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Community assembly after intervention by coral transplantation
The potential of transplantation of scleractinian corals in restoring degraded reefs has been widely recognized. Levels of success of coral transplantation have been highly variable due to variable environmental conditions and interactions with other reef organisms. The community structure of the area being restored is an emergent outcome of the interaction of its components as well as of processes at the local level. Understanding the
coral reef as a complex adaptive system is essential in understanding how patterns emerge from processes at local scales. Data from a coral transplantation experiment will be used to develop an individual-based model of coral community development. The objectives of the model are to develop an understanding of assembly rules, predict trajectories and discover unknown properties in the development of coral reef communities in the context of reef restoration. Simulation experiments will be conducted to derive insights on community trajectories under different disturbance regimes as well as initial transplantation configurations. The model may also serve as a decision-support tool for reef restoration.
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.
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Hi. I’m Wolf. I’m the Argelander (Tenure-Track Assistant) Professor for Integrated System Modeling for Sustainability Transitions at the University of Bonn, Germany.
We reshape human-environment modeling to identify critical leverage points for sustainability transitions.
Cooperation at scale – in which large collectives of intelligent actors in complex environments seek ways to improve their joint well-being – is critical for a sustainable future, yet unresolved.
To move forward with this challenge, we develop a mathematical framework of collective learning, bridging ideas from complex systems science, multi-agent reinforcement learning, and social-ecological resilience.
General Question:
Without Central Control is self organization possible?
Specific Case:
Considering the seemingly preplanned, densely aggregated communities of the prehistoric Puebloan Southwest, is it possible that without centralized authority (control), that patches of low-density communities dispersed in a bounded landscape could quickly self-organize and construct preplanned, highly organized, prehistoric villages/towns?
Andrew Bell (Ph.D. 2010, Michigan) was a Research Fellow in the Environment and Production Technology Division at the International Food Policy Research Institute (IFPRI) in Washington, DC. His current research portfolio focuses on the use of field instruments – such as discrete choice experiments, framed field experiments, randomized control trials – to inform behavior in agent-based models of coupled human-natural systems. Prior to this post, Andrew was a post-doctoral research fellow at The Earth Institute at Columbia University, where he focused on developing applications for paleo-climate histories.
My profound interest in networks convinced me to work in these subjects and start my master project on an application of social network analysis for detecting organized fraud in Automobile insurance, which helps to flag groups of fraudsters. The key point of this project is simply to find fraudulent rings, while the most of traditional methods have only taken opportunistic fraud into consideration. My duty in research is to design an algorithm for identifying cyclic components, then to be compared with theoretical ones. This project showed me how networks are used in the analysis of relations.
I obtained my undergraduate degree in Mathematics at Worcester College, Oxford University. I then worked for 9 years for the UK government before returning to university to study for a MSc and PhD at UCL. On leaving UCL I started working in the insurance industry, where I develop models of cyber catastrophe events.
Key research interests are how to build models of complex human behaviour.
My PhD research project was focussed on building a model of the process by which people develop the propensity to commit acts of crime or terrorism, from which came a computer simulation of the radicalisation process.
My current research interest is on creating models of cyber threats.
Moscow City University, Professor: Institute of Digital Education - http://digida.mgpu.ru
National Research University Higher School of Economics, Professor: Institute of Education / Department of Educational Programmes. Leading Expert: Institute of Education / Laboratory for Digital Transformation of Education - 2019 – present
2016 – present Leading Researcher at Moscow City University, Educational policies & educational practices
2018 – 2020 World Bank, Consultant. Children Learning to Code: Essential for 21st Century Human Capital
2011 - 2019 - Co-founder, chief community officer at WikiVote!
Educational network - Letopisi.org 2006 – present, Co-founder, chief community officer
Scientific project “Mobile and ubi-learning”, 2009 - 2011
ABM, wiki, NetLogo, StarLogo Nova, R, Collaboration
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