arXiv subjects

Önder Gürcan

Publications and source records attributed to Önder Gürcan.

At least 19 recordsLinked to original sources

arXiv2026

A Role-Based Multi-Agent Model for Climate Adaptation Deliberation Across Living Labs

Climate governance processes involve complex interactions between heterogeneous citizens, advocacy groups, media actors, and political decision-makers. While agent-based models (ABMs) have been widely used to study environmental policy and socio-ecological systems, many existing approaches focus either on institutional dynamics or individual behavioural mechanisms in isolation. This paper presents a modular multi-level agent-based architecture that integrates empirically grounded cognitive decision models with strategic institutional behaviour within a unified simulation framework. The architecture combines (i) motive-based individual decision-making operationalised through the HUMAT and MOA frameworks, (ii) socially embedded influence processes via demographic homophily networks, and (iii) institutional strategy modules for environmental non-governmental organisations (NGOs), media agents, and politicians. Political decisions emerge from the aggregation of multiple signals, including expert input, public mobilisation, party alignment, and media framing. The model is designed to be empirically calibrated through synthetic populations derived from survey data and and institutional parameters informed through Living Lab stakeholder engagement, and to support scenario-based exploration of climate-relevant land-use governance processes. Rather than presenting empirical results, this paper focuses on the architectural design principles, modular structure, and integration logic of the model. We discuss how this multi-layered approach contributes to the modelling of democratic climate governance and outline pathways for generalization and future validation.

cs.MA↗

arXiv2026

A Multi-Level Agent-Based Architecture for Climate Governance Integrating Cognitive and Institutional Dynamics

cs.CY↗

arXiv2026

LLM-enabled Social Agents

Large Language Models (LLMs) have transformed agent-agent and human-agent interaction by enabling software, physical, and simulation agents to communicate and deliberate through natural language. Yet fluent language use does not by itself yield socially intelligible behaviour. Most current systems remain weakly grounded in roles, norms, intentions, and contextual constraints, limiting their capacity for meaningful participation in social environments. This paper develops a conceptual baseline for LLM-enabled social agents by arguing that they should be grounded in role definitions operationalized through persona descriptions. On this basis, we outline research directions for representation, hybrid control, and evaluation. The paper concludes that persona-based role definitions are a necessary foundation for turning language competence into social behaviour.

cs.MA↗

arXiv2025

Anemoi: A Semi-Centralized Multi-agent System Based on Agent-to-Agent Communication MCP server from Coral Protocol

Recent advances in generalist multi-agent systems (MAS) have largely followed a context-engineering plus centralized paradigm, where a planner agent coordinates multiple worker agents through unidirectional prompt passing. While effective under strong planner models, this design suffers from two critical limitations: (1) strong dependency on the planner's capability, which leads to degraded performance when a smaller LLM powers the planner; and (2) limited inter-agent communication, where collaboration relies on prompt concatenation rather than genuine refinement through structured discussions. To address these challenges, we propose Anemoi, a semi-centralized MAS built on the Agent-to-Agent (A2A) communication MCP server from Coral Protocol. Unlike traditional designs, Anemoi enables structured and direct inter-agent collaboration, allowing all agents to monitor progress, assess results, identify bottlenecks, and propose refinements in real time. This paradigm reduces reliance on a single planner, supports adaptive plan updates, and minimizes redundant context passing, resulting in more scalable execution. Evaluated on the GAIA benchmark, Anemoi achieved 52.73% accuracy with a small LLM (GPT-4.1-mini) as the planner, surpassing the strongest open-source baseline OWL (43.63%) by +9.09% under identical LLM settings. Our implementation is publicly available at https://github.com/Coral-Protocol/Anemoi.

cs.MA↗

arXiv2025

Agent-Based Exploration of Recommendation Systems in Misinformation Propagation

This study uses agent-based modeling to examine the impact of various recommendation algorithms on the propagation of misinformation on online social networks. We simulate a synthetic environment consisting of heterogeneous agents, including regular users, bots, and influencers, interacting through a social network with recommendation systems. We evaluate four recommendation strategies: popularity-based, collaborative filtering, and content-based filtering, along with a random baseline. Our results show that popularity-driven algorithms significantly amplify misinformation, while item-based collaborative filtering and content-based approaches are more effective in limiting exposure to fake content. Item-based collaborative filtering was found to perform better than previously reported in related literature. These findings highlight the role of algorithm design in shaping online information exposure and show that agent-based modeling can be used to gain realistic insight into how misinformation spreads.

