Research into the awareness and understanding of the general population regarding the work of St. Jude. Research into the behavior of donors to St. Jude.
Sudhira’s research has been primarily on urban land-use and land cover change studies exploring their consequences on environmental sustainability and understanding their inter-relationship with transportation. His broader research addresses the evolution and growth of towns and cities invoking complexity sciences, understanding planning practices and studying the effect of varied governance structures.
Understanding agent-based modeling
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.
Behavioural ecology and modelling of ant behaviour, with an emphasis on understanding how individual-level complexity affects collective decision-making
My PHD project focuses on understanding factors influencing individual sustainable consumption behaviour and how these factors could promote a sustainability transition.
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.
I have a background in social science and training in modeling coupled human and natural systems, and apply both to advance our understanding of how interactions among cognitive, behavioral, social, and demographic processes influence human adaptation to climate change.
agent-based modeling, cognition
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 research focuses on building a systemic understanding of coupled human-natural systems. In particular, I am interested in understanding how patterns of land-use and land-cover change emerge from human alterations of natural processes and the resulting feedbacks. Study systems of interest include those undergoing agricultural to urban conversion, typically known as urban sprawl, and those in which protective measures, such as wildfire suppression or flood/storm impact controls, can lead to long-term instability.
Dynamic agent- and process-based simulation models are my primary tools for studying human and natural systems, respectively. My past work includes the creation of dynamic, process-based simulation models of the wildland fires along the urban-wildland interface (UWI), and artificial dune construction to protect coastal development along a barrier island coastline. My current research involves the testing, refinement, extension of an economic agent-based model of coupled housing and land markets (CHALMS), and a new project developing a generalized agent-based model of land-use change to explore local human-environmental interactions globally.