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This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field's impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka's law is reflected in the author's contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford's Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape. [ABSTRACT FROM AUTHOR]

One of the main pathways that cities are taking to reduce greenhouse gas emissions is the decarbonisation of the electricity supply in conjunction with the electrification of transport and heat services. Estimating these future electricity demands, greatly influenced by end-users' behaviour, is key for planning energy systems. In this context, support tools can help decision-makers assess different scenarios and interventions during the design of new planning guidelines, policies, and operational procedures. This paper presents a novel bottom-up decision support framework using an agent-based modelling and simulation approach to evaluate, in an integrated way, transport and heat electrification scenarios in urban areas. In this work, an open-source tool named SmartCityModel is introduced, where agents represent energy users with diverse sociodemographic and technical attributes. Based on agents' behavioural rules and daily activities, vehicle trips and building occupancy patterns are generated together with electric vehicle charging and building heating demands. A representative case study set in London, UK, is shown in detail, and a summary of more than ten other case studies is presented to highlight the flexibility of the framework to generate high-resolution spatiotemporal energy demand profiles in urban areas, supporting decision-makers in planning low-carbon and sustainable cities. [ABSTRACT FROM AUTHOR]

Prediction markets are heralded as powerful forecasting tools, but models that describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong to a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable of replicating the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, which is an exchange platform whose data have never been used before to validate models of opinion diffusion. [ABSTRACT FROM AUTHOR]

Urbanised city transportation simulation needs a wide range of factors to reflect the influence of certain real-life events accurately. The vehicle composition and the timing of the traffic light signal scheduling play an important role in controlling the traffic flow and facilitate road users, particularly in densely populated urban cities. Since road capacity in urban cities changes throughout the day, an optimal traffic light signal duration might be different. Hence, in this paper, the effect of vehicle composition and traffic light phases on traffic flow during peak and off-peak hours in Georgetown, Penang, one of the highly populated cities in Malaysia, is investigated. Through Agent-Based Modelling (ABM), this complex system is simulated by integrating the driver's behaviour into the model using the GIS and Agent-Based Modelling Architecture (GAMA) simulation platform. The result of predicted traffic flow varies significantly depending on the vehicle composition while the duration of the traffic signal timing has little impact on traffic flow during peak hours. However, during off-peak hour, it is suggested that 20 s duration of green light provides the highest flow compared to 30 s and 40 s duration of green light. This concludes that the planning for traffic light phasing should consider multiple factors since the vehicle composition and traffic light timing for an effective traffic flow varies according to the volume of road users. [ABSTRACT FROM AUTHOR]

Rising demand for optimized digital marketing strategies has led firms in a hunt to harvest every possible aspect indicating users’ experience and preference. People visit, regularly through the day, numerous websites using both desktop and mobile devices. For businesses to acknowledge device’s usage rates is extremely important. Thus, this research is focused on analyzing each device’s usage and their effect on airline firms’ digital brand name. In the first phase of the research, we gathered web data from 10 airline firms during an observation period of 180 days. We then proceeded in developing an exploratory model using Fuzzy Cognitive Mapping, as well as a predictive and simulation model using Agent-Based Modeling. We inferred that various factors of airlines’ digital brand name are affected by both desktop and mobile usage, with mobile usage having a slightly bigger impact on most of them, with gradually rising values. Desktop device usage also appeared to be quite significant, especially in traffic coming from referral sources. The paper’s contribution has been to provide a handful of time-accurate insights for marketeers, regarding airlines’ digital marketing strategies. [ABSTRACT FROM AUTHOR]

This literature review is dedicated to the subject of agent-based modelling for the system of international migration, and of the modelling of policies that are known to aid in its management. The reason for the selection of agent-based modelling as a framework for studying international migration is that the system of international migration presents the characteristics of a complex system: notably, its property of emergence, which therefore imposes the usage of a methodology for its modelling that is capable of reflecting its emergent traits. The policies that we study are those that intervene in the country of origin of emigrants and that are aimed at decreasing the aggregate volume of emigrants from that country. The reason for this choice is that policies in the countries of origin have become particularly attractive today, especially in European countries, under the assumption that it should be possible to prevent the migrants from reaching the point of destination of their journey if some kind of action is undertaken before the migrants arrive. We start by discussing the theoretical constraints that suggest how this approach may only partially be valid. Then, to assist the development of future agent-based models that study migration, we identify via topic mining the ten topics that are most commonly discussed in the literature on the application to the international migration of agent-based models; this lets us highlight the characteristics of an agent-based model that should be included when the research task relates to the usage of ABM to study international migration and its associated policies. Finally, we indicate why the existing literature on the modelling of international migration is missing a key aspect that is required to correctly model policies: the integration between agent-based approaches and systems dynamics. [ABSTRACT FROM AUTHOR]

Citizen sensing applications need to have a number of users defined that ensures their effectiveness. This is not a straightforward task because neither the relationship between the size of the userbase or its effectiveness is easily quantified, nor is it clear which threshold for the number of users would make the application 'effective'. This paper presents an approach for estimating the number of users needed for location-based crowdsourcing applications to work successfully, depending on the use case, the circumstances, and the criteria of success. It circumvents various issues, ethical or practical, in performing real-world controlled experiments and tackles this challenge by developing an agent-based modelling and simulation framework. This framework is tested on a specific scenario, that of missing children and the search for them. The search is performed with the contribution of citizens being made aware of the disappearance through a mobile application. The result produces an easily reconfigurable testbed for the effectiveness of citizen sensing mobile applications, allowing the study of the marginal utility of new users of the application. The resulting framework aims to be the digital twin of a real urban scenario, and it has been designed to be easily adapted and support decisions on the feasibility, evaluation, and targeting of the deployment of spatial crowdsourcing applications. [ABSTRACT FROM AUTHOR]

Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling's segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling's model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour. [ABSTRACT FROM AUTHOR]

Sustainability in cities, and its accurate and exhaustive assessment, represent a major keystone of environmental sciences and policy making in urban planning. This study aims to provide methods for a reproducible, descriptive, predictive and prescriptive analysis of urban residential choices and mobility, which are key components of an urban system's sustainability. Using the DPSIR framework for building agent evolution rules, we design an agent-based model of the canton of Geneva, Switzerland. The model leverages real geographical data for the canton of Geneva and its public transportation network. The resulting simulations show the dynamics of the relocation choices of commuters, in terms of the function of their travel time by public transportation to their workplace. Results show that areas around the city centre are generally preferred, but high rent prices and housing availability may prevent most residents from relocating to these areas. Other preferred housing locations are distributed around major tram and train lines and where rent prices are generally lower. The model and its associated tools are capable of spatialising aggregated statistical datasets, inferring spatial correlations, and providing qualitative and quantitative analysis of relocation dynamics. Such achievements are made possible thanks to the efficient visualisation of our results. The agent-based modelling methodology represents an adequate solution for understanding complex phenomena related to sustainability in urban systems, which can be used as guidance for policy making. [ABSTRACT FROM AUTHOR]

Water pricing is an economic instrument traditionally used to reduce water demand. However, its effective implementation requires knowledge of the extent to which users reduce water consumption with increasing water prices. The price elasticity of water demand has been estimated using econometric regression, which relies on cross-sectional and time-series water data. As an alternative, we propose the use of agent-based modelling, which does not require reliable historical data on water prices and consumption and enables the simulation of multiple scenarios with different consumer profiles, behaviour profiles and water price changes, thereby allowing comprehensive understanding of price elasticity estimates. To illustrate the potential use of agent-based modelling for the estimation of water demand price elasticity, we performed an empirical application to a residential area in Chile. Price elasticity estimates ranged from −0.0159 to −0.1036 (mean −0.0250), indicating that residential water consumption is inelastic to price changes. This result is consistent with previous findings. Agent-based modelling is an alternative for the ex-ante assessment of the potential effectiveness of water pricing policies intended to reduce residential water demand. [ABSTRACT FROM AUTHOR]

Multi-agent systems (MAS) allow and promote the development of distributed and intelligent applications in complex and dynamic environments. Applications of this kind have a crucial role in our everyday life, as witnessed by the broad range of domains they are deployed to—such as manufacturing, management sciences, e-commerce, biotechnology, etc. Despite heterogeneity, those domains share common requirements such as autonomy, structured interaction, mobility, and openness—which are well suited for MAS. Therein, in fact, goal-oriented processes can enter and leave the system dynamically and interact with each other according to structured protocols. This special issue gathers 17 contributions spanning from agent-based modelling and simulation to applications of MAS in situated and socio-technical systems. [ABSTRACT FROM AUTHOR]

Dynamic Stochastic General Equilibrium (DSGE) models are widely used as a tool for policy decision-making. These models lost their fame when they could not predict the crisis in 2008 and could not address policy problems afterward. Meanwhile, the Agent-Based Modelling (ABM) approach emerged as an alternative to DSGE models. Between 2000 and 2020, this study examined scholarly research on the topic of ABM in economics. The information is gathered using the SCOPUS database. Numerous bibliometric indicators are provided, including the total number of publications and citations. The study reveals that agent-based modelling in economics research has grown in recent years. The majority of active research occurs in countries such as the United States of America, and collaboration allows researchers to reach out to many more countries. ABM has the potential to be applied in a wide range of economic fields. ABM also necessitates research into its own development to be used to better understand economic phenomena. [ABSTRACT FROM AUTHOR]

Given massive events, such as demonstrations in coastal cities exposed to tsunamigenic earthquakes, it is essential to explore pedestrian motion methods to help at-risk coastal communities and stakeholders understand the current issues they face to enhance disaster preparedness. This research targets SDG 11 Sustainable Cities and Communities. It strengthens resilience in coastal areas by implementing a social force model using a microscopic agent-based model to assess the impact of human behaviour on evacuation performance by introducing evacuation stress levels due to a tsunami triggered in central Chile. Two scenarios with two environments and three crowd sizes are implemented in NetLogo. In Scenario 1, pedestrians walk at a relaxed velocity. In Scenario 2, tsunami evacuation stress is incorporated, resulting in pedestrians walking at a running velocity, taking, on average, four times less time to evacuate. We explored more realistic settings by considering the internal susceptibility of each agent to spread tsunami evacuation stress among other evacuees. Results from Scenario 2 show that internal susceptibility effects almost double the mean evacuation time for 200 agents. Findings suggest a trade-off between realism and the minimization of evacuation time. This research is considered a first step toward including stress in tsunami evacuations for sustainable evacuation planning. [ABSTRACT FROM AUTHOR]

