FUTURES & FORESIGHT SCIENCE最新文献

Kees van der Heijden is an icon in the futures and foresight academic and practitioner community. Educated at the Technische Universiteit Delft, his work at Royal Dutch Shell, the Global Business Network, the Strathclyde Business School at University of Strathclyde, the Saïd Business School and the Templeton College at University of Oxford, and the Netherlands Business School at Nijenrode University has shaped the scholarly field of futures studies as well as the practical world of scenario facilitation. This article is a 25-year reflective and retrospective book review of Kees van der Heijden's seminal text Scenarios: The Art of Strategic Conversation. The authors conducted interviews with colleagues, coworkers, collaborators, students, and friends of Kees van der Heijden to add depth and dimension to this retrospective work. To bring van der Heijden's work into scholarly conversation with the extant literature, we also situated this return to Scenarios in the context of related works and other reviews of both editions of the book.

Cross-impact analysis is widely employed to inform management and policy decisions based on the formulation of scenarios, defined as combinations of outcomes of relevant uncertainty factors. In this paper, we argue that the use of nonprobabilistic variants of cross-impact analysis is problematic in the context of risk assessment where the usual aim is to produce conservative risk estimates which may exceed but are not smaller than the actual risk level. Then, building on the characterization of probabilistic dependencies, we develop an approach to probabilistic cross-impact analysis which (i) admits several kinds of probabilistic statements about the outcomes of relevant uncertainty factors and their dependencies; (ii) maps such statements into constraints on the joint probability distribution over all possible scenarios; (iii) provides support for preserving the consistency of elicited statements; and (iv) uses mathematical optimization to compute lower and upper bounds on the overall risk level. This approach—which is illustrated with an example from the context of nuclear waste repositories—is useful in that it retains the informativeness of cross-impact statements while ensuring that these statements are interpreted within the coherent framework of probability theory.

Organizations increasingly adopt technologies and logistics measures to improve their social sustainability, reduce transportation-related carbon emissions, or even design their supply chains carbon negative. However, the speed at which this adoption is progressing is not fast enough to address the sustainability challenges of the world today. To gain deeper knowledge about the adoption process of sustainability-related measures in road freight transportation, this paper examines barriers currently hindering a broader market penetration and derives expected timeframes when mass adoption of 14 relevant technologies will occur. Furthermore, the technologies' impacts on the economic, environmental, and social dimensions of sustainability are discussed providing recommendations, on which technologies the respective stakeholders might focus on in the future. Since we want to examines a long-term technology perspective of the next 25 years, we rely on systematic foresight methodology. More specifically, we collect empirical data by use of a real-time Delphi survey, which particularly suits complex and uncertain environments. Our global panel includes 116 experts from 25 different countries. The assessments are grouped into short-term, midterm, midterm to long-term, and long-term developments. The categories shed light on potential drivers and barriers to the implementation of the surveyed technologies. The findings emphasize the need to systematically select suitable measures and promote those that are expected to deliver short-term sustainability improvements to address current deficits in a timely manner. The article further outlines the crucial role of politics in developing necessary regulative frameworks to drive sustainability in road freight transportation.

This paper outlines new perspectives for data-supported foresight by combining participatory expert-based futures dialogues with the power of artificial intelligence (AI) in what we call the hybrid AI-expert-based foresight approach. To this end, we present a framework of five typical steps in a fully fledged foresight process ranging from scoping to strategizing and show how AI can be integrated into each of the steps to enable the hybrid AI-expert foresight approach. Building on this, we present experiences gained from two recent research projects of TNO and Fraunhofer ISI that deal with aspects of the hybrid AI-expert foresight approach and give insights into the opportunities and challenges of the new perspectives for data-supported foresight that this approach enables. Finally, we summarize open questions and challenges for future research.

Forecasting plays a vital role in intelligence assessment and contributes to national security decision-making by improving strategic foresight. Remarkably, most intelligence organizations do not proactively track their forecasting accuracy and, therefore, do not know how accurate their forecasts are or what types of biases intelligence analysts (or organizations) might exhibit. We review research on geopolitical forecasting and a roughly decade-long program of research to assess the accuracy of strategic intelligence forecasts produced by and for the Government of Canada. This research is described in three phases corresponding to previously published research, following which novel analyses (drawing from the data used in the earlier phases) are reported. The findings reveal a high degree of forecasting accuracy as well as significant underconfidence. These results were evident regardless of whether analysts assigned numeric probabilities to their forecasts. However, the novel analyses clarified that there is a substantial cost to accuracy if end-users rely on their own interpretations of verbal probability terms used in the forecasts. We recommend that intelligence organizations proactively track forecasting accuracy as a means of supporting accountability and organizational learning. We further recommend that intelligence organizations use numeric probabilities in their forecasts to support better comprehension of these estimates by end-users.

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=/).