Exploring the Potential of Conversational AI Support for Agent-Based Social Simulation Model Design

• URL: http://arxiv.org/abs/2405.08032v2
• Date: Tue, 07 Oct 2025 17:52:42 GMT
• Title: Exploring the Potential of Conversational AI Support for Agent-Based Social Simulation Model Design
• Authors: Peer-Olaf Siebers,
• Abstract summary: The research presented here demonstrates how CAISs can facilitate the development of innovative conceptual ABSS models.<n>We have constructed a comprehensive prompt script that enables the design of conceptual ABSS models with or by the CAIS.<n>Despite occasional inaccuracies and conversational divergence, the CAIS proved to be a valuable companion for ABSS modellers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: ChatGPT, the AI-powered chatbot with a massive user base of hundreds of millions, has become a global phenomenon. However, the use of Conversational AI Systems (CAISs) like ChatGPT for research in the field of Social Simulation is still limited. Specifically, there is no evidence of its usage in Agent-Based Social Simulation (ABSS) model design. This paper takes a crucial first step toward exploring the untapped potential of this emerging technology in the context of ABSS model design. The research presented here demonstrates how CAISs can facilitate the development of innovative conceptual ABSS models in a concise timeframe and with minimal required upfront case-based knowledge. By employing advanced prompt engineering techniques and adhering to the Engineering ABSS framework, we have constructed a comprehensive prompt script that enables the design of conceptual ABSS models with or by the CAIS. A proof-of-concept application of the prompt script, used to generate the conceptual ABSS model for a case study on the impact of adaptive architecture in a museum environment, illustrates the practicality of the approach. Despite occasional inaccuracies and conversational divergence, the CAIS proved to be a valuable companion for ABSS modellers.

