Developing the needed scientific theory will not be easy: A commentary on Fergnani and Chermack 2021

Ahti Salo
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On this point, I tend to agree with them and warmly welcome their contribution.</p><p>Yet foresight and futures studies are not alone in having faced criticisms concerning the lack of theoretical soundness in offering decision and policy advice. Analogous arguments have been raised in macroeconomics, for example. Specifically, Colander (<span>2011</span>) addresses reasons why some researchers have concluded that macroeconomics does not exhibit the attributes which are required of fundamental science. One of these reasons is that the scientific understanding of a phenomenon as complex as the real macro economy remains limited. Furthermore, Colander argues that the standard macroeconomics has confounded fundamental science and policy applications in ways that undermine both. Indeed, juxtaposing these two papers shows that there are notable parallels between the concepts “macroeconomics” and “models” in Colander (<span>2011</span>) and “futures studies” and “scenarios” in Fergnani and Chermack (<span>2021</span>), respectively.</p><p>One implication from Colander (<span>2011</span>) is that if the complexity of the macro economy (as a real phenomenon) is why macroeconomics (as a scientific discipline) has encountered difficulties in ensuring its soundness, then it should not be surprising that futures studies should encounter difficulties, too, because macro economy is but a part of the world at large about which futurists make future-oriented policy relevant statements.</p><p>A trend in many countries is that researchers are increasingly encouraged and even pressured to derive policy implications from their research and to communicate these to policy makers and stakeholders in society. For instance, the Strategic Research Council of the Academy of Finland has been established as a funding instrument which stresses policy impacts. All projects funded by this instrument need to carry out well-planned efforts to exert demonstrable impact on policy and society. Embedded in this requirement is the possibility that the crucial steps in deriving policy recommendations from research are less sound than the execution of the underlying research proper. In part, this is because the transition from “what has been observed” to “what should be done” is an uneasy one. This transition calls for a thorough understanding of stakeholders’ values and goals as well as multidirectional and collaborative processes of interpretation which—even if facilitated by scholars with strong scientific credentials—can be questioned for lack of theory—just like the endeavors of futurists.</p><p>The above possibility may be aggravated due to the fact that academic career systems tend to reward for scientific quality and productivity, thereby creating incentives which may deter researchers from making the time-consuming investment to gain a sound understanding of the sociopolitical context to which their research results are injected. This is particularly the case if the projects are relatively short or if there are but modest prospects for benefiting from such contextual knowledge later on.</p><p>Still, the establishment of such funding instruments indicates that there is a legitimate space for translating conclusive scientific findings and other forms of knowledge to defensible policy recommendations. It would be unfortunate if this space were to be occupied primarily by scientists from other fields if their respective practices in transforming scientific findings to policy implications are no less immune to criticism than those of futurists’. Rather, there should be more collaboration and cross-fertilization in both directions, that is, not only by seeking to strengthen the theoretical foundations of futures studies by building on management and organization sciences, but also by fostering the awareness and uptake of the tested intellectual and methodological apparatus that the field of futures studies can offer to other disciplines.</p><p>In theory building, there is a challenge in that it may be impossible to subject the processes by which scientific findings are translated into policy recommendations to scientific scrutiny <i>at the time when these translations are carried out</i>. For example, the attention span that policy makers can afford to futurists may be fully consumed by the urgent pressures to make timely and defensible decisions. This would undermine opportunities for engaging them in contemporaneous research activities that are not equally instrumental for the decisions at hand. While there may be prospects for conducting such research activities later on, the motivation may be lacking and, once some time passed, the later recollections may not capture the nuances of the process as it unfolded.</p><p>Importantly, these real contexts—as well as the ways in which foresight can be carried out—are very diverse. As a result, the outcomes of futurists’ interventions (such as the efficacy and perceived usefulness of such-and-such a scenario methodology) are dependent on numerous contextual variables of which many cannot be controlled in real-life contexts. In particular, the number of these variables and ways of encoding them can be quite high relative to the number of real-life case studies which can be meaningfully compared with regard to the impacts of different methods, for example. This limits possibilities for reaching statistically significant and generalizable conclusions about which methods are “better”.</p><p>On the other hand, the design, execution, and analysis of controlled experiments makes it possible to arrive at statistically proven results. There are related research traditions in human forecasting (see, e.g., Leitner &amp; Wildburger, <span>2011</span>) and multi-criteria decision analysis (see, e.g Ishizaka &amp; Siraj, <span>2018</span>; Salo et al., <span>2021</span>). Yet the objectives, tasks, and outcomes addressed through such experiments appear narrower than those in typical foresight processes. 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As an example, some of the many variants of scenario analysis (see, e.g. Bunn &amp; Salo, <span>1993</span>) are highly quantitative due to the needs arising from contexts such as nuclear waste repositories (Tosoni et al., <span>2019</span>).</p><p>Given the challenges humanity is faced with, it is imperative to make continued progress to improve our capabilities for “making sense of the future.” Strengthening of the theoretical foundations of futures studies by building on the management and organizational studies can be an important part in this endeavor. 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引用次数: 1

Abstract

It is sobering to realize that the age-old human imperative to “make sense of the future” is much older than the institutional manifestations of science. One could even argue that there is an evolutionary demand for the kinds of capabilities that are fostered by foresight and futures studies.

