Sinfield Joseph V, Sheth Ananya, Kotian Romika R
College of Engineering Innovation and Leadership Studies Program, Purdue University, West Lafayette, IN, United States.
Innovation Science Laboratory, Purdue University, West Lafayette, IN, United States.
Sustain Futur. 2020;2:100037. doi: 10.1016/j.sftr.2020.100037. Epub 2020 Aug 25.
Complex socio-technical challenges, often referred to as grand challenges or wicked problems, lack a robust method for their holistic framing. Current approaches to framing fall into two primary categories. On one hand, models grounded in reductionist perspectives tend to oversimplify the problems and thus fall short of capturing the true complexity that must be understood to make tangible progress. On the other, notable attempts to achieve holism are more effective at incorporating contextual nuance, but still lack systematicity to identify and drive effective inclusion of critical issues, and also tend to suffer from the inherent bias of select expert input. In this article, we report on an extension of holistic problem framing techniques called comprehensive success factor analysis (CSFA) that makes-sense of web-mined information reflective of both expert and general population perspectives as well as pattern-informed ontological knowledge organization structure, to yield 'rich pictures' of grand challenges. This method has been developed and refined over a seven-year period by application to a variety of distinct socio-technical challenges, and emphasizes that framing complex problems requires one to embrace multiple levels of abstraction, a plurality of perspectives, careful contextualization, and an overarching system view. The CSFA method results in 'success factor trees' that are more comprehensive than seen otherwise and present a holistic view of the essential factors that need to be considered when engaging in large scale socio-technical problems. The success factor trees provide common grounds for meaningful collaboration and discourse on grand challenges, facilitate more informed resource allocation decisions, and provide guidance for designing solutions through careful consideration of system factors that are not always apparent. The paper illustrates CSFA applied to the challenge of 'food security for a nation in a low- to middle-income country context' to ascertain the value of the approach and finds that it results in a robust view of the challenge that greatly exceeds perspectives arrived at in the literature using current framing methods, on dimensions of scope, levels of abstraction, plurality, and context detail.
复杂的社会技术挑战,通常被称为重大挑战或棘手问题,缺乏一种全面构建其框架的稳健方法。当前构建框架的方法主要分为两类。一方面,基于还原论观点的模型往往会过度简化问题,因此无法捕捉到为取得切实进展而必须理解的真正复杂性。另一方面,显著的整体论尝试在纳入情境细微差别方面更有效,但仍缺乏系统性来识别和推动关键问题的有效纳入,并且还往往受到特定专家意见固有偏差的影响。在本文中,我们报告了一种称为综合成功因素分析(CSFA)的整体问题构建技术的扩展,该技术能够理解反映专家和普通民众观点以及模式驱动的本体知识组织结构的网络挖掘信息,以生成重大挑战的“丰富图景”。这种方法在七年的时间里通过应用于各种不同的社会技术挑战得到了发展和完善,并强调构建复杂问题需要接受多个抽象层次、多种观点、仔细的情境化以及总体系统观。CSFA方法产生的“成功因素树”比其他方法更全面,呈现了在处理大规模社会技术问题时需要考虑的基本因素的整体视图。成功因素树为关于重大挑战的有意义合作和讨论提供了共同基础,有助于做出更明智的资源分配决策,并通过仔细考虑并非总是显而易见的系统因素为设计解决方案提供指导。本文通过将CSFA应用于“低收入至中等收入国家背景下一个国家的粮食安全”挑战来说明该方法的价值,并发现它在范围、抽象层次、多元性和情境细节等维度上,对该挑战产生了一种稳健的观点,大大超越了使用当前构建方法在文献中得出的观点。