The human brain is arguably the most complex "machine" to ever exist. Its detailed functioning is yet to be fully understood, let alone modelled. Neurological processes have logical signal-processing and biophysical aspects, and both affect the brain's structure, functioning and adaptation. Mathematical approaches based on both information and graph theory have been extensively used in an attempt to approximate its biological functioning, along with Artificial Intelligence frameworks inspired by its logical functioning. In this article, an approach to model some aspects of the brain learning and signal processing is presented, mimicking the metastability and backpropagation found in the real brain while also accounting for neuroplasticity. Several simulations are carried out with this model to demonstrate how dynamic neuroplasticity, neural inhibition and neuron migration can reshape the brain's logical connectivity to synchronise signal processing and obtain certain target latencies. This work showcases the importance of dynamic logical and biophysical remodelling in brain plasticity. Combining mathematical (agents, graph theory, topology and backpropagation) and biomedical ingredients (metastability, neuroplasticity and migration), these preliminary results prove complex brain phenomena can be reproduced—under pertinent simplifications—via affordable computations, which can be construed as a starting point for more ambitiously accurate simulations. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are increasing the interdependence of transportation policies and the electricity market dimension. In this paper, an Electricity Market Model with Electric Vehicles (EMMEV) was developed, exploiting an agent-based model that analyzes how carbon reduction policy in transportation may increase the number of Electric Vehicles and how that would influence electricity price. Agents are Energy Service Providers (ESCOs) which can distribute fuels and their objective is to maximize their profit. In this paper, the EMMEV is used to analyze the impacts of the Low-Carbon Fuel Standard (LCFS), a performance-based policy instrument, on electricity prices and EV sales volume. The agents in EMMEV are regulated parties in LCFS should meet a certain Carbon Intensity (CI) target for their distributed fuel. In case they cannot meet the target, they should buy credits to compensate for their shortfall and if they exceed it, they can sell their excess. The results, considering the assumptions and limitations of the model, show that the banking strategy of the agents contributing in the LCFS might have negative impact on penetration of EVs, unless there is a regular Credit Clearance to trade credits. It is also shown that the electricity price, as a result of implementing the LCFS and increasing number of EVs, has increased between 2% and 3% depending on banking strategy. [ABSTRACT FROM AUTHOR]

On-demand multi-passenger shared transport options are increasingly being promoted as an influential strategy to reduce traffic congestion and emissions and improve the convenience and travel experience for passengers. These services, often referred to as on-demand public transport, are aimed at meeting personal travel demands through the use of shared vehicles that run on flexible routes using advanced tools for dynamic scheduling. This paper presents an agent-based traffic simulation model that was developed to evaluate the performance of on-demand public transport and compare it with existing scheduled bus services using a case study of the inner city of Melbourne in Australia. The key performance measures used in the comparative evaluation included quality of service and passenger experience in terms of waiting times, the efficiency of service and operations in terms of hourly vehicle utilization, and system efficiency in terms of trip completion rates, passenger kilometers travelled and total passenger trip times. The results showed significant benefits for passengers who use on-demand bus services compared to scheduled bus services. The on-demand bus service was found to reduce average total passenger waiting times by 89% during the Morning Peak; by 78% during the Mid-Day period; by 81% during the Afternoon Peak; and by more than 95% during other periods of the day. From an operator's perspective, the on-demand services were found to achieve around 70% vehicle utilization rates during peak hours compared to a utilization rate not exceeding 16% for the scheduled bus services. Even during off-peak periods, the occupancies for on-demand services were almost twice the vehicle occupancies for scheduled bus services. In terms of system efficiency, the on-demand services achieved a trip completion rate of 85% compared to a trip completion rate of 67% for the scheduled bus services. The total passenger-kilometers travelled was similar for both scheduled and on-demand bus services, which refutes claims that on-demand bus services induce more kilometers of travel. The trip completion times were around 55% shorter for on-demand bus services compared to scheduled services, which represents a significant saving in travel time for users. Finally, the paper presents average emissions per completed trip for both types of services and shows a significant reduction in emissions for on-demand services compared to conventional bus services. These include, on average, a 48% reduction in CO2 emissions per trip; 82% reduction in NO emissions per trip; and 41% reduction in p.m.10 emissions per trip. These findings clearly demonstrate the superior benefits of on-demand bus services compared to scheduled bus services. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has raised many questions on how to manage an epidemiological and economic crisis around the world. Since the beginning of the COVID-19 pandemic, scientists and policy makers have been asking how effective lockdowns are in preventing and controlling the spread of the virus. In the absence of vaccines, the regulators lacked any plausible alternatives. Nevertheless, after the introduction of vaccinations, to what extent the conclusions of these analyses are still valid should be considered. In this paper, we present a study on the effect of vaccinations within the dynamic stochastic general equilibrium model with an agent-based epidemic component. Thus, we validated the results regarding the need to use lockdowns as an efficient tool for preventing and controlling epidemics that were obtained in November 2020. [ABSTRACT FROM AUTHOR]

The integration of early warning and evacuation systems (EWES) with estimations for mitigating the loss of life in flood risk assessments marks an advancement towards developing robust emergency action plans for dam breaks. Through the simulation of diverse EWES scenarios, the impact of these systems, coupled with community preparedness, on minimising the potential for loss of life could be calculated. This study was conducted in the theoretical context of a dam break located upstream from a densely populated urban region in Brazil. Hydrodynamic and agent-based models were utilised to estimate potential loss of life across various scenarios and simulations. The Monte Carlo approach, in combination with the LifeSim model, was applied to assess how factors such as warning issuance timing, evacuation strategies and community responses impact the model's outcomes. Sensitivity analysis was performed considering the overall exposed area and specific areas at risk for different spatiotemporal EWES strategies. The results of simulations highlighted the EWES' great potential for risk mitigation and displayed optimal times for warning issuance. The warning diffusion and the protective action initiation parameters proved crucial for improving EWES. The spatiotemporal sectorisation of the alert and evacuation was also an effective strategy to optimise EWES. This methodology should allow for further similar tests and incite EWES improvements based on consistent loss of life alleviation simulations. [ABSTRACT FROM AUTHOR]

