FUTURES & FORESIGHT SCIENCE最新文献

Comparative approaches are rarely utilized in futures studies despite the distinctive nature of different policy problems. Issues like climate change, infrastructure investments, and governance of emerging technology are frequently grouped under the umbrella of the “long-term problems” without adequate consideration for their distinct spatial and temporal attributes. To address this research gap, this paper presents a framework to systematically compare long-term policy problems, such as the risks of climate change and artificial intelligence (AI). I conduct a comparative analysis of the risks of climate change and AI—both widely regarded as pivotal questions of our time—focusing on how they differ across eight attributes that affect their governance: scientific certainty, spatiality, temporality, linearity, path dependence, accountability, capacity to address and the costs involved. The findings suggest that climate change involves a more evident intergenerational conflict between generations than risks of AI and might therefore be a more challenging long-term governance problem. Yet, both problems risk triggering irreversible lock-in effects, specifically in extreme scenarios such as crossing climate tipping points or misaligned advanced AI systems. Mitigating these uncertain lock-in effects requires precautionary governance measures, highlighting the potential of comparative approaches at the intersection of foresight and policy analysis.

Waste crime is a pressing concern for the waste and resource industry as it is undermining investment, growth and jobs within the industry and threatening the natural environment. However, there is little knowledge of the scale of the problem, the types of criminality and motivations involved, and the precise nature of crime. Environmental regulators are building foresight capabilities to better understand the effect of current and future changes in markets, in technology and in the legislative environment on waste crime and associated behaviours. At the heart of this paper is the question: how can horizon scanning be adopted by environmental regulators to shape decision processes and build resilience to waste crime? We report our efforts to build a toolkit and guidance for conducting horizon scanning, aimed at supporting environmental regulators, investigators and intelligence analysts to build an understanding of—and interpretation of the consequences of—behavioural, market, technological and pollution trends in the waste sector. A review of the academic and grey literature provided insights to organisational approaches and design principles for public sector horizon scanning. Outputs guided discussion at a stakeholder workshop with waste regulators, criminal intelligence and industry professionals to explore institutional challenges and to agree broad design principles for a horizon scanning process. The toolkit supports environmental regulators in applying horizon scanning to policy and wider operational and delivery-focused challenges; learning how to: (1) spot weak signals and emerging trends quickly, (2) examine the evidence around potential threats and opportunities for the future, and (3) take action on strategically important issues to minimise the impact of crime on the environment, society and business. The paper sets out further research needed to integrate horizon scanning with data analytics (e.g., predictive and hotspot analyses) to challenge assumptions about the patterns of change, based largely on historical trends, and to better manage these so there is greater adaptability to current and future trends.

People are often overconfident in their probabilistic judgments of future events or the state of their own knowledge. Some training methods have proven effective at reducing bias, but these usually involve intensive training sessions with experienced facilitators. This is not conducive to a scalable and domain-general training program for improving calibration. In two experiments (N₁ = 610, N₂ = 871), we examined the effectiveness of a performance feedback calibration training paradigm based on the Practical scoring rule, a modification of the logarithmic scoring rule designed to be more intuitive to facilitate learning. We examined this training regime in comparison to a control group and an outcome feedback group. Participants were tasked with selecting which of two world urban agglomerations had a higher population and to provide their confidence level. The outcome feedback group received information about the correctness of their choice on a trial-by-trial basis as well as a summary of their percent correct after each experimental block. The performance feedback group received this information plus the Practical score on a trial-by-trial basis and information about their overall over- or underconfidence at the end of each block. We also examined whether Actively Open-Minded Thinking (AOMT) was predictive of calibration and its change across blocks. We found no improvement in calibration due to either training regime. Good calibration overall was predicted by AOMT, but not its change across blocks. The results shed light on the generalizability of other findings showing positive effects of performance training using the Practical scoring rule.

Public administration in governments strives to develop forward-looking capacities. Foresight has come to the fore as a set of practices that is mobilized to counter political myopia. Despite this increasing interest in governmental foresight, research suggests that diverging institutional practices, organizational structures, and epistemic cultures between foresight practices and policy-making result in loose coupling. This article aims to contribute to understanding these complicated foresight-policy interactions. To this end, we draw on findings from public administration scholarship. The concept of institutionalization is employed to analyze how foresight aligns with working practices and routines in government. Based on an in-depth case study on the institutionalization of foresight practices within the German Federal Government, we demonstrate that scrutinizing the multiple dimensions of institutionalization helps not only to understand where and why loose ends occur but also provides new insights into some of the causes of the lacking impact. The article does not aim to provide an easy fix. Instead, we want to sensitize foresight practitioners, reduce both disappointment and overstating regarding the role of foresight in policy-making, and provide a nuanced understanding of foresight practices in government.

Corporate foresight (CF) can be considered a future-oriented capability that incorporates perceiving and prospecting. Extant CF-related studies tackle the CF−innovation relationship but do not provide details on how CF relates to innovation climate. As we assume that the innovation climate of companies is a relevant antecedent to innovation, we conduct a quantitative empirical study with 147 upper-tier managers to investigate how CF and the respective training of managers relate to a corporate's innovation climate. Results show that strong perceiving and prospecting positively influence the innovation climate, whereby prospecting is of particular importance. Further, we find that training managers in future-oriented capabilities is only under certain circumstances (i.e., low prospecting) beneficial to the innovation climate in companies.

Scenarios are commonly used for decision support and future exploration of complex systems. Using simulation models to generate these scenarios, called scenario discovery, has received increased attention in the literature as a principled method of capturing the uncertainty, complexity, and dynamics inherent in such problems. However, current methods of incorporating dynamics into scenario discovery are limited to a single outcome of interest. Furthermore, there is little work on the post-generation evaluation of the generated scenarios. In this work, we extend scenario discovery to multiple dynamic outcomes of interest, and present a number of visual and statistical approaches for evaluating the resulting scenario sets. These innovations make model-based scenario generation more widely applicable in decision support for complex societal problems, and open the door to multimethod scenario generation combining model-based and model-free methods such as Intuitive Logics or futures cones.