FUTURES & FORESIGHT SCIENCE最新文献

This commentary aims to discuss challenges and opportunities for theory development in the field of futures and foresight by drawing on design theory. The values of theorization are acknowledged by many scholars in the field. These would include promoting a common understanding of futures and foresight among researchers both within the community and across different communities, helping researchers and practitioners select appropriate futures and foresight methods to execute a project being addressed, and providing an educational and training foundation to equip people such as novices and students with futures and foresight methods and approaches in a more systematic manner.

To date, a number of methods are available in the field, such as scenarios, Delphi method, roadmapping, and backcasting (van der Duin, 2016; Glenn & Gordon, 2009; Popper, 2008). However, less attention has been paid to theory development because, historically, there is a tendency that higher priority is placed on practicality (e.g., engaging with projects to “change the world” as described in Fergnani & Chermack, 2021) rather than academic contributions (e.g., writing scientific papers). In an attempt to further stimulate discussions on this important topic, we raise some challenges to be considered and then suggest an approach to theory development in the field through the lens of design theory (Tomiyama et al., 2009).

As Fergnani and Chermack (2021) pointed out, the field has not yet made enough efforts to develop theories for several reasons. While agreeing on such reasons raised there, we want to note three challenges that should be considered, which come from the key features of the field.

Firstly, it is not an easy task to test the validity of theory since the phenomenon of interest is about the future. Often, the community's interests lie not in the accuracy of a prediction¹, but rather in causal relations about how a certain future (or possible futures) might happen from the present, as discussed in scenarios and scenario planning literature (Bradfield et al., 2005; Spaniol & Rowland, 2019). From the viewpoint of management and organization sciences, it is of particular importance “to distinguish predicting the future and predicting the outcomes of futures and foresight interventions and capabilities with scientific theory (Chermack, 2007).” When the main purpose is not to predict an accurate future, the phenomena we want to study may be relevant to either of the following questions:

To the best of our knowledge, both of these questions have not been sufficiently addressed in previous research. It should be noted that Fergnani and Chermack (2021) focused on (II), such as organizational-level learning effects by futures and foresight methods.

Secondly, the definitions of t

Lustick and Tetlock (2021) argue for the use of theory-guided simulation for aiding geopolitical planning and decision-making. This is a very welcome contribution, as in our experience, geopolitical analyses are often qualitative in nature and prone to group think. The complexities of geopolitical issues, however, really call for the use of theory-guided simulations. These issues are too complex for mental simulation (Atkins et al., 2002; Sterman, 1989, 1994). Dynamic simulations, if properly grounded in appropriate theories and well-motivated assumptions, can derive the possible dynamics from interacting nonlinear processes, and thus aid human reasoning about system behavior (Sterman, 2002). Moreover, since geopolitical issues are subject to uncertainty, scarce data, and conflicting information, using an ensemble modeling approach is appropriate. An ensemble of simulations enables reasoning across alternative assumptions consistent with the available data and information. Such an ensemble can capture much more of the available theories, information, and educated guesses than any single model in isolation (Bankes, 2002). With the rising computational power, ensemble modeling is increasingly a feasible research strategy.

Despite our broad agreement with Lustick and Tetlock (2021), we have three major comments on their work. First, from the broader perspective of modeling and simulation, they offer little that is truly novel or surprising. The envisioned approach of ensemble simulations is already well established under the label of exploratory modeling. By not engaging with this literature, the authors have deprived themselves from a rich set of analytical techniques that could have substantially strengthened their case study, as well as relevant theories and concepts which would have strengthened the appeal of the manifesto. Second, we content that validating simulation models of complex systems with partially open system boundaries should focus of perceived usefulness, not supposedly predictive accuracy captured through brier scores. Third, in interpreting results from simulation models it is possible and useful to try and increase understanding between the system's structural characteristics and theories, instead of using simulations as point predictions.

There is ample literature that has emerged over the last 30 years on the use of computational experimentation with simulation models to aid planning and decision-making. Hodges (1991) identified six things that could be done with simulation models in the absence of good data. Hodges and Dewar (1992) identified a seventh use case. Bankes (1993) moved away from enumerating the number of use cases, and simply spoke of exploratory modeling. Since these formative ideas from the early 1990s, a large body of literature on exploratory modeling has emerged (se

Lustick and Tetlock (2021) present a distinctive, persuasive case for theory-guided simulation and its use in the intelligence community (IC). The usefulness of their insight can be expanded by recognizing the relationship between the forecasting project, the decision-aiding project, and the knowledge project. We point out further issues for consideration with theory-guided simulation and the foresight project. We argue that when uncertainty prevails to the extent that forecasting becomes challenged, the real measures of merit should be better decisions, not better predictions. Recent advances in decision-making under deep uncertainty (DMDU) offer the prospect of providing just such aid to planning and decision. This, in turn, suggests a reconsideration of the IC (and other knowledge project endeavors’) roles in supporting policy deliberations. Potential intersection and cross-fertilization between DMDU concepts and methods and forecasting technique might prove to be to the mutual benefit of both the forecasting and decision-aiding projects and transformative to conceptualization of the nature of the knowledge project.

We very much welcome the publication of Fergnani and Chermack (2021) as it makes an important contribution to the development of the foresight discipline. The important role of scientific theories in social science and across all disciplines has been reflected for decades (see, e.g., Parsons, 1938). With scientific theories, researchers can link the abstract world (the world of concepts/ideas) and the concrete world (the empirical/observable world) (Chibucos et al., 2005). It is, therefore, more than appropriate to question along with Fergnani and Chermack (2021, p. 1) and colleagues, “why the field of futures and foresight has not been successful at becoming part of the social scientific establishment”? The lack of a theoretical grounding in futures and foresight studies (FFS) has been mentioned and critically discussed by several authors (Hideg, 2007; Marien, 2010; Mermet et al., 2009; Öner, 2010; Piirainen & Gonzalez, 2015). With their recent conceptual analysis, Fergnani and Chermack (2021) build upon their observation and derive argumentative explanations as well as propose recommendations for further evolution in the scientific domain. They underline that “weak theoretical foundations prevent the field from becoming a recognized academic discipline of study in the academic establishment” (Fergnani & Chermack, 2021, p. 1). We have made similar observations in our own studies of the foresight field. However, despite some bibliometric analyses of the futures research domain in general (see, e.g., Fergnani, 2019) and various focused analyses of selected techniques (see, e.g., Flostrand et al., 2020), no such examination of scientific theory in FFS exists. Our commentary supports the underlying observations by Fergnani and Chermack (2021) by adopting a brief bibliometric lens on 50 years of cumulative scholarship (1973–January 2021) in 22 selected journals. These 22 journals include a total of 47,049 articles that were scanned. Based on our search criteria, we found 151 article matches (only 0.32 percent), of which a subset of 28 articles applied scientific theories from different disciplines.

We chose the approach of bibliometric analysis, including bibliographic coupling, to measure and illustrate the resistance to scientific theory in futures and foresight. This method can be used to reconstruct the structural landscape of an academic field, which is why this approach is most appropriate for mapping research streams (Zupic & Čater, 2015). Bibliometric analysis includes the investigation of a body of literature regarding quantitative indicators such as citations, thematic associations, authorships, and geographical and institutional patterns (Ellegaard & Wallin, 2015).

A total of 151 publication