Adrian Groza Member since: Monday, April 29, 2013

Phd in Computer Science

Flexible agent communication
Argumentation in multi-agent systems
Knowledge representation and reasoning
Ontologies for agents
Mediation and Dispute Resolution

Talal Alsulaiman Member since: Friday, February 27, 2015

Bachelor of Science in Systems Engineering, Master of Science in Industrial Engineering, Master of Science in Financial Engineering

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.

Manuel Castañón-Puga Member since: Wednesday, October 16, 2019 Full Member

Ph.D. Computer Science, Universidad Autónoma de Baja California, México., MSC Computer Science, Tecnológico Nacional de México, México., ENG Industrial, Tecnológico Nacional de México, México.

I´m a full Professor at Chemistry and Engineering School at the Universidad Autónoma de Baja California in Mexico. I teach computer sciences and software engineering in graduate and undergraduate academic programs.

  • Computational science
  • Computational social science
  • Social-inspired ICT
  • Social computation
  • Agents technology
  • Computational intelligence and hybrid-intelligent agents
  • Complexity and complex systems
  • Multi-agent systems
  • Computational modeling
  • Context-oriented programming
  • Knowledge Management
  • Software engineering

Arend Ligtenberg Member since: Thursday, April 09, 2015


Agent Based Modelling for spatial systems

Rikard Roitto Member since: Tuesday, July 23, 2013

PhD in Religious Studies

Historical studies of Early Christianity. Simulations of social agents aids my interpretation of history.

kianercy Member since: Wednesday, January 04, 2012

Msc. Mechanical Eng., Msc. Chemical Eng.

Adapting Agents on Evolving Networks: An evolutionary game theory approach

Rodolphe Buda Member since: Monday, February 04, 2013

Doctor in Economic Science

Main Research Topics :
1) Agent-based Modeling (Communication between agents)
2) Economic and Econometric Algorithms and Software Development
3) Optimal International Trade Configuration

Carole Adam Member since: Friday, February 03, 2017

PhD in Artificial Intelligence

Improving agent models and architectures for agent-based modelling and simulation applied to crisis management. In particular modelling of BDI agents, emotions, cognitive biases, social attachment, etc.

Jordi Sabater-Mir Member since: Tuesday, November 07, 2017

PhD in Artificial Intelligence

My research is focused on autonomous agents and multiagent systems. Specifically: Trust and reputation models, cognitive architectures, cognitive models and social simulation.

Brent Auble Member since: Friday, December 17, 2010

B.S. Computer Science, Lafayette College, MAIS, Computational Social Science, George Mason University

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).

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