Modified opioid agonist therapy (OAT) guidelines that were initially introduced during the COVID-19 pandemic allow prescribers to increase the number of take-home doses to fulfill their need for physical distancing and prevent treatment discontinuation. It is crucial to evaluate the consequence of administering higher take-home doses of OAT on treatment retention and opioid-related harms among OAT recipients to decide whether the new recommendations should be retained post-pandemic. This study used an agent-based model to simulate individuals dispensed daily or weekly OAT (methadone or buprenorphine/naloxone) with a prescription over a six-month treatment period. Within the model simulation, a subset of OAT recipients was deemed eligible for receiving increased take-home doses of OAT at varying points during their treatment time course. Model results demonstrated that the earlier dispensing of increased take-home doses of OAT were effective in achieving a slightly higher treatment retention among OAT recipients. Extended take-home doses also increased opioid-related harms among buprenorphine/naloxone-treated individuals. The model results also illustrated that expanding naloxone availability within OAT patients' networks could prevent these possible side effects. Therefore, policymakers may need to strike a balance between expanding access to OAT through longer-duration take-home doses and managing the potential risks associated with increased opioid-related harms. [ABSTRACT FROM AUTHOR]

In the modern digitalised era, the total number of businesses and organisations utilising crowdsourcing services has risen, leading to an increase of their website traffic. In this way, there is plenty of space for marketers and strategists to capitalise big data from both their own and the crowdsourcer's websites. This can lead to a comprehension of factors affecting their brand name, sustainability (gross profit) and consequently visitor influence. The first of the three staged contexts, based on web data, includes the retrieval of web data analytics and metrics from five air forwarding and five crowdsourcing websites in 210 observation days. At stage two, we deployed a diagnostic-exploratory model, through Fuzzy Cognitive Mapping (FCM), and in the last stage, an Agent-Based Model is deployed for data prediction and simulation. We concluded that crowdsourcing referral traffic increases air forwarders' top 3 keywords volume, and decreases social traffic and total keywords volume, which then boosts their global web rank and gross profit. The exact opposite results occur with crowdsourcing search traffic. To sum up, the contribution of this paper is to offer realistic and well-informed insights to marketers about SEO and SEM strategies for brand name and profit enhancement, based on harvesting crowdsourcing platform traffic. [ABSTRACT FROM AUTHOR]

The airplane turnaround time costs money to the airline companies and, over the years, it has been determined that the best way to reduce it is by using efficient boarding strategies. Many boarding strategies have been proposed but a consensus as to which is the best method has not been reached yet. The aim of this paper is to gather and test all these strategies considering the same initial conditions in order to help airline companies identify the best boarding strategy. Minimizing the costs not only contributes to airlines’ sustainability and long-term performance, but also influences their ticketing policy, and has an impact on their customers’ choices. Moreover, airports can benefit from the fact that the airline companies are optimizing their boarding strategies as they can extend the number of services offered to more airlines during one day without investing in new infrastructure. Thus, this paper considers 24 boarding strategies and builds a configurable agent-based model using NetLogo 6.0.2 to perform different simulations. The results are analysed using grey systems theory considering the aircraft dimensions, aircraft occupancy and whether or not passengers are carrying hand luggage that may need storage. [ABSTRACT FROM AUTHOR]

The significant progress in scientific research and innovation has led to the need for a new paradigm to legitimise the innovation process in society and politics. The European Union, with the Horizon 2020 framework program and Horizon Europe, institutionalises this change by defining the concept of responsible research and innovation (RRI), aiming at greater inclusiveness and sustainability in the research and innovation processes. This paper aimed to present an agent-based model (ABM) to simulate the dynamics between the different actors that cooperate within networks during the innovation process, taking the inclinations toward RRI practices into account. The different types of agent, their characteristics, and the different strategies that they follow have been formulated within the Horizon 2020 project I AM RRI-Webs of Innovation Value Chains (IVCs) of Additive Manufacturing (AM) under consideration of RRI. Besides, some experiments are reported to validate the model, ensuring its rigor and making our model a useful tool for policymakers, assisting them in defining strategic guidelines for disseminating and encouraging RRI best practices and defining the critical factors of the innovative cooperative process. [ABSTRACT FROM AUTHOR]

The classification and recycling of household waste becomes a major issue in today's urban environmental protection and domestic waste disposal. Although various policies promoting household waste classification have been introduced, the recovery rate failed to reach the expected result. Existing studies on incentive policies for household waste recycling tried to integrate subjective and objective factors in human behavior decisions. To explore how effective interventions can promote household waste classification in communities, this article developed an Agent-Based Model (ABM) based on Theory of Planned Behavior (TPB) to simulate the participation of households under eight different policy scenarios. The result shows that: monetary incentive is most effective in inducing participation, while social norms have different impacts on household decision under different policy intervention. Under policy stimulus, the participation rate of garbage sorting increased from 18% to 76%. This model has been applied into an online community-based participatory virtual simulation 3D system, which aims to help university students better understand how policies affect household recycling behaviors, which end up affecting the environment. [ABSTRACT FROM AUTHOR]

In this paper, we present an agent-based model for exploring the interplay of basic structural and socio-cognitive factors and conventional water saving measures in the evolution of domestic water use in metropolitan areas. Using data of Barcelona, we discuss three scenarios that involve plausible demographic and cultural trends. Results show that, in the three scenarios, aggregate outcomes are consistent with available conventional modelling (while total water use grows, per capita water use declines); however, the agent-based simulation also reveals, for each scenario, the different dynamics of simple policy measures with population growth, cultural trends and social influence; thus providing unexpected insights for policy design. [ABSTRACT FROM AUTHOR]

Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: 'attribute,' 'actor,' and 'autonomous agent'. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3 × 3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: 'association,' 'intervention,' and 'counterfactual'. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step. [ABSTRACT FROM AUTHOR]

