Applications of agent-based modeling and complexity theory to real-world problems. I am particular interested in stigmergic polyagents, their relation to the path integral formalization of quantum physics, and their application to combinatorially explosive problems, but also work extensively in modeling social systems.

As a Master’s Thesis student, I am intended to apply Artificial Intelligence to an already existing model with the aim of making it more accurate.

Even though I do not have the focus point and the scope of the research clear yet, the road map is set to start from a very simple model to validate the technology and methodology used and then continue with more abitiuos projects.

I like the co-operation that I have found in this space and I think that I could both learn a lot from the community and add value with my novel trials and findings.

Of course I would be pleased to update the status of my project and I would try to help if I have the proper knowledge or different angle to other peers who seek for seconds opinions.

Thank you,
Francisco

Agent Based Modeling

My broad research interests are in human-environmental interactions and land-use change. Specifically, I am interested in how people make land-use decisions, how those decisions modify the functioning of natural systems, and how those modifications feedback on human well-being, livelihoods, and subsequent land-use decisions. All of my research begins with a complex systems background with the aim of understanding the dynamics of human-environment interactions and their consequences for environmental and economic sustainability. Agent-based modeling is my primary tool of choice to understand human-environment interactions, but I also frequently use other land change modeling approaches (e.g., cellular automata, system dynamics, econometrics), spatial statistics, and GIS. I also have expertise in synthesis methods (e.g., meta-analysis) for bringing together leveraging disparate forms of social and environmental data to understand how specific cases (i.e., local) of land-use change contribute to and/or differ from broader-scale (i.e. regional or global) patterns of human-environment interactions and land change outcomes.

My main research areas are agent-based modelling, urban analytics and complex decision making enabled by AI. I am interested in the bottom-up transition of complex urban systems under major socio-economic and environmental shocks, such as climate change and the fourth industrial revolution. I want to understand how cities as self-organised complex systems respond to external shocks and evolve under a constantly changing environment. In the past, I have looked at various aspects of urban systems, including the housing market, the labour market, transport and energy system. I am also interested in decision making in complex systems. For example, I have studied the decision to become a vegetarian/vegan under social influence. I have also looked at global food trade in a complex trade network and the resulting food and nutrition security. Recently, I am interested in applying AI algorithms especially reinforcement learning in multi-agent systems, including applications of AI in urban adaptation to climate change, housing market dynamics and criminal behaviour in an urban system.

My research interests concern the computational study of financial markets (microstructure, systemic properties and behavioral bias), of social Interactions on complex networks (theory and experiments), the evolution of cooperation in networks (theory and experiments) and the study of companies strategies in the digital economy.

My research focus is on the economic analysis of agri-environmental policy instruments as means to navigate ecosystem service trade-offs in multifunctional landscapes. In this context, I am particularly interested in identifying policy instruments and instrument mixes allowing to align societal preferences with biophysical potential of landscapes to provide multiple ecosystem services. Here, the mutual relationship between regulatory and incentive-based instruments is of much interest. Using agent-based modelling, but also more qualitative approaches, I look at the emerging landscape-level patterns that result from various policy mixes given realistic descriptions of farmers’ behaviour and institutional settings.