FUTURES & FORESIGHT SCIENCE最新文献

To mobilize science and technology for sustainability, it is essential to develop a method for explicitly considering the needs and preferences of future generations in designing research strategies and technology innovations. In this study, we conducted a participatory deliberation experiment on research strategy design of hydrothermal reactions and slag, to analyze whether the adoption of imaginary future generations (IFGs), which is a social system that has been proven to be effective for overcoming shortsightedness and activating futurability of people and society in pursuit of sustainability, could change the direction of research and development (R&D) and thereby innovation. A questionnaire survey was administered to the participants after each deliberation session to verify whether treatments, such as analyzing past R&D initiatives and adopting IFGs in deliberations, would change participants’ perceptions about criteria related to designing R&D programs. The results of the deliberation experiment showed that the contents and ideas of research strategies, such as research visions, methodologies and anticipated benefits, were changed by the adoption of IFGs. The criteria used for designing R&D also altered according to changes in research strategy. The findings showed that adopting IFGs and examining issues from the viewpoint of “futurability” could shift the direction of research agendas and technological innovation. Furthermore, the findings could provide insights into how to design R&D strategies and generate innovations in pursuit of sustainability by reflecting upon the needs of and benefits to future generations.

“Postnormal” currents of thought, which are herein used in reference to the post-normal science and the postnormal times frameworks, have been tremendously useful to help us understand the limits of science and the nature of societal change. Yet a blanket adherence to these frameworks without scrutiny risks falling into an unsubstantiated ideology. In response to and as a prevention of this risk, this article explains and critiques the two frameworks. It explains that post-normal science is both a description of a recent trend in science applied to policy contexts and a prescriptive response to new conditions of scientific inquiry. It also explains that postnormal times is both description of societal change and expression of subjective feelings elicited by such change. The two frameworks' merits and limitations are also discussed. The article's conclusion is that while the post-normal science framework can be rescued with some further qualifications, the postnormal times framework is particularly problematic. The discussion of the two frameworks' limitations is used as a warning against ideological positions that prevent fruitful research in the field of futures and foresight, and to encourage a more informed use of the term “postnormal.”

Skeptics see long-range geopolitical forecasting as quixotic. A more nuanced view is that although predictability tends to decline over time, its rate of descent is variable. The current study gives geopolitical forecasters a sporting chance by focusing on slow-motion variables with low base rates of change. Analyses of 5, 10, and 25-year cumulative-risk judgments made in 1988 and 1997 revealed: (a) specialists beat generalists at predicting nuclear proliferation but not shifting nation-state boundaries; (b) some counterfactual interventions—for example, Iran gets the bomb before 2022—boosted experts’ edge but others—for example, nuclear war before 2022—eliminated it; (c) accuracy fell faster on topics where expertise conferred no edge in shorter-range forecasts. To accelerate scientific progress, we propose adversarial collaborations in which clashing schools of thought negotiate Bayesian reputational bets on divisive issues and use Lakatosian scorecards to incentivize the honoring of bets.

The Delphi technique is a commonly applied method for (among the various uses) achieving consensus from a group of knowledgeable experts. This approach is frequently employed to generate and prioritize ideas, identify potential solutions, and make decisions in various contexts through a series of iterative rounds. In the Futures Studies (FS) context, the Delphi method is regularly used in combination with the scenario method to explore different futures, implementing strategies in the present with the aim of averting dystopian outcomes and/or facilitating normative scenarios. Nevertheless, assuming that the convergence of opinions can also occur in spatial contexts, a shortcoming of the method is the deficiency of spatial references useful in the planning process. In this paper, we introduce the Real-Time Geo-Spatial Consensus System, a novel web-based open platform useful to develop Delphi-based Spatial Scenarios (DBSS), in an interactive and innovative interface. The platform adopts the Real-Time spatial Delphi technique to obtain a spatial convergence of opinions among experts to offer researchers, decision-makers, policymakers, and local authorities a new tool for complex spatial decisions. The primary innovations of the platform, including its architecture, statistical algorithms, tools, features, and outcomes, are demonstrated through a preliminary application focused on potential future climatic hazards in Dublin, Ireland.

