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Information diffusion and behavior adoption mechanisms driven by risk preferences in two-layer complex networks
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DOI：10.1016/j.physa.2026.131804.png)
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Abstract
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In real-world social interactions, individuals' risk preferences play a crucial role in shaping information dissemination and behavioral adoption. Traditional social contagion models typically assume individual homogeneity and thus fail to capture the effects of heterogeneous risk preferences on the diffusion process. To address this limitation, this study proposes a two-layer susceptible-adopted-recovered (SAR) model that incorporates risk preference heterogeneity. Individuals are divided into two types: risky and conservative, and four types of adoption probability functions are developed based on interaction types to characterize the behavior adoption mechanisms among individuals with different risk preferences. Within the two-layer network framework, social reinforcement is further introduced to depict the accumulation effect of multi-channel information exposure. The model is theoretically derived based on edge-based compartmental theory and cavity theory, and the analytical results are verified through numerical simulations on Erdős–Rényi (ER) and scale-free (SF) networks. The results demonstrate that increasing the proportion of risk nodes in the network promotes the expansion of behavioral adoption. As the threshold in the information dissemination process increases, the final adoption scale becomes constrained. Compared with global social reinforcement, local social reinforcement has a more significant impact on the final behavioral adoption scale. Finally, increasing the degree exponents in the two-layer SF network is conducive to the large-scale diffusion of behavior.
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