Towards a standard model for research in agent-based modeling and simulation

Abstract

Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decisionmaking agent. ABMs are very sensitive to implementation details. Thus, it is very easy to inadvertently introduce changes which modify model dynamics. Such problems usually arise due to the lack of transparency in model descriptions, which constrains how models are assessed, implemented and replicated. In this paper, we present PPHPC, a model which aims to serve as a standard in agent based modeling research, namely, but not limited to, conceptual model specification, statistical analysis of simulation output, model comparison and parallelization studies. This paper focuses on the first two aspects (conceptual model specification and statistical analysis of simulation output), also providing a canonical implementation of PPHPC. The paper serves as a complete reference to the presented model, and can be used as a tutorial for simulation practitioners who wish to improve the way they communicate their ABMs.

micompr: An R Package for Multivariate Independent Comparison of Observations

“…The package includes test data produced by several implementations of the Predator-Prey for High Performance Computing (PPHPC) simulation model (Fachada et al, 2015). The data is provided in rdata format, and is readily available on loading the package.…”

“…PPHPC is a reference model for studying and evaluating implementation strategies for spatial agentbased models, capturing important characteristics such as agent movement and local agent interactions (Fachada et al, 2015). The model describes a prototypical predator-prey system, and has six outputs, namely prey population, P s , predator population, P w , cell-bound food quantity, P c , mean prey energy, E s , mean predator energy, E w , and mean cell-bound food levels, C. Here we show the main comparison cases discussed in a previous article (Fachada et al, 2017), in which the model implementations are parameterized with size 400 and parameter set 1 (Fachada et al, 2015). A canonical PPHPC realization, implemented in NetLogo (Wilensky, 1999), is compared with three configurations of a parallel Java implementation (Fachada et al, 2016).…”

<code>SimOutUtils</code> – Utilities for Analyzing Time Series Simulation Output

“…These partial tables can be merged into larger tables, with custom features such 0 500 as additional rows, headers and/or footers. Tables 8 to 11 of reference [4] were generated with this function.…”

“…In other words, aid in the process of docking simulation models [2]. 4. From the previous points, produce publication quality L A T E X tables and figures (the latter via the matlab2tikz script [3]).…”

“…These utilities were originally developed to study the Predator-Prey for High-Performance Computing (PPHPC) agent-based model [4], namely by statistically analyzing its outputs for a number of different parameters and comparing the dynamical behavior of different implementations [4,5,6]. They were later generalized to be usable with any stochastic simulation model with time serieslike outputs.…”