FUTURES & FORESIGHT SCIENCE最新文献

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.

“Values” play an oversized role in strategic foresight: they help define scanning frameworks, direct scanning efforts, inform change driver and scenario development, and underpin change within various systems and domains (e.g., politics, society, etc.). And yet, values are largely understudied within foresight. They are rarely defined consistently or explored with reference to a theoretical model of how values emerge or evolve. Rather, values are researched using dissimilar methods depending on the foresight research at hand, which can lead to gaps in analysis and inconsistency between foresight projects. Moral Foundations Theory (MFT), a social psychological theory that identifies common human moral values, offers a solution. MFT describes six moral values or “foundations”—care, fairness, loyalty, authority, sanctity, and liberty—each explained through the evolutionary development of humanity and detectable across cultures. Within foresight, MFT can be applied to understand and identify shifts in the influence of different values, which can result in more novel and unexpected conclusions. With these potential benefits available, we propose adopting and adapting MFT for use within the foresight to improve the way it approaches, identifies, and utilizes values. Our article unpacks MFT into its core tenets and illustrates how it can be used to inform scanning, change driver development, and scenario construction.

Since overall image and attractiveness can play a key role in developing a destination's tourism market, the current study attempts to identify the main factors influencing Iran's overall image and attractiveness from a supply-side perspective. To reduce the uncertainties in Iran's future tourism market development, we first used factor analysis to identify factors that affect Iran's tourism market. Second, we used multiple linear regressions to examine the relationships between the identified factors and Iran's overall image and attractiveness as a destination. According to the findings, political, product, and service factors significantly affect Iran's overall image and attractiveness. Third, we provided scenarios for improving Iran's overall image and attractiveness based on cross-impact balanced analysis.

Since its introduction decades ago, the Delphi method has become a popular instrument of broad application within the field of technology foresight and for wider science, technology, and innovation policy-making. The Delphi method has also attracted interest from a methodological perspective. The literature on the Delphi method demonstrates a research gap on the issue of Delphi statements and respondents' understanding of those statements. Based on a systematic literature review, this paper examines the existing knowledge and practices in constructing Delphi statements. A key contribution of the paper is a set of practical recommendations for constructing Delphi statements for technology foresight. Furthermore, the paper suggests future research on the topic.

Designing backcasting scenarios is a powerful approach to the development of sustainable visions and pathways for governments or enterprises in the early stage of their policy-making or strategic decision-making process. To date, a number of scholars have proposed various backcasting methods, in which workshops are often used to reflect the voices of stakeholders. However, it is still a challenge to test the validity of scenarios because the process of designing backcasting scenarios is not transparent or shared among involved stakeholders. This also prevents reusing knowledge and intermediate outputs generated during the scenario design process. To solve these problems, this paper aims to develop a method for supporting a backcasting scenario design by introducing computational assistance. A scenario design support system called the sustainable society scenario (3S) simulator is used to visualize a scenario's logical sequence of the scenario in graph format. To demonstrate the proposed method, a case study for the city of Toyama, a Japanese municipality, was performed using three workshops with citizen participation. The results showed that two different scenarios, involving future visions and associated pathways, were developed by reflecting the diversified values of local citizens. The usage of the 3S simulator visualized the logical relations of the described scenarios, which consist of five blocks—problem definition, subgoals to achieve visions, measures to attain these subgoals, verification, and conclusions. This visualization is effective to increase the verifiability and reusability of the scenarios for evidence-based policy-making processes.

Averaging quantiles as a way of combining experts' judgments is studied both mathematically and empirically. Quantile averaging is equivalent to taking the harmonic mean of densities evaluated at quantile points. A variance shrinkage law is established between equal and harmonic weighting. Data from 49 post-2006 studies are extended to include harmonic weighting in addition to equal and performance-based weighting. It emerges that harmonic weighting has the highest average information and degraded statistical accuracy. The hypothesis that the quantile average is statistically accurate would be rejected at the 5% level in 28 studies and at the 0.1% level in 15 studies. For performance weighting, these numbers are 3 and 1, for equal weighting 2 and 1.

Large organizations usually spread their research and development (R&D) around the world, hindering coordination efforts when performing future-oriented technology analysis (FTA) studies. FTA is commonly used in R&D planning, especially on research prioritization. Therefore, we developed an FTA framework for large and geographically spread organizations, named Tiamat. The Tiamat framework is composed of a simplified organization model, which focuses on internal and external interactions during FTA, and an FTA process, which indicates the steps performed by the decision-maker need until the delivery of the FTA final report from the organization. Based on the Tiamat framework, we developed the Tiamat software, a modular tool that implements generic FTA using the concept of workflows of FTA methods. Thus, we describe the architecture and collaboration aspects of the Tiamat software. The Tiamat framework (i.e., model and process) and the Tiamat software were applied in real situations to evaluate its FTA support capability. Besides, we show non-FTA uses of the Tiamat software to showcase its flexibility. Among the contributions, we highlight the use of the Tiamat framework in the management of a standardized FTA process, based on the concept of workflows. The framework was developed to be easily adapted to many types of organizations, such as businesses, universities, and public institutions, either civil or military. The Tiamat software has shown to be capable to support distributed and local FTA studies, as well non-FTA studies.