeling, Epstein has authored seminal books including Growing Artificial Societies: Social Science from the Bottom Up, with Robert Axtell (MIT Press, 1996); Generative Social Science: Studies in Agent-Based Computational Modeling (Princeton Press, 2006); and recently Agent_Zero: Toward Neurocognitive Foundations for Generative Social Science (Princeton University Press, 2013). He holds a Ph.D. from MIT, has taught at Princeton and lectured worldwide. In 2008, he received an NIH Director's Pioneer Award and in 2010 an Honorary Doctorate of Science from Amherst College, his alma mater.

Monday, December 7, 8:00am
Agent_Zero and Generative Social Science
Agent_Zero is a formal alternative to the rational actor model that has dominated social science since the 1940s. This software individual is the first to be endowed with distinct affective, deliberative, and social modules. Grounded in neuroscience, these internal facets interact to produce far-from-rational individual behavior. And when ensembles of these agents interact spatially they generate a panoply of social dynamics from genocide to financial panic to vaccine refusal. Epstein will discuss the background of Agent_Zero, demonstrate its application to an array of fields, and discuss future research directions including large-scale modeling in the economic, behavioral, and health sciences.

Titans of Simulation

Averill M. Law is President of Averill M. Law & Associates, the world leader in simulation training. He has presented 535 simulation seminars in 20 countries on topics such as system design and analysis, model validation, and agent-based simulation. He is the author of the book Simulation Modeling and Analysis, with more than 158,000 copies in print and 15,000 citations. He was awarded the 2009 INFORMS Simulation Society Lifetime Professional Achievement Award. Previously, he was a faculty member at University of Wisconsin-Madison and University of Arizona, and has a Ph.D. in operations research from University of California at Berkeley.

Monday, December 7, 12:20pm
Discrete-Event and Agent-Based Simulation and Where to Use Each
Discrete-event simulation (DES) has been used since the late 1950s. In contrast, agent-based simulation (ABS) is much newer but has been the “hottest” topic in simulation since 2005, despite a lack of agreement on what is an agent or ABS. We carefully define DES and ABS, and discuss their similarities/differences. We argue that emergence is not a fundamental tenet of ABS, as is often suggested. We give three general situations where ABS will probably be required, and relate these to actual applications. The talk concludes with a discussion of the most-important developments in simulation technology in the last five years.

Pierre L’Ecuyer is Professor in the Département d’Informatique et de Recherche Opérationnelle, the Université de Montréal, Canada. He holds the Canada Research Chair in Stochastic Simulation and Optimization. He is a member of the CIRRELT and GERAD research centers, and also benefits from an Inria International Chair in Rennes, France. His main research interests are random number generation, quasi-Monte Carlo methods, efficiency improvement via variance reduction, sensitivity analysis and optimization of discrete-event stochastic systems, and discrete-event simulation in general. He has served as Editor-in-Chief for ACM Transactions on Modeling and Computer Simulation from 2010 to 2013. He is currently Associate Editor for ACM Transactions on Mathematical Software, Statistics and Computing, and International Transactions in Operational Research. He has published over 250 scientific articles and book chapters, and has been a referee for over 130 different scientific journals.

Tuesday, December 8, 12:20pm
Imitation Challenges: From Uniform Random Variables to Complex Systems
In stochastic simulation, we construct mathematical models to imitate the behavior of real systems, use computers to sample behavioral histories (sample paths) of these models, and exploit those samples to improve decision making with the real system. The imitation part can be very challenging, in particular for modeling uncertainty. Fitting univariate probability distribution to data is far from sufficient. Modeling the dependence is very important and much more challenging. It involves multivariate distributions, copulas, stochastic processes, and other complicated stochastic objects. Simulating the model on a computer also involves an imitation game, to simulate the realizations of random variables and stochastic processes with deterministic algorithms on a computer. Random number generation involves writing deterministic computer programs that can imitate simple probabilistic models such as independent uniform random variables uniformly distributed over the interval (0, 1). An “exact” algorithmic implantation of such models is theoretically impossible, so we settle for a reasonable fake. The talk will give snapshots and expose ideas collected from the author’s journey through stochastic simulation. The tour will start with random number generation and visit some challenging problems such as stochastic modeling, simulation-based optimization, rare events, simulation on parallel processors, and future challenges.