scholarly journals Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling

2018 ◽  
Vol 53 (9) ◽  
pp. 560-569 ◽  
Author(s):  
Adam Hulme ◽  
Jason Thompson ◽  
Rasmus Oestergaard Nielsen ◽  
Gemma J M Read ◽  
Paul M Salmon
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Computational Modelling ◽  
Sports Injury ◽  
Physical Workload ◽  
Complex Nature ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Starting Point ◽  
Injury Research

ObjectivesThere have been recent calls for the application of the complex systems approach in sports injury research. However, beyond theoretical description and static models of complexity, little progress has been made towards formalising this approach in way that is practical to sports injury scientists and clinicians. Therefore, our objective was to use a computational modelling method and develop a dynamic simulation in sports injury research.MethodsAgent-based modelling (ABM) was used to model the occurrence of sports injury in a synthetic athlete population. The ABM was developed based on sports injury causal frameworks and was applied in the context of distance running-related injury (RRI). Using the acute:chronic workload ratio (ACWR), we simulated the dynamic relationship between changes in weekly running distance and RRI through the manipulation of various ‘athlete management tools’.ResultsThe findings confirmed that building weekly running distances over time, even within the reported ACWR ‘sweet spot’, will eventually result in RRI as athletes reach and surpass their individual physical workload limits. Introducing training-related error into the simulation and the modelling of a ‘hard ceiling’ dynamic resulted in a higher RRI incidence proportion across the population at higher absolute workloads.ConclusionsThe presented simulation offers a practical starting point to further apply more sophisticated computational models that can account for the complex nature of sports injury aetiology. Alongside traditional forms of scientific inquiry, the use of ABM and other simulation-based techniques could be considered as a complementary and alternative methodological approach in sports injury research.

scholarly journals Organisational plasticity: can we really model human–agent behaviours?

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dale Richards
Keyword(s):  
Sudden Change ◽  
Computational Modelling ◽  
Human Behaviour ◽  
Complex Nature ◽  
Key Factors ◽  
Content Type ◽  
Agent Based ◽  
Human Agent ◽  
External Pressures ◽  
Behaviour Models

PurposeThe ability for an organisation to adapt and respond to external pressures is a beneficial activity towards optimising efficiency and increasing the likelihood of achieving set goals. It can also be suggested that this very ability to adapt to one's surroundings is one of the key factors of resilience. The nature of dynamically responding to sudden change and then to return to a state that is efficient may be termed as possessing the characteristic of plasticity. Uses of agent-based systems in assisting in organisational processes may have a hand in facilitating an organisations' plasticity, and computational modelling has often been used to try and predict both agent and human behaviour. Such models also promise the ability to examine the dynamics of organisational plasticity through the direct manipulation of key factors. This paper discusses the use of such models in application to organisational plasticity and in particular the relevance to human behaviour and perception of agent-based modelling. The uses of analogies for explaining organisational plasticity is also discussed, with particular discussion around the use of modelling. When the authors consider the means by which the authors can adopt theories to explain this type of behaviour, models tend to focus on aspects of predictability. This in turn loses a degree of realism when we consider the complex nature of human behaviour, and more so that of human–agent behaviour.Design/methodology/approachThe methodology and approach used for this paper is reflected in the review of the literature and research.FindingsThe use of human–agent behaviour models in organisational plasticity is discussed in this paper.Originality/valueThe originality of this paper is based on the importance of considering the human–agent-based models. When compared to agent-based model approaches, analogy is used as a narrative in this paper.

scholarly journals Situating agent-based modelling in population health research

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Eric Silverman ◽  
Umberto Gostoli ◽  
Stefano Picascia ◽  
Jonatan Almagor ◽  
Mark McCann ◽  
...  
Keyword(s):  
Complex Systems ◽  
Population Health ◽  
Health Research ◽  
Intervention Development ◽  
Epidemiological Methods ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Complex Systems Theory ◽  
Current State ◽  
Core Concepts

