computer simulations of biological macroevolution; dynamics and evolution in social and systems, also memetics and macromemetics - evolution of culture
I am an anthropological archaeologist with broad interests in hunter-gatherers, lithic technology, human evolution, and complex systems theory. I am particularly interested in understanding processes of long term social, evolutionary, and adaptational change among hunter-gatherers, specifically by using approaches that combine archaeological data, ethnographic data, and computational modeling.
Behavioural aspects of environmental problems: Use of evolutionary approaches to investigate how people react to environmental policy.
Climate-economic Models: Understand how economic agents think and decide about climate change and climate protection
Adapting Agents on Evolving Networks: An evolutionary game theory approach
My research focuses on using generic social science in creating models of social reality, in particular self-organization of social systems.
I am major in Management Science and Engineering. My interests lie in agent-based modeling, collective intelligence, knowledge diffusion, and cooperation evolution.
My current interests include: agent-based modeling, simulating social complexity, land use, dynamic networks, social and cultural anthropology, HIV transmission dynamics, socio-political conflicts and social movements
I have only just started becoming active in research/agent based modeling.
I find agent based computational economics interesting. I would also be interested in combining agent based modeling to explore cultural anthropology, government policies, socioeconomic stratification, and the diffusion of information.
Kenneth D. Aiello is a postdoctoral research scholar with the Global BioSocial Complexity Initiative at ASU. Kenneth’s research contributes to cross disciplinary conversations on how historical developments in biological, social, and cultural knowledge systems are governed by processes that transform the structure, dynamics, and function of complex systems. Applying computational historical analysis and epistemology to question what scientific knowledge is and how we can analyze changes in knowledge, he uses text analysis, social network analysis, and machine learning to measure similarities and differences between the knowledge claims of individual agents and groups. His work builds on how to assess contested knowledge claims and measure the evolution of knowledge across complex systems and multiple dimensions of scale. This approach also engages in dynamic new debates about global and local structures of knowledge shaped by technological innovation within microbiology related to public policy, shrinking resources given to biomedical ideas as opposed to “translation”, and the ethics of scientific discovery. Using interdisciplinary methods for understanding historical content and context rich narratives contributes to understanding new domains and major transitions in science and provides a richer understanding of how knowledge emerges.