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1. #NETLOGO MODELING COMMONS SOFTWARE#
2. #NETLOGO MODELING COMMONS CODE#
3. #NETLOGO MODELING COMMONS PROFESSIONAL#

This understanding is no longer as meaningful as it once was: many parts of NetLogo are in fact compiled ( Stonedahl et al. The belief likely originated in part with the understanding that NetLogo and the Java language it is based on are interpreted instead of compiled. This belief that NetLogo is inherently unsuited for large models is not well supported ( Tisue and Wilensky 2004). But for computationally intensive “serious” ABMs, there is a belief that NetLogo’s execution speed is such a constraint that models will need to be re-implemented in lower-level languages (e.g., Sklar 2007 Bouquet et al. Therefore, NetLogo appears to have a reputation as especially suited for relatively simple ABMs intended mainly to communicate ideas.

#NETLOGO MODELING COMMONS SOFTWARE#

It is widely accepted that NetLogo can make model-based science efficient by greatly reducing programming effort and making it easier to test both the software and model design.

#NETLOGO MODELING COMMONS PROFESSIONAL#

The reasons for NetLogo’s popularity include its professional design and packaging, comprehensive documentation, high-level programming language with many built-in commands and data types specialized for ABMs, integrated graphical user interface, integrated tool for performing simulation experiments, and active user community.ĭespite its many advantages, NetLogo has a reputation as not suitable for large or complex models. The CoMSES Net Computational Model Library ( ), to which this journal encourages submitting models, appears to be dominated by models implemented in NetLogo and two recent textbooks on ABMs ( Railsback and Grimm 2012 Wilensky and Rand 2015) use NetLogo as the platform.

Īgent-based models (ABMs) have become essential tools in social (and other) sciences, and NetLogo ( Wilensky 1999) is probably now the most widely used software platform for ABMs. Further updates and additions to this article are at. 9.1) have changed dramatically due to improvements in NetLogo versions 6.0.1 and later. The speed costs of using the "in-radius" primitive (Sect. NetLogo also is supported by efficient analysis tools, such as BehaviorSearch and RNetLogo, that can reduce the number of model runs and the effort to set them up for (e.g.) parameterization and sensitivity analysis.Ĭorrigendum : Further investigation indicates that the speed advantage of using state variables instead of links stated in Sects. NetLogo’s BehaviorSpace tool makes it very easy to conduct multiple-model-run experiments in parallel on either desktop or high performance cluster computers, so even quite slow models can be executed thousands of times.

For models in which most agents do nothing on most time steps, discrete event simulation-facilitated by the time extension to NetLogo-can dramatically increase speed. Programming the same behavior in a different way can sometimes provide order-of-magnitude speed increases. For models with extensive initialization methods, reorganizing the setup procedure can reduce the initialization effort in simulation experiments. Avoiding or improving agent filtering statements can often produce dramatic speed improvements. We recommend a five-step process for quantifying execution speed, identifying slow parts of code, and writing faster code.

#NETLOGO MODELING COMMONS CODE#

NetLogo programs often do run very slowly when written to minimize code length and maximize clarity, but relatively simple and easily tested changes can almost always produce major increases in execution speed. Our experience does not support that belief. Individuals then imitate their neighbors and change their money based on what money they use.Experiments through the use of Behavior Space have shown that under certain properties community currency can spread all over the network crowding out the dominant type of money.This model is used as part of a Phd research on monetary innovation.NetLogo has become a standard platform for agent-based simulation, yet there appears to be widespread belief that it is not suitable for large and complex models due to slow execution. At each iteration, each individual make exchanges by using either money “0” or money “1” to the neighbors in the network. Money users interact with each other based on whom they are connected to in the network. In this model, there are two monetary variants in competition within the social network – one variant generated by “money 0” (or legal tender) and the other generated by “money 1” (community currency). This modified Language_Change NetLogo model portrays money exchange as language exchange.It explores how the properties of money users (agents) and the structure of their social networks can affect the course of money usage change.