#Netlogo breeds software#
#Netlogo breeds code#

There are already two ways to couple NetLogo and R to benefit from both software assets for agent-based modeling. Users can benefit from the power and versatility of this medium level language which includes many powerful, user-generated packages to build their SE-ABMs. Using the R language, already familiar to many ecologists, to implement SE-ABMs instead of using another software has advantages. R has a huge open-source user and developer community that continuously builds on and improves the software with core enhancements and new extension packages, therefore increasing the software's potential and facilitating its use.

R (R Core Team) is one of the globally leading open source data and statistical languages (SQL and Python being the other two) and it is widely used in many disciplines, including ecology, to explore and analyze data. NetLogo has many other advantages such as a built-in visual interface, several extensions to broaden the language, and it executes quickly. This makes the software relatively easy to learn and use, as many actions are already pre-coded into these primitives.

#Netlogo breeds code#

NetLogo has a well-defined structure to represent model agents and a high-level language composed of less than 500 words (called ‘primitives’) to be used to code a model. NetLogo (Wilensky 1999), in particular, offers great potential in SE-ABMs implementation, both for research and as a teaching tool (Tisue and Wilensky 2004). Several modeling environments already exist to build agent-based models such as NetLogo (Wilensky 1999), SELES (Fall and Fall 2001), NOVA (), GAMA (), Capsis (), Repast (Collier and North 2013, North et al. 2012), and models of vegetation dynamics (Wallentin et al. 2012), analyses of movement and habitat selection patterns for animals (McIntire et al. SE-ABMs have been widely used by ecologists to study populations and communities with, for example, research on human–wildlife interactions (Chion et al. These models can include as many agent-level mechanisms as wanted and they are especially useful when population-level patterns are difficult to understand (DeAngelis and Grimm 2014). When they are ‘spatially-explicit’, there is an effect of the landscape and the fate of the entities is therefore constrained by their environment (McIntire et al.

Population-level patterns emerge from the agent-level mechanism simulations (Railsback and Grimm 2012). These entities have state-dependent behaviors and they can interact with each other and/or their environment. Agent-based models are bottom–up models that simulate the fate of unique, autonomous entities. Many scientific disciplines have shown great interests in spatially explicit agent-based models (SE-ABMs) (DeAngelis and Grimm 2014).