My research interests centre on the study of individual preferences in economics and on understanding how preferences evolve through interaction, learning, and social context. I am particularly interested in how seemingly weak or latent preferences—such as attitudes toward environmental attributes, prices, or social norms—can become amplified through feedback mechanisms and generate non-linear aggregate outcomes. A core methodological focus of my work is the use of agent-based modelling and economic simulation as a bridge between economic theory and experimentation. By treating agent-based models as computational laboratories, I explore how heterogeneous preferences, habit formation, peer influence, and firm behaviour interact dynamically, allowing theoretical mechanisms to be tested, stress-tested, and compared under controlled but flexible conditions that are difficult to achieve using purely analytical or empirical approaches.

Modeling and simulation of agents and other discrete systems.

My research centers on isolating how and to what extent political institutions themselves shape policy. I use computational modeling (agent-based and simulation) to gain theoretical leverage on the issue. This approach allows me to place groups of actors with given preferences into different institutional settings in order to gauge the effect of the rules of the game on political outcomes. Most of my research examines the ways in which legislative processes affect issues of political economy, such as income redistribution.

Area: Complex Biological, Social and Sociotechnical Systems
Specific focus: Origins of intelligent behavior

My research focused on pricing strategy and its impact on Supply Chain (SC) using the Agent-Based Modeling and Simulation (ABMS) approach. Currently, I’m working on an ABMS model to analyze the impact of SC Coordination on SC performance when intelligent retailers may offer price discounts based on the market’s states using Q-learning algorithm.

I am an Associate Professor of Data Analytics at Trinity Business School, Trinity College Dublin, The University of Dublin and a Senior Fellow of the Higher Education Academy. I was the Director of Postgraduate Teaching at the Department of Management Science, Lancaster University Management School overseeing MSc programmes in Business Analytics, Management Science and Marketing Analytics, Logistics and Supply Chain Management, e-Business and Innovation, and Project Management.

My research interests lie in the areas of predictive analytics using simulation. I am particularly interested in simulation modelling methodology (symbiotic simulation, hybrid modelling, agent-based simulation, discrete-event simulation) with applications in operations and supply chain management (e.g. hospital, manufacturing, transportation, warehouse) and social dynamics (e.g. diffusion of perception). Currently, I am the associate editor of the Journal of Simulation and the secretary of The OR Society‘s Special Interest Group in Simulation. I am the track coordinator of Agent-Based Simulation for the Winter Simulation Conference 2018.

Becky is a Research Associate at the Imperial Centre for Energy Policy and Technology (ICEPT). She investigates economic, social and technical aspects of energy policy in the UK and abroad.

Becky’s current research is focussed on transitions in the UK bioenergy system and on biofuels for aviation. She is involved with two major projects: Bioenergy Value Chains: Whole Systems Analysis and Optimisation, an EPSRC SUPERGEN Bioenergy Challenge Project; and Renewable Jet Fuel Supply Chain Development and Flight Operations (RENJET), a project for EIT Climate-KIC. Becky has also worked on projects for the UK Energy Research Centre – International Renewable Energy Agency (UKERC-IRENA) collaboration, investigating issues such as economic value creation, policy evaluation metrics, innovation theory and rural electrification. She is particularly interested in the role of renewable technologies for developing countries, having lived and worked in Mali and Senegal.

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

My research interests stand between natural resource management and ecological economics. The aim of my PhD project responds to the increasing demand for cross-disciplinary agent-based models that examine the disjunction between economic growth and more sustainable use of natural resources.

My research attempts to test the effectiveness of different governance and economic frameworks on managing natural resources sustainably at both regional and national levels. The goal is to simulate how communities and institutions manage the commons in complex socio-ecological systems through several case-studies, e.g. rainforest management in Australia. It is hoped that the models will highlight which combination of variables lead to positive trends in both economic and environmental indicators, which could stimulate more sustainable practices by governments, private sectors and civil society.

High performance parallel computing, parallel applications, performance models, automatic performance analysis, dynamic tuning. Performance tools for automatic and dynamic performance tuning on HPC systems. Agent-based modelling systems.