The big picture question driving my research is how do complex systems of interactions among individuals / agents result in emergent properties and how do those emergent properties feedback to affect individual / agent decisions. I have explored this big picture question in a number of different contexts including the evolution of cooperation, suburban sprawl, traffic patterns, financial systems, land-use and land-change in urban systems, and most recently social media. For all of these explorations, I employ the tools of complex systems, most importantly agent-based modeling.

My current research focus is on understanding the dynamics of social media, examining how concepts like information, authority, influence and trust diffuse in these new media formats. This allows us to ask questions such as who do users trust to provide them with the information that they want? Which entities have the greatest influence on social media users? How do fads and fashions arise in social media? What happens when time is critical to the diffusion process such as an in a natural disaster? I have employed agent-based modeling, machine learning, geographic information systems, and network analysis to understand and start to answer these questions.

Mathematical modeling
agent-based modeling
coupling of agent-based models and mathematical models
machine learning algorithms
deep learning algorithms
Statistical inference
infectious diseases modeling

• Agent-Based Simulation
• Machine Learning
• UAVs
• Drones
• Swarms

• Applied data science (machine/deep learning applications) and computational modeling (agent-based
  modeling) in U.S. Government
• Geographic Information Systems and analysis of dense urban environments and complex terrain
• Complexity theory and computational organizational design of distributed enterprise teams.
• Human Capital Management and Talent Management policy development

Sedar will be focusing on data analytics and smart cities, developing a programming library to try simulate how policies can impact a small world of autonomous intelligent agents to try deduce positive or negative impact in the long run. If the impact is positive and this is conveyed collectively taking into consideration the agent’s health, happiness and other social characteristics then the policy can be considered. Furthermore, he will work on agent based modelling to solve and provide faster solutions to economic and social elements of society, establishing applied and theoretical answers. Some other interests are:

• Multi-agent systems
• Intelligent agents
• Natural language processing
• Artificial intelligence planning
• Machine learning
• Neural networks
• Genetic programming
• Geocomputation
• Argumentation theory
• Smart cities

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.

Community and Infrastructure Resilience
Disaster Recovery
Complex Systems Modeling
Agent-Based Modeling
System Dynamics
Machine Learning
Pattern Recognition
Data Mining
Spatial Analysis and Modeling
Construction Management
Building Information Modeling

Simulation, machine learning, systems modeling, big data.

• Agent-based Modeling
• Automated Planning and Distributed Problem Solving
• Natural Language Processing
• Machine Learning
• Internet of Things and Cloud-based Distributed Architectures

Generative AI, LLMs, Multi-Agent Modeling, Agent-Based Modeling, Cellular Automata, Graph Networks, Deep Learning, Social Sciences