This paper presents an agent-based model (ABM) for residential end-users, which is part of a larger, interdisciplinary co-simulation framework that helps to investigate the performance of future power distribution grids (i.e., smart grid scenarios). Different modes of governance (strong, soft and self-organization) as well as end-users' heterogeneous behavior represent key influential factors. Feedback was implemented as a measure to foster grid-beneficial behavior, which encompasses a range of monetary and non-monetary incentives (e.g., via social comparison). The model of frame selection (MFS) serves as theoretical background for modelling end-users' decision-making. Additionally, we conducted an online survey to ground the end-user sub-model on empirical data. Despite these empirical and theoretical foundations, the model presented should be viewed as a conceptual framework, which requires further data collection. Using an example scenario, representing a lowly populated residential area (167 households) with a high share of photovoltaic systems (30%), different modes of governance were compared with regard to their suitability for improving system stability (measured in cumulated load). Both soft and strong control were able to decrease overall fluctuations as well as the mean cumulated load (by approx. 10%, based on weekly observation). However, we argue that soft control could be sufficient and more societally desirable. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has raised numerous questions concerning the shape and range of state interventions the goals of which are to reduce the number of infections and deaths. The lockdowns, which have become the most popular response worldwide, are assessed as being an outdated and economically inefficient way to fight the disease. However, in the absence of efficient cures and vaccines, there is a lack of viable alternatives. In this paper we assess the economic consequences of the epidemic prevention and control schemes that were introduced in order to respond to the COVID-19 pandemic. The analyses report the results of epidemic simulations that were obtained using the agent-based modelling methods under the different response schemes and their use in order to provide conditional forecasts of the standard economic variables. The forecasts were obtained using the dynamic stochastic general equilibrium model (DSGE) with the labour market component. [ABSTRACT FROM AUTHOR]

Due to an ever-increasing movement of containers across the globe in line with the economic boom, the trade imbalance and issues related to empty containers have become inevitable. Empty containers accumulated at specific ports cannot only generate profit but also increase the environmental footprint. This research proposes a Maritime Empty Container Reposition Modelling Framework by integrating the agent-based modelling (ABM) paradigm to model the global movements of empty containers. An agent-based maritime logistic empty container redistribution model was developed to help minimize the total relevant costs for empty container movement in the planning horizon. In the system, ports, shipping companies, customers, and empty containers were identified as critical agents. Using simulated annealing (SA), shipping line agents were able to optimise empty container repositioning to determine the best sequence for moving containers. The model was applied to the Asia–Middle East region to simulate global empty containers repositioning in the region. The results comparison shows that the proposed optimised empty container repositioning framework can significantly reduce the shipping line's costs and make full use of empty containers. [ABSTRACT FROM AUTHOR]

Landscape patterns and composition were identified as key drivers of fire risk and fire regimes. However, few studies have focused on effective policymaking aimed at encouraging landowners to diversify the landscape and make it more fire-resilient. We propose a new framework to support the design of wildfire mitigation policies aimed at promoting low-risk fire regimes based on land use/land cover choices by landowners. Using the parishes of a fire-prone region in central Portugal as analysis units, a two-step modelling approach is proposed, coupling an agent-based model that simulates land use/land cover choice and a logistic model that predicts fire regimes from a set of biophysical variables reported as important fire regime drivers in the literature. The cost-effectiveness of different policy options aimed at promoting low-risk fire regimes at the parish level is assessed. Our results are in line with those of previous studies defending the importance of promoting landscape heterogeneity by reducing forest concentration and increasing agricultural or shrubland areas as a measure to reduce the risk of wildfire. Results also suggest the usefulness of the framework as a policy simulation tool, allowing policymakers to investigate how annual payments supporting agricultural or shrubland areas, depending on the policy mix, can be very cost-effective in removing a substantial number of parishes from high-risk fire regimes. [ABSTRACT FROM AUTHOR]

Industrial Symbiosis (IS) is a collaborative cross-sectoral approach to connect the resource supply and demand of various industries in order to optimize the resource use through exchange of materials, energy, water and human resources across different companies, while generating ecological, technical, social and economic benefits. One of the main goals of IS is the set-up of advanced circular/cascading systems, in which the energy and material flows are prolonged for multiple utilization within industrial systems in order to increase resource productivity and efficiency, while reducing the environmental load. Many Information Communication Technology (ICT) tools have been developed to facilitate IS, but they predominantly focus on the as-is analysis of the IS system, and do not consider the development of a common desired target vision or corresponding possible future scenarios as well as conceivable transformation paths from the actual to the defined (sustainability) target state. This gap shall be addressed in this paper, presenting the software requirements engineering results for a holistic IT-supported IS tool covering system analysis, transformation simulation and goal-setting. This new approach goes beyond system analysis and includes the use of expert systems, system dynamics and Artificial Intelligence (AI) techniques, which turn the IT-supported IS tool to be developed into a comprehensive and holistic instrument with which future scenarios and transformation paths can be simulated. [ABSTRACT FROM AUTHOR]

