Social Learning Rules and the Effectiveness of Behavioural Policy: An Agent-Based Model
The same seeding intervention can stall, drift or cascade depending on the learning ecology.
Behaviour-change interventions unfold in social systems where people learn from others. This article develops a stylised agent-based model to examine how four canonical social learning rules—conformist transmission, prestige-biased copying, payoff-biased copying, and random copying—shape the impact of a simple seeding intervention. Two arms evolve under identical conditions, differing only in initial adoption. Across homogeneous populations, mixed ecologies, and parameter sweeps, the same seeding intervention can stall, drift, or cascade depending on the learning ecology: conformist dynamics exhibit threshold effects that erase treatment gains, prestige-biased and random copying can preserve positive lift when diffusion remains incomplete, and payoff-biased copying mainly changes the diffusion regime. The findings motivate policy heuristics that evaluate interventions relative to local diffusion potential and tailor seeding to the prevailing mix of learning rules.
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