Complex Adaptive Systems, Data Analytics and Visualization
Complex adaptive systems, complexity, systems science, creativity, data mining, machine learning, economic and health systems, science education
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 Using interdisciplinary methods for understanding historical content and context rich narratives contributes to understanding new domains and major transitions in science and provides a richer understanding of how knowledge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.
Agent-based computing in economics and finance
Large-scale agent-based models
Agent models calibrated by micro-data
Complex adaptive systems
Mathematical analysis of agent systems
Postdoctoral researcher at Institute of Economics, Polish Academy of Sciences and in Macroprudential Research Division at National Bank of Poland. She graduated in Mathematics (Jagiellonian University, Poland) and in Economics (University of Alcala, Spain). In 2017 she obtained Fulbright Advanced Research Award. In the United States, she carried out research on systemic risk and complex systems. Her doctoral dissertation was about the measurement and modeling of systemic risk using simulation methods and complex systems approach (the results to be published by Palgrave Macmillan US). Previously, she gained experience on agent-based modeling while working with Juan Luis Santos on the European Commission FP 7 MOSIPS project (http://www.mosips.eu/).
Mathematics, complex systems, financial modeling, agent-based modeling, econometrics, macroprudential policies, systemic risk, cental banking
I am broadly interested in using Agent-based Modelling, Microsimulation, Geosimulation or a hybrid of these approaches as methodology to investigate complex dynamics of systems in various domains. I am also interested in exploring the potential of simulation models as decision support and policy-informing tools.
With my research, I aim to improve scientific understanding of the role interactions among cognitive, behavioral, social, and demographic processes play in human adaptation to social-ecological change. Currently, I hold a Postdoctoral Research Fellow position at University of Michigan’s School for Environment and Sustainability and an Instructor position at Portland State University’s Systems Science Program. I have a Ph.D. in Systems Science (2018) from Portland State University, and an M.A. in Economics (2007) and a B.S. in Management (1999) from Boston University.
Cognitive Social Science, Social-Ecological Systems, Multi-Agent Modeling, Complex Adaptive Systems