Mode Changes in Agent-Based Simulation Models

The scope of the Technical Forum Group (TFG) on Agents in Bioinformatics (BIOAGENTS) was to inspire collaboration between the agent and bioinformatics communities with the aim of creating an opportunity to propose a different (agent-based) approach to the development of computational frameworks both for data analysis in bioinformatics and for system modelling in computational biology. During the day, the participants examined the future of research on agents in bioinformatics primarily through 12 invited talks selected to cover the most relevant topics. From the discussions, it became clear that there are many perspectives to the field, ranging from bio-conceptual languages for agent-based simulation, to the definition of bio-ontology-based declarative languages for use by information agents, and to the use of Grid agents, each of which requires further exploration. The interactions between participants encouraged the development of applications that describe a way of creating agent-based simulation models of biological systems, starting from an hypothesis and inferring new knowledge (or relations) by mining and analysing the huge amount of public biological data. In this report we summarize and reflect on the presentations and discussions.

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Scoping the Future with Theory-Driven Models—Where’s the Uncertainty?: Commentary on Lustick and Tetlock 2021

10.31234/osf.io/ew5c8 ◽

2021 ◽

Author(s):

David R. Mandel

Keyword(s):

National Security ◽

Policy Decision ◽

Simulation Models ◽

Insufficient Data ◽

Agent Based Simulation ◽

Agent Based ◽

Strategic Intelligence ◽

The Future ◽

Policy Decision Making ◽

Rich Data

Lustick and Tetlock outline an intellectually ambitious approach to scoping the future. They are particularly interested in sectors of national security and foreign policy decision-making that require anticipatory strategic intelligence that is difficult to produce because there is insufficient data, even if relevant theories are available. They propose that in these theory-rich/data-impoverished cases, there can be great value in developing agent-based simulation models that incorporate probabilistic rules that cohere with postulates of the theory or theories that are brought to bear on the intelligence challenge. This is the gist of the “simulation manifesto.” The aim of this commentary is to focus on the assessment and representation of key uncertainties in such models and I outline several ways in which uncertainty may arise in the process of simulation model construction.

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“Engineering” Agent-Based Simulation Models?

Lecture Notes in Computer Science - Agent-Oriented Software Engineering XIII ◽

10.1007/978-3-642-39866-7_11 ◽

2013 ◽

pp. 179-196 ◽

Cited By ~ 2

Author(s):

Franziska Klügl

Keyword(s):

Simulation Models ◽

Agent Based Simulation ◽

Agent Based

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The Agent Object Relationship Simulation as a Business Process

Handbook of Research on Emerging Rule-Based Languages and Technologies ◽

10.4018/978-1-60566-402-6.ch015 ◽

2010 ◽

pp. 348-370

Author(s):

Emilian Pascalau ◽

Adrian Giuca ◽

Gerd Wagner

Keyword(s):

Business Process ◽

Process Modeling ◽

Simulation Models ◽

Use Case ◽

Agent Based Simulation ◽

Agent Based ◽

Simulation Process ◽

Simulation Paradigm ◽

Business Process Modeling Notation ◽

Object Relationship

The use of agent-based simulation models is growing and attracted a lot of attention recently both for researchers and business management. Agent-Object Relationship (AOR) is an agent-based simulation paradigm that uses reaction rules to model agents’ behavior. The goal of this chapter, besides exemplifying the AOR concepts by means of a use case, is to investigate the use of business process modeling notation (BPMN) to model the AOR simulation process. Moreover it discusses aspects of a distributed architecture for an AOR simulation system. The chapter concludes with the fact that BPMN is well suited to model the AOR simulation process.

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Exploring the behavior space of agent-based simulation models using random forest metamodels and sequential sampling

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2018.12.006 ◽

2019 ◽

Vol 92 ◽

pp. 62-81 ◽

Cited By ~ 2

Author(s):

Mert Edali ◽

Gönenç Yücel

Keyword(s):

Random Forest ◽

Sequential Sampling ◽

Simulation Models ◽

Agent Based Simulation ◽

Agent Based

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PARALLEL AGENT-BASED SIMULATION ON A CLUSTER OF WORKSTATIONS

Parallel Processing Letters ◽

10.1142/s0129626403001562 ◽

2003 ◽

Vol 13 (04) ◽

pp. 629-641 ◽

Cited By ~ 5

Author(s):

Konstantin Popov ◽

Mahmoud Rafea ◽

Fredrik Holmgren ◽

Per Brand ◽

Vladimir Vlassov ◽

...

Keyword(s):

Web Sites ◽

Large Scale ◽

Parallel Implementation ◽

Simulation Models ◽

Agent Based Simulation ◽

Time Step ◽

Agent Based ◽

Simulation Step ◽

Cluster Of Workstations ◽

Concurrent Simulation

We discuss a parallel implementation of an agent-based simulation. Our approach allows to adapt a sequential simulator for large-scale simulation on a cluster of workstations. We target discrete-time simulation models that capture the behavior of Web users and Web sites. Web users are connected with each other in a graph resembling the social network. Web sites are also connected in a similar graph. Users are stateful entities. At each time step, they exhibit certain behaviour such as visiting bookmarked sites, exchanging information about Web sites in the "word-of-mouth" style, and updating bookmarks. The real-world phenomena of emerged aggregated behavior of the Internet population is studied. The system distributes data among workstations, which allows large-scale simulations infeasible on a stand-alone computer. The model properties cause traffic between workstations proportional to partition sizes. Network latency is hidden by concurrent simulation of multiple users. The system is implemented in Mozart that provides multithreading, dataflow variables, component-based software development, and network-transparency. Currently we can simulate up to 106 Web users on 104 Web sites using a cluster of 16 computers, which takes few seconds per simulation step, and for a problem of the same size, parallel simulation offers speedups between 11 and 14.

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