Interactive applications & predefined models!
- Intuitive, small, yet powerful and simple-to-learn API for agent based models.
- Universal model structure where agents are identified by a unique id:
- Support for many types of space: arbitrary graphs, regular grids, or continuous space.
- Multi-agent support, for interactions between disparate agent species.
- Scheduler interface (with default schedulers), making it easy to activate agents in a specific order (e.g. by the value of some property)
- Automatic data collection in a
DataFrameat desired intervals
- Aggregating collected data during model evolution
- Distributed computing
- Batch running and batch data collection
- Visualize agent distributions on regular grids, arbitrary graphs or continuous space.
- Interactive applications for any agent based model (in continuous or grid space), which are created with only 5 lines of code and look like this:
The package is in Julia's package list. Install it using this command:
Implementing an ABM framework in Julia has several advantages:
- Using a general purpose programming language instead of a custom scripting language, such as NetLogo's, removes a learning step and provides a single environment for building the models and analyzing their results.
- Julia has a rich ecosystem for data analysis and visualization, implemented and maintained independently from Agents.jl.
- Julia is easier-to-use than Java (used for Repast and MASON), and provides a REPL (Read-Eval-Print-Loop) environment to build and analyze models interactively.
- Unlike Python (used for Mesa), Julia is fast to run. This is a crucial criterion for models that require considerable computations.
- Because the direct output of Agents.jl is a
DataFrame, it makes it easy to use tools such as DataVoyager.jl, which provide an interactive environment to build custom plots from
DataFrames. (and of course the
DataFrameitself is a tabular data format similar to Python's Pandas).
Agents.jl is lightweight and modular. It has a short learning curve, and allows one to extend its capabilities and express complicated modeling scenarios. Agents.jl was originally inspired by the Mesa framework for Python, but has since then departed in design, leading to a dramatically simpler and cleaner API and a shorter learning curve, besides having obvious performance benefits (more than 10 times better performance than Mesa, see our Agents.jl vs Mesa: speed comparison).
An agent-based (or individual-based) model is a computational simulation of autonomous agents that react to their environment (including other agents) given a predefined set of rules . ABMs have been adopted and studied in a variety of research disciplines. One reason for their popularity is that they enable a relaxation of many simplifying assumptions usually made by mathematical models. Relaxing such assumptions of a "perfect world" can change a model's behavior .
Agent-based models are increasingly recognized as the approach for studying complex systems [3,4,5,6]. Complex systems cannot be fully understood using traditional mathematical tools which aggregate the behavior of elements in a system. The behavior of a complex system depends on both the behavior of and interactions between its elements (agents). Small changes in the input to complex systems or the behavior of its agents can lead to large changes in outcome. That is to say, a complex system's behavior is nonlinear, and that it is not only the sum of the behavior of its elements. Use of ABMs have become feasible after the availability of computers and has been growing ever since, especially in modeling biological and economical systems, and has extended to social studies and archaeology.
An ABM consists of autonomous agents that behave given a set of rules. A classic example of an ABM is Schelling's segregation model, which we implement as an example here. This model uses a regular grid and defines agents as the cells of the grid. Agents can be from different social groups. Agents are happy/unhappy based on the fraction of their neighbors that belong to the same group as they are. If they are unhappy, they keep moving to new locations until they are happy. Schelling's model shows that even small preferences of agents to have neighbors belonging to the same group (e.g. preferring that at least 30% of neighbors to be in the same group) could lead to total segregation of neighborhoods. This is an example of emergent behavior from simple interactions of agents that can only be captured in an agent-based model.
If you use this package in a publication, please cite the paper below:
R. Vahdati, Ali (2019). Agents.jl: agent-based modeling framework in Julia. Journal of Open Source Software, 4(42), 1611, https://doi.org/10.21105/joss.01611