FUTURES & FORESIGHT SCIENCE最新文献

This paper revisits a 1997 Harvard Business Review article, “Strategy Under Uncertainty,” 25 years after publication, to selectively and critically extract its insights for the current era in futures and foresight work. It relates the original article to ongoing purpose and methodological issues in the futures field and outlines the ways its concepts remain pertinent in academic futures understanding and organizational futures practice.

The Delphi method is a technique of social research that seeks to obtain a reliable group opinion from experts. It was first created for military purposes in the mid-1950s. Since then, its use in the scientific field has continued to spread to different disciplines and aims. Despite this expansion, however, not set of indicators of the quality of Delphi studies has yet to be developed that might provide the reader—whether an expert in the technique or not—with some framework of reference whereby to gauge what credibility should be afforded to the results of the study. In this paper, following a thorough review of the literature on the criteria used to assess Delphi studies and the items of evaluation recommended for inclusion in Delphi reports, we determine what characteristics a quality evaluation indicator for this technique should have and propose a battery of indicators based on these characteristics, which should for preference be included in the final report of a Delphi study. The proposed indicators focus on three areas that are particularly relevant to the quality of Delphi research: the quality of the panel of participating experts, the way in which relevant information is obtained from the experts, and the quality of the interaction generated among the experts.

Prediction is hard, especially about the future. But not always. Predicting human behavior at the extremes is fairly easy. Within reason, it's quite straightforward to predict what someone will do tomorrow, at least with respect to their day-to-day routine. It's called a “routine” for a reason. At the other extreme, over eons of human existence, it's quite plausible to predict that the continents will reconnect, dramatically altering the current geographic balance of power. Even further out, although humans could well explore the universe and even establish new homes outside of Earth, we also know, at least according to our current knowledge, that the universe will suffer from heat death.

However, those extremes are not what we care about. The relevant time frame, as acknowledged by the Tetlock et al. piece, is between these extremes, say several years or even a few decades from now. On the one hand, examples of amazingly accurate predictions based on long-term forecasts do seem possible. Perhaps the classic example is John Maynard Keynes' Economic Consequences of the Peace. Noting that the Treaty of Versailles had “nothing to make the defeated Central Empires into good neighbors, nothing to stabilize the new States of Europe, nothing to reclaim Russia,” he predicted, quite ominously and perhaps more accurately than even he realized, that “great privation and great risks to society have become unavoidable” (Keynes, 1919, pp. 226 & 255).

And yet, for each prediction that exhibits such accuracy, there many that are, quite frankly, way off. Consider a data rich enterprise in which accurate forecasts are sought after and valued: population growth. Forecasts of population growth over decades are notoriously difficult despite great effort to make them sound. The uncertainty in such forecasts needs to be explicit, because, as demographer Lee (2011, p. 572) observed, “population projections motivate painful decisions about tax increases, benefit cuts, retirement age, and measures to offset global warming, we need careful measures of their uncertainty”.

Rather than “cherry picking” a particularly good or bad prediction from the past, Tetlock et al. provide systematic assessment of medium-term prediction accuracy. Specifically, they offer an assessment of the Expert Political Judgment project, evaluating the forecasts offered by project participants in 2 years, 1988 and 1997. Moreover, rather than considering a range of topics, the authors reassess the experts’ predictive judgments on two “slower moving” topics: stability versus change in national borders, and nuclear-power status. By the year 2022, 25 years had passed since the later set of forecasts and 34 years had passed since the first set of forecasts. This offers ample time for the predictions offered in those years to pan out. If medium term geopolitical forecasting is in any way possible, it will be found here.

What they find encourag

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.