Agent-Based Modeling of Innovation Diffusion in Social Networks

Agent-based modelling has become a cornerstone methodology for exploring how innovations—ranging from consumer products to digital platforms—propagate through social networks. By simulating populations of autonomous agents, each endowed with distinct characteristics and behavioural rules, researchers can capture the heterogeneity of real-world users and the nuanced dynamics of peer influence. Agents interact according to prescribed network structures, which may be static or evolve over time, and make adoption decisions based on personal thresholds, perceived benefits, and observed behaviours of their neighbours. Such bottom-up approaches reveal emergent patterns—such as tipping points, threshold effects and clustering—that classical aggregate models cannot easily reproduce. Applications span marketing strategy, public-health interventions, technology uptake in rural communities and policy design for sustainable innovation. With advances in computational power and data availability, contemporary models increasingly integrate empirical social-network data, behavioural economics and cognitive theories to enhance predictive accuracy and to inform targeted interventions that accelerate or moderate diffusion processes at scale.

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Agent-Based Modeling of Innovation Diffusion in Social Networks publication trend

The graph below shows the total number of articles in agent-based modeling of innovation diffusion in social networks across all publications each year (not limited to Nature Index journals).

Technical terms

Agent-based model: A computational framework simulating a system as interacting autonomous entities following defined behavioural rules.

Diffusion: The process by which an innovation spreads through a population over time via interpersonal influence and external drivers.

Social network: A representation of individuals or organisations (nodes) connected by social ties such as communication, trust or collaboration.

Emergent behaviour: System-level phenomena that arise from local interactions among agents rather than from centralised coordination.

Network topology: The structural arrangement of nodes and links in a network, which shapes pathways for information or innovation flow.

References

1. An agent-based model with social interactions for scalable probabilistic prediction of performance of a new product. International Journal of Information Management Data Insights (2022).
2. Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms. Annals of Operations Research (2021).
3. Online Social Network Information Dissemination Integrating Overconfidence and Evolutionary Game Theory. IEEE Access (2021).