Even though biomass is characterised as renewable energy, it produces anthropogenic greenhouse gas (GHG) emissions, especially from biomass logistics. Lifecycle assessment (LCA) is used as a tool to quantify the GHG emissions from logistics but in the past the majority of LCAs have been steady-state and linear, when in reality, non-linear and temporal aspects (such as weather conditions, seasonal biomass demand, storage capacity, etc.) also have an important role to play. Thus, the objective of this paper was to optimise the environmental sustainability of forest biomass logistics (in terms of GHG emissions) by introducing the dynamic aspects of the supply chain and using the geographical information system (GIS) and agent-based modelling (ABM). The use of the GIS and ABM adds local conditions to the assessment in order to make the study more relevant. In this study, GIS was used to investigate biomass availability, biomass supply points and the road network around a large-scale combined heat and power plant in Naantali, Finland. Furthermore, the temporal aspects of the supply chain (e.g., seasonal biomass demand and storage capacity) were added using ABM to make the assessment dynamic. Based on the outcomes of the GIS and ABM, a gate-to-gate LCA of the forest biomass supply chain was conducted in order to calculate GHG emissions. In addition to the domestic biomass, we added imported biomass from Riga, Latvia to the fuel mixture in order to investigate the effect of sea transportation on overall GHG emissions. Finally, as a sensitivity check, we studied the real-time measurement of biomass quality and its potential impact on overall logistical GHG emissions. According to the results, biomass logistics incurred GHG emissions ranging from 2.72 to 3.46 kg CO2-eq per MWh, depending on the type of biomass and its origin. On the other hand, having 7% imported biomass in the fuel mixture resulted in a 13% increase in GHG emissions. Finally, the real-time monitoring of biomass quality helped save 2% of the GHG emissions from the overall supply chain. The incorporation of the GIS and ABM helped in assessing the environmental impacts of the forest biomass supply chain in local conditions, and the combined approach looks promising for developing LCAs that are inclusive of the temporal aspects of the supply chain for any specific location. [ABSTRACT FROM AUTHOR]

Assessment of welfare effects of macroprudential policy seems the most important application of the Dynamic Stochastic General Equilibrium (DSGE) framework of macro-modelling. In particular, the DSGE-3D model, with three layers of default (3D), was developed and used by the European Systemic Risk Board and European Central Bank as a reference tool to formally model the financial cycle as well as to analyze effects of macroprudential policies. Despite the extreme importance of incorporating financial constraints in Real Business Cycle (RBC) models, the resulting DSGE-3D construct still embraces the representative agent idea, making serious analyses of diversity of economic entities impossible. In this paper, we present an alternative to DSGE modelling that seriously departs from the assumption of the representativeness of agents. Within an Agent Based Modelling (ABM) framework, we build an environment suitable for performing counterfactual simulations of the impact of macroprudential policy on the economy, financial system and society. We contribute to the existing literature by presenting an ABM model with broad insight into heterogeneity of agents. We show the stabilizing effects of macroprudential policies in the case of economic or financial distress. [ABSTRACT FROM AUTHOR]

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods. [ABSTRACT FROM AUTHOR]

An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling. [ABSTRACT FROM AUTHOR]

Synchromodal transport incorporates real-time events in a dynamic manner in order to facilitate the most suitable selection of modes, routes and handling points. Up until now, current assessments rely on analytical models. Most of these models average distances for barges and trains via route mapping platforms that provide realistic distances for road only. To reflect on real-world developments more accurately, new thinking and modelling approaches are necessary to bridge academic models with physical transport processes. This paper introduces a computational model which computes movements of agents in geographically referenced space. The model captures stochastic parallel processes for each mode, and simulates decentralized delivery performance of each order in terms of cost, time and emissions at an operational level. Furthermore, we study the routing of individual orders and their responsiveness to disruptions. Computational experiments are performed within a case study which concerns imports of retail goods by unimodal truck transport from France to Belgium. Our findings show that dynamic synchromodal solutions cope with disturbances better, but unnecessary deviations and pro-activeness can also lead to negative effects when compared to static intermodal solutions [ABSTRACT FROM AUTHOR]

Seat and aisle interferences are assumed to be linked with a prolonged boarding time along with several other aspects related to airplane boarding such as: luggage handling, luggage distribution inside the cabin, number of passengers, passengers' physique characteristics, group behavior, seat selection, aircraft occupancy, aircraft design, etc. Based on these assumptions, a series of proposed boarding methods, underlying their efficiency starting from the absence or limited presence of these types of interferences, are proposed. The present paper aims to analyze whether the different types of seat or aisle interferences do matter for the overall boarding time by considering 24 boarding methods proposed in the literature. A series of specific elements related to interferences, such as: the average waiting time, the average number of interferences based on their types, and the average number of interference-affected passengers, have been considered. Also, the presence of multiple interferences in different parts of the aircraft has been analyzed in order to offer a complete picture of the considered situation. An agent-based model in NetLogo 6.0.4, fed with values form field trials within the literature is created and used for simulations, which enables the agents to act like real passengers involved in an airplane boarding process. [ABSTRACT FROM AUTHOR]

As the complexity, breadth of expertise and number of agents involved in megaprojects grow, collaborative innovation models become invaluable for helping to achieve sustainable project development. On this basis, the purpose of this study is to explore the innovation output mechanisms used for collaborative innovation in megaproject (CIMP) systems by the agent-based modelling (ABM) approach, and to promote the efficiency and effectiveness of organisational collaborative innovation through variable controls. A multi-agent simulation CIMP model was developed using the NetLogo tool. The model encompasses the behavioural factors and interaction rules that affect organisational CIMP. Four simulations were conducted, and the results showed that (1) the innovation environment, including policy environment, cultural climate, and engineering demand, has a positive effect on the output rate of CIMP; (2) a larger scale of innovative network organisation accelerates innovation output; (3) innovative organisations must avoid enforcing high standards for cooperation, communication, and recognition abilities when selecting partner organisations; (4) innovative organisations' ability to absorb technology, information, and knowledge is positively related to output, while an increase in behavioural costs reduces the scale of innovative network organisations, thereby affecting their output. This study developed its CIMP theory from the perspective of organisational behaviour. The findings are expected to provide methodological and practical guidance for the selection of innovation agents, behavioural patterns, and for guaranteeing efficient innovation in collaborative megaproject organisations. [ABSTRACT FROM AUTHOR]