AbstractToday’s most troublesome population health challenges are often driven by social and environmental determinants, which are difficult to model using traditional epidemiological methods. We agree with those who have argued for the wider adoption of agent-based modelling (ABM) in taking on these challenges. However, while ABM has been used occasionally in population health, we argue that for ABM to be most effective in the field it should be used as a means for answering questions normally inaccessible to the traditional epidemiological toolkit. In an effort to clearly illustrate the utility of ABM for population health research, and to clear up persistent misunderstandings regarding the method’s conceptual underpinnings, we offer a detailed presentation of the core concepts of complex systems theory, and summarise why simulations are essential to the study of complex systems. We then examine the current state of the art in ABM for population health, and propose they are well-suited for the study of the ‘wicked’ problems in population health, and could make significant contributions to theory and intervention development in these areas.

scholarly journals Agent-based modelling using naming game for language evolution studies

2021 ◽  
Vol 127 ◽  
pp. 01014
Author(s):  
Alexander Ioilyevich Ilyinsky ◽  
Galina Vladimirovna Klimova ◽  
Evgeniy Sergeevich Smakhtin ◽  
Marina Aleksandrovna Amurskaya ◽  
Ekaterina Yurievna Rozhina
Keyword(s):  
Language Evolution ◽  
Computational Modelling ◽  
Foreign Languages ◽  
Contemporary Society ◽  
Research Papers ◽  
Naming Game ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Vast Literature ◽  
Language Emergence

The article describes approaches to applying agent-based modelling and, particularly, the case of Naming Game, in linguistic studies and within teaching foreign languages. Computational modelling implementation has become a comprehensive and ambitious field of research, as its methods are applicable to solving tasks set within various aspects of contemporary society and science. The main purpose of this paper is to perform an analysis of Naming Game implementation in language emergence and evolution studies. To achieve this purpose we set several tasks: to present a vast literature review on agent-based modelling in linguistics and other adjacent sciences; to give an overview and description of the Naming Game; to perform simulations within the Naming Game and present their outcomes. As the main methodology the article uses simulations. The paper concludes that a clear hysteresis effect is present in the dependence of the size of the population vocabulary from the size of vocabulary of its average agent. At the point where the population vocabulary transitions into the uniform distribution the average agent’s vocabulary reaches saturation and plateaus. Those dynamics also change as the population vocabulary grows and declines. Agent-based modelling is a relatively novel direction for linguistics with a modest number of research papers. Results, presented in the paper, give a fresh angle on the issues of language emergence and evolution.

scholarly journals Validation techniques of agent based modelling for geospatial simulations

2014 ◽  
Vol XL-2/W3 ◽  
pp. 91-95 ◽  
Author(s):  
M. Darvishi ◽  
G. Ahmadi
Keyword(s):  
Modelling And Simulation ◽  
Human Cognition ◽  
Complex Nature ◽  
Agent Based ◽  
The Real ◽  
Agent Based Modelling ◽  
Emergence Patterns ◽  
Wide Range ◽  
Geospatial Modelling ◽  
Gis Software

One of the most interesting aspects of modelling and simulation study is to describe the real world phenomena that have specific properties; especially those that are in large scales and have dynamic and complex behaviours. Studying these phenomena in the laboratory is costly and in most cases it is impossible. Therefore, Miniaturization of world phenomena in the framework of a model in order to simulate the real phenomena is a reasonable and scientific approach to understand the world. Agent-based modelling and simulation (ABMS) is a new modelling method comprising of multiple interacting agent. They have been used in the different areas; for instance, geographic information system (GIS), biology, economics, social science and computer science. The emergence of ABM toolkits in GIS software libraries (e.g. ESRI’s ArcGIS, OpenMap, GeoTools, etc) for geospatial modelling is an indication of the growing interest of users to use of special capabilities of ABMS. Since ABMS is inherently similar to human cognition, therefore it could be built easily and applicable to wide range applications than a traditional simulation. But a key challenge about ABMS is difficulty in their validation and verification. Because of frequent emergence patterns, strong dynamics in the system and the complex nature of ABMS, it is hard to validate and verify ABMS by conventional validation methods. Therefore, attempt to find appropriate validation techniques for ABM seems to be necessary. In this paper, after reviewing on Principles and Concepts of ABM for and its applications, the validation techniques and challenges of ABM validation are discussed.

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