Related papers

• TongSIM: A General Platform for Simulating Intelligent Machines [59.27575233453533]
  Embodied intelligence focuses on training agents within realistic simulated environments.<n>TongSIM is a high-fidelity, general-purpose platform for training and evaluating embodied agents.
  arXiv  Detail & Related papers  (2025-12-23T10:00:43Z)
• Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems [0.6526824510982802]
  This paper explores the emergent paradigm of human-AI co-creation, where AI is not merely used for automation or efficiency gains, but actively participates in ideation, visual conceptualization, and decision-making.<n>Specifically, we investigate the use of large language models (LLMs) like GPT-4 and multimodal diffusion models such as Stable Diffusion as creative agents that engage designers in iterative cycles of proposal, critique, and revision.
  arXiv  Detail & Related papers  (2025-07-22T04:29:33Z)
• Modeling Open-World Cognition as On-Demand Synthesis of Probabilistic Models [93.1043186636177]
  We explore the hypothesis that people use a combination of distributed and symbolic representations to construct bespoke mental models tailored to novel situations.<n>We propose a computational implementation of this idea -- a Model Synthesis Architecture''<n>We evaluate our MSA as a model of human judgments on a novel reasoning dataset.
  arXiv  Detail & Related papers  (2025-07-16T18:01:03Z)
• AI Flow: Perspectives, Scenarios, and Approaches [51.38621621775711]
  We introduce AI Flow, a framework that integrates cutting-edge IT and CT advancements.<n>First, device-edge-cloud framework serves as the foundation, which integrates end devices, edge servers, and cloud clusters.<n>Second, we introduce the concept of familial models, which refers to a series of different-sized models with aligned hidden features.<n>Third, connectivity- and interaction-based intelligence emergence is a novel paradigm of AI Flow.
  arXiv  Detail & Related papers  (2025-06-14T12:43:07Z)
• SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning [53.16179295245888]
  We introduce SIV-Bench, a novel video benchmark for evaluating the capabilities of Multimodal Large Language Models (MLLMs) across Social Scene Understanding (SSU), Social State Reasoning (SSR), and Social Dynamics Prediction (SDP)<n>SIV-Bench features 2,792 video clips and 8,792 meticulously generated question-answer pairs derived from a human-LLM collaborative pipeline.<n>It also includes a dedicated setup for analyzing the impact of different textual cues-original on-screen text, added dialogue, or no text.
  arXiv  Detail & Related papers  (2025-06-05T05:51:35Z)
• Toward Knowledge-Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human-AI Synergy [0.0]
  We argue for a new generation of design systems that transcend black-box modeling by integrating domain knowledge, physics-informed learning, and intuitive human-AI interfaces.<n>Through illustrative examples and conceptual frameworks, we advocate that inverse design in manufacturing should evolve into a unified ecosystem.
  arXiv  Detail & Related papers  (2025-05-29T08:15:27Z)
• AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
  The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
  arXiv  Detail & Related papers  (2025-05-17T15:01:33Z)
• Personalized Artificial General Intelligence (AGI) via Neuroscience-Inspired Continuous Learning Systems [3.764721243654025]
  Current approaches largely depend on expanding model parameters, which improves task-specific performance but falls short in enabling continuous, adaptable, and generalized learning. This paper reviews the state of continual learning and neuroscience-inspired AI, and proposes a novel architecture for Personalized AGI that integrates brain-like learning mechanisms for edge deployment. Building on these insights, we outline an AI architecture that features complementary fast-and-slow learning modules, synaptic self-optimization, and memory-efficient model updates to support on-device lifelong adaptation.
  arXiv  Detail & Related papers  (2025-04-27T16:10:17Z)
• AI Automatons: AI Systems Intended to Imitate Humans [54.19152688545896]
  There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness. The research, design, deployment, and availability of such AI systems have prompted growing concerns about a wide range of possible legal, ethical, and other social impacts.
  arXiv  Detail & Related papers  (2025-03-04T03:55:38Z)
• A Survey of Model Architectures in Information Retrieval [59.61734783818073]
  The period from 2019 to the present has represented one of the biggest paradigm shifts in information retrieval (IR) and natural language processing (NLP)<n>We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)<n>We conclude with a forward-looking discussion of emerging challenges and future directions.
  arXiv  Detail & Related papers  (2025-02-20T18:42:58Z)
• TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models [14.861568843321598]
  We present TrueReason, an exemplar personalised learning system that integrates a multitude of specialised AI models. The proposed system demonstrates the first step in building sophisticated AI systems.
  arXiv  Detail & Related papers  (2025-01-23T13:25:44Z)
• Converging Paradigms: The Synergy of Symbolic and Connectionist AI in LLM-Empowered Autonomous Agents [55.63497537202751]
  Article explores the convergence of connectionist and symbolic artificial intelligence (AI) Traditionally, connectionist AI focuses on neural networks, while symbolic AI emphasizes symbolic representation and logic. Recent advancements in large language models (LLMs) highlight the potential of connectionist architectures in handling human language as a form of symbols.
  arXiv  Detail & Related papers  (2024-07-11T14:00:53Z)
• Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptation [9.36651659099834]
  We propose a new unified set of dimensions through which to analyze and describe human-AI systems. Our conceptual model is centered around three high-level aspects - agency, interaction, and adaptation.
  arXiv  Detail & Related papers  (2024-04-18T10:12:18Z)
• LVLM-Interpret: An Interpretability Tool for Large Vision-Language Models [50.259006481656094]
  We present a novel interactive application aimed towards understanding the internal mechanisms of large vision-language models. Our interface is designed to enhance the interpretability of the image patches, which are instrumental in generating an answer. We present a case study of how our application can aid in understanding failure mechanisms in a popular large multi-modal model: LLaVA.
  arXiv  Detail & Related papers  (2024-04-03T23:57:34Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence [0.5898893619901381]
  We 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 such as ChatGPT to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model.
  arXiv  Detail & Related papers  (2023-09-20T16:43:05Z)
• CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection [0.07499722271664144]
  We propose a novel framework for automation of High Level Context (HLC) reasoning across intelligent systems at scale. The design of the framework supports the sharing and inter context among intelligent systems and the components for handling CSMs and the management of hierarchy, relationship, and transition. The implementation of the framework experiments on the HLC reasoning into vector and matrix computing and presents the potential to reach next level of automation.
  arXiv  Detail & Related papers  (2023-08-21T22:21:15Z)
• Navigating the Complexity of Generative AI Adoption in Software Engineering [6.190511747986327]
  The adoption patterns of Generative Artificial Intelligence (AI) tools within software engineering are investigated. Influencing factors at the individual, technological, and societal levels are analyzed.
  arXiv  Detail & Related papers  (2023-07-12T11:05:19Z)
• Perspectives on AI Architectures and Co-design for Earth System Predictability [3.844947514183043]
  A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Co-design' session and associated outcomes.
  arXiv  Detail & Related papers  (2023-04-07T17:36:24Z)
• End-to-End Speech Recognition: A Survey [68.35707678386949]
  The goal of this survey is to provide a taxonomy of E2E ASR models and corresponding improvements. All relevant aspects of E2E ASR are covered in this work, accompanied by discussions of performance and deployment opportunities.
  arXiv  Detail & Related papers  (2023-03-03T01:46:41Z)
• Analogical Concept Memory for Architectures Implementing the Common Model of Cognition [1.9417302920173825]
  We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts.
  arXiv  Detail & Related papers  (2022-10-21T04:39:07Z)
• A Model-Driven Engineering Approach to Machine Learning and Software Modeling [0.5156484100374059]
  Models are used in both the Software Engineering (SE) and the Artificial Intelligence (AI) communities. The main focus is on the Internet of Things (IoT) and smart Cyber-Physical Systems (CPS) use cases, where both ML and model-driven SE play a key role.
  arXiv  Detail & Related papers  (2021-07-06T15:50:50Z)
• PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception [50.551003004553806]
  We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions. Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events. As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
  arXiv  Detail & Related papers  (2021-03-02T18:44:57Z)
• Characterizing an Analogical Concept Memory for Architectures Implementing the Common Model of Cognition [1.468003557277553]
  We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts.
  arXiv  Detail & Related papers  (2020-06-02T21:54:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.