A theoretical formalism for analyzing agent-based models

Abstract

Purpose: Following Holland, complex adaptive systems (CASs) are collections of interacting, autonomous, learning decision makers embedded in an interactive environment. Modeling CASs is challenging for a variety of reasons including the presence of heterogeneity, spatial relationships, nonlinearity, and, of course, adaptation. The challenges of modeling CASs can largely be overcome by using the individual-level focus of agent-based modeling. Agent-based modeling has been used successfully to model CASs in many disciplines. Many of these models were implemented using agent-based modeling software such as Swarm, Repast 3, Repast Simphony, Repast for High-Performance Computing, MASON, NetLogo, or StarLogo. All of these options use modular imperative architectures with factored agents, spaces, a scheduler, logs, and an interface. Many custom agent-based models also use this kind of architecture. This paper's contribution is to introduce and apply a theoretical formalism for analyzing modular imperative agent-based models of CASs. This paper includes an analysis of three example models to show how the formalism is useful for predicting the execution time and space requirements for representations of common CASs.Method: The paper details the formalism and then uses it to prove several new findings about modular imperative agent-based models.Results: It is proven that the asymptotic time and space performance of modular imperative agent-based modeling studies is computationally optimal for a common class of problems. Here 'optimal' means that no other technique can solve the same problem computationally using less asymptotic time or space. Modular imperative agent-based models are shown to be universal models, subject to the correctness of the Church-Turing thesis. Several other results are also proven about the time and space performance of modular imperative agent-based models. The formalism is then used to predict the performance of three models and the results are found to compare closely to the measured performance.Conclusions: This paper's contribution is to introduce, analyze, and apply a theoretical formalism for proving findings about agent-based models with modular agent scheduler architectures. Given that this kind of modeling is both computationally optimal and a natural structural match for many modeling problems, it follows that it is the best modeling method for such problems.

Formal specification and analysis of take-off procedure using VDM-SL

“…Further, when an enhancement to the system is required regression testing will be needed to perform which recommends that the complete set of simulations must be re-performed. Formal specification and analysis techniques help to ensure that the models and simulations are correct and reliable to overcome the disadvantages of testing and simulation (North 2014). Therefore, it is required to apply formal approaches which provide an exhaustive support for verification of algorithms before the simulation.…”

An agent-based algorithm exploiting multiple local dissimilarities for clusters mining and knowledge discovery

“…In particular LD-ABCD identifies clusters on a dataset that is represented through a labeled graph: graph clustering is a well-known problem and it has been addressed in many other works [10,46,18,20,21,38]. Such clusters are discovered by different agents, which operate according to a paradigm inspired by the multi-agent systems that can be found in the literature [6,36,13,12,16,2,38].…”

“…Such clusters are discovered by different agents, which operate according to a paradigm inspired by the multi-agent systems that can be found in the literature [6,36,13,12,16,2,38]. Each agent examines the patterns by performing a RW [2,19] on the graph that represents the dataset and tries to group them in different clusters.…”

“…Clustering [27,37,11,39,9,26,51] is a well-established approach that can be used to this end. Among the many solutions available in this field, it is worth citing those clustering techniques based on graph-theoretical results and multi-agent systems [10,46,18,20,21,6,38,1,22]. Graph-based techniques have the fundamental advantage of mapping the original problem onto a "dimensionless" object: the graph.…”

Dynamic agent composition for large-scale agent-based models

“…Many toolkits are available to support the development of agent-based models (Nikolai and Madey 2009;Berryman 2008;Railsback et al 2006;Najlis et al 2001;North 2013;Luke et al 2005) with most containing the following features: agents, a scheduler, an interaction space, random number streams, logging and a user interface (North 2013). In these toolkits, agents are generally made up of a unique identifier, behaviours that can be activated and attributes that can be modified.…”