In recent years, interest in non-meat diets has been growing at an exponential rate in many countries. There is a wide consensus now that increased meat consumption is linked to higher health risks and environmental impact. Yet humans are social animals. Even the very personal decision of whether to eat meat or not is influenced by others around them. Using data from the British Social Attitude Survey, we develop an agent-based model to study the effect of social influence on the spread of meat-eating behaviour in the British population. We find that social influence is crucial in determining the spread of different meat-eating behaviours. According to the model, in order to bring about large-scale changes in meat-eating behaviours at the national level, people need to (1) have a strong openness to influences from others who have different meat-eating behaviour and (2) have a weak tendency to reinforce their current meat-eating behaviour after observing others in their own social group sharing the same behaviour. [ABSTRACT FROM AUTHOR]

Subscription decision in the telecom market is quite complex and cumbersome, invoking decision inertia in consumers and resulting in suboptimal choices. We implemented choice inertia and consumer interaction as an agent-based model to better understand the process. The model illustrates that with adequate peer interactions with active consumers, inactive consumers could overcome their inertia significantly and switch to a better alternative. Furthermore, the newly converted active consumers influenced their inert neighbors as a ripple effect. Active consumers contribute to firm profits and healthy market competition. Moreover, in environments with low neighborhood effects and a stronger inertia threshold, firms are able to maintain profits by retaining inert consumers. We show that apart from the attractiveness of market offerings, firms can benefit from understanding consumer inertia and devising means to reduce it. [ABSTRACT FROM AUTHOR]

Boarding is one of the major processes of airplane turnaround time, with a direct influence on the airline companies' costs. From a sustainable point of view, a faster completion of the boarding process has impact not only on the airline company's long-term performance, but also on customers' satisfaction and on the airport's possibility of offering more services without additional investments in new infrastructure. Considering the airplane boarding strategies literature, it can be observed that the latest papers are dealing with developing faster boarding strategies, most of them considering boarding using just one-door of the aircraft. Even though boarding on one-door might be feasible for the airports having the needed infrastructure and sufficient jet-bridges, the situation is different in European airports, as the use of apron buses is fairly common. Moreover, some of the airline companies have adapted their boarding pass in order to reflect which door one should board once they get down from the bus. While using these buses, the boarding strategies developed in the literature are hard to find their applicability. Thus, a new method for boarding on two-door airplanes when apron buses are used is proposed and tested against the actual boarding method. A model is created in NetLogo 6.0.4, taking advantages of the agent-based modeling and used for simulations. The results show a boarding time reduction of 8.91%. [ABSTRACT FROM AUTHOR]

An agent is an autonomous computer system situated in an environment to fulfill a design objective. Multi-Agent Systems aim to solve problems in a flexible and robust way by assembling sets of agents interacting in cooperative or competitive ways for the sake of possibly common objectives. Multi-Agent Systems have been applied to several domains ranging from many industrial sectors, e-commerce, health and even entertainment. Agent-Based Modeling, a sort of Multi-Agent Systems, is a technique used to study complex systems in a wide range of domains. A natural or social system can be represented, modeled and explained through a simulation based on agents and interactions. Such a simulation can comprise a variety of agent architectures like reactive and cognitive agents. Despite cognitive agents being highly relevant to simulate social systems due their capability of modelling aspects of human behaviour ranging from individuals to crowds, they still have not been applied extensively. A challenging and socially relevant domain are the Disaster-Rescue simulations that can benefit from using cognitive agents to develop a realistic simulation. In this paper, a Multi-Agent System applied to the Disaster-Rescue domain involving cognitive agents based on the Belief–Desire–Intention architecture is presented. The system aims to bridge the gap in combining Agent-Based Modelling and Multi-Agent Systems approaches by integrating two major platforms in the field of Agent-Based Modeling and Belief-Desire Intention multi-agent systems, namely, NetLogo and Jason. [ABSTRACT FROM AUTHOR]

System dynamics and agent-based simulation modelling approaches have a potential as tools to evaluate the impact of policy related decision making in food value chains. The context is that a food value chain involves flows of multiple products, financial flows and decision making among the food value chain players. Each decision may be viewed from the level of independent actors, each with their own motivations and agenda, but responding to externalities and to the behaviours of other actors. The focus is to show how simulation modelling can be applied to problems such as fairness and power asymmetries in European food value chains by evaluating the outcome of interventions in terms of relevant operational indicators of interorganisational fairness (e.g., profit distribution, market power, bargaining power). The main concepts of system dynamics and agent-based modelling are introduced and the applicability of a hybrid of these methods to food value chains is justified. This approach is outlined as a research agenda, and it is demonstrated how cognitive maps can help in the initial conceptual model building when implemented for specific food value chains studied in the EU Horizon 2020 VALUMICS project. The French wheat to bread chain has many characteristics of food value chains in general and is applied as an example to formulate a model that can be extended to capture the functioning of European FVCs. This work is to be further progressed in a subsequent stream of research for the other food value chain case studies with different governance modes and market organisation, in particular, farmed salmon to fillet, dairy cows to milk and raw tomato to processed tomato. [ABSTRACT FROM AUTHOR]

In recent years, the research on collaborative networks has been pointing to the need to put more emphasis on the social interactions of its participants, along with technical features, as a potential direction to finding solutions to prevent failures and potential conflicts. In this context, a modelling framework called Collaborative EMOtion modelling framework (C-EMO), conceived for appraising the collaborative network emotions that might be present in a collaborative networked environment, is presented, and an implementation approach, based on system dynamics and agent-based simulation modelling techniques, for estimating both the collaborative network emotional state and each member's emotions, is described. The work is divided in two parts: the first considers the design of the models and the second comprises the transformation of these conceptual models into a computer model, providing the proposed simulation model. In order to validate the simulation model, and taking into consideration the novelty of the research area, experiments are undertaken in different scenarios representing several aspects of a collaborative environment and a sensitivity analysis and discussion of the results is performed. [ABSTRACT FROM AUTHOR]

