Agent-based Simulation Platforms: Review and Development Recommendations

By Steven F. Railsback, S. Lytinen, Stephen K. Jackson. International Conference on Advances in System Simulation, 2006

812 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/7bc3d31e7e84d6a1464e29e6bf3fd0dad3f57354

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. William Rand
2. Matthew J. Hoffmann
3. Roland T. Rust

In Computer Networks and Communications

1. William Rand
2. Matthew J. Hoffmann
3. Roland T. Rust

In Management Science and Operations Research

1. William Rand
2. Matthew J. Hoffmann
3. Roland T. Rust

Citations in the corpus

• Tools of the Trade: A Survey of Various Agent Based Modeling Platforms (2009, background): “One of the seminal papers has been the investigation by Railsback, Lytinen, and Jackson (2006).”
• Agent-based models in translational systems biology (2009, neutral): “21.”
• Agent Based Modelling and Simulation tools: A review of the state-of-art software (2017, background): “In [29], five platforms (NetLogo, MASON, Repast, Java Swarm,”
• Systems Science Methods in Public Health (2012, neutral): “, RePast), much of the academic ABM projects are based on handwritten software that utilizes object-oriented programming techniques (116).”
• Agent-based Modeling (2008, result): “There are good summaries in the literature which compare and contrast the various ABM tools [17] [18].”
• Agent-Based Modeling in Marketing: Guidelines for Rigor (2011, methodology): “Because ABM is a computational method, it is possible to embed a machine learning approach within each agent that allows that agent to dynamically adopt the rules under which it operates (Rand, 2006).”
• Predicting monarch butterfly (Danaus plexippus) movement and egg-laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory (2018, background): “Agent-based modeling is well-suited to address the effects of different spatially-explicit landscape configurations on animal 4    population dynamics (DeAngelis and Mooij, 2005, Grimm et al., 2005, Railsback et al., 2006; Railsback and Grimm, 2011).”
• Modelling climate change impacts on marine fish populations: process-based integration of ocean warming, acidification and other environmental drivers (2016, background): “Various IBM 530 programming packages focus on agent-environment interactions (Railsback et al., 2006, 531 Arunachalam et al., 2008).”