NetLogo, a Multi-agent Simulation Environment. NetLogo [Wilensky, 1999] is a multi-agent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper we focus on NetLogo as a tool for research and for teaching at the undergraduate level and higher. We outline the principles behind our design and describe recent and planned enhancements

References in zbMATH (referenced in 58 articles )

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  1. Dougherty, Francis L.; Ambler, Nathaniel P.; Triantis, Konstantinos P.: A complex adaptive systems approach for productive efficiency analysis: building blocks and associative inferences (2017)
  2. Galán, Severino F.: Simple decentralized graph coloring (2017)
  3. Kimbrough, Steven Orla; Lau, Hoong Chuin: Business analytics for decision making (2016)
  4. Lenhart, Suzanne; Xiong, Jie; Yong, Jiongmin: Optimal controls for stochastic partial differential equations with an application in population modeling (2016)
  5. P. Degenne, D. Lo Seen: Ocelet: Simulating processes of landscape changes using interaction graphs (2016)
  6. Rousset, Alban; Herrmann, Bénédicte; Lang, Christophe; Philippe, Laurent: A survey on parallel and distributed multi-agent systems for high performance computing simulations (2016)
  7. An, Gary; Kulkarni, Swati: An agent-based modeling framework linking inflammation and cancer using evolutionary principles: description of a generative hierarchy for the hallmarks of cancer and developing a bridge between mechanism and epidemiological data (2015)
  8. Foo, Jasmine; Haskell, Cymra; Komarova, Natalia L.; Segal, Rebecca A.; Wood, Karen E.: Modeling sympatric speciation in quasiperiodic environments (2015)
  9. Hoyles, Celia; Noss, Richard: A computational lens on design research (2015) MathEduc
  10. Monk, Travis; Paulin, Michael G.; Green, Peter: Ecological constraints on the origin of neurones (2015)
  11. Oremland, Matthew; Laubenbacher, Reinhard: Optimal harvesting for a predator-prey agent-based model using difference equations (2015)
  12. Salgado, Mauricio: The evolution of paternal care can lead to population growth in artificial societies (2015)
  13. Berland, Matthew; Baker, Ryan S.; Blikstein, Paulo: Educational data mining and learning analytics: applications to constructionist research (2014) ioport
  14. Dong, Mianxiong; Ota, Kaoru; Li, He; Du, Suguo; Zhu, Haojin; Guo, Song: RENDEZVOUS: towards fast event detecting in wireless sensor and actor networks (2014) ioport
  15. Jiang, Guoyin; Ma, Feicheng; Shang, Jennifer; Chau, Patrick Y.K.: Evolution of knowledge sharing behavior in social commerce: an agent-based computational approach (2014)
  16. Lai, Kevin; White, Tobin: How groups cooperate in a networked geometry learning environment (2014) MathEduc
  17. Lipinski-Paes, Thiago; de Souza, Osmar Norberto: MASTERS: a general sequence-based multiagent system for protein tertiary structure prediction (2014) ioport
  18. Moein, Sara; Logeswaran, Rajasvaran: KGMO: a swarm optimization algorithm based on the kinetic energy of gas molecules (2014) ioport
  19. Patil, Akshay P.; Dongre, Alpana R.: Emergent properties of the public realm and encroachments in the urban environment (2014) ioport
  20. Wang, Pei; Tian, Chengeng; Lu, Jun-An: Identifying influential spreaders in artificial complex networks (2014)

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