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from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 1 Workshop 13 ; Agent-based models have recently gained in popularity for modelling military operations. They purposefully leave out detailed physical attributes of the military entities concerned if this is not expected to have any bearing on the study at hand. This allows scenarios to be run relatively fast, over many excursions in order to discover unique situations or tactics where friendly forces can achieve dominance over an enemy. Another key feature of agent-based models is that, although the one-to-one interaction between various agents and their environment may be quite simple, the combined effect of many agents interacting can lead to complicated group dynamics and emergent behaviour. In this regard, agent-based models have the potential to represent the more chaotic and intangible aspects of military conflicts.

Tesis Doctoral le?da en la Universidad Rey Juan Carlos de Madrid en 2013. Director de la Tesis: Sascha Ossowski ; Governments around the globe are heavily investing in upgrading the ageing infrastructure of the electricity grid. The imperative for this is driven primarily by regulatory requirements and the high cost of inefficiently delivering energy. The infrastructure is continuously improving, as more and more smart meters are installed, coupled with the proliferation of controllable loads and distributed generation. However, network operators, utilities, as well as end-consumers and small-scale producers are struggling to extract value from such systems and are exploring new ways for optimizing the performance of their deployed assets. This thesis introduces a multiagent approach for modelling the emerging complexity of the energy industry. The multiagent system paradigm is an ideal candidate for delivering a framework that captures the inherent distributed and dynamic nature of smart grids. While the traditionally centralized management of the system becomes less viable in the context of distributed generation and controllable loads, the underlying thread of this thesis advocates the design and implementation of coordination mechanisms capable to integrate and manage a large-scale integration of such devices via agent-based control. We begin by proposing dynamic micro-grids, a new conceptual organization of the network, adequate to integrate today?s traditional users into an interactive, internetlike system, in the sense that power flow will become bidirectional and energy management will become distributed in the grid due to the many actors involved in the operation of the system. The mechanisms proposed for micro-grid formation are oriented towards producing sub-systems of the grid that are exhibiting reduced transmission losses and an efficient utilization of renewables, as well as endowing the system with self-adaptation techniques for coping with dynamic environments. We further aim to enhance the ...

Land use activity is a major issue and challenge for town and country planners. Modelling and managing urban growth is a complex problem. Cities are now recognised as complex, non-linear and dynamic process systems. The design of a system that can handle these complexities is a challenging prospect. Local governments that implement urban growth models need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban uses within the boundary. There are so many negative implications related to the type of inappropriate urban development, such as increased traffic and demand for mobility, reduced landscape attractiveness, land use fragmentation, loss of biodiversity and alterations of the hydrological cycle. The aim of this study was to use an agent-based model as a powerful tool for simulating urban growth patterns. Our study area was Sanandaj city located in the west of Iran. Landsat imageries acquired in 2000 and 2006 were used. The dataset used included distance to principle roads, distance to residential areas, elevation, slope, distance to green spaces and distance to region centres, land price and distance to fault. In this study, an appropriate methodology for urban growth modelling using satellite remotely sensed data was presented and evaluated. Percent correct match (PCM), figure of merit and kappa statistics were used to evaluate the simulation results. DOI: http://dx.doi.org/10.5755/j01.erem.71.2.11504

Transparency is an important factor in democratic societies composed of characteristics such as accessibility, usability, informativeness, understandability and auditability. In this research we focus on auditability since it plays an important role for citizens that need to understand and audit public information. Although auditability has been a subject of discussion when designing systems, there is a lack of systematization in its specification. We propose an approach to systematically add auditability requirements specification during the goal-oriented agent-based Tropos methodology. We used the Transparency Softgoal Interdependency Graph that captures the different facets of transparency while considering their operationalization. An empirical evaluation was conducted through the design and implementation of LawDisTrA system that distributes lawsuits among judges in an appellate court. Experiments included the distribution of over 300,000 lawsuits at the Brazilian Superior Labor Court. We theorize that the presented approach for auditability provides adequate techniques to address the cross-organizational nature of transparency.

Many agent-based models (ABMs) try to explain large-scale phenomena by reducing them to behaviors at lower scales. At these scales in social systems are functional groups such as households, religious congregations, coops and local governments. The intra-group dynamics of functional groups often generate inefficient or unexpected behavior that cannot be predicted by modeling groups as basic units. We introduce a framework for modeling intra-group decision-making and its interaction with social norms, using the household as our focus. We select phenomena related to women's empowerment in agriculture as examples influenced by both intra-household dynamics and gender norms. Our framework proves more capable of replicating these phenomena than two common types of ABMs. We conclude that it is not enough to build multi-scale models; explaining social behaviors entails modeling intra-scale dynamics.

