ACEGES

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The ACEGES Graphical User Interface

The ACEGES model (Agent-based Computational Economics of the Global Energy System) is a decision support tool for energy policy by means of controlled computational experiments.[1] The ACEGES tool is designed to be the foundation for large custom-purpose simulations of the global energy system. The ACEGES methodological framework, developed by Voudouris (2011)[2] by extending Voudouris (2010),[3] is based on the agent-based computational economics (ACE) paradigm. ACE is the computational study of economies modeled as evolving systems of autonomous interacting agents.[4][5]

The ACEGES tool is written in Java and runs on Windows, Mac OS and Linux platforms. The ACEGES tool is based on:

  • The MASON library – a discrete-event multiagent simulation library
  • The R Project for statistical computing
  • The GAMLSS framework

It is important to clarify that although the ACEGES model builds on the available scholarly and policy literature, it does not strictly follow any existing approach.

History[edit]

The ACEGES project was conceived by Vlasios Voudouris (with contributions by Michael Jefferson). Voudouris is now Affiliate Professor (Energy and Commodities) at the ESCP Europe. The first version of the ACEGES decision-support tool was written in 2010. The ACEGES models energy demand and supply of 218 countries. The ACEGES tool was the main output of the ACEGES Project. The overall aim of the ACEGES project was to develop, test and disseminate an agent-based computational laboratory for the systematic experimental study of the global energy system through the mechanism of Energy Scenarios. In particular, the intention was to show how the ACEGES framework and prototype can be used to help leaders in government, business and civil society better understand the challenging outlook for energy through controlled computational experiments.

Demonstrations[edit]

The ACEGES tool has been used, for example, to test the peak oil theory and to develop plausible scenarios of conventional oil production by means of demonstration at:

Details about the ACEGES decision-support tool (including supporting documentation) are available from ABM Analytics.

References[edit]

  1. ^ Voudouris, V., Stasinopoulos, D., Rigby, R. and Di Maio, C., (2011), "The ACEGES laboratory for Energy Policy: Exploring the production of crude oil", Energy Policy. Available from: Link
  2. ^ Voudouris, V. (2011), "Towards a conceptual synthesis of dynamic and geospatial models: fusing the agent-based and Object – Field models", Environment and Planning B: Planning and Design 38(1) 95 – 114 Link
  3. ^ Voudouris, V. (2010), "Towards a unifying formalisation of geographic representation: The Object-Field model with uncertainty and semantics", International Journal of Geographical Information Science, Vol. 24, No. 12, pp. 1811-1828 Link
  4. ^ Tesfatsion, L., 2006. Handbook of Computational Economics Volume 2: Agent-Based Computational Economics. Elsevier, Amsterdam, Ch. Agent-based Computational Economics: A constructive approach to economic theory.
  5. ^ Tesfatsion, L and Judd, K. (2006), Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics, North Holland