在科学,初创企业和安全方面量化失败动态
Yian Yin1,2,3, Yang Wang1,2,4, James A Evans5,6
1Center for Science of Science and Innovation, Northwestern University, Evanston, IL, USA.
Nature
|November 1, 2019
概括
了解失败动态是成功的关键. 这项研究揭示了重复尝试的不同模式, 将进展与停滞区分开来, 并提供预测最终结果的早期信号.
科学领域:
- 复杂系统科学
- 创新研究
- 行为动力学
背景情况:
- 人类的成就往往涉及许多失败,
- 现有的创新,人力动力学和学习研究为探索失败模式提供了基础.
- 要了解失败的复杂动态, 需要一种量化方法.
研究的目的:
- 开发和验证一个预测成功或停滞的不同失败动态的模型.
- 识别早期信号, 区分那些走向成功的人与最终失败的人.
- 调查不同领域的失败动态的普遍性.
主要方法:
- 开发了一个单参数分析模型,模拟未来的成功尝试如何建立在过去的努力上.
- 分析了分离进展和停滞动态的阶段过渡模型.
- 收集和分析来自三个不同领域的大规模实证数据:NIH资助申请,初创企业退出以及恐怖袭击伤亡索赔.
主要成果:
- 该模型预测了故障动态的阶段过渡,将其分类为进展 (增量提炼) 或停滞 (分离探索).
- 实证数据来自NIH的资助,初创企业和恐怖袭击, 始终支持模型的预测.
- 在故障动态中识别出明显的,可检测的早期信号,预测最终的成功或失败.
结论:
- 失败动态表现出可预测的模式, 有关值区分系统进步和无目的探索.
- 尽管早期的尝试看起来相似,但可能会显示出根本上不同的失败动态.
- 这项研究为理解失败提供了定量框架,并为预测各种复杂努力的结果提供了早期指标.
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