Community

J Glaubius Member since: Friday, May 22, 2015

Human and environmental interactions in the Mediterranean region. Currently working on influence of agricultural terraces on landscape evolution.

Roger Cremades Member since: Wednesday, April 01, 2020 Full Member

Dr. Roger Cremades is a complex systems scientist and heterodox global change economist integrating human-Earth interactions across systems and scales into modular quantitative tools, e.g. connecting drought risks in cities with land use at the river basin scale. He is co-Chair of the Development Team of the Finance and Economics Knowledge-Action Network of Future Earth (2020-2022), the largest global research programme in global change. Roger coordinated research and co-production projects above €1M, and published in top journal like PNAS, Nature Climate Change, and Nature Geoscience.

Global change, human-Earth interactions, complex systems.

Leigh Tesfatsion Member since: Wednesday, August 28, 2013

PhD in Economics

Agent-based computational economics (ACE); development and use of ACE test beds for the study of electric power market operations; development and use of ACE test beds for the study of water, energy, and climate change

Geo Kocheril Member since: Tuesday, October 01, 2019

Research fellow, PhD Candidate (University of Kassel)

Energy system transiton modelling * stakeholder and market modelling, governance and policy modelling, * agent-based modelling (ABM), optimisation, * model coupling, open and integrative modelling framework, * open source, S4F

S Johnson Member since: Thursday, October 23, 2014

Biological Sciences, Bioengineering

ABM modelling of molecular and cellular interactions in Lymph Nodes

Simone Righi Member since: Tuesday, March 17, 2015

Ph.D. In Economics

Networks Theory, Applied Microeconomics, Industrial Organization and Social Interactions.

Talal Alsulaiman Member since: Friday, February 27, 2015

Bachelor of Science in Systems Engineering, Master of Science in Industrial Engineering, Master of Science in Financial Engineering

In this paper, we explore the dynamic of stock prices over time by developing an agent-based market. The developed artificial market comprises of heterogeneous agents occupied with various behaviors and trading strategies. To be specific, the agents in the market may expose to overconfidence, conservatism or loss aversion biases. Additionally, they may employ fundamental, technical, adaptive (neural network) strategies or simply being arbitrary agents (zero intelligence agents). The market has property of direct interaction. The environment takes the form of network structure, namely, it takes the manifestation of scale-free network. The information will flow between the agents through the linkages that connect them. Furthermore, the tax imposed by the regulator is investigated. The model is subjected to goodness of fit to the empirical observations of the S\&P500. The fitting of the model is refined by calibrating the model parameters through heuristic approach, particularly, scatter search. Conclusively, the parameters are validated against normality, absence of correlations, volatility cluster and leverage effect using statistical tests.

Mind Mingles Member since: Monday, July 19, 2021 Full Member

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mikegolf Member since: Sunday, January 08, 2012

Emergence Paris

William Rand Member since: Wednesday, October 24, 2007 Full Member Reviewer

PhD, Computer Science, University of Michigan, Certificate of Study, Center for the Study of Complex Systems, University of Michigan, MS, Computer Science, University of Michigan, BS, Computer Science, Michigan State University, BA, Philosophy, Michigan State University

The big picture question driving my research is how do complex systems of interactions among individuals / agents result in emergent properties and how do those emergent properties feedback to affect individual / agent decisions. I have explored this big picture question in a number of different contexts including the evolution of cooperation, suburban sprawl, traffic patterns, financial systems, land-use and land-change in urban systems, and most recently social media. For all of these explorations, I employ the tools of complex systems, most importantly agent-based modeling.

My current research focus is on understanding the dynamics of social media, examining how concepts like information, authority, influence and trust diffuse in these new media formats. This allows us to ask questions such as who do users trust to provide them with the information that they want? Which entities have the greatest influence on social media users? How do fads and fashions arise in social media? What happens when time is critical to the diffusion process such as an in a natural disaster? I have employed agent-based modeling, machine learning, geographic information systems, and network analysis to understand and start to answer these questions.

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