Quantifying the dynamics of failure across science, startups and security
Quantifying the dynamics of failure across science, startups and security
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DOI：
10.1038/s41586-019-1725-y
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发表时间：
2019-11-07
期刊：
影响因子：
64.8
通讯作者：
Wang, Dashun
中科院分区：
文献类型：
--
作者：
Yin, Yian;Wang, Yang;Wang, Dashun
人类的成就往往是在一次次失败的尝试之后取得的，但人们对控制失败动力学的机制知之甚少。在这里，基于先前与创新（1-7），人类动力学（8-11）和学习（12- 1 - 7）相关的研究，我们开发了一个简单的单参数模型，该模型模拟了未来成功的尝试如何建立在过去的努力之上。解决这个模型分析表明，相变分离的动态失败到区域的进展或停滞，并预测，在临界阈值附近，代理人谁共享相似的特征和学习策略可能会遇到根本不同的结果失败后。在临界点之上，代理人利用渐进的改进来系统地走向成功，而在临界点之下，他们探索不相交的机会，而没有改进模式。该模型提出了几个经验可检验的预测，表明那些最终成功的人和那些没有成功的人最初可能看起来相似，但在与每次后续尝试相关的效率和质量方面，其特征可以是根本不同的失败动态。我们从三个不同的领域收集了大规模数据，并追踪了调查人员多次试图获得美国国立卫生研究院（NIH）的赠款以资助他们的研究，创新者成功退出他们的初创企业，以及恐怖组织声称在暴力袭击中伤亡。我们在所有三个领域都找到了广泛一致的经验支持，这系统地验证了我们模型的每个预测。总之，我们的研究结果揭示了可检测的但以前未知的早期信号，使我们能够识别导致最终成功或失败的失败动态。由于失败的普遍存在的性质和缺乏定量的方法来理解它，这些结果代表了第一步，更深入地了解复杂的动态失败的基础。
Human achievements are often preceded by repeated attempts that fail, but little is known about the mechanisms that govern the dynamics of failure. Here, building on previous research relating to innovation(1-7), human dynamics(8-11) and learning(12-17), we develop a simple one-parameter model that mimics how successful future attempts build on past efforts. Solving this model analytically suggests that a phase transition separates the dynamics of failure into regions of progression or stagnation and predicts that, near the critical threshold, agents who share similar characteristics and learning strategies may experience fundamentally different outcomes following failures. Above the critical point, agents exploit incremental refinements to systematically advance towards success, whereas below it, they explore disjoint opportunities without a pattern of improvement. The model makes several empirically testable predictions, demonstrating that those who eventually succeed and those who do not may initially appear similar, but can be characterized by fundamentally distinct failure dynamics in terms of the efficiency and quality associated with each subsequent attempt. We collected large-scale data from three disparate domains and traced repeated attempts by investigators to obtain National Institutes of Health (NIH) grants to fund their research, innovators to successfully exit their startup ventures, and terrorist organizations to claim casualties in violent attacks. We find broadly consistent empirical support across all three domains, which systematically verifies each prediction of our model. Together, our findings unveil detectable yet previously unknown early signals that enable us to identify failure dynamics that will lead to ultimate success or failure. Given the ubiquitous nature of failure and the paucity of quantitative approaches to understand it, these results represent an initial step towards the deeper understanding of the complex dynamics underlying failure.