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Smarzhevskiy Ivan, born 1961, graduated from the Faculty of Mechanics and Mathematics of Moscow State University in 1983. Ph.D. in Economic Sciences since 2000.
Research interests: individual and collective behavior in the organization, decision making, sociology of small groups.
decision making, sociology of small groups, agent based models
Environmental Economics, Resource Economics, Behaviour Economics, Social Security/ Health Economics, Sustainability, Development Economics
Currently I develop ABM models to follow up issues raised in my previous research on trade between hunting groups and long-distance trade, territoriality and migration patterns.
My research centers on isolating how and to what extent political institutions themselves shape policy. I use computational modeling (agent-based and simulation) to gain theoretical leverage on the issue. This approach allows me to place groups of actors with given preferences into different institutional settings in order to gauge the effect of the rules of the game on political outcomes. Most of my research examines the ways in which legislative processes affect issues of political economy, such as income redistribution.
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 am a Professor in the School of Sustainability and the Director of the Center for Behavior, Institutions and the Environment. I want to understand how people solve collective problems at different levels of scale, especially those problems related to sustainability of our environment. Our society experience unprecedented challenged to sustain common resource for future generations at a scale we have never experienced before. What makes groups cooperate? What is the role of information? How does the ecological context affect the social fabric? How do they deal with a changing environment? How can we use these insight to address global challenges? To do this research I combine behavioral experiments, agent-based modeling and case study analysis.
I am fascinated by unraveling water-scarcity patterns. I am an expert in Integrated Assessment Modelling and Water Footprint Assessment. The concepts and tools that I have developed and applied all aim at availing knowledge at scales relevant to decision-makers in the water sector. During my PhD at the University of Twente I evaluated how spatiotemporal patterns of water availability relate to patterns of water use for a river basin in the semi-arid Northeast of Brazil. I have used agent-based modelling and developed the downstreamness concept to analyze the emergence of basin closure. This concept is helpful to water managers for identifying priority locations for intervention inside a river basin system. As a postdoc I continued to evaluate the relation between water use and availability and further broadened my scope to a wider range of related topics.
In my research I focus on understanding human behaviour in group(s) as a part of a complex (social) system. My research can be characterised by the overall question: ‘How does group or collective behaviour arise or change given its social and physical context?‘ More specifically, I have engaged with: ‘How is (individual) human behaviour affected by being in a crowd?’, ‘Why do some groups (cooperatively) use their resources sustainably, whereas others do not?‘, ‘What is the role of (often implicit simplistic) assumptions regarding human behaviour for science and/or management?’
To address these questions, I use computational simulations to integrate and reflect synthesised knowledge from literature, empirics and experts. Models, simulation and data analysis are my tools for gaining a deeper understanding of the mechanisms underlying such systems. More specifically, I work with agent-based modelling (ABM), simulation experiments and data analysis of large datasets. Apart from crowd modelling and social-ecological modelling, I also develop methodological tools to analyse social simulation data and combining ABM with other methods, such as behavioural experiments.
My core research interest is to understand how humans and other living creature perceive and behave; respond and act upon their environment and how this dynamic interplay shapes us into who we are. In recognition of the broad scope of this question I am a strong believer in the need for inter- and multi-disciplinary approaches and have worked at research groups in a wide range of departments and institutions, including university departments of Physics as well as Psychology, a bio-medical research lab, a robotics research laboratory and most recently the RIKEN Brain Science Institute. Though my work has primarily taken the form of computational neuroscience I have also performed psychophysical experiments with healthy human subjects, been involved in neural imaging experiments and contributed towards the development of a humanoid robot.
Based on the philosophy of ‘understanding through creating’ I believe that bio-mimetic and biologically inspired computational and robotic engineering can teach us not only how to build more flexible and robust tools but also how actual living creatures deal with their environment. I am therefore a strong believer in the fertile information exchange between scientific as well as engineering research disciplines.
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.
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