cs.MA↗

arXiv2025

Coral Protocol: Open Infrastructure Connecting The Internet of Agents

Coral Protocol is an open and decentralized collaboration infrastructure that enables communication, coordination, trust and payments for The Internet of Agents. It addresses the growing need for interoperability in a world where organizations are deploying multiple specialized AI agents that must work together across domains and vendors. As a foundational platform for multi-agent AI ecosystems, Coral establishes a common language and coordination framework allowing any agent to participate in complex workflows with others. Its design emphasizes broad compatibility, security, and vendor neutrality, ensuring that agent interactions are efficient and trustworthy. In particular, Coral introduces standardized messaging formats for agent communication, a modular coordination mechanism for orchestrating multi-agent tasks, and secure team formation capabilities for dynamically assembling trusted groups of agents. Together, these innovations position Coral Protocol as a cornerstone of the emerging "Internet of Agents," unlocking new levels of automation, collective intelligence, and business value through open agent collaboration.

cs.MA↗

arXiv2025

Towards an ABM on Proactive Community Adaptation for Climate Change

We present an agent-based model (ABM) simulating proactive community adaptation to climate change in an urban context. The model is applied to Bergen, Norway, represented as a complex socio-ecological system. It integrates multiple agent types: municipal government (urban planners and political actors), civil society (individual citizens), environmental NGOs and activists, and media. Agents interact during urban planning processes - particularly the evaluation and approval of new development proposals. Urban planners provide technical assessments, while politicians (organized by party) make final decisions to approve, modify, or reject projects. Environmental NGOs, activist groups, and the media shape public perception and influence policymakers through campaigns, lobbying, protests, and news coverage. Individual citizens decide whether to engage in collective action based on personal values and social influences. The model captures the resulting decision-making ecosystem and reveals feedback loops and leverage points that determine climate-adaptive outcomes. By analyzing these dynamics, we identify critical intervention points where targeted policy measures can facilitate systemic transformation toward more climate-resilient urban development.

cs.CY↗

arXiv2025

Large Language Models for Agent-Based Modelling: Current and possible uses across the modelling cycle

The emergence of Large Language Models (LLMs) with increasingly sophisticated natural language understanding and generative capabilities has sparked interest in the Agent-based Modelling (ABM) community. With their ability to summarize, generate, analyze, categorize, transcribe and translate text, answer questions, propose explanations, sustain dialogue, extract information from unstructured text, and perform logical reasoning and problem-solving tasks, LLMs have a good potential to contribute to the modelling process. After reviewing the current use of LLMs in ABM, this study reflects on the opportunities and challenges of the potential use of LLMs in ABM. It does so by following the modelling cycle, from problem formulation to documentation and communication of model results, and holding a critical stance.

cs.MA↗

arXiv2025

Reliability Analysis of Smart Contract Execution Architectures: A Comparative Simulation Study

The industrial market continuously needs reliable solutions to secure autonomous systems. Especially as these systems become more complex and interconnected, reliable security solutions are becoming increasingly important. One promising solution to tackle this challenge is using smart contracts designed to meet contractual conditions, avoid malicious errors, secure exchanges, and minimize the need for reliable intermediaries. However, smart contracts are immutable. Moreover, there are different smart contract execution architectures (namely Order-Execute and Execute-Order-Validate) that have different throughputs. In this study, we developed an evaluation model for assessing the security of reliable smart contract execution. We then developed a realistic smart contract enabled IoT energy case study. Finally, we simulate the developed case study to evaluate several smart contract security vulnerabilities reported in the literature. Our results show that the Execute-Order-Validate architecture is more promising regarding reliability and security.

cs.CR↗

arXiv2025

Towards an LLM-powered Social Digital Twinning Platform

We present Social Digital Twinner, an innovative social simulation tool for exploring plausible effects of what-if scenarios in complex adaptive social systems. The architecture is composed of three seamlessly integrated parts: a data infrastructure featuring real-world data and a multi-dimensionally representative synthetic population of citizens, an LLM-enabled agent-based simulation engine, and a user interface that enable intuitive, natural language interactions with the simulation engine and the artificial agents (i.e. citizens). Social Digital Twinner facilitates real-time engagement and empowers stakeholders to collaboratively design, test, and refine intervention measures. The approach is promoting a data-driven and evidence-based approach to societal problem-solving. We demonstrate the tool's interactive capabilities by addressing the critical issue of youth school dropouts in Kragero, Norway, showcasing its ability to create and execute a dedicated social digital twin using natural language.

cs.CY↗

arXiv2025

UAV Marketplace Simulation Tool for BVLOS Operations

We present a simulation tool for evaluating team formation in autonomous multi-UAV (Unmanned Aerial Vehicle) missions that operate Beyond Visual Line of Sight (BVLOS). The tool models UAV collaboration and mission execution in dynamic and adversarial conditions, where Byzantine UAVs attempt to disrupt operations. Our tool allows researchers to integrate and compare various team formation strategies in a controlled environment with configurable mission parameters and adversarial behaviors. The log of each simulation run is stored in a structured way along with performance metrics so that statistical analysis could be done straightforwardly. The tool is versatile for testing and improving UAV coordination strategies in real-world applications.