An agent-based tool for micro-level simulation of transport chains (TAPAS) is described. It is more powerful than traditional approaches as it is able to capture the interactions between individual actors of a transport chain, as well as their heterogeneity and decision making processes. Whereas traditional approaches rely on assumed statistical correlation between different parameters, TAPAS relies on causality, i.e., the decisions and negotiations that lead to the transports being performed. An additional advantage is that TAPAS is able to capture time aspects, such as, the influence of timetables, arrival times, and time-differentiated taxes and fees. TAPAS is composed of two layers, one layer simulating the physical activities taking place in the transport chain, e.g., production, storage, and transports of goods, and another layer simulating the different actors' decision making processes and interaction. The decision layer is implemented as a multi-agent system using the JADE platform, where each agent corresponds to a particular actor. We demonstrate the use of TAPAS by investigating how the actors in a transport chain are expected to act when different types of governmental control policies are applied, such as, fuel taxes, road tolls, and vehicle taxes. By analyzing the costs and environmental effects, TAPAS provides guidance in decision making regarding such control policies. We argue that TAPAS may also complement existing approaches in different ways, for instance by generating input data such as transport demand. Since TAPAS models a larger part of the supply chain, the transport demand is a natural part of the output. Studies may concern operational decisions like choice of consignment size and frequency of deliveries, as well as strategic decisions like where to locate storages, terminals, etc., choice of producer, and adaptation of vehicle fleets.

AbstractWe discuss the emerging new opportunity for building feedback‐rich computational models of social systems using generative artificial intelligence. Referred to as generative agent‐based models (GABMs), such individual‐level models utilize large language models to represent human decision‐making in social settings. We provide a GABM case in which human behavior can be incorporated into simulation models by coupling a mechanistic model of human interactions with a pre‐trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful dynamic models of various social systems that include realistic human reasoning and decision‐making. © 2024 System Dynamics Society.

International audience ; The global consequences of climate change calls for adoption of new technologies across all sectors of society. However, it is important to know how fast new technologies are adopted to reach the political climate goals. This paper presents a method for designing agent-based simulations for studying technology adoption. The method includes the design of simulation population, agent adoption logic, and external factors influencing agents' adoption decisions. A case study of the Danish commercial greenhouses' adoption of a smart energy solution is investigated. The result is presented with Roger's technology adoption curve which can be used to demonstrate the desired adoption rate by regulating different simulation parameters. The adoption rate of 50% with the return on the investment of 3 and 5 years is investigated. The greenhouse segment of growing pot plants with an area of more than 20,000 m2 is found to be the innovators. The simulation result shows that the population categories of the 'innovators', 'early adopters', and 'early majority' don't change between the return on investment time of 3 and 5 years. Furthermore, the relation of the return on investment time and the initial cost is close to linear. Hence, a lower/higher initial cost will result in a shorter/longer return on investment time.

International audience ; The global consequences of climate change calls for adoption of new technologies across all sectors of society. However, it is important to know how fast new technologies are adopted to reach the political climate goals. This paper presents a method for designing agent-based simulations for studying technology adoption. The method includes the design of simulation population, agent adoption logic, and external factors influencing agents' adoption decisions. A case study of the Danish commercial greenhouses' adoption of a smart energy solution is investigated. The result is presented with Roger's technology adoption curve which can be used to demonstrate the desired adoption rate by regulating different simulation parameters. The adoption rate of 50% with the return on the investment of 3 and 5 years is investigated. The greenhouse segment of growing pot plants with an area of more than 20,000 m2 is found to be the innovators. The simulation result shows that the population categories of the 'innovators', 'early adopters', and 'early majority' don't change between the return on investment time of 3 and 5 years. Furthermore, the relation of the return on investment time and the initial cost is close to linear. Hence, a lower/higher initial cost will result in a shorter/longer return on investment time.

International audience ; The global consequences of climate change calls for adoption of new technologies across all sectors of society. However, it is important to know how fast new technologies are adopted to reach the political climate goals. This paper presents a method for designing agent-based simulations for studying technology adoption. The method includes the design of simulation population, agent adoption logic, and external factors influencing agents' adoption decisions. A case study of the Danish commercial greenhouses' adoption of a smart energy solution is investigated. The result is presented with Roger's technology adoption curve which can be used to demonstrate the desired adoption rate by regulating different simulation parameters. The adoption rate of 50% with the return on the investment of 3 and 5 years is investigated. The greenhouse segment of growing pot plants with an area of more than 20,000 m2 is found to be the innovators. The simulation result shows that the population categories of the 'innovators', 'early adopters', and 'early majority' don't change between the return on investment time of 3 and 5 years. Furthermore, the relation of the return on investment time and the initial cost is close to linear. Hence, a lower/higher initial cost will result in a shorter/longer return on investment time.

The impact of cultural processes on personal and social changes is one of the important research issues not only in contemporary social sciences but also for simulation of future development scenarios and evidence-based policy decision making. In the context of the theoretical concept of cultural values, based on the system theory and theory of social capital, the impact of cultural events could be analyzed and simulated by focussing on the construction/deconstruction of social capital, which takes place throughout the actor's cultural participation. The main goal of this research is the development of measuring metrics, and agent-based simulation model aimed at investigation of the social impact of cultural processes. This paper provides new insights of modeling the social capital changes in a society and its groups, depending on cultural participation. The proposed measurement metrics provide the measurement facility of three key components: actors, cultural events and events flow and social capital. It provides the initial proof of concept simulation results, - simplified agent-based simulation model showcase. The NetLogo MAS platform is used as a simulation environment.