Master student in Sustainable Development at Uppsala University
Matteo Richiardi is an internationally recognised scholar in micro-simulation modelling (this includes dynamic microsimulations and agent-based modelling). His work on micro-simulations involves both methodological research on estimation and validation techniques, and applications to the analysis of distributional outcomes, the functioning of the labour market and welfare systems. He is Chief Editor of the International Journal of Microsimulation. Examples of his work are the two recent books “Elements of Agent-based Computational Economics”, published by Cambridge University Press (2016), and “The political economy of work security and flexibility: Italy in comparative perspective”, published by Policy Press (2012).
My interests lie in the intersection of economics, networks, and computation. I am currently studying labour dynamics as a process where people flow throughout the economy by moving from one firm to another. I study these flows by looking at detailed data about employment histories of each individual and every firm in entire economies. Using this information, I construct networks of firms in order to map the roads that people take throughout their careers. This allows to study labour markets at an unprecedented fine-grained level of detail. I employ agent-based computing methods to understand how economic shocks and policies alter labour flows, which eventually translate into unemployment and other related problems.
Dissertation: Narrative Generation for Agent-Based Models
Abstract: This dissertation proposes a four-level framework for thinking about having agent-based models (ABM) generate narrative describing their behavior, and then provides examples of models that generate narrative at each of those levels. In addition, “interesting” agents are identified in order to direct the attention of researchers to the narratives most likely to be worth spending their time reviewing. The focus is on developing techniques for generating narrative based on agent actions and behavior, on techniques for generating narrative describing aggregate model behavior, and on techniques for identifying “interesting” agents. Examples of each of these techniques are provided in two different ABMs, Zero-Intelligence Traders (Gode & Sunder, 1993, 1997) and Sugarscape (Epstein & Axtell, 1996).
System of Systems and Complex Systems
My field of interests concerns two axes:
First, epistemology of computational modeling and simulation of complex systems. I am particularly interested in a sociological inquiry about social implication of knowledge derived from complex systems’ study.
Second, assessing the possibilities and limits of studying social complexity with complex systems tools, particularly, agent-based modeling and simulation.
Multi-agent Systems, Agent Based Modeling, Artificial Intelligence
Complex adaptive systems, complexity, systems science, creativity, data mining, machine learning, economic and health systems, science education
Angelos Chliaoutakis received his PhD in Electronic & Computer Engineering in 2020 at Technical University of Crete (TUC), Greece. During 2005-2020 he was a research assistant at the Intelligent Systems Laboratory of TUC, participating in several research projects associated with NLP, semantic similarity and ontology based information systems. Since 2010 he is also a research assistant at the Laboratory of Geophysical - Satellite Remote Sensing and Archaeo-environment (GeoSat ReSeArch Lab) of the Institute for Mediterranean Studies of Foundation for Research and Technology (IMS-FORTH), were he is involved in various research projects related to the full-stack development of Geographical Information Systems (GIS), web-based GIS applications and Geoinformatics in the cultural and archaeological domain. This ultimately transformed his interest and research direction towards computational archaeology, in particular, agent-based modeling and simulation, while intertwining ideas and approaches from Artificial Intelligence, Multi-agent Systems and GIS.
Research activities range between Computer Science, Information Systems and Natural Language Processing (NLP), Agent-based modeling/simulation (ABM), Artificial Intelligence (AI) and Multi-Agent Systems (MAS) and Geographical Information Science (GIScience).