Agent-based Simulation Platforms: Review and Development Recommendations

Abstract

Five software platforms for scientific agent-based models (ABMs) were reviewed by implementing example models in each. NetLogo is the highest-level platform, providing a simple yet powerful programming language, built-in graphical interfaces, and comprehensive documentation. It is designed primarily for ABMs of mobile individuals with local interactions in a grid space, but not necessarily clumsy for others. NetLogo is highly recommended, even for prototyping complex models. MASON, Repast, and Swarm are “framework and library” platforms, providing a conceptual framework for organizing and designing ABMs and corresponding software libraries. MASON is least mature and designed with execution speed a high priority. The Objective-C version of Swarm is the most mature library platform and is stable and well organized. Objective-C seems more natural than Java for ABMs but weak error-handling and the lack of developer tools are drawbacks. Java Swarm allows Swarm’s Objective-C libraries to be called from Java; it does not seem to combine the advantages of the two languages well. Repast provides Swarm-like functions in a Java library and is a good choice for many, but parts of its organization and design could be improved. A rough comparison of execution speed found MASON and Repast usually fastest (MASON 1-35% faster than Repast), Swarm (including Objective-C) fastest for simple models but slowest for complex ones, and NetLogo intermediate. Recommendations include completing the documentation (for all platforms except NetLogo), strengthening conceptual frameworks, providing better tools for statistical output and automating simulation experiments, simplifying common tasks, and researching technologies for understanding how simulation results arise.

Instruction in Computer Modeling Can Support Broad Application of Complex Systems Knowledge

“…Similarly, students acting out honey bee behavior show improvements in complex systems reasoning (Danish, 2014). Students can also learn about complex systems by using agent-based computer models that show numerous distinct agents moving and interacting to result in a system that globally changes over time (Epstein and Axtell, 1996;Wilensky, 2001;Goldstone and Janssen, 2005;Railsback et al, 2006;Epstein, 2007;Miller and Page, 2007). Agent-based models perceptually ground learners' understanding by visually displaying results so that learners can track the evolution of systems in time.…”

Volunteer Computing Simulation using Repast and Mason

“…Some of them are oriented to specific kinds of simulation (see [12,14]): e.g., simulating of movement of entities with 3D visualisation (see e.g., breve, [9]), networking (see e.g, ns2/ns3, [4]), possibility of visual programming (see e.g. SeSam, [17]).…”

Educational Resource Scheduling Based on Socio-inspired Agents

“…-Simulation A Multi-Agent Simulation Toolkit (MASON) has been used to deploy and test our proposed solution [6,8].…”