当少数异类触发数字采纳级联：企业主体基模型中网络可达性、阈值异质性与绩效条件复杂传染

中文摘要 AI 辅助

为什么一种优越的技术有时会传播开来，有时却停滞不前，即使其预期回报要高得多？我们开发了一个主体基模型，在该模型中，企业通过一种快速节俭的启发式方法模仿成功的邻居来采纳数字技术。我们比较了基于频率的模仿（企业遵循本地多数）与基于绩效条件的模仿（企业遵循表现更好的邻居）。我们将级联定义为通过快速、加速的起飞而非缓慢积累达到的高最终采纳率。在校准机制下，出现了四个结果。首先，动态级联在基于绩效条件的模仿下出现，但在外生采纳风险或基于频率的模仿下则不会出现。其次，在度控制网络上，一旦网络提供足够的可达性，采纳就会触发，小世界重连阈值接近0.41，95%自助置信区间为0.39至0.43。这反映了短路径、低聚类和高可达节点，而非单一图统计量。第三，在固定平均阈值下，将接受性集中在低尾部分可以产生同质阈值无法产生的级联。第四，级联概率强烈依赖于初始采纳者和低阈值先驱者的位置。在人类尺度观察度下，该机制是真正的复杂传染：采纳需要来自多个成功范例的强化，当由聚类临界质量启动时，能承受至少两个成功数字邻居的要求，并在邓巴尺度邻域之外减弱。该模型识别了干预可能产生杠杆作用的点，而非预测行业。系统范围的采纳不需要广泛的文化变革：一小部分接受性强的企业，位于网络能够传递成功的位置，就足够了。简而言之，少数异类可以改变世界。

英文摘要

Why does a superior technology sometimes spread and sometimes stall, even when its expected returns are much higher? We develop an agent-based model in which firms adopt a digital technology by imitating successful neighbours through a fast-and-frugal heuristic. We compare frequency-based imitation, where firms follow the local majority, with performance-conditioned imitation, where they follow better-performing neighbours. We define a cascade as high final adoption reached through rapid, accelerating takeoff rather than slow accumulation. In the calibrated regime, four results emerge. First, dynamic cascades arise under performance-conditioned imitation, but not under an exogenous adoption hazard or frequency-based imitation. Second, on degree-controlled networks, adoption tips once the network provides sufficient reach, at a small-world rewiring threshold near 0.41, with a 95 percent bootstrap confidence interval of 0.39 to 0.43. This reflects short paths, low clustering, and high-reach nodes rather than one graph statistic. Third, at a fixed mean threshold, concentrating receptiveness in the lower tail can generate cascades that homogeneous thresholds cannot. Fourth, cascade probability depends strongly on the positions of initial adopters and low-threshold pioneers. At human-scale observation degrees, the mechanism is genuine complex contagion: adoption requires reinforcement from multiple successful exemplars, survives a requirement of at least two successful digital neighbours when seeded by a clustered critical mass, and weakens beyond Dunbar-scale neighbourhoods. The model identifies where interventions may have leverage rather than forecasting industries. Broad cultural change is not required for system-wide adoption: a small minority of receptive firms, positioned where the network can transmit success, can be enough. In short, a few misfits can change the world.

发表机构

• Esade – URL（ESADE-URL）
• Safe Intelligence
• UPC – BSC（加泰罗尼亚理工大学–巴塞罗那超级计算中心）

机构由 AI 辅助整理，请以论文原文为准。