Lessons learned on development and application of agent-based models of complex dynamical systems

Williams, Richard A.

doi:10.1016/j.simpat.2017.11.001

Cited by 27 publications

(13 citation statements)

References 64 publications

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“…Furthermore, one of the key strengths of the agent-based approach is that it allows us to model the complex non-deterministic and heterogeneous behaviours, which allows us to investigate the role of differences in chracteristics (e.g., demographic or behavioural) of individuals within social systems and develop testable hypotheses for new empirical research. See Williams [4] for lessons learned on development and application of ABMs of complex dynamical systems.…”

Section: Related Workmentioning

confidence: 99%

“…One of the main strengths of the systems approach to investigating complex systems, is that it focuses on three key properties when modelling the systems: 1) system structures, 2) system dynamics, and 3) system control [3] . Indeed, previous discussions around Agent-Based Modelling and Simulation (ABMS) advise that it can be used to test working theories of the underlying mechanics of component interactions within systems and their resulting dynamics [4] , [5] . As such, we believe that ABMS is the preferred approach to facilitate the micro-to-macro mapping of complex dynamical systems, in particular, social systems [6] .…”

Section: Introductionmentioning

confidence: 99%

“…We therefore believe that ABMS provides a low-cost computational approach for developing hypotheses of the real-world system. With ABMS having recently become accepted as a scientific instrument for investigation [7] , it is now imperative that agent-based models (ABMs) adhere to the scientific method through a principled approach to their design, development and use, to ensure they are fit-for-purpose [4] .…”

Section: Introductionmentioning

confidence: 99%

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User experiences using FLAME: A Case study modelling conflict in large enterprise system implementations

Williams

2021

Simulation Modelling Practice and Theory

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The complexity of systems now under consideration (be they biological, physical, chemical, social, etc), together with the technicalities of experimentation in the real-world and the non-linear nature of system dynamics, means that computational modelling is indispensible in the pursuit of furthering our understanding of complex systems. Agent-based modelling and simulation is rapidly increasing in its popularity, in part due to the increased appreciation of the paradigm by the non-computer science community, but also due to the increase in the usability, sophistication and number of modelling frameworks that use the approach. The Flexible Large-scale Agent-based Modelling Environment (FLAME) is a relatively recent addition to the list. FLAME was designed and developed from the outset to deal with massive simulations, and to ensure that the simulation code is portable across different scales of computing and across different operating systems. In this study, we report our experiences when using FLAME to model the development and propagation of conflict within large multi-partner enterprise system implementations, which acts as an example of a complex dynamical social system. We believe FLAME is an excellent choice for experienced modellers, who will be able to fully harness the capabilities that it has to offer, and also be competent in diagnosing and solving any limitations that are encountered. Conversely, because FLAME requires considerable development of instrumentation tools, along with development of statistical analysis scripts, we believe that it is not suitable for the novice modeller, who may be better suited to using a graphical user interface driven framework until their experience with modelling and competence in programming increases.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User experiences using FLAME: A Case study modelling conflict in large enterprise system implementations

Williams

2021

Simulation Modelling Practice and Theory

Self Cite

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“…Besides, one of the main strength of the agent-based approach is its capacity to model a huge number of agents, allowing to reproduce realworld systems in a limited computational time. All of these reasons explain why the field of agent-based modelling has gained a significant following in recent years (Williams, 2018).…”

Section: Introductionmentioning

confidence: 99%

To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

Micolier

Loubet

Taillandier

et al. 2019

Journal of Cleaner Production

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Life cycle assessment (LCA) has proven its worth in modelling the entire value chain associated with the production of goods and services. However, modelling the consumption system, such as the use phase of a product, remains challenging due to uncertainties in the socioeconomic context. Agent-based models (ABMs) can reduce these uncertainties by improving the consumption system modelling in LCA. So far, no systematic study is available on how ABM can contribute towards a behavior-driven modelling in LCA. This paper aims at filing this gap by reviewing all papers coupling both tools. A focus is carried out on 18 case studies which are analysed according to criteria derived from the four phases of LCA international standards. Criteria specific to agent-based models and the coupling of both tools, such as the type and degree of coupling, have also been selected. The results show that ABMs have been coupled to LCA in order to model foreground systems with too many uncertainties arising from a behaviour-driven use phase, local variabilities, emerging technologies, to explore scenarios and to support consequential modelling. Foreground inventory data have been mainly collected from ABM at the use phase. From this review, we identified the potential benefits from ABM at each LCA phase: (i) scenario exploration, (ii) foreground inventory data collection, (iii) temporal and/or spatial dynamics simulation, and (iv) data interpretation and communication. Besides, methodological guidance is provided on how to choose the type and degree of coupling during the goal and scope phase. Finally, challenging LCA areas of research that could benefit from the agent-based approach to include behaviour-driven dynamics at the inventory and impact assessment phase have been identified.

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“…Indeed, in the area of complex software systems, it has been extensively argued that dynamical systems modeling can be a powerful tool to analyze the behavior of complex systems [23], and several studies exist in that direction (e.g., [19]). SOTA commits to the above perspective, but it adopts a totally different endeavour.…”

Section: Introductionmentioning

confidence: 99%

Engineering Collectives of Self-driving Vehicles: The SOTA Approach

Abeywickrama

Mamei

Zambonelli

2018

Lecture Notes in Computer Science

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Future cities will be populated by myriads of autonomous self-driving vehicles. Although individual vehicles have their own goals to pursue in autonomy, they may also be part of a collective of vehicles, as in the case of a fleet of vehicles of a car sharing company. Accordingly, they may also be required to act in a coordinated way towards the achievement of specific collective goals, or to meet specific city-level objectives. This raises the issue of properly engineering the behavior of such collective of vehicles, by properly capturing their collective requirements also in consideration of their individual goals, and understanding which knowledge about the state of the collective they must be provided with. In this context, this paper shows how the SOTA model can be a very effective tool to support the engineering of self-driving vehicle collectives. SOTA, by bringing together the lessons of goal-oriented requirements engineering, context-aware systems, and dynamical systems modeling, has indeed the potential for acting as a general reference model to help tackle some key issues in the design and development of complex collective systems immersed in dynamic environments, as collectives of self-driving vehicles are.

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Lessons learned on development and application of agent-based models of complex dynamical systems

Cited by 27 publications

References 64 publications

User experiences using FLAME: A Case study modelling conflict in large enterprise system implementations

User experiences using FLAME: A Case study modelling conflict in large enterprise system implementations

To what extent can agent-based modelling enhance a life cycle assessment? Answers based on a literature review

Engineering Collectives of Self-driving Vehicles: The SOTA Approach

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