My research interests lay within the interface of social, water and modelling sciences. I have created tools that explore the effects of water demand management policies in domestic urban water demand behaviour and the effects of civil decision making in flood risk management. I am interested in agent based modelling, artificial intelligence techniques, the creation of ABM tools for civil society, Circular Economy, distributed water technologies and overall urban water management.

My interests lie in the areas of market structure, macroprudential and microprudential policies and their interactions, international macroeconomics, political economy, international financial integration, banking, and systemic risk.

Two themes unite my research: a commitment to methodological creativity and innovation as expressed in my work with computational social sciences, and an interest in the political economy of “globalization,” particularly its implications for the ontological claims of international relations theory.

I have demonstrated how the methods of computational social sciences can model bargaining and social choice problems for which traditional game theory has found only indeterminate and multiple equilibria. My June 2008 article in International Studies Quarterly (“Coordination in Large Numbers,” vol. 52, no. 2) illustrates that, contrary to the expectation of collective action theory, large groups may enjoy informational advantages that allow players with incomplete information to solve difficult three-choice coordination games. I extend this analysis in my 2009 paper at the International Studies Association annual convention, in which I apply ideas from evolutionary game theory to model learning processes among players faced with coordination and commitment problems. Currently I am extending this research to include social network theory as a means of modeling explicitly the patterns of interaction in large-n (i.e. greater than two) player coordination and cooperation games. I argue in my paper at the 2009 American Political Science Association annual convention that computational social science—the synthesis of agent-based modeling, social network analysis and evolutionary game theory—empowers scholars to analyze a broad range of previously indeterminate bargaining problems. I also argue this synthesis gives researchers purchase on two of the central debates in international political economy scholarship. By modeling explicitly processes of preference formation, computational social science moves beyond the rational actor model and endogenizes the processes of learning that constructivists have identified as essential to understanding change in the international system. This focus on the micro foundations of international political economy in turn allows researchers to understand how social structural features emerge and constrain actor choices. Computational social science thus allows IPE to formalize and generalize our understandings of mutual constitution and systemic change, an observation that explains the paradoxical interest of constructivists like Ian Lustick and Matthew Hoffmann in the formal methods of computational social science. Currently I am writing a manuscript that develops these ideas and applies them to several challenges of globalization: developing institutions to manage common pool resources; reforming capital adequacy standards for banks; and understanding cascading failures in global networks.

While computational social science increasingly informs my research, I have also contributed to debates about the epistemological claims of computational social science. My chapter with James N. Rosenau in Complexity in World Politics (ed. by Neil E. Harrison, SUNY Press 2006) argues that agent-based modeling suffers from underdeveloped and hidden epistemological and ontological commitments. On a more light-hearted note, my article in PS: Political Science and Politics (“Clocks, Not Dartboards,” vol. 39, no. 3, July 2006) discusses problems with pseudo-random number generators and illustrates how they can surprise unsuspecting teachers and researchers.

Agent-based modeling, Participatory modeling, Socio-technical systems, Complexity, Sustainability, Circular Economy, Design Science, Action research.

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.

Quantitative Economy

My research focuses on the interdisciplinary nexus of artificial intelligence (AI), digital economy, technological innovation, and industrial economics, with an emphasis on understanding how digital technologies reshape economic structures, policy frameworks, and societal norms. Key areas of interest include:

1. Artificial Intelligence & Digital Transformation
   Ethical and Governance Challenges of AI: Investigating algorithmic bias, data privacy, and accountability in AI systems; proposing frameworks for ethical AI development and deployment.
   AI Adoption and Economic Impact: Analyzing how AI-driven automation and innovation influence productivity, labor markets, and industrial competitiveness.
2. Digital Economy & Platform Markets
   Crowdfunding, Sharing Economy, and Digital Platforms: Examining user behavior, market dynamics, and performance drivers in emerging digital ecosystems (e.g., crowdfunding campaigns, app markets).
   Digital Innovation and Entrepreneurship: Studying the role of technological innovation in firm growth, particularly in internet-based industries.
3. Technological Innovation & Industrial Policy
   Innovation-Driven Industrial Competitiveness: Developing quantitative models (e.g., DEA, CGE) to assess the efficiency and competitiveness of emerging industries under technological disruption.
   Policy Evaluation and Simulation: Using computational modeling to analyze the economic and industrial impacts of trade policies, environmental regulations, and technological standards.
4. Resource Economics & Sustainable Development
   Water Resource Management and Policy: Evaluating the economic and environmental trade-offs of water conservation policies through general equilibrium modeling.
   Global Trade and Food Security: Assessing the impacts of international trade regulations (e.g., food safety standards) on domestic industries and global supply chains.
5. Cross-Disciplinary Methodological Innovation
   Integrating econometrics, data science, and behavioral economics to enhance the rigor and relevance of industrial and policy research.
   Leveraging big data analytics, machine learning, and agent-based modeling to uncover complex relationships in digital markets and technological ecosystems.

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).

Researcher in sustainable production and consumption, the service economy, energy markets, and electricity balancing mechanisms.