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.

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.

Agent-Based Modeling of Flood Insurance Futures

2020 ◽  
Author(s):  
Linda Geaves
Keyword(s):  
Autonomous Agents ◽  
Insurance Industry ◽  
Model Development ◽  
Policy Instruments ◽  
Flood Insurance ◽  
Agent Based Models ◽  
Online Data ◽  
Agent Based ◽  
Artificial Intelligence Technology ◽  
The Impact

Agent-based models have facilitated greater understanding of flood insurance futures, and will continue to advance this field as modeling technology develops further. As the pressures of climate-change increase and global populations grow, the insurance industry will be required to adapt to a less predictable operating environment. Complicating the future of flood insurance is the role flood insurance plays within a state, as well as how insurers impact the interests of other stakeholders, such as mortgage providers, property developers, and householders. As such, flood insurance is inextricably linked with the politics, economy, and social welfare of a state, and can be considered as part of a complex system of changing environments and diverse stakeholders. Agent-based models are capable of modeling complex systems, and, as such, have utility for flood insurance systems. These models can be considered as a platform in which the actions of autonomous agents, both individuals and collectives, are simulated. Cellular automata are the lowest level of an agent-based model and are discrete and abstract computational systems. These automata, which operate within a local and/or universal environment, can be programmed with characteristics of stakeholders and can act independently or interact collectively. Due to this, agent-based models can capture the complexities of a multi-stakeholder environment displaying diversity of behavior and, concurrently, can cater for the changing flood environment. Agent-based models of flood insurance futures have primarily been developed for predictive purposes, such as understanding the impact of introductions of policy instruments. However, the ways in which these situations have been approached by researchers have varied; some have focused on recreating consumer behavior and psychology, while others have sought to recreate agent interactions within a flood environment. The opportunities for agent-based models are likely to become more pronounced as online data becomes more readily available and artificial intelligence technology supports model development.

scholarly journals Case studies, cross-site comparisons, and the challenge of generalization: comparing agent-based models of land-use change in frontier regions

2008 ◽  
Vol 3 (1) ◽  
pp. 41-72 ◽  
Author(s):  
Dawn C. Parker ◽  
Barbara Entwisle ◽  
Ronald R. Rindfuss ◽  
Leah K. Vanwey ◽  
Steven M. Manson ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Case Studies ◽  
Agent Based Models ◽  
Agent Based ◽  
Cross Site

scholarly journals Agent-Based Modelling of Urban District Energy System Decarbonisation – A Systematic Literature Review

2021 ◽  
Author(s):  
Ardak Akhatova ◽  
Lukas Kranzl ◽  
Fabian Schipfer ◽  
Charitha Buddhika Heendeniya
Keyword(s):  
Urban Areas ◽  
Energy Transition ◽  
Energy System ◽  
Social Characteristics ◽  
Solar Panels ◽  
Urban District ◽  
Agent Based ◽  
Agent Based Modelling ◽  
District Energy ◽  
Industrial Energy

There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

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.

Analytic Versus Computational Cognitive Models: Agent-Based Modeling as a Tool in Cognitive Sciences

2019 ◽  
Vol 28 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Jens Koed Madsen ◽  
Richard Bailey ◽  
Ernesto Carrella ◽  
Philipp Koralus
Keyword(s):  
Complex Systems ◽  
Closed Form ◽  
Model Development ◽  
Cognitive Models ◽  
System Level ◽  
Model Parameters ◽  
Social Psychological ◽  
Agent Based Models ◽  
Agent Based ◽  
Closed Form Solutions

Computational cognitive models typically focus on individual behavior in isolation. Models frequently employ closed-form solutions in which a state of the system can be computed if all parameters and functions are known. However, closed-form models are challenged when used to predict behaviors for dynamic, adaptive, and heterogeneous agents. Such systems are complex and typically cannot be predicted or explained by analytical solutions without application of significant simplifications. In addressing this problem, cognitive and social psychological sciences may profitably use agent-based models, which are widely employed to simulate complex systems. We show that these models can be used to explore how cognitive models scale in social networks to calibrate model parameters, to validate model predictions, and to engender model development. Agent-based models allow for controlled experiments of complex systems and can explore how changes in low-level parameters impact the behavior at a whole-system level. They can test predictions of cognitive models and may function as a bridge between individually and socially oriented models.

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