FUTURES & FORESIGHT SCIENCE最新文献

Tetlock et al.'s balanced contribution to the debate over the possibility of long-range geopolitical forecasting provides a useful roadmap at a critical time. An especially challenging geopolitical juncture compels heightened attention to systematic efforts of this sort that both identify limits on expert judgment and offer ways to overcome them. The task may be extremely difficult—skeptics abound—but is nevertheless vital for a social science true to the mission of enhancing peace and security. Tetlock et al. report findings from a previous study suggesting that expertise in nuclear weapons improved accuracy in predicting long-range proliferation. In particular, they argue, experts did not cry wolf; they exhibited low False-Alarm rates. Meliorists could find some reassurance there. And yet this field of inquiry has also seen significant and repeated overestimation of the odds of nuclear proliferation cascades. In 1963 President Kennedy foresaw the potential of between 15 and 25 nuclear weapons states by 1975. Yet rather than actual proliferation cascades, it is only predictions of imminent cascades, chains, and dominoes that have proliferated since (Potter & Mukhatzhanova, 2008). Such predictions have failed to materialize thus far for well over 60 years, a significantly long range. Radical Skeptics might find this vindicating. Understanding the sources of over-predicted proliferation on the one hand, and of the more accurate tally in Tetlock et al.'s findings on the other, may shed light on these two distinctive (past) readings of the future. It may also further improve the Meliorist's case for long-range predictions.

Over-predictions of nuclear proliferation may run the epistemological-ontological gamut, but I focus here on one systematic source of bias leading to massive False Positives for over 60 years. This record is especially, though not uniquely, the domain of a brand of neorealist theory alluring for its simplicity—“it's all about systemic anarchy and balance of power.” Anarchy presumably renders all states insecure, compelling self-help while acquiring nuclear weapons provides security, helps balance power, delivers stability, and minimizes the chances of war (the classic and most impressive locus is Waltz, 1979, 1981). Yet this analytical foundation has proved fatally flawed in its predictive tally. The massive number of predicted False Positives and anomalies in neorealist studies include Ukraine, Poland, Germany, Japan, and many, many more cases.1 If one abides by the theory's core tenets, anarchy, uncertainty, and self-help should have led most if not all states to acquire nuclear weapons. Yet an overwhelming majority (191 states!) have renounced them while nine have acquired them. Even more modest predictions (Waltz, 1981) of 18 to 30 nuclear weapons states have not materialized; and even acutely vulnerable states (e.g., Vietnam, Egypt, Taiwan, and man

This is a book review of Strategic Planning for Dynamic Supply Chains: Preparing for Uncertainty Using Scenarios by Shardul S. Phadnis, Yossi Sheffi, and Chris Caplice (Cham, Switzerland, 226 p, 2022). The book covers three case studies, presented as vignettes, which illustrate three unique applications of a seven-step approach to scenario planning, modeled after the Intuitive Logics School. The book is aimed at executives and business leaders, as well as academics, and scenario planning practitioners. This review discusses the unique aspects the scenario team brings to the strategic space, the strengths of their pragmatic process, and key elements in practice that are often left out of the larger academic scholarship.

Is prediction possible in world politics—and, if so, when? Tetlock et al. (2023) report some of the first systematic evidence on long-range political forecasting. Asked to guess which countries would get nuclear weapons within 25 years and which would undergo border changes due to war or secession, both experts and educated generalists outperformed chance. On nuclear proliferation—but not border changes—the experts beat the generalists, and the difference grew as the time scale increased from 5 to 25 years. What are we to make of this? The authors see messages for both “skeptics,” who consider the political future irreducibly opaque, and “meliorists,” who acknowledge the difficulties but think expertise can still improve predictions. Moreover, they suggest progress could be made through adversarial collaboration between scholars of the two persuasions, which would push both to specify their priors and adopt falsifiable positions.

It's hard not to admire a research paper that has been more than 25 years in the making—and one can only rejoice that picky referees did not insist the experiment be rerun from scratch. The results prompt two broader questions. First, what makes something easier or harder to predict? Second, when does expertise help? At the risk of restating the obvious, let me offer a few thoughts.

For clarity, consider a task like those in the article.¹ Respondents at time t must guess the value of a variable $���Y_{�i�,�t�+�N�}�\in �\{�\mathrm{Yes}�,�\mathrm{No}�\}��$, N years in the future, for I countries indexed by i. The “success rate” is the proportion of countries for which the respondent chooses correctly. A task of this kind, A, is “easier” for a given individual than another task, B, if that individual's success rate on A tends to be higher than his success rate on B.

When will that be the case? The authors give a few examples of easy and difficult tasks. That New Zealand and Norway will not fight a war is “trivially obvious” (p. 1). That anyone could guess who will be US president in 25 years is “far-fetched” (p. 2). They sought challenges for their re

As one of the approaches to scenario analysis, cross-impact methods provide a structured approach to building scenarios as combinations of outcomes for selected uncertainty factors. Although they vary in their details, cross-impact methods are similar in that they synthesize expert judgments about probabilistic or causal dependencies between pairs of uncertainty factors and seek to focus attention on scenarios that can be deemed consistent. Still, most cross-impact methods do not associate probabilities with scenarios, which limits the possibilities of integrating them in risk and decision analysis. Motivated by this recognition, we develop a cross-impact method that derives a joint probability distribution over all possible scenarios from probabilistically interpreted cross-impact statements. More specifically, our method (i) admits a broad range of probabilistic statements about the realizations of uncertainty factors, (ii) supports the process of eliciting such statements, (iii) synthesizes these judgments by solving a series of optimization models from which the corresponding scenario probabilities are derived. The resulting scenario probabilities can be used to construct Bayesian networks, which expands the range of analyses that can be carried out. We illustrate our method with a real case study on the impacts of three-dimensional (3D)-printing on the Finnish Defense Forces. The scenarios, their probabilities, and the associated Bayesian network resulting from this case study helped explore alternative futures and gave insights into how the Defence Forces could benefit from 3D-printing.

Scenario planning has a long history of academic inquiry and practice in numerous fields and industries; however, its future as a tool to manage strategic uncertainty may well have reached an impasse. While the academic community perpetuates the view that the field is characterized by methodological chaos, the practitioner community is concerned only with how scenario planning can help solve an organizational problem. This paper argues that the academic community would benefit from adopting a philosophical orientation that is “pragmatic” where theoretical and methodological sophistication should be traded-off against the need to produce a practical outcome that addresses a specific organizational problem. This would enable more academics to generate new knowledge that was “useful” rather than “generalizable.” Adopting a Pragmatic Philosophy would also address three primary issues asserted in literature on the process, content, and implementation of scenario-informed strategizing. This position paper provides a reflective account of how the narrative on scenario planning theory can be moved more effectively into scenario planning practice by illustrating the author's commitment to developing scenario-based actionable knowledge, high levels of implementable validity, and instrumental impact with organizations. As such, it presents a reflection on interventions that demonstrate how scenario-informed strategies were developed and implemented with successful organizational outcomes.

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.