Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part IIntroduction to Behavioural Data Science
- Part IIHuman Behaviour
- Part IIIAlgorithmic Behaviour
- Part IVSystems and Culture
- 19Systems, Culture and Human–Machine Teaming
- 20Cognitive Networks as Models of Cognition and Behaviour
- 21Agent-Based Modelling in Social Networks
- 22Modelling Context-Dependent Behaviour
- 23A Short Primer on Historical Natural Language Processing
- 24Behavioural Data in Complex Economic and Business Systems
- 25Applications of Statistical Mechanics and Cyber-Physical Systems to Behaviour
- 26Systems Behaviour for Sustainable AI
- 27Systems Behaviour and Experimentation
- 28Quantum Mechanics of Human Perception, Behaviour and Decision-Making
- Part VApplications
- Part VIConcluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
21 - Agent-Based Modelling in Social Networks
from Part IV - Systems and Culture
Published online by Cambridge University Press: 29 May 2026
Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part IIntroduction to Behavioural Data Science
- Part IIHuman Behaviour
- Part IIIAlgorithmic Behaviour
- Part IVSystems and Culture
- 19Systems, Culture and Human–Machine Teaming
- 20Cognitive Networks as Models of Cognition and Behaviour
- 21Agent-Based Modelling in Social Networks
- 22Modelling Context-Dependent Behaviour
- 23A Short Primer on Historical Natural Language Processing
- 24Behavioural Data in Complex Economic and Business Systems
- 25Applications of Statistical Mechanics and Cyber-Physical Systems to Behaviour
- 26Systems Behaviour for Sustainable AI
- 27Systems Behaviour and Experimentation
- 28Quantum Mechanics of Human Perception, Behaviour and Decision-Making
- Part VApplications
- Part VIConcluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
Summary
Agent-based modelling (ABM) in social networks offers a powerful framework for simulating individual behaviours and emergent collective patterns within dynamic and interconnected populations. This chapter explores the conceptual foundations, methodological innovations and behavioural implications of ABM in the context of social networks, with a particular focus on the modelling of communication, influence, cooperation and contagion. Drawing from the fields of behavioural science, network theory and computational social science, the chapter presents ABM as a generative tool for understanding how micro-level decision-making rules produce macro-level phenomena. It also critically evaluates the increasing use of AI-driven agents – including large language model (LLM)-powered agents and synthetic personas – in simulating realistic and context-sensitive behaviours within artificial societies. The chapter engages with ethical and methodological challenges, including representation, explainability and the problem of behavioural validity. A worked example is included, illustrating how agent-based simulations can be applied to study misinformation diffusion and norm formation in online social networks. Ultimately, the chapter argues that agent-based modelling in social networks not only advances Behavioural Data Science methodologically, but also fosters new forms of interpretive insight into the dynamics of collective behaviour in an age of digital mediation.
Information
- Type
- Chapter
- Information
- The Cambridge Handbook of Behavioural Data Science , pp. 381 - 395Publisher: Cambridge University PressPrint publication year: 2026
References
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