FUTURES & FORESIGHT SCIENCE最新文献

In a turbulent environment characterized by unexpected events, such as the COVID-19 pandemic, a sharp oil price decline, and dramatic economic crises, many organizations face not only challenges but also an “identity crisis” as they are unable to respond to the question "who are we as an organization?" Some organizations may concurrently hold multiple identities without being clear on whether these are shared and agreed upon, and on their role in potentially disruptive futures. In response to this challenge and addressing a methodological gap in the broader futures and foresight field, we draw on both futures and foresight as well as management and organization literature to propose a new organizational foresight method: Identity Wind Tunneling (IWT). Designed as an enhancement of wind tunneling approaches, IWT allows an organization to understand, test, and manage multiple organizational identities. Furthermore, IWT facilitates the alignment of organizational identities across a range of possible futures created during a scenario planning process with the ultimate goal of finding a strategic identity. This represents the central, enduring, and distinctive role that the organization embodies while pursuing a long-term and holistic strategic direction that is robust across all scenarios. The strategic identity either clarifies or reimagines the identity of the organization. The paper provides a step-by-step explanation of how to implement IWT and ​presents a case study of its application. The method is also presented with a series of open-source templates created on the whiteboard collaborative platform Miro (https://miro.com/app/board/o9J_kqakYjs=/).

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