论文标题

网络结构和集体智能在创新的传播中

Network Structure and Collective Intelligence in the Diffusion of Innovation

论文作者

Becker, Joshua

论文摘要

当多项创新争夺收养时,导致早期优势的历史机会会产生锁定效果,从而使次优创新以牺牲优越的替代方案为代价。关于创新未受扩散的研究已经确定了许多可能的早期优势来源,但是这些机制可以使最佳和最佳创新受益。本文超越了偶然的解释,以确定系统地影响最佳策略传播的可能性的结构原理。创新扩散的正式模型表明,组织关系的网络结构可能会系统地影响广泛采用的创新将是最佳回报的可能性。本文以先前的扩散研究为基础,重点介绍中央参与者的作用,即良好的人或公司。尽管扩散的传染模型突出了中央参与者进一步更快地传播创新的好处,但目前的分析揭示了这种影响的阴暗面:仅仅中央参与者在网络中的存在会增加采用率,但也增加了次优成果的可能性。但是,由于密集的网络既快速又最佳,因此这种效果并不代表速度的折衷方案。这一发现与相关研究一致,表明网络集中化破坏了集体智能。

When multiple innovations compete for adoption, historical chance leading to early advantage can generate lock-in effects that allow suboptimal innovations to succeed at the expense of superior alternatives. Research on the diffusion of innovafacetion has identified many possible sources of early advantage, but these mechanisms can benefit both optimal and suboptimal innovations. This paper moves beyond chance-as-explanation to identify structural principles that systematically impact the likelihood that the optimal strategy will spread. A formal model of innovation diffusion shows that the network structure of organizational relationships can systematically impact the likelihood that widely adopted innovations will be payoff optimal. Building on prior diffusion research, this paper focuses on the role of central actors i.e. well-connected people or firms. While contagion models of diffusion highlight the benefits of central actors for spreading innovations further and faster, the present analysis reveals a dark side to this influence: the mere presence of central actors in a network increases rates of adoption but also increases the likelihood of suboptimal outcomes. This effect, however, does not represent a speed-optimality tradeoff, as dense networks are both fast and optimal. This finding is consistent with related research showing that network centralization undermines collective intelligence.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源