Thus, regardless of the extent to which these capabilities are scrutinized scientifically (or through the lens of social sciences, as promoted by Fergnani and Chermack (2021)), it lies in the interest of individuals and societies that the processes of building and harnessing these capabilities are guided by an accumulating body of knowledge. As noted by Fergnani and Chermack, this body of knowledge is best built by developing and testing adequate scientific theories. On this point, I tend to agree with them and warmly welcome their contribution.

Yet foresight and futures studies are not alone in having faced criticisms concerning the lack of theoretical soundness in offering decision and policy advice. Analogous arguments have been raised in macroeconomics, for example. Specifically, Colander (2011) addresses reasons why some researchers have concluded that macroeconomics does not exhibit the attributes which are required of fundamental science. One of these reasons is that the scientific understanding of a phenomenon as complex as the real macro economy remains limited. Furthermore, Colander argues that the standard macroeconomics has confounded fundamental science and policy applications in ways that undermine both. Indeed, juxtaposing these two papers shows that there are notable parallels between the concepts “macroeconomics” and “models” in Colander (2011) and “futures studies” and “scenarios” in Fergnani and Chermack (2021), respectively.

One implication from Colander (2011) is that if the complexity of the macro economy (as a real phenomenon) is why macroeconomics (as a scientific discipline) has encountered difficulties in ensuring its soundness, then it should not be surprising that futures studies should encounter difficulties, too, because macro economy is but a part of the world at large about which futurists make future-oriented policy relevant statements.

A trend in many countries is that researchers are increasingly encouraged and even pressured to derive policy implications from their research and to communicate these to policy makers and stakeholders in society. For instance, the Strategic Research Council of the Academy of Finland has been established as a funding instrument which stresses policy impacts. All projects funded by this instrument need to carry out well-planned efforts to exert demonstrable impact on policy and society. Embedded in this requirement is the possibility that the crucial steps in deriving policy recommendations from research are less sound than the execution of the underlying research proper. In part, this is because the transition from “what has been observed” to “what should be done” is an uneasy one. This transition calls for a thorough understanding of stakeholders’ values and goals as well as multidirectional and collaborative processes of interpretation which—even if facilitated by scholars with strong scientific credentials—can be questioned for lack of theory—just like the endeavors of futurists.

The above possibility may be aggravated due to the fact that academic career systems tend to reward for scientific quality and productivity, thereby creating incentives which may deter researchers from making the time-consuming investment to gain a sound understanding of the sociopolitical context to which their research results are injected. This is particularly the case if the projects are relatively short or if there are but modest prospects for benefiting from such contextual knowledge later on.

Still, the establishment of such funding instruments indicates that there is a legitimate space for translating conclusive scientific findings and other forms of knowledge to defensible policy recommendations. It would be unfortunate if this space were to be occupied primarily by scientists from other fields if their respective practices in transforming scientific findings to policy implications are no less immune to criticism than those of futurists’. Rather, there should be more collaboration and cross-fertilization in both directions, that is, not only by seeking to strengthen the theoretical foundations of futures studies by building on management and organization sciences, but also by fostering the awareness and uptake of the tested intellectual and methodological apparatus that the field of futures studies can offer to other disciplines.

In theory building, there is a challenge in that it may be impossible to subject the processes by which scientific findings are translated into policy recommendations to scientific scrutiny at the time when these translations are carried out. For example, the attention span that policy makers can afford to futurists may be fully consumed by the urgent pressures to make timely and defensible decisions. This would undermine opportunities for engaging them in contemporaneous research activities that are not equally instrumental for the decisions at hand. While there may be prospects for conducting such research activities later on, the motivation may be lacking and, once some time passed, the later recollections may not capture the nuances of the process as it unfolded.

Importantly, these real contexts—as well as the ways in which foresight can be carried out—are very diverse. As a result, the outcomes of futurists’ interventions (such as the efficacy and perceived usefulness of such-and-such a scenario methodology) are dependent on numerous contextual variables of which many cannot be controlled in real-life contexts. In particular, the number of these variables and ways of encoding them can be quite high relative to the number of real-life case studies which can be meaningfully compared with regard to the impacts of different methods, for example. This limits possibilities for reaching statistically significant and generalizable conclusions about which methods are “better”.

