Agent-based modelling with NetLogo

The 2022 course is fully booked. Registration is no longer possible!
Registration for the 2023 course will start in May 2023.


Topic: Computer course on agent-based modelling with the software NetLogo
Level: Beginners (BSc, MSc, Phd, ...)
Language: English
Duration: 6 days (+ 2 days Pre-seminar)
Frequency: Annual, usually in August or September
Place: Faculty of Forest Sciences, University of Göttingen, Germany
Teachers: Katrin Meyer & Ecosystem Modelling Department
2022 course Course: 19 September - 23 September 2022 and 26 September, 9:00-18:00, online
Pre-Seminar: 15-16 September 2022, 9:00-18:00, online

More information on this course:

    Learning outcome / core skills:
  • Comprehensive knowledge of agent-based modelling for beginners;
  • Ability to select, conceptualize, apply, implement, and document agent-based modelling techniques in NetLogo with respect to a given question (with a focus on ecological questions);
  • Development of an own agent-based modelling project;
  • Development of interdisciplinary analytical thinking;
  • Critical analysis and evaluation of the potentials and limitations of agent-based models based on the scientific literature;
  • Refined presentation skills.

    Examination to obtain 6 credits (or a graded certificate):
  • Presentation (ungraded) of a classic modelling paper in the Pre-Seminar AND
  • Presentation (graded) on lessons learnt during the course

(The course can also be attended without the Pre-Seminar. In that case, an ungraded presentation on lessons learnt during the course must be given, resulting in an ungraded certificate of attendance.)

    Schedule:
  • Thursday/Friday: Pre-Seminar with paper presentations by participants
  • Saturday/Sunday: free
  • Monday: Models and Modelling, Model questions, Model concepts, Data, Modelling projects
  • Tuesday: Implementation, NetLogo exercises, Modelling projects
  • Wednesday: Documentation, Debugging, Testing & Validation
  • Thursday: Model analysis, Sensitivity analysis, NetLogo extensions, Failed projects
  • Friday: Modelling projects
  • Saturday/Sunday: free
  • Monday: Project presentations

    General info:
  • The course itself is for free

    If the course is on site (not applicable in 2022):
  • Address: Buesgenweg 4, University of Goettingen North Campus, Goettingen, Germany
  • Participants have to organize and pay travel, accommodation etc. by themselves (we can help if required)
  • The maximum number of participants is 20, half of which is reserved for students from the University of Göttingen
  • Computers are available, but own laptops can be brought
  • Internet connection via eduroam is possible
  • We will work with the software NetLogo 6

    If the course is online (as it is in 2022):
  • The maximum number of participants is 20, half of which is reserved for students from the University of Göttingen
  • Participants need own internet access, a computer, a microphone, and, ideally but not compulsorily, a camera
  • Participants have to install the free software NetLogo 6 on their computers
  • We will use the learning platform and integrated meeting software of the University of Goettingen, which can all be accessed via browser
  • More details will follow via email to registered participants

Recommended readings (strictly voluntary!):

    Books:
  • Grimm, V., & Railsback, S. F. (2005). Individual-based modeling and ecology. New Jersey: Princeton University Press.
  • Grimm, V., & Railsback, S. F. (2012). Agent-based and individual based modeling: a practical introduction. New Jersey: Princeton University Press.
  • Wilensky, U., & Rand, W. (2015). An introduction to agent-based modeling: Modeling natural, social, and engineered complex systems with NetLogo. Massachusetts: MIT Press.

