This page contains links to valuable information on natural resource modeling. These may be links to working papers, databases or any other information members think would be of interest to fellow members.


 International Federation of Operational Research Societies

IFORS 2017

The site includes the society newsletter, journals, meetings, job announcements, etc. There is an Educational Resources Project underway, a developing countries newsletter, and a recreational operations research page with puzzles, problems, and games.


Nova Modeler Platform

Nova is a new Java-based modeling platform that naturally supports the creation of models in the system dynamics, spatial and agent-based modeling paradigms in a single desktop application. Nova uses a visual language to express model design, and provides automatic conversion for such models to script form for execution. Nova’s architecture promotes hierarchical design, code reuse, and extensibility through the use of plug-ins.

Nova is fundamentally a dynamic modeling system that is extended through hierarchical design to express spatial and agent-based architectures. A Nova model can be built using the visual language, and then by using its capture function be automatically converted into a runnable script for immediate execution, or possible deployment over a network or on a supercomputer. Nova focuses on the creation of a modular unit called a capsule. Each capsule is a complete model that interacts with its environment through an interface consisting of input and output channels. The simplest capsule might contain a stock-and-flow model similar to one built in Stella. However, capsule instances (called chips) may appear in other capsules (as long as there is no circularity), communicating with their hosts through their I/O channels. Each chip introduces into its host the functionality of that chip’s encapsulated model. Capsules may also be exported and reused in other projects.


Agent-based and Individual-based Modeling: A Practical Introduction


Railsback and Grimm – This is a textbook on scientific applications of agent-based (or “individual-based”; we use the terms synonymously) modeling to study complex systems. It is intended for classes at upper-undergraduate or higher levels, and for self-instruction by students and scientists.

Our book uses Wilensky’s NetLogo software (Wilensky, 1999) as the platform for building and analyzing models. This is not just a book on NetLogo, but a book on scientific modeling that includes learning to use NetLogo software.

The book is now available through your local bookstore, its site at Princeton University Press, and on-line bookstores. You can view the Table of Contents, download Chapter 1 (PDF), and see a list of reviews and endorsements at its site at Princeton University Press.


AD Model Builder and Template Model Builder

The ADMB project supports the application of automatic differentiation (AD) for solutions to non-linear statistical modeling and optimization problems.

AD Model Builder, or ADMB, is a C++ application which implements AD using specialized classes and operator overloading. ADMB can be downloaded from this site. For further information about ADMB, see ADMB citation
Fournier, D.A., Skaug, H.J., Ancheta, J., Ianelli, J., Magnusson, A., Maunder, M.N., Nielsen, A., and Sibert, J. 2012. AD Model Builder: using automatic differentiation for statistical inference of highly parameterized complex nonlinear models. Optim. Methods Softw. 27:233-249.

Template Model Builder, or TMB, is a statistical modeling package which implements AD using C++ templates and is integrated with the R statistical language. TMB can be downloaded from from the TMB github site. For further information about TMB, see the TMB Wiki and the primary publication, see TMB citation
Kristensen, K., Nielsen, A., Berg, C.W., Skaug, H.J., Bell, B. 2016, TMB: Automatic Differentiation and Laplace Approximation.


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The R Project for Statistical Computing


R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. To download R, please choose your preferred CRAN mirror.