In this paper, we explore the dynamic of stock prices over time by developing an agent-based market. The developed artificial market comprises of heterogeneous agents occupied with various behaviors and trading strategies. To be specific, the agents in the market may expose to overconfidence, conservatism or loss aversion biases. Additionally, they may employ fundamental, technical, adaptive (neural network) strategies or simply being arbitrary agents (zero intelligence agents). The market has property of direct interaction. The environment takes the form of network structure, namely, it takes the manifestation of scale-free network. The information will flow between the agents through the linkages that connect them. Furthermore, the tax imposed by the regulator is investigated. The model is subjected to goodness of fit to the empirical observations of the S\&P500. The fitting of the model is refined by calibrating the model parameters through heuristic approach, particularly, scatter search. Conclusively, the parameters are validated against normality, absence of correlations, volatility cluster and leverage effect using statistical tests.

I obtained a PhD in database information theory from the University of the West of Scotland in 2015, and have been a researcher at the James Hutton Institute ever since. My areas of research are agent-based-modelling (ABM), data curation, effective use of infrastructure as a service (IaaS), and semantic information representation and extraction using formal structures such as computerised ontologies, relational databases and any other structured or semi-structured data representations. I primarily deal with social and agricultural models and was originally taken on in the role of knowledge engineer in order to create the ontology for the H2020 project, Green Lifestyles, Alternative Models and Upscaling Regional Sustainability (GLAMURS). Subsequent work, for the Scottish Government has involved the use of IaaS, more commonly referred to as the “cloud” to create rapidly deployable and cheap alternatives to in-house high-performance computing for both ABM and Geographical Information System models.

It is the mixture of skills and interests involving modelling, data organisation and computing infrastructure expertise that I believe will be highly apposite in the duties associated with being a member of the CoMSES executive. Moreover, prior to joining academia, I spent about 25 years as a developer in commercial IT, in the agricultural, entertainment and banking sectors, and feel that such practical experience can only benefit the CoMSES network.

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).

Research topics: Inmersive Technologies, Educational Technologies, Web Accessibility and Usability, Sematic Web, Artificial Intelligence.

My research focuses pn the intersection between game theory, social networks, and multi-agent simulations. The objectives of this scientific endeavor are to inform policy makers, generate new technological applications, and bring new insight into human and non-human social behavior. My research focus is on the transformation of cultural conventions, such as signaling and lexical forms, and on many cell models models of stem cell derived clonal colony.

Because the models I analyze are formally defined using game theory and network theory, I am able to approach them with different methods that range from stochastic process analysis to multi-agent simulations.

Ecology - Natural Resources Management (Community-based management)

I worked on natural resources management modelling in STELLA. I developed a technical and scientific model to analyze soil, climate and biological conditions to explain how Bamboo ecosystem works and how people in Cundinamarca, Colombia could focus on a sustainable model for use and manage forestry resources.
Also, I worked on the seventh framework program named: Community-based management of Environmental Challenges in Latin America -COMET-LA-. The project built a learning arena with scientists, civil society and government to identify sustainable models for governance of natural resources in social-ecological systems located in a rural context from Colombia, México and Argentina.

I am interesting in research on Modelling of governance and Community-based management of natural resources.

My main research field is health economic modeling with the main focus on sexually transmitted diseases. We are trying to build a agent-based model using the FLAME-framework (www.flame.ac.uk).

My main research interests are the theoretical and experimental analysis of the dynamics of social networks, in relation to problems of cooperation and conflict.

Doing research on how the flood insurance system in the UK should be structured in the future to make it resilient for environmental change.