cs.RO↗

arXiv2025

A Generic Modelling Framework for Last-Mile Delivery Systems

Large-scale social digital twinning projects are complex with multiple objectives. For example, a social digital twinning platform for innovative last-mile delivery solutions may aim to assess consumer delivery method choices within their social environment. However, no single tool can achieve all objectives. Different simulators exist for consumer behavior and freight transport. Therefore, we propose a high-level architecture and present a blueprint for a generic modelling framework. This includes defining modules, input/output data, and interconnections, while addressing data suitability and compatibility risks. We demonstrate the framework's effectiveness with two real-world case studies.

cs.MA↗

arXiv2024

Modelisation a base d'Agent Augmentes par LLM pour les Simulations Sociales: Defis et Opportunites

As large language models (LLMs) continue to make significant strides, their better integration into agent-based simulations offers a transformational potential for understanding complex social systems. However, such integration is not trivial and poses numerous challenges. Based on this observation, in this paper, we explore architectures and methods to systematically develop LLM-augmented social simulations and discuss potential research directions in this field. We conclude that integrating LLMs with agent-based simulations offers a powerful toolset for researchers and scientists, allowing for more nuanced, realistic, and comprehensive models of complex systems and human behaviours.

physics.soc-ph↗

arXiv2024

A Guide to Re-Implementing Agent-based Models: Experiences from the HUMAT Model

Replicating existing agent-based models poses significant challenges, particularly for those new to the field. This article presents an all-encompassing guide to re-implementing agent-based models, encompassing vital concepts such as comprehending the original model, utilizing agent-based modeling frameworks, simulation design, model validation, and more. By embracing the proposed guide, researchers and practitioners can gain a profound understanding of the entire re-implementation process, resulting in heightened accuracy and reliability of simulations for complex systems. Furthermore, this article showcases the re-implementation of the HUMAT socio-cognitive architecture, with a specific focus on designing a versatile, language-independent model. The encountered challenges and pitfalls in the re-implementation process are thoroughly discussed, empowering readers with practical insights. Embrace this guide to expedite model development while ensuring robust and precise simulations.

cs.MA↗

arXiv2024

On Using Agent-based Modeling and Simulation for Studying Blockchain Systems

There is a need for a simulation framework, which is develop as a software using modern engineering approaches (e.g., modularity --i.e., model reuse--, testing, continuous development and continuous integration, automated management of builds, dependencies and documentation) and agile principles, (1) to make rapid prototyping of industrial cases and (2) to carry out their feasibility analysis in a realistic manner (i.e., to test hypothesis by simulating complex experiments involving large numbers of participants of different types acting in one or several blockchain systems).

cs.SE↗

arXiv2024

Multi-Agent eXperimenter (MAX)

We present a novel multi-agent simulator named Multi-Agent eXperimenter (MAX) that is designed to simulate blockchain experiments involving large numbers of agents of different types acting in one or several environments. The architecture of MAX is highly modular, enabling easy addition of new models.

cs.MA↗

arXiv2024

Towards Secure and Trusted-by-Design Smart Contracts

Distributed immutable ledgers, or blockchains, allow the secure digitization of evidential transactions without relying on a trusted third-party. Evidential transactions involve the exchange of any form of physical evidence, such as money, birth certificate, visas, tickets, etc. Most of the time, evidential transactions occur in the context of complex procedures, called evidential protocols, among physical agents. The blockchain provides the mechanisms to transfer evidence, while smart contracts - programs executing within the blockchain in a decentralized and replicated fashion - allow encoding evidential protocols on top of a blockchain. As a smart contract foregoes trusted third-parties and runs on several machines anonymously, it constitutes a highly critical program that has to be secure and trusted-by-design. While most of the current smart contract languages focus on easy programmability, they do not directly address the need of guaranteeing trust and accountability, which becomes a significant issue when evidential protocols are encoded as smart contracts.

cs.CR↗

arXiv2024

Multi-Agent Optimization for Safety Analysis of Cyber-Physical Systems: Position Paper

Failure Mode, Effects and Criticality Analysis (FMECA) is one of the safety analysis methods recommended by most of the international standards. The classical FMECA is made in a form of a table filled in either manually or by using safety analysis tools. In both cases, the design engineers have to choose the trade-offs between safety and other development constraints. In the case of complex cyber-physical systems (CPS) with thousands of specified constraints, this may lead to severe problems and significantly impact the overall criticality of CPS. In this paper, we propose to adopt optimization techniques to automate the decision making process conducted after FMECA of CPS. We describe a multi-agent based optimization method which extends classical FMECA for offering optimal solutions in terms of criticality and development constraints of CPS.

cs.AI↗