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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.

While neorealism and world-system theory both claim to be "structural" theories of international relations, they embody very different understandings of system structure and structural explanation. Neorealists conceptualize system structures in individualist terms as constraining the choices of preexisting state agents, whereas world-system theorists conceptualize system structures in structuralist terms as generating state agents themselves. These differences stem from what are, in some respects, fundamentally opposed solutions to the "agent-structure" or "micromacro" problem. This opposition, however, itself reflects a deeper failure of each theory to recognize the mutually constitutive nature of human agents and system structures—a failure which leads to deep-seated inadequacies in their respective explanations of state action. An alternative solution to the agent-structure problem, adapted from "structuration theory" in sociology, can overcome these inadequacies by avoiding both the reduction of system structures to state actors in neorealism and their reification in world-system theory. Structuration theory requires a philosophical basis in scientific realism, arguably the "new orthodoxy" in the philosophy of natural science, but as yet largely unrecognized by political scientists. The scientific realist/structuration approach generates an agenda for "structural-historical" research into the properties and dispositions of both state actors and the system structures in which they are embedded.

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.

Neorealism and world-system theory both claim to be 'structural' theories of international relations, yet they embody very different understandings of system structure and structural explanation. These differences stem from fundamentally opposed solutions to the 'agent-structure' or 'micro-macro' problem. An alternative solution to the agent-structure problem, adapted from 'structuration theory' in sociology, can overcome these inadequacies. Discusses its approach. (Abstract amended)

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.

[EN] In multi-agent systems, agents perform complex tasks that require different levels of intelligence and give rise to interactions among them. From these interactions, conflicts of opinion can arise, especially when these systems become open, with heterogeneous agents dynamically entering or leaving the system. Therefore, agents willing to participate in this type of system will be required to include extra capabilities to explicitly represent and generate agreements on top of the simpler ability to interact. Furthermore, agents in multiagent systems can form societies, which impose social dependencies on them. These dependencies have a decisive influence in the way agents interact and reach agreements. Argumentation provides a natural means of dealing with conflicts of interest and opinion. Agents can reach agreements by engaging in argumentation dialogues with their opponents in a discussion. In addition, agents can take advantage of previous argumentation experiences to follow dialogue strategies and persuade other agents to accept their opinions. Our insight is that case-based reasoning can be very useful to manage argumentation in open multi-agent systems and devise dialogue strategies based on previous argumentation experiences. To demonstrate the foundations of this suggestion, this paper presents the work that we have done to develop case-based dialogue strategies in agent societies. Thus, we propose a case-based argumentation framework for agent societies and define heuristic dialogue strategies based on it. The framework has been implemented and evaluated in a real customer support application. ; This work is supported by the Spanish Government Grants [CONSOLIDER-INGENIO 2010 CSD2007-00022, and TIN2012-36586-C03-01] and by the GVA project [PROMETEO 2008/051]. ; Heras Barberá, SM.; Jordán Prunera, JM.; Botti, V.; Julian Inglada, VJ. (2013). Case-Based strategies for argumentation dialogues in agent societies. Information Sciences. 223:1-30. https://doi.org/10.1016/j.ins.2012.10.007 ; S ; 1 ; 30 ; 223

In recent years, UK governments have implemented policies that emphasise the ability of parents to choose which school they wish their child to attend. Inherently spatial school-place allocation rules in many areas have produced a geography of inequality between parents that succeed and fail to get their child into preferred schools based upon where they live. We present an agent-based simulation model developed to investigate the implications of distance-based school-place allocation policies. We show how a simple, abstract model can generate patterns of school popularity, performance and spatial distribution of pupils which are similar to those observed in local education authorities in London, UK. The model represents 'school' and 'parent' agents. Parental 'aspiration' to send their child to the best performing school (as opposed to other criteria) is a primary parent agent attribute in the model. This aspiration attribute is used as a means to constrain the location and movement of parent agents within the modelled environment. Results indicate that these location and movement constraints are needed to generate empirical patterns, and that patterns are generated most closely and consistently when schools agents differ in their ability to increase pupil attainment. Analysis of model output for simulations using these mechanisms shows how parent agents with above-average – but not very high – aspiration fail to get their child a place at their preferred school more frequently than other parent agents. We highlight the kinds of alternative school-place allocation rules and education system policies the model can be used to investigate.