NetLogo

Draft

Introduction

“NetLogo is a cross-platform multi-agent programmable modeling environment. NetLogo was authored by Uri Wilensky in 1999 and is under continuous development at the CCL (the people who brought you StarLogoT). NetLogo also powers the HubNet participatory simulation system”. Netlogo can be used both to teach programing, model building and understanding of complex phenomena through models.

NetLogo can be described as a programming microworld. It is officially described as a programmable modeling environment for simulating natural and social phenomena. “NetLogo is particularly well suited for modeling complex systems developing over time. Modelers can give instructions to hundreds or thousands of "agents" all operating independently. This makes it possible to explore the connection between the micro-level behavior of individuals and the macro-level patterns that emerge from the interaction of many individuals.” (What is NetLogo?, retrieved 09:54, 18 September 2009 (UTC))

“NetLogo is a cross-platform multi-agent programmable modeling environment. NetLogo was authored by Uri Wilensky in 1999 and is under continuous development at the CCL (the people who brought you StarLogoT). NetLogo also powers the HubNet participatory simulation system”.

NetLogo is a free environment and as of Feb 2019 is still under active development and a large model library. We tested this under Windows 10 and Ubuntu 18. - Daniel K. Schneider (talk) 11:40, 11 March 2019 (CET)

See also:

• AgentSheets, a commercial educational environment with somewhat similar aims.
• Agent-based modelling and simulation
• System Dynamics (FreeStyler), a free alternative for the system dynamics module
• System Dynamics (NetLogo), a more detailed introduction to system dynamics modeling with NetLogo

The software

NetLogo is free and runs on must systems (since it is programmed in Java).

As of Feb 2019, this is a live projects, its last update in June 2018.

You also can use NetLogo Web, a browser-based platform. However, it does not work as well as the the desktop platform and it does have some restrictions

I suggest donating some money to this open source project since it its authors managed to create a useful environment and keep it going. - Daniel K. Schneider (talk) 16:32, 14 March 2019 (CET)

Creating simple agents

It has an easy to use graphical interface to create simple simulations. You may create a world and parametrize turtles that move around. At some point one then can add programming logic to these objects. Have them move around more smartly, interact with others or with the place on which they currently sit. Turtles can have any shape, i.e. a car.

Netlogo simulations

In addition to a learning environment for simple agent-based programming, NetLogo is a modeling and simulation tool of several kinds.

(1) Firstly, by definition, a multi-turtle environment like NetLogo is an Agent-based modelling and simulation (ABMS) environment since the turtles interact with each other and the environment. You may play with parameters and use the “BehaviorSpace [...] software tool integrated with NetLogo that allows you to perform experiments with models. It runs a model many times, systematically varying the model's settings and recording the results of each model run. This process is sometimes called "parameter sweeping". It lets you explore the model's "space" of possible behaviors and determine which combinations of settings cause the behaviors of interest.” (BehaviorSpace)

A default installation contains many models from several subject areas, including a large section of curricular materials.

(2) The system dynamics modeler allows creating system dynamics models

The System Dynamics manual (retrieved 09:54, 18 September 2009 (UTC)) defines the difference between the two models - ABMS vs. system dynamics like this: “With the agent-based approach we usually use in NetLogo, you program the behavior of individual agents and watch what emerges from their interaction. In a model of Wolf-Sheep Predation, for example, you provide rules for how wolves, sheep and grass interact with each other. When you run the simulation, you watch the emergent aggregate-level behavior: for example, how the populations of wolves and sheep change over time.”. See System Dynamics (NetLogo) for an example.

(3) With the Hubnet model you may “run participatory simulations in the classroom. In a participatory simulation, a whole class takes part in enacting the behavior of a system as each student controls a part of the system by using an individual device, such as a networked computer or Texas Instruments graphing calculator.” (HubNet, retrieved 09:54, 18 September 2009 (UTC)).

Netlogo includes additional features, consult the NetLogo web site.

Example

The following screen capture shows a comparison between an agent-based and a simulation-based predation model made by Wilensky (2005). . In the simple agent model the wolves at some point will eat all the sheep and then die out. In the simulation model, populations can recover since fractions of a single wolf or sheep are allowed. A second, more comple sheep - wolves - grass model is more stable.

Agent vs. system dynamcis. Only 3 sheep left in the agent model after 307 ticks

We discuss both of types of models in the NetLogo Wolf Sheep Predation model and System Dynamics (NetLogo).

For researchers

NetLogo has a configurable logging feature to study user behavior and interaction.

Educational benefits

When NetLogo is used as modeling tool by students, it “promotes several processes of reasoning that are central to science: developing original hypotheses, formalizing ideas, researching existing so-lutions, and critical analysis of results.” (Wilensky & Reisman 2006:205)

Moreover, we also can find a "learning through building" approach that is popular in engineering sciences: “If you can’t build it, then you don’t under-stand it. Our approach of modeling underlying mechanisms takes the engineer’sdictum seriously. To model a system, it is not sufficient to understand only a hand-ful of isolated facts about it. Rather, one must understand many facts and concepts about the system and, most important, how these relate to each other.The process of modeling is inherently about developing such conceptual relations and seeking out new facts and concepts when a gap in one’s knowledge is discovered.” (Wilensky & Reisman 2006:202)

Classical vs. embodied models

According to Wilensky and Reisman (2006), , embodied, agent-based models do have some advantages over system-dynamics (or similar) models. In a study, “using agent-based, embodied modeling tools, students model the microrules underlying a biological phenomenon and observe the resultant aggregate dynamics.” In the two cases described, “students framed hypotheses, constructed multiagent models that incorporate these hypotheses, and tested these by running their models and observing the outcomes. Contrasting these cases against traditionally used, classical equation-based approaches, we argue that the embod-ied modeling approach connects more directly to students’ experience, enables ex-tended investigations as well as deeper understanding, and enables “advanced” top-ics to be productively introduced into the high school curriculum.”

Links

• NetLogo HomePage. This website includes the downloads, user manual, extensions, ready models from a library, etc.

Tutorials:

• Pdf tutorial document describing how to build a predator-prey model using NetLogo. Created by Erin McNelis, Associate Professor, Mathematics and Computer Science Department, Western Carolina University, Cullowhee, NC.

Bibliography

Lotka, A.J. (1956) Elements of Mathematical Biology. New York: Dover.

Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Wilensky, U & Rand, W. (2015). Introduction to Agent-Based Modeling: Modeling Natural, Social and Engineered Complex Systems with NetLogo. Cambridge, MA. MIT Press.

Kornhauser, D., Wilensky, U., & Rand, W. (2009). Design guidelines for agent based model visualization. Journal of Artificial Societies and Social Simulation, JASSS, 12(2), 1.

Wilensky, U. & Reisman, K. (1999). Connected Science: Learning Biology through Constructing and Testing Computational Theories – an Embodied Modeling Approach. International Journal of Complex Systems, M. 234, pp. 1 - 12. (This model is a slightly extended version of the model described in the paper.)

Wilensky, U., & Reisman, K. (2006). Thinking Like a Wolf, a Sheep, or a Firefly: Learning Biology Through Constructing and Testing Computational Theories—An Embodied Modeling Approach. Cognition and Instruction, 24(2), 171–209. https://doi.org/10.1207/s1532690xci2402_1 . A free version is available at http://ccl.northwestern.edu/papers/2006/Thinking_Like_a_Wolf(1).pdf (retrieved Feb 2019).