What every agent-based modeller should know about floating point arithmetic

Authored by J Gary Polhill, LR Izquierdo, NM Gotts

Date Published: 2006-03

DOI: 10.1016/j.envsoft.2004.10.011

Sponsors: No sponsors listed

Platforms: Swarm

Model Documentation: UML Other Narrative Mathematical description

Model Code URLs: http://www.macaulay.ac.uk/fearlus/floating-point/charity-world/

Abstract

Floating point arithmetic is a subject all too often ignored, yet, for agent-based models in particular, it has the potential to create misleading results, and even to influence emergent outcomes of the model. Using a simple demonstration model, this paper illustrates the problems that accumulated floating point errors can cause, and compares a number of techniques that might be used to address them. We show that inexact representation of parameter values, imprecision in calculation results, and differing implementations of mathematical expressions can significantly influence the behaviour of the model, and create issues for replicating results, though they do not necessarily do so. None of the techniques offer a failsafe approach that can be applied in ally situation, though interval arithmetic is the most promising. (c) 2004 Elsevier Ltd. All rights reserved.

Tags

Agent-based modelling emergence Floating Point Arithmetic interval arithmetic