scholarly journals Why Sociology Should Use Agent Based Modelling

2013 ◽  
Vol 18 (3) ◽  
pp. 31-41 ◽  
Author(s):  
Edmund Chattoe-Brown
Keyword(s):  
Quantitative Data ◽  
Theory Building ◽  
Agent Based Models ◽  
Sociological Practice ◽  
Agent Based ◽  
Qualitative And Quantitative ◽  
Agent Based Modelling ◽  
Schelling Model ◽  
Social Causes ◽  
Sociology Journals

Although Agent Based Models (hereafter ABM) are now regularly reported in sociology journals, explaining the approach, describing models and reporting results leaves little opportunity to examine wider implications of ABM for sociological practice. This article uses an established ABM (the Schelling model) for this. The first part argues that ABM integrates qualitative and quantitative data distinctively, provides novel tools for understanding social causes and offers a significantly different perspective on theory building. The second part shows how the emerging ABM methodology is compatible with existing sociological practice while undermining several criticisms of ABM perceived to limit its sociological relevance.

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 GETTING AWAY FROM NUMBERS: USING QUALITATIVE OBSERVATION FOR AGENT-BASED MODELING

2008 ◽  
Vol 11 (02) ◽  
pp. 175-185 ◽  
Author(s):  
LU YANG ◽  
NIGEL GILBERT
Keyword(s):  
Social Sciences ◽  
Quantitative Data ◽  
Qualitative Data ◽  
Participant Observation ◽  
Agent Based Modeling ◽  
Agent Based Models ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Qualitative Observation

Although in many social sciences there is a radical division between studies based on quantitative (e.g. statistical) and qualitative (e.g. ethnographic) methodologies and their associated epistemological commitments, agent-based simulation fits into neither camp, and should be capable of modelling both quantitative and qualitative data. Nevertheless, most agent-based models (ABMs) are founded on quantitative data. This paper explores some of the methodological and practical problems involved in basing an ABM on qualitative participant observation and proposes some advice for modelers.

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.

Agent-Based Modeling

2017 ◽  
Vol 8 (4) ◽  
pp. 387-395 ◽  
Author(s):  
Joshua Conrad Jackson ◽  
David Rand ◽  
Kevin Lewis ◽  
Michael I. Norton ◽  
Kurt Gray
Keyword(s):  
Nonlinear Dynamics ◽  
Hypothesis Testing ◽  
Statistical Power ◽  
Group Behavior ◽  
Agent Based Modeling ◽  
Theory Building ◽  
Collective Phenomena ◽  
Agent Based Models ◽  
Agent Based ◽  
Over Time

Agent-based modeling is a long-standing but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to precisely specify the behavior of any number of agents and observe their interactions over time. ABMs are especially useful when investigating group behavior or evolutionary processes and can uniquely reveal nonlinear dynamics and emergence—the process whereby local interactions aggregate into often-surprising collective phenomena such as spatial segregation and relational homophily. We review several illustrative ABMs, describe the strengths and limitations of this method, and address two misconceptions about ABMs: reductionism and “you get out what you put in.” We also offer maxims for good and bad ABMs, give practical tips for beginner modelers, and include a list of resources and other models. We conclude with a seven-step guide to creating your own model.

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.

An Enchantment of Digital Archaeology: Raising the Dead with Agent-Based Models, Archaeogaming and Artificial Intelligence

2020 ◽  
Author(s):  
Shawn Graham ◽  
Keyword(s):  
Artificial Intelligence ◽  
Video Games ◽  
Unintended Consequences ◽  
Agent Based Models ◽  
Roman Archaeology ◽  
Agent Based ◽  
Agent Based Modelling ◽  
The Past ◽  
Digital Archaeology ◽  
The Dead

The use of computation in archaeology is a kind of magic, a way of heightening the archaeological imagination. Agent-based modelling allows archaeologists to test the ‘just-so’ stories they tell about the past. It requires a formalization of the story so that it can be represented as a simulation; researchers are then able to explore the unintended consequences or emergent outcomes of stories about the past. Agent-based models are one end of a spectrum that, at the opposite side, ends with video games. This volume explores this spectrum in the context of Roman archaeology, addressing the strengths, weaknesses, and opportunities of a formalized approach to computation and archaeogaming.

Review of Agent Based Modelling of Social Attachment in Crisis Situations

2019 ◽  
Vol 11 (1) ◽  
pp. 35-64 ◽  
Author(s):  
Julius M Bañgate ◽  
Julie Dugdale ◽  
Elise Beck ◽  
Carole Adam
Keyword(s):  
Attachment Theory ◽  
Human Behaviour ◽  
Agent Architecture ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Model ◽  
Agent Based Modelling ◽  
Social Attachment ◽  
Bdi Agent ◽  
Multi Agent

Human behaviour during crisis evacuations is social in nature. In particular, social attachment theory posits that proximity of familiar people, places, objects, etc., promotes calm and a feeling of safety, while their absence triggers panic or flight. In closely bonded groups such as families, members seek each other and evacuate as one. This makes attachment bonds necessary in the development of realistic models of mobility during crises. This article presents a review of evacuation behaviour, theories on social attachment, crisis mobility, and agent-based models. It was found that social attachment influences mobility in the different stages of evacuation (pre, during and post). Based on these findings, a multi-agent model of mobility during seismic crises (SOLACE) is being developed, and it is implemented using the belief, desire and intention (BDI) agent architecture.

scholarly journals Combining social network analysis and agent-based modelling to explore dynamics of human interaction: A review

2020 ◽  
Vol 2 ◽  
pp. 16325 ◽  
Author(s):  
Meike Will ◽  
Jürgen Groeneveld ◽  
Karin Frank ◽  
Birgit Müller
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Human Interaction ◽  
Extensive Literature ◽  
Agent Based Models ◽  
Agent Based ◽  
Agent Based Modelling ◽  
The Impact

Agent-based modelling (ABM) and social network analysis (SNA) are both valuable tools for exploring the impact of human interactions on a broad range of social and ecological patterns. Integrating these approaches offers unique opportunities to gain insights into human behaviour that neither the evaluation of social networks nor agent-based models alone can provide. There are many intriguing examples that demonstrate this potential, for instance in epidemiology, marketing or social dynamics. Based on an extensive literature review, we provide an overview on coupling ABM with SNA and evaluating the integrated approach. Building on this, we identify current shortcomings in the combination of the two methods. The greatest room for improvement is found with regard to (i) the consideration of the concept of social integration through networks, (ii) an increased use of the co-evolutionary character of social networks and embedded agents, and (iii) a systematic and quantitative model analysis focusing on the causal relationship between the agents and the network. Furthermore, we highlight the importance of a comprehensive and clearly structured model conceptualization and documentation. We synthesize our findings in guidelines that contain the main aspects to consider when integrating social networks into agent-based models.

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