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

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  1. An, G.; Fitzpatrick, B.G.; Christley, S.; Federico, P.; Kanarek, A.; Neilan, R.Miller; Oremland, M.; Salinas, R.; Laubenbacher, R.; Lenhart, S.: Optimization and control of agent-based models in biology: a perspective (2017)
  2. Dougherty, Francis L.; Ambler, Nathaniel P.; Triantis, Konstantinos P.: A complex adaptive systems approach for productive efficiency analysis: building blocks and associative inferences (2017)
  3. Galán, Severino F.: Simple decentralized graph coloring (2017)
  4. Kimbrough, Steven Orla; Lau, Hoong Chuin: Business analytics for decision making (2016)
  5. Lenhart, Suzanne; Xiong, Jie; Yong, Jiongmin: Optimal controls for stochastic partial differential equations with an application in population modeling (2016)
  6. 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)
  7. Foo, Jasmine; Haskell, Cymra; Komarova, Natalia L.; Segal, Rebecca A.; Wood, Karen E.: Modeling sympatric speciation in quasiperiodic environments (2015)
  8. Hoyles, Celia; Noss, Richard: A computational lens on design research (2015) MathEduc
  9. Monk, Travis; Paulin, Michael G.; Green, Peter: Ecological constraints on the origin of neurones (2015)
  10. Oremland, Matthew; Laubenbacher, Reinhard: Optimal harvesting for a predator-prey agent-based model using difference equations (2015)
  11. Salgado, Mauricio: The evolution of paternal care can lead to population growth in artificial societies (2015)
  12. Berland, Matthew; Baker, Ryan S.; Blikstein, Paulo: Educational data mining and learning analytics: applications to constructionist research (2014) ioport
  13. 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
  14. Jiang, Guoyin; Ma, Feicheng; Shang, Jennifer; Chau, Patrick Y.K.: Evolution of knowledge sharing behavior in social commerce: an agent-based computational approach (2014)
  15. Lai, Kevin; White, Tobin: How groups cooperate in a networked geometry learning environment (2014) MathEduc
  16. Lipinski-Paes, Thiago; de Souza, Osmar Norberto: MASTERS: a general sequence-based multiagent system for protein tertiary structure prediction (2014) ioport
  17. Moein, Sara; Logeswaran, Rajasvaran: KGMO: a swarm optimization algorithm based on the kinetic energy of gas molecules (2014) ioport
  18. Patil, Akshay P.; Dongre, Alpana R.: Emergent properties of the public realm and encroachments in the urban environment (2014) ioport
  19. Wang, Pei; Tian, Chengeng; Lu, Jun-An: Identifying influential spreaders in artificial complex networks (2014)
  20. Wilkerson-Jerde, Michelle Hoda: Construction, categorization, and consensus: student generated computational artifacts as a context for disciplinary reflection (2014) MathEduc

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