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 105 articles )

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  1. Goy, Natascha; Glaser, Simone M.; Grüter, Christoph: The adaptive value of tandem communication in ants: insights from an agent-based model (2021)
  2. Imirzian, Natalie; Hughes, David P.: An agent-based model shows zombie ants exhibit search behavior (2021)
  3. Stummer, Christian; Kiesling, Elmar: An agent-based market simulation for enriching innovation management education (2021)
  4. Alamino, R. C.: An agent-based lattice model for the emergence of anti-microbial resistance (2020)
  5. Bañuelos, Selenne; Bush, Mathew; Martinez, Marco V.; Prieto-Langarica, Alicia: Undergraduate research in mathematical epidemiology (2020)
  6. Jani, Arpan: An extension of Schelling’s segregation model: modeling the impact of individuals’ intolerance in the presence of resource scarcity (2020)
  7. Kopp, Thomas; Salecker, Jan: How traders influence their neighbours: modelling social evolutionary processes and peer effects in agricultural trade networks (2020)
  8. Lenhart, Suzanne; Tang, Xiao; Xiong, Jie; Yong, Jiong-min: Controlled stochastic partial differential equations for rabbits on a grassland (2020)
  9. Lukas Riedel; Benjamin Herdeanu; Harald Mack; Yunus Sevinchan; Julian Weninger: Utopia: A Comprehensive and Collaborative Modeling Framework for Complex and Evolving Systems (2020) not zbMATH
  10. Oldham, Matthew: Quantifying the concerns of Dimon and Buffett with data and computation (2020)
  11. Rigato, Emanuele; Fusco, Giuseppe: A heuristic model of the effects of phenotypic robustness in adaptive evolution (2020)
  12. Ali R. Vahdati: Agents.jl: agent-based modeling framework in Julia (2019) not zbMATH
  13. Arroyo-Esquivel, Jorge; Sanchez, Fabio; Barboza, Luis A.: Infection model for analyzing biological control of coffee rust using bacterial anti-fungal compounds (2019)
  14. Brailsford, Sally C.; Eldabi, Tillal; Kunc, Martin; Mustafee, Navonil; Osorio, Andres F.: Hybrid simulation modelling in operational research: a state-of-the-art review (2019)
  15. Herrera-Restrepo, Oscar; Triantis, Konstantinos: Enterprise design through complex adaptive systems and efficiency measurement (2019)
  16. Izquierdo, Luis R.; Izquierdo, Segismundo S.; Sandholm, William H.: An introduction to \textitABED: agent-based simulation of evolutionary game dynamics (2019)
  17. Kaper, Hans G.; Engler, Hans: Modeling food systems (2019)
  18. Kostylenko, Olena; Rodrigues, Helena Sofia; Torres, Delfim F. M.: The spread of a financial virus through Europe and beyond (2019)
  19. Kraaijer, Rutger; van Kreveld, Marc; Meulemans, Wouter; van Renssen, André: Geometry and generation of a new graph planarity game (2019)
  20. Premo, L. S.; Brown, Justin R.: The opportunity cost of walking away in the spatial iterated prisoner’s dilemma (2019)

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