Agent-based modelling with NetLogo

Registration for the 2024 course will start on May 6, 2024.

Note: In 2024, this course will be tought 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:
• Friday: Pre-Seminar with paper presentations by participants
• 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: Project presentations

General info:
• The course itself is for free

If the course is on site (NOT applicable in 2024):
• 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 (applicable in 2024):
• 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