FUTURES & FORESIGHT SCIENCE最新文献

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.

The existence of free will and its prerequisites have entertained countless debates. The position one takes in these debates dictates their outlook on limitations of predicting human psychology. Sam Harris (2012), in his book, misses out on exploring these consequences accepting or rejecting free will has for the predictability of human decision-making and action-taking. Likewise, referencers of his work addressed this topic only peripherally and, to the best of my knowledge, have never explored its consequences in depth. Therefore, in the present review, I aim to demonstrate and formalize the following: If we understand human cognition and behavior as the result of environmental and individual causes that both are knowable, adding a per definition indeterminable source, namely, free will, can only reduce the maximum possible power of any used prediction model. Accepting a free human will renders error-free foresight theoretically, and empirically, impossible. This has consequences for all forecasting and planning methods that concern human agents as well as their tools’ utility (e.g., scenarios and psychological analysis). I address the consequences free will belief has conceptually for three application fields: management and leadership, historic analysis, and theory building.

An already pressing need to evidence the effectiveness of futures and foresight tools has been further amplified by the coronavirus pandemic, which highlighted more mainstream tools' difficulty with uncertainty. In light of this, the recent discussion in this journal on providing futures and foresight science with a stronger scientific basis is welcome. In this discussion critical realism has been proffered as a useful philosophical foundation and experiments a useful method for improving this field's scientific basis. Yet, experiments seek to isolate specific causal effects through closure (i.e., by controlling for all extraneous factors) and this may cause it to jar with critical realism's emphasis on uncertainty and openness. We therefore extend the recent discussion on improving the scientific basis of futures and foresight science by doing three things. First, we elaborate on critical realism and why the experimental method may jar with it. Second, we explain why the distinction between a conceptual and a direct replication can help overcome this jarring, meaning experiments can still be a valuable research tool for a futures and foresight science underpinned by critical realism. Third, we consider the appropriate unit of analysis for experiments on futures and foresight tools. In so doing, we situate the recent discussion on improving the scientific basis of futures and foresight science within the much longer running one on improving the scientific basis of business, management and strategy research more broadly. We use the case of scenario planning to illustrate our argument in relation to futures and foresight science.