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 decision-making 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.
Highlights
Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent
As suggested by Radax & Rengs (2010), we focus on providing a detailed assessment of the distributional properties of the different focal measures (FMs), namely whether they are sufficiently “normal” such that normality-assuming statistical techniques can be applied, for confidence interval (CI) estimation, and for model comparison purposes
Each replication r was performed with a pseudo-random number generators (PRNGs) seed obtained by taking the MD5 checksum of r and converting the resulting hexadecimal string to a 32-bit integer, guaranteeing some independence between seeds, and between replications
Summary
Agent-based modeling (ABM) is a bottom-up modeling approach, where each entity of the system being modeled is uniquely represented as an independent decision-making agent. Each agent analyzes its current situation (e.g., what resources are available, what other agents are in the neighborhood), and acts appropriately, based on a set of rules. The most well known standard ABM is the “StupidModel,” which consists of a series of 16 pseudo-models of increasing complexity, ranging from simple moving agents to a full predator-prey-like model. It was developed by Railsback, Lytinen & Grimm (2005) as a teaching tool and template for real applications, as it includes a set of features commonly used in ABMs of real systems. Its multiple versions and user-interface/visualization goals limit the series appeal as a pure computational model for the goals described in the introduction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
