The GitHub page of the NetLogo platform4 gives one possible list of examples. Examples of typical NetLogo extensions There are many different types of extension. The usage of this API is presented in detail in section 1.3. That allows models to be loaded, executed and gives access to their variables and methods. To do this, NetLogo provides a Java APIĬhapter written by Benoit G AUDOU, Christophe L ANG, Nicolas Guilhelm S AVIN, Sébastien R EY C OYREHOURCQ and Jean-Marc N ICOD. Conversely, NetLogo can also be called and controlled by other programs, such as OpenMole1, Python2 and R3. We will discuss this in more detail in section 1.2. These extensions are developed with an open Java API. But many modelers have also developed their own extensions to tackle specific problems that are of interest to them. We will explore some of these extensions later in this chapter. An official library of extensions is available on the official NetLogo Website. There is a vast library of extensions available to users, allowing them to integrate additional functionality that is not present in the native version of NetLogo, but which might nonetheless be necessary for the development of a given model. Additionally, to make up for any missing features, NetLogo is compatible with other platforms and libraries, as we will demonstrate throughout this book. ![]() NetLogo offers a wide range of features and generic operators to its users. Introduction to extensions in NetLogo NetLogo is a generic simulation environment in the sense that it was not designed with any specific domain of application in mind. To be read and reread without moderation!ġ NetLogo, an Open Simulation Environmentġ.1. In the same spirit as Volume 1, this second volume includes examples of NetLogo code and GitHub links for each of the models encountered. Finally, Chapter 6 brings the book to a close by presenting a number of protocols for exploring complex models in NetLogo. Chapter 5 focuses on solving so-called “swarm” problems. Chapter 4 explores the notion of network in much more depth, considering fundamental principles of graph theory but also more advanced features like dynamic graphs. The scientific material this book relies on was developed within various research and pedagogic projects, which benefited from the financial or logistic support of several institutions: Mission pour l’interdisciplinarité du CNRS/PEPS HUMAIN CNRS (), LabeX DynamiTe ( en/the-labex/), ISC-PIF (), RNSC (), MAPS Network ().Ĭhapter 2 discusses the question of multiscale modeling, with applications in road traffic management, and Chapter 3 focuses on coupling macro and micro models based on networks, with applications in spatial epidemiology. Chapters 2–5 explore in depth the opportunities for extending and coupling NetLogo presented in the first chapter, situating them within a number of fundamental perspectives.Ĭhapter written by Arnaud BANOS, Christophe L ANG and Nicolas M ARILLEAU. Readers will be offered a slightly atypical and unconventional presentation of NetLogo that emphasizes the aspect of being an open simulation environment (Chapter 1). The objective of this second book is to give an educational presentation of these two important dimensions of agent-based spatial simulation with NetLogo. The second form, more typically consisting of internal resources, arises from the suitability of NetLogo, its language and its architecture for developing models that are intrinsically more advanced. External resources allow specialized extensions to be directly constructed and/or exploited from within NetLogo, and allow NetLogo to be dynamically coupled with other libraries, software programs or platforms. These resources take two different forms. NetLogo also houses a number of commonly unexpected and underestimated resources that fully justify its status as a platform for agent-based modeling and simulation. ![]() Volume 1, Agent-based Spatial Simulation with NetLogo 1, specifically focused on this remarkable quality. The NetLogo platform is perfect for rapidly and effectively prototyping simple models. So, to get the density to vary you can just modify the 5 (or add a slider as was done in the original 2D life.Agent-based Spatial Simulation with NetLogo 2Īgent-based Spatial Simulation with NetLogo 2 Advanced ConceptsĪrnaud Banos Christophe Lang Nicolas Marilleau if a random number between 0 and 100 is less than If the number drawn is less than the initial-density, the cell is "born." So, you can basically do the same thing in 3D- with this simplified setup: to setup Essentially, in the 2D version the density is determined by getting each cell to randomly draw a number between 0 and 100, and compare that to the value in the initial-density slider. I think you're okay to convert this more or less directly to 3D without using a different primitive- random-float or random should still do the trick.
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