Source note1 min read
Social Simulations: from Agent-Based Modeling to Digital Twins
Source
arXiv
arxiv.org/abs/2607.13693v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Erica Cau, Andrea Failla, Valentina Pansanella, Giulio Rossetti
- Venue: arXiv: cs.MA
- Published: 2026-07-15
- Collected: 2026-07-16
- URL: http://arxiv.org/abs/2607.13693v1
- PDF: https://arxiv.org/pdf/2607.13693v1
- DOI: UNKNOWN
Plain-Language Brief
The text discusses the evolution of social simulation from traditional agent-based modeling to more advanced AI-enhanced simulations and Social Digital Twins, focusing on their methodological foundations, applications, advantages, and limitations.
What the Document Says
- discussion on evolution of social simulation
- from classical agent-based models
- to AI-enhanced simulations
- Large Language Models
- Social Digital Twins
- high-fidelity, data-driven representations
- real-world socio-technical systems
Key Concepts Mentioned
- social simulation
- agent-based modeling
- AI-enhanced simulations
- Large Language Models
- Social Digital Twins
- high-fidelity simulations
- socio-technical systems
Claims Made by the Document
- The evolution of social simulation has progressed from abstract models to increasingly realistic computational representations.
Evidence Used
- discussion on the shift from abstract models to more realistic computational representations
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly defined behavioral rules, to AI-enhanced simulations based on Large Language Models and, ultimately, Social Digital Twins: high-fidelity, data-driven representations of real-world socio-technical systems. Along this trajectory, we discuss the main methodological foundations, applications, advantages, and limitations of each paradigm, highlighting the progressive shift from abstract models designed to investigate general social mechanisms toward increasingly realistic computational representations of specific social systems.
