Date and LocationAug 13, 2018 - 3:00pm to 4:00pm
We consider models of growing multilevel systems wherein the growth process is driven by rules of tournament selection. A system can be conceived as an evolving tree with a new agent being attached to a contestant agent at the best hierarchy level (a level nearest to the tree root). The proposed evolution reflects limited information on system properties available to new agents. It can also be expressed in terms of population dynamics. Two models are studied in details: a constant tournament (CT) model wherein the number of tournament participants is constant throughout system evolution, and a proportional tournament (PT) model where this number increases proportionally to the growing size of the system itself. The results of analytical calculations based on a rate equation fit well to numerical simulations for both models. In the CT model all hierarchy levels emerge, but the birth time of a consecutive hierarchy level increases exponentially or faster for each new level. The number of agents at the first hierarchy level grows logarithmically in time, while the size of the last, “worst” hierarchy level oscillates quasi-log-periodically. In the PT model, the occupations of the first two hierarchy levels increase linearly, but worse hierarchy levels either do not emerge at all or appear only by chance in the early stage of system evolution to further stop growing at all.
The results allow us to conclude that information available to each new agent in tournament dynamics restrains the emergence of new hierarchy levels and that it is the absolute amount of information, not relative, which governs such behaviour: the larger the amount of available information, the slower the growth of consecutive hierarchies. This behavior resembles models of group cooperation, where easy access to information causes a hierarchy to become shallower provided that system resources are evenly distributed.
Professor Janusz Hołyst leads the Physics in Economy and Social Sciences Lab at the Faculty of Physics, Warsaw University of Technology, and coordinates a Project of the Russian Scientific Foundation at ITMO University in Saint Petersburg. His current research includes simulations of evolving networks, models of collective opinion and emotion formation, econophysics, non-equilibrium statistical physics and data science. He is one of the pioneers in applying physical methods to economic and social systems. He was a scientific advisor of 12 completed Ph.D. thesis and his list of publications includes around 150 papers in peer reviewed journals that have been cited over 2500 times (h-index=29).
Hołyst maintains a close research collaboration with several Institutes in Germany, UK, Switzerland, Slovenia, Russia, Singapure, Japan and USA, where he spent over six years as a Visiting Professor, Fellow of Humboldt Foundation or Guest Scientist. He is an associate editor in the Journal of Computational Science and a past Editor of the European Physical Journal B and of the European Physical Journal Data Science. Hołyst was a coordinator or partner in several EU Projects, e.g. FP7 FET ICT Integrated Project Collective Emotions in Cyberspace (CYBEREMOTIONS, 2009 – 2013, www.cyberemotions.eu ) or RENOIR, Reverse Engineering of Social Information Processing (H2020 UE MSCA-RISE, 2016 –2019, renoirproject.eu). He is the President of KRAB (National Council for Research Projects Coordinators in Poland) and a past Chairman of FENS (Physics in Economy and Social Sciences, Division of the Polish Physical Society).
Prof. Holyst has worked as a leader of an advisory team on the modeling of marketing and economic processes for the American Company, Bunge.
Host: Ambuj Singh