FUTURES & FORESIGHT SCIENCE最新文献

This paper performs a backcasting wheel analysis of the issue of democratic backsliding. It identifies an interacting set of proximate causes of democratic backsliding, and then a set of complementary strategies for addressing the root causes of each of these. It takes an interdisciplinary systems-based approach throughout. The paper is grounded in an extensive survey of several literatures in multiple disciplines. It shows how the backcasting wheel complements other methods employed in anticipation and future studies more generally.

Our study emphasizes the evolving nature and increasing relevance of corporate foresight (CF) in a volatile, uncertain, complex, and ambiguous world. This research, therefore, contributes to the foresight literature, concerned with the antecedents and motivators of CF as it investigates how external events that are characterized by great uncertainty influence the CF practice of large companies. On the example of the COVID-19 pandemic and based on a study of 25 interviews, this empirical research reveals that such events provoke an intensified engagement with the future within companies as implied by a greater resource commitment and interest in foresight. Thereby, CF and especially the development of multiple future scenarios, are considered helpful in countering uncertainty and facilitating responsiveness.

We describe an exploratory study examining the effectiveness of an interactive app and a novel training process for improving calibration and reducing overconfidence in probabilistic judgments. We evaluated the training used in the app by conducting an American college football forecasting tournament involving 153 business school students making 52 forecasts over 11 weeks. A coarsened exact matching analysis found statistical evidence that, in under 30 min, the more challenging training was able to modestly reduce overconfidence, improve calibration and improve the accuracy of probabilistic judgments (measured by the Brier score). The experimental results also suggest that the generic training can generalize across domains and that effective calibration training is possible without expert facilitators or pedagogical training materials. Although no previous studies have reported similar results, due to the modest effect, we conclude that these results should only be interpreted as a proof of concept and that further evaluation and validation of mechanisms of the app's effect is necessary.

System dynamics (SD) is a methodology to generate qualitative and quantitative models. SD has two main concepts that are highly suitable to use with scenarios: feedback processes that define the structure of sociotechnical systems and accumulation processes that are responsible for the dynamic behavior of systems over time. This article discusses a framework that integrates methodologically scenarios and SD. The integration can take multiple forms depending on the use of SD for creating or supporting scenarios. The framework is illustrated with multiple examples. Since SDs' practice uses processes similar to scenario practice, mutual enrichment between the communities can be highly successful.

Urban living is changing. Citizens explore new lifestyles in practice and envision alternative urban futures. Companies, as significant stakeholders in cities, are building urban infrastructure and serving citizens' needs. To succeed in the future, companies must be able to navigate the complexity and diversity of evolving cities. However, former corporate foresight research on cities is sparse. This participatory corporate foresight study examines city life in 2040 by engaging a group of London citizens with a lead user profile. A qualitative multimethod approach, consisting of online diaries, a futures imagining workshop, and in-depth interviews, explores citizens' images of the future and lifestyle changes. The lifestyle adjustments experienced during the COVID-19 pandemic period have acted as a catalyst for novel personal and communal futures. The broad range of visions included both continuity and discontinuity, with a moderately optimistic undertone, and featured communal, infrastructural, ecological, and technological aspects of life. These visions emphasized sustainability in multiple areas of life and demonstrated the dynamic relationship of the past, present, and future. The methodological contribution of this paper lies in its multimethod approach, which enabled an agile collection of textual and audio-visual datasets in both online and face-to-face contexts. This agility is particularly relevant in a corporate foresight context, where companies must balance efficiency, depth, and applicability while operating under resource constraints. The article also extends the lead user approach's use from individual products and services to the city environment and urban lifestyle in general.

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.

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