Agent-based models and self-organisation: addressing common criticisms and the role of agent-based modelling in urban planning

2016 ◽  
Vol 87 (3) ◽  
pp. 321-338 ◽  
Author(s):  
Sara Levy ◽  
Karel Martens ◽  
Rob van der Heijden
Keyword(s):  
Urban Planning ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Self Organisation

Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques

2012 ◽  
pp. 301-326
Author(s):  
Mitchell Welch ◽  
Paul Kwan ◽  
A.S.M. Sajeev ◽  
Graeme Garner
Keyword(s):  
Large Scale ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Livestock Disease ◽  
Interacting Agents ◽  
National Model ◽  
Complex Phenomena ◽  
Modelling System

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

scholarly journals Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6133
Author(s):  
Georg Holtz ◽  
Christian Schnülle ◽  
Malcolm Yadack ◽  
Jonas Friege ◽  
Thorben Jensen ◽  
...  
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Stakeholder Involvement ◽  
Energy System ◽  
Agent Based Models ◽  
Agent Based ◽  
Industrial Economy ◽  
Agent Based Modelling ◽  
Model Studies ◽  
Phase Out

The German Energiewende is a deliberate transformation of an established industrial economy towards a nearly CO2-free energy system accompanied by a phase out of nuclear energy. Its governance requires knowledge on how to steer the transition from the existing status quo to the target situation (transformation knowledge). The energy system is, however, a complex socio-technical system whose dynamics are influenced by behavioural and institutional aspects, which are badly represented by the dominant techno-economic scenario studies. In this paper, we therefore investigate and identify characteristics of model studies that make agent-based modelling supportive for the generation of transformation knowledge for the Energiewende. This is done by reflecting on the experiences gained from four different applications of agent-based models. In particular, we analyse whether the studies have improved our understanding of policies’ impacts on the energy system, whether the knowledge derived is useful for practitioners, how valid understanding derived by the studies is, and whether the insights can be used beyond the initial case-studies. We conclude that agent-based modelling has a high potential to generate transformation knowledge, but that the design of projects in which the models are developed and used is of major importance to reap this potential. Well-informed and goal-oriented stakeholder involvement and a strong collaboration between data collection and model development are crucial.

scholarly journals The FLAME-accelerated Signalling Tool (FaST): A tool for facile parallelisation of flexible agent-based models of cell signalling

2019 ◽  
Author(s):  
Gavin Fullstone ◽  
Cristiano Guttà ◽  
Amatus Beyer ◽  
Markus Rehm
Keyword(s):  
Cell Signalling ◽  
Parallel Applications ◽  
Spatial Effects ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Central Processing ◽  
Innovative Tool ◽  
Graphical Processing ◽  
Complex Features

AbstractAgent-based modelling is particularly adept at modelling complex features of cell signalling pathways, where heterogeneity, stochastic and spatial effects are important, thus increasing our understanding of decision processes in biology in such scenarios. However, agent-based modelling often is computationally prohibitive to implement. Parallel computing, either on central processing units (CPUs) or graphical processing units (GPUs), can provide a means to improve computational feasibility of agent-based applications but generally requires specialist coding knowledge and extensive optimisation. In this paper, we address these challenges through the development and implementation of the FLAME-accelerated signalling tool (FaST), a software that permits easy creation and parallelisation of agent-based models of cell signalling, on CPUs or GPUs. FaST incorporates validated new agent-based methods, for accurate modelling of reaction kinetics and, as proof of concept, successfully converted an ordinary differential equation (ODE) model of apoptosis execution into an agent-based model. We finally parallelised this model through FaST on CPUs and GPUs resulting in an increase in performance of 5.8× (16 CPUs) and 53.9× respectively. The FaST takes advantage of the communicating X-machine approach used by FLAME and FLAME GPU to allow easy alteration or addition of functionality to parallel applications, but still includes inherent parallelisation optimisation. The FaST, therefore, represents a new and innovative tool to easily create and parallelise bespoke, robust, agent-based models of cell signalling.

AI-Driven Agent-Based Models to Study the Role of Vaccine Acceptance in Controlling COVID-19 Spread in the US

2021 ◽  
Author(s):  
Parantapa Bhattacharya ◽  
Dustin Machi ◽  
Jiangzhuo Chen ◽  
Stefan Hoops ◽  
Bryan Lewis ◽  
...  
Keyword(s):  
Agent Based Models ◽  
Vaccine Acceptance ◽  
Agent Based ◽  
The Us

scholarly journals Leveraging social influence to address overweight and obesity using agent-based models: The role of adolescent social networks

2015 ◽  
Vol 125 ◽  
pp. 203-213 ◽  
Author(s):  
J. Zhang ◽  
L. Tong ◽  
P.J. Lamberson ◽  
R.A. Durazo-Arvizu ◽  
A. Luke ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Overweight And Obesity ◽  
Agent Based Models ◽  
Agent Based

scholarly journals Simulating Transport and Land Use Interdependencies for Strategic Urban Planning—An Agent Based Modelling Approach

Systems ◽  
2015 ◽  
Vol 3 (4) ◽  
pp. 177-210 ◽  
Author(s):  
Nam Huynh ◽  
Pascal Perez ◽  
Matthew Berryman ◽  
Johan Barthélemy
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Modelling Approach

scholarly journals Modelling COVID-19 epidemic and its social consequences

2021 ◽  
Vol 4 ◽  
pp. 18-27
Author(s):  
Olena Pugachova
Keyword(s):  
Social Learning ◽  
Computer Experiments ◽  
Social Consequences ◽  
Economic Consequences ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Social Patterns ◽  
The Impact

The paper studies different approaches to modelling COVID-19 transmission. It is emphasized that the variety of models proposed for forecasting the dynamics of epidemic and its long-term socio-economic consequences deals with the complexity of the object under investigation. So the multiplicity of models makes it possible to describe different aspects of complex reality. It is also highlighted that agent-based simulation is more suitable for modelling social aspects of the processes (human behaviour, social interactions, collective behaviour, and opinion diffusion) in the situation of deep uncertainty.The computer experiments with the parameters of the model are analysed on the basis of a number of agent-based models in NetLogo, namely epiDEM and ASSOCC. It is demonstrated that the dynamics of COVID-19 has different scenarios, and agent-based modelling is a powerful tool in political decisionmaking, taking into account social complexity that often exhibits unpredictable output of intervention policy. The role of agent-based modelling in social learning is also discussed. It is pointed out that social learning can reduce the impact of unsubstantiated statements and rumors that are not always adequate to the situation. It is also stressed that social learning could influence social behaviour that, in turn, facilitates the development of social patterns that reduces the likelihood of disease spreading. Attention is paid to the idea that involving people into the modelling process is a part of effective anti-epidemic policy because of the sensitivity of the output of political intervention to the behavioural reaction. It has been shown that today the ideas of agent-based modelling are widely used by social scientists worldwide. The aim of this endeavour is not only to overcome the current pandemic and its long-term socioeconomic consequences but also to prepare for new challenges in the future. The paper is also aimed at paying attention to the lack of agent-based models in Ukraine that could help policy-makers in developing practical recommendations and avoiding undesirable scenarios.

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