Multi‐method modelling: AnyLogic

Borshchev, Andrei

doi:10.1002/9781118762745.ch12

Cited by 200 publications

(158 citation statements)

References 1 publication

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“…System dynamics modelling methods were created in the 1950s by Jay Forrester in the field of engineering. System dynamics modelling utilises feedback loops (causal loop diagrams) and stock (accumulations) and flow diagrams to represent complex systems [ 6 , 23 , 72 ]. This modelling method represents the dynamics of the system at a high level of abstraction [ 6 ], making them an efficient form of modelling in terms of computing resources.…”

Section: Methodsmentioning

confidence: 99%

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Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol

Freebairn

Atkinson

Kelly

et al. 2016

Health Res Policy Sys

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BackgroundEvidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making.Methods/DesignThe study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants’ engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change.DiscussionThe study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings.

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Section: Methodsmentioning

confidence: 99%

“…Agent-based modelling (ABM) methods have been developed more recently and allow for representation of individuals or agents within the system. The model can be built from the ground up by defining agents, their behaviours and their interactions [ 6 , 72 ]. ABM is a computational method used to examine the actions of agents (e.g.…”

Section: Methodsmentioning

confidence: 99%

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol

Freebairn

Atkinson

Kelly

et al. 2016

Health Res Policy Sys

View full text Add to dashboard Cite

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“…A full list of commands can be found in the Extensions section of the NetLogo Manual. Another extension allows NetLogo to be interfaced with the R statistical computing platform (Thiele and Grimm 2010), which has packages that can be used to perform many common techniques in spatial analysis, as well as some more rarefied ones (Bivand et al 2013).…”

Section: Interacting With Gis Datamentioning

confidence: 99%

Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3

Davies¹,

Romanowska²,

Harris³

et al. 2019

Preprint

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Archaeologists are using spatial data in increasingly sophisticated analyses and invoking more explicit considerations of space in their interpretations. Geographic information systems (GIS) have become standard technology for professional archaeologists in the collection and management of spatial data. Many calls have been made to develop and adapt digital geospatial technologies for interpretation and understanding past social dynamics, but this has been limited to some extent by the static nature of map-oriented GIS approaches. Here, we illustrate how coupling GIS with agent-based modeling (ABM) can assist with more dynamic explorations of past uses of space and geospatial phenomena.

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“…It provides flexibility of incorporating several other modelling approach such as Discrete Event Simulation (DES), System Dynamics (SD), Analytical Modelling or Mathematical Optimisation in the agent or in the parallel simulation. With such flexibility, different components of ABM can be modelled in comprehensive manner [9]. For instance, model like SparkCity where several users charge their electric vehicles with different charging strategies which involves certain constraints can be modelled with mathematical programming in to an agent.…”

Section: Agent-based Modellingmentioning

confidence: 99%

Assessing the Monetary Value of Vehicle-to-Grid Considering Battery Degradation: Agent-Based Approach

Raj¹

2019

Preprint

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Electrification of the transport sector is increasing the electricity demand in the energy system. On the other hand, penetration of renewable energy sources is increasing the intermittency in the power system, posing the challenge in the real-time balancing of the electricity supply and demand. With V2G or bi-directional charging, it is possible to link both the system in a way that can provide synergistic benefits to both. In V2G research, system benefits have been studied thoroughly in the past compared to EV user benefits of V2G services. Moreover, research on EV user benefits lacks the socio-technical approach that takes into account user behaviour, social values, and business models.Considering above mentioned research gaps, in this thesis, V2G monetary benefits are researched for the EV users in the Netherlands. The study specifically focuses on EV users trading at spot market in the Netherlands while providing V2G storage (arbitrage). Moreover, simplistic battery degradation cost and lifetime economic analysis have also taken into consideration for the calculation of V2G net benefits. This research has been conducted under SparkCity agent-based model framework, which has been developed at the Eindhoven University of Technology and currently maintained by Zenmo simulations in the Netherlands.Agent-based modelling approach comprises the heterogeneous behaviours and characteristics of the agents (EV users), their collective influence on the spot market and individual agent’s V2G benefits which are affected by the global change in prices of the spot market. V2G charging strategy has been compared with baseline charging strategy and cost-based charging strategy for the comparison of the benefits. EV users with diverse characteristics in the neighbourhood have been compared to study the effects of individual characteristics on monetary benefits. Simulation has been run for three different years with varying EV adoption rate and renewable energy penetration into the energy system.Results show V2G as a promising solution for the energy system with high renewable and EVs. It can provide a tremendous amount of flexibility and storage to the system and offset the storage costs into the future energy system. However, V2G benefits for EV users under spot market are lower comparing to other market mechanisms. It might be less attractive for users to provide storage for a more extended period with comparably less profit gain. Moreover, battery degradation costs are higher taking into consideration current battery prices. However, with more PV installation in the renewable mix, the potential to gain benefits during the day time increase significantly.

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Multi‐method modelling: AnyLogic

Cited by 200 publications

References 1 publication

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol

Combining Geographic Information Systems and Agent-Based Models in Archaeology: Part 2 of 3

Assessing the Monetary Value of Vehicle-to-Grid Considering Battery Degradation: Agent-Based Approach

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