On the other hand, the design, execution, and analysis of controlled experiments makes it possible to arrive at statistically proven results. There are related research traditions in human forecasting (see, e.g., Leitner & Wildburger, 2011) and multi-criteria decision analysis (see, e.g Ishizaka & Siraj, 2018; Salo et al., 2021). Yet the objectives, tasks, and outcomes addressed through such experiments appear narrower than those in typical foresight processes. Furthermore, most experiments have been carried out by recruiting students, that is, relatively homogeneous subjects who are faced with stakes that may not be high enough to be viewed as realistic.

As for the breadth of the scientific foundations of futures studies, emphasizing management and organization studies exclusively may be limiting and even stifling. Rather, the underpinning theoretical disciplines need to be broad enough, including, for instance, mathematical methods for quantifying, propagating, and understanding uncertainties. That is, if the scope of theoretically sound futures science were to be confined solely to management and organization studies, the field of futures studies could be perceived as unwelcoming by those who work on quantitative and computational techniques, thereby unnecessarily propping rather than eroding barriers. As an example, some of the many variants of scenario analysis (see, e.g. Bunn & Salo, 1993) are highly quantitative due to the needs arising from contexts such as nuclear waste repositories (Tosoni et al., 2019).

Given the challenges humanity is faced with, it is imperative to make continued progress to improve our capabilities for “making sense of the future.” Strengthening of the theoretical foundations of futures studies by building on the management and organizational studies can be an important part in this endeavor. To this end, it is pertinent to strengthen connections between methodological advances and accompanying real case studies which are systematically evaluated to support theory building and also to guide further methodological work.

Within the broader landscape of scientific disciplines, futures studies is set to improve its status by demonstrating intellectual rigor while ensuring that the boundaries between disciplines remain fluid enough to foster cross-fertilization, cumulative learning, and multidisciplinary achievements.

发展所需的科学理论并不容易:费尔格尼和切尔马克评论2021
例如,决策者能够负担得起的对未来主义者的关注时间可能会被做出及时和合理决策的紧迫压力所完全消耗。这将破坏让他们参与同时期的研究活动的机会,这些活动对手头的决策没有同等的帮助。虽然以后可能会有进行这种研究活动的前景,但动机可能会缺乏,而且一旦一段时间过去,后来的回忆可能无法捕捉到展开过程中的细微差别。重要的是,这些真实的环境——以及实现远见的方式——是非常多样化的。因此,未来学家干预的结果(如某某情景方法的有效性和感知有用性)依赖于许多情境变量,其中许多变量在现实生活中无法控制。特别是,这些变量的数量及其编码方式可能相对于实际案例研究的数量相当高,例如,可以对不同方法的影响进行有意义的比较。这限制了得出关于哪种方法“更好”的具有统计意义和可推广的结论的可能性。另一方面,控制实验的设计、执行和分析使得出经统计证实的结果成为可能。在人类预测方面有相关的研究传统(例如,莱特纳&Wildburger, 2011)和多标准决策分析(参见,例如Ishizaka &Siraj 2018;Salo et al., 2021)。然而,通过这些实验解决的目标、任务和结果似乎比典型的预见过程更狭隘。此外,大多数实验都是通过招募学生进行的,也就是说,相对同质的受试者面临的风险可能不够高,不足以被视为现实。至于期货研究的科学基础的广度,只强调管理和组织研究可能是有限的,甚至是令人窒息的。相反,基础理论学科需要足够广泛,例如,包括量化、传播和理解不确定性的数学方法。也就是说,如果理论上合理的期货科学的范围仅仅局限于管理和组织研究,期货研究领域可能会被那些从事定量和计算技术工作的人视为不受欢迎,从而不必要地支撑而不是消除障碍。例如,情景分析的一些变体(参见,例如Bunn &Salo, 1993)是高度定量的,因为核废料储存库等情况产生了需求(Tosoni等人,2019)。考虑到人类面临的挑战,我们必须不断取得进步,提高我们“理解未来”的能力。在管理和组织研究的基础上,加强期货研究的理论基础是期货研究的重要组成部分。为此目的,加强方法进步与相关实际案例研究之间的联系是有必要的,这些案例研究将得到系统评估,以支持理论建设,并指导进一步的方法工作。在科学学科的广阔前景中,未来研究将通过展示知识的严谨性来提高其地位,同时确保学科之间的界限保持足够的流动性,以促进交叉受精、累积学习和多学科成就。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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