The impact of the external environment on strategy is a long debate in the “traditional” strategic management theory. In contrast, future studies have a clearer view, having established that the environment is perceived and has a direct impact on how the future is anticipated. The same field has grown significantly, in the last two decades, with regard to how companies should foresight the future, looking into their external environment. However, we observe that there is a lack of retrospective research on how companies have embraced extreme environmental events, surprises, and wildcards which led to crisis with regard to their perception of the external environment and the strategies developed for the future. This study fills this gap with a multimethodological approach, combining survey and archival data, to examine the relationship between the perceived environmental turbulence and corporate strategy in some of the largest UK-listed companies. We use the recession of the UK economy in 2008 as the key phenomenon to compare the relationship between perceptions of the environment and corporate strategy before the economic recession (2007) and after (2009). With our analysis, we provide evidence of how the environment influences corporate strategy and we show how a wildcard, like the recession, changed the perception of environmental turbulence, which resulted in greater adoption of the risk averse, retrenchment strategies.

The authors of this thoughtfully crafted article argue cogently that increased efforts should be taken to strengthen the role of experiments in building an accumulated body of knowledge of scenario planning. While such efforts can foster the emergence of promising research results, it is pertinent to remain cognizant of the realities which put limits on what experiments can contribute to the advancement such knowledge. Many of these realities ensue from the distinctive characteristics of scenario planning as an intervention (or, using the terminology of experimental design, the “treatment” as the independent variable). Such interventions can be carried out in alternative ways to promote desired outcomes (e.g., inducing changes in the participants' mental models). Apart from the intervention, these outcomes also depend on contextual factors of which some may not be under the experimenter's control. For instance, because scenario planning is typically a group activity, the outcomes depend not only the selected scenario method but also on how well the participants are able to communicate with each other, which in turn depends on their linguistic skills, cognitive abilities, and educational background, including familiarity with futures studies.

For starters, one can posit that the variables in terms of which the interventions, outcomes, and contextual factors are characterized should be similar enough to those encountered in the practice of scenario planning. This would be a prerequisite for interpreting experiments from the viewpoint of practice and for inferring tentative generalizations. Without such a correspondence, there is a potential danger that the experimental research would evolve as a semi-independent activity which—despite fostering the emergence of a continuing stream of empirical experiments as such—would have limited impact in informing the work of practitioners who would continue to rely on their accumulated body of expertise and the insights that they have gained from the many sources of information at their disposal, including anecdotal evidence in reported case studies.

There is an inherent challenge in that if the interventions (e.g., variants of scenario processes), their outcomes (e.g., impacts on mental models), and contextual factors (e.g., participants’ level of trust in each other) are specified with a higher level of granularity, it becomes exceedingly laborious to carry out sufficiently many experimental runs to arrive at validated—perhaps statistically significant—conclusions about the likely outcomes of a given scenario approach in a specific planning context.

To illustrate this point, consider a setting in which there are five participants in each scenario group and four alternative interventions to scenario development based on two variables, (i) the number of scenarios (small vs. large) and (ii) the approach to the characterization of uncertainties (quantitative vs. qualitative). Furthermore, as

This “reflections from practice” piece explores some of the implications of emerging, artificially intelligent tools for the futures and foresight prac-ademic community. The authors provide background on these emerging, artificially intelligent tools, and explore, with special emphasis on scenarios, a specific tool named “Chat Generative Pre-trained Transformer” (hereafter, ChatGPT). The authors examine the utility of scenarios generated by artificial intelligence (AI) and explore whether or not the futures and foresight prac-ademic community should selectively embrace advances in AI to assist in the generation of scenarios. In particular, the authors will consider (1) the utility of using scenarios generated completely by AI, (2) whether what is produced, in fact, constitute scenarios, based on conventional definitions, and (3) assess the utility of using AI to assist in the production of scenarios. At this point in time, artificially intelligent tools can now generate numerous scenarios on seemingly any topic at essentially zero cost to the user. Still, the authors insist that the utility of those scenarios is largely predicated on the user's ability to coax the appropriate “raw material” from the artificially intelligent bot, which implicates, the authors contend, that such bots can usefully provide base material for the development of scenarios but are unlikely to fully eclipse scenarists in the production of scenarios. Additionally, the authors recommend that the futures and foresight prac-ademic community pay especially close attention to artificially intelligent tools for novel insights with regard to the differences in human cognition and, in this case, the logic of large language model outputs.