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 78 articles )

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  1. Arroyo-Esquivel, Jorge; Sanchez, Fabio; Barboza, Luis A.: Infection model for analyzing biological control of coffee rust using bacterial anti-fungal compounds (2019)
  2. Sean A. Fulop, Hannah Scott: Vowel System Sandbox: Complex System Modelling of Language Change (2019) not zbMATH
  3. Barmpalias, George; Elwes, Richard; Lewis-Pye, Andrew: Minority population in the one-dimensional Schelling model of segregation (2018)
  4. Cronin, Adam L.: An agent-based model of nest-site selection in a mass-recruiting ant (2018)
  5. Falletta, J.; Woodcock, S.: A simulation study of Texas hold ’em poker: what Taylor Swift understands and James bond doesn’t (2018)
  6. Hinze, Thomas: The Java environment for nature-inspired approaches (JENA): a workbench for biocomputing and biomodelling enthusiasts (2018)
  7. Monk, Travis: Martingales and the fixation probability of high-dimensional evolutionary graphs (2018)
  8. Ramírez-Ávila, G. M.; Kurths, Jürgen; Deneubourg, J. L.: Fireflies: a paradigm in synchronization (2018)
  9. Andrianakis, Ioannis; McCreesh, Nicky; Vernon, Ian; McKinley, Trevelyan J.; Oakley, Jeremy E.; Nsubuga, Rebecca N.; Goldstein, Michael; White, Richard G.: Efficient history matching of a high dimensional individual-based HIV transmission model (2017)
  10. Castro, Mario; Lythe, Grant; Molina-París, Carmen: The T cells in an ageing virtual mouse (2017)
  11. Dougherty, Francis L.; Ambler, Nathaniel P.; Triantis, Konstantinos P.: A complex adaptive systems approach for productive efficiency analysis: building blocks and associative inferences (2017)
  12. Galán, Severino F.: Simple decentralized graph coloring (2017)
  13. Kuznetsov, A. V.: A simplified combat model based on a cellular automaton (2017)
  14. Ponte, Borja; Sierra, Enrique; de la Fuente, David; Lozano, Jesús: Exploring the interaction of inventory policies across the supply chain: an agent-based approach (2017)
  15. Smaldino, Paul E.; Janssen, Marco A.; Hillis, Vicken; Bednar, Jenna: Adoption as a social marker: innovation diffusion with outgroup aversion (2017)
  16. Farjam, Mike; Mill, Wladislaw; Panganiban, Marian: Ignorance is bliss, but for whom? The persistent effect of good will on cooperation (2016)
  17. Keane, Christopher: Chaos in collective health: fractal dynamics of social learning (2016)
  18. Kimbrough, Steven Orla; Lau, Hoong Chuin: Business analytics for decision making (2016)
  19. Lenhart, Suzanne; Xiong, Jie; Yong, Jiongmin: Optimal controls for stochastic partial differential equations with an application in population modeling (2016)
  20. P. Degenne, D. Lo Seen: Ocelet: Simulating processes of landscape changes using interaction graphs (2016) not zbMATH

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