    Papers:
  • Augusiak, J., Van den Brink, P. J., & Grimm, V. (2014). Merging validation and evaluation of ecological models to “evaludation”: A review of terminology and a practical approach. Ecological Modelling, 280, 117–128.
  • Evans, M. R., Grimm, V., Johst, K., Knuuttila, T., de Langhe, R., Lessells, C. M., … Benton, T. G. (2013). Do simple models lead to generality in ecology? Trends in Ecology & Evolution, 28(10), 578–583.
    … and a reply to this article:
  • Lonergan, M. (2014). Data availability constrains model complexity, generality, and utility: a response to Evans et al. Trends in Ecology & Evolution, 29(6), 301–302.
  • Grimm, V., Augusiak, J., Focks, A., Frank, B. M., Gabsi, F., Johnston, A. S. A., … Railsback, S. F. (2014). Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE. Ecological Modelling, 280, 129–139.
  • Grimm, V., Berger, U., DeAngelis, D. L., Polhill, J. G., Giske, J., & Railsback, S. F. (2010). The ODD protocol: A review and first update. Ecological Modelling, 221(23), 2760–2768.
  • Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W. M., Railsback, S. F., … DeAngelis, D. L. (2005). Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science, 310(5750), 987–991.
  • Müller, B., Bohn, F., Dreßler, G., Groeneveld, J., Klassert, C., Martin, R., Schlüter, M., Schulze, J., Weise, H., Schwarz, N. (2013). Describing human decisions in agent-based models – ODD + D, an extension of the ODD protocol. Environmental Modelling & Software 48, 37–48.
  • Pe’er, G., Saltz, D., Münkemüller, T., Matsinos, Y. G., & Thulke, H.-H. (2013). Simple rules for complex landscapes: the case of hilltopping movements and topography. Oikos, 122(10), 1483–1495.
  • Salecker, J., Sciaini, M., Meyer, K.M., Wiegand, K. (2019) The nlrx R package: A next-generation framework for reproducible NetLogo model analyses. Methods in Ecology and Evolution, 10: 1854–1863. 2019, doi: 10.1111/2041‐210X.13286.
  • Schmolke, A., Thorbek, P., DeAngelis, D. L., & Grimm, V. (2010). Ecological models supporting environmental decision making: a strategy for the future. Trends in Ecology & Evolution, 25(8), 479–486.
  • Thiele, J. C., Kurth, W., & Grimm, V. (2014). Facilitating Parameter Estimation and Sensitivity Analysis of Agent-Based Models: A Cookbook Using NetLogo and R. Journal of Artificial Societies and Social Simulation, 17(3), 11.

Supplemental readings:

    Books:
  • O’Sullivan, D., & Perry, G. L. W. (2013). Spatial Simulation: Exploring Pattern and Process. John Wiley & Sons.

    Papers:
  • DeAngelis, D. L., & Yurek, S. (2017). Spatially Explicit Modeling in Ecology: A Review. Ecosystems, 20(2), 284–300.
  • Epstein, J.M. (2008). Why model? Journal of Artificial Societies and Social Simulation, 11(4), 12.
  • Grimm, V., Frank, K., Jeltsch, F., Brandl, R., Uchmański, J., & Wissel, C. (1996). Pattern-oriented modelling in population ecology. Science of The Total Environment, 183(1), 151–166.
  • Jackson, L. J., Trebitz, A. S., & Cottingham, K. L. (2000). An Introduction to the Practice of Ecological Modeling. BioScience, 50(8), 694–706.
  • Peck, S. L. (2004). Simulation as experiment: a philosophical reassessment for biological modeling. Trends in Ecology & Evolution, 19(10), 530–534.
  • Thiele, J. C. & Grimm, V. (2015) Replicating and breaking models: good for you good for ecology. Oikos, 124(6), 691-696.
  • Winsberg, E. (2009). Computer Simulation and the Philosophy of Science. Philosophy Compass, 4(5), 835–845.

    Published papers on NetLogo models:
  • Anderson JH, Downs JA, Loraamm R, Reader S. 2017. Agent-based simulation of Muscovy duck movements using observed habitat transition and distance frequencies. Computers, Environment and Urban Systems 61:49–55.
  • Baggio, J. A., Salau, K., Janssen, M. A., Schoon, M. L., & Bodin, Ö. (2011). Landscape connectivity and predator–prey population dynamics. Landscape Ecology, 26(1), 33–45.
  • Hovel, K. A., & Regan, H. M. (2008). Using an individual-based model to examine the roles of habitat fragmentation and behavior on predator–prey relationships in seagrass landscapes. Landscape Ecology, 23(1), 75–89.

More NetLogo papers: http://ccl.northwestern.edu/netlogo/references.shtml