As technology advances towards new paradigms such as the Internet of Things, there is a desire among business leaders for a reliable method to determine the value of supporting these ventures. Traditional simulation and analysis techniques cannot model the complex systems inherent in fields such as infrastructure asset management, or suffer from a lack of data on which to build a prediction. Agent-based modelling, through an integration with data science, presents an attractive simulation method to capture these underlying complexities and provide a solution. The aim of this work is to investigate this integration as a refined process for answering practical business questions. A specific case study is addressed to assess the return on investment of installing condition monitoring sensors on lift assets in a London Underground station. An agent-based model is developed for this purpose, supported by analysis from historical data. The simulation results demonstrate how returns can be achieved and highlight features induced as a result of stochasticity in the model. Suggestions of future research paths are additionally outlined. [ABSTRACT FROM AUTHOR]

Conventionally, agent-based modelling approaches start from a conceptual model capturing the theoretical understanding of the systems of interest. Simulation outcomes are then used "at the end" to validate the conceptual understanding. In today's data rich era, there are suggestions that models should be data-driven. Data-driven workflows are common in mathematical models. However, their application to agent-based models is still in its infancy. Integration of real-time sensor data into modelling workflows opens up the possibility of comparing simulations against real data during the model run. Calibration and validation procedures thus become automated processes that are iteratively executed during the simulation. We hypothesize that incorporation of real-time sensor data into agent-based models improves the predictive ability of such models. In particular, that such integration results in increasingly well calibrated model parameters and rule sets. In this contribution, we explore this question by implementing a flocking model that evolves in real-time. Specifically, we use genetic algorithms approach to simulate representative parameters to describe flight routes of homing pigeons. The navigation parameters of pigeons are simulated and dynamically evaluated against emulated GPS sensor data streams and optimised based on the fitness of candidate parameters. As a result, the model was able to accurately simulate the relative-turn angles and step-distance of homing pigeons. Further, the optimised parameters could replicate loops, which are common patterns in flight tracks of homing pigeons. Finally, the use of genetic algorithms in this study allowed for a simultaneous data-driven optimization and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Emerging sensor networks (ESNs) are an inevitable trend with the development of the Internet of Things (IoT), and intend to connect almost every intelligent device. Therefore, it is critical to study resource allocation in such an environment, due to the concern of efficiency, especially when resources are limited. By viewing ESNs as multi-agent environments, we model them with an agent-based modelling (ABM) method and deal with resource allocation problems with market models, after describing users' patterns. Reinforcement learning methods are introduced to estimate users' patterns and verify the outcomes in our market models. Experimental results show the efficiency of our methods, which are also capable of guiding topology management. [ABSTRACT FROM AUTHOR]

Understanding subsurface hydrocarbon migration is a crucial task for petroleum geoscientists. Hydrocarbons are released from deeply buried and heated source rocks, such as shales with a high organic content. They then migrate upwards through the overlying lithologies. Some hydrocarbon becomes trapped in suitable geological structures that, over a geological timescale, produce viable hydrocarbon reservoirs. This work investigates how intelligent agent models can mimic these complex natural subsurface processes and account for geological uncertainty. Physics-based approaches are commonly used in petroleum system modelling and flow simulation software to identify migration pathways from source rocks to traps. However, the problem with these simulations is that they are computationally demanding, making them infeasible for extensive uncertainty quantification. In this work, we present a novel dynamic screening tool for secondary hydrocarbon migration that relies on agent-based modelling. It is fast and is therefore suitable for uncertainty quantification, before using petroleum system modelling software for a more accurate evaluation of migration scenarios. We first illustrate how interacting but independent agents can mimic the movement of hydrocarbon molecules using a few simple rules by focusing on the main drivers of migration: buoyancy and capillary forces. Then, using a synthetic case study, we validate the usefulness of the agent modelling approach to quantify the impact of geological parameter uncertainty (e.g., fault transmissibility, source rock location, expulsion rate) on potential hydrocarbon accumulations and migrations pathways, an essential task to enable quick de-risking of a likely prospect. [ABSTRACT FROM AUTHOR]

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. However, it is a largely complex and versatile methodology which hinders its broader uptake by researchers as well as improved results. 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. Hence, this work contributes to better understanding and further improving the research on transition towards decarbonised society. [ABSTRACT FROM AUTHOR]

To achieve the climate goals of the Paris Agreement, greenhouse gas emissions from the electricity sector must be substantially reduced. We develop an agent-based model of the electricity system with heterogeneous agents who invest in power generating capacity under uncertainty. The heterogeneity is characterised by the hurdle rates the agents employ (to manage risk) and by their expectations of the future carbon prices. We analyse the impact of the heterogeneity on the transition to a low carbon electricity system. Results show that under an increasing CO2 tax scenario, the agents start investing heavily in wind, followed by nuclear and to some extent in natural gas fired power plants both with and without carbon capture and storage as well as biogas fired power plants. However, the degree to which different technologies are used depend strongly on the carbon tax expectations and the hurdle rate employed by the agents. Comparing to the case with homogeneous agents, the introduction of heterogeneity among the agents leads to a faster CO2 reduction. We also estimate the so called "cannibalisation effect" for wind and find that the absolute value of wind does not drop in response to higher deployment levels, but the relative value does decline. [ABSTRACT FROM AUTHOR]