FUTURES & FORESIGHT SCIENCE最新文献

Intelligence analysis has traditionally relied on inside-view, case-specific modes of thinking: why did this actor—say, the USSR—do that and what might it do next? After 9/11, however, analysts faced a vastly wider range of threats that necessitated outside-view, statistical modes of reasoning: how likely are threats to emerge from actors of diverse types operating in situations of diverse types? Area-study specialists (who staffed most geopolitical desks) were ill-equipped for answering these questions. Thanks to advances in long-range sensing, digitization, and computing, the intelligence community was flooded with data, but lacked clear ideas about how to render it relevant. Empiricism, whether grounded in deep inside-view knowledge of particular places or broad outside-view knowledge of statistical patterns across the globe, could not and cannot solve the problem of anticipating high-impact, rare events, like sneak attacks and pandemics. Contingency planning for these threats requires well-calibrated conditional forecasts of the impact of policy interventions that in turn require synthesizing inside- and outside-view analytics. Such syntheses will be best achieved by refining computer simulations that permit replays of history based on the interplay among initial conditions, chance, and social-science models of causation. We offer suggestions for accelerating the development and application of theory-guided simulation techniques.

We offer an argumentative explanation of the reasons why the field of futures and foresight has not been successful at becoming part of the social scientific establishment. We contend that the very set of norms, beliefs, and epistemological foundations of futures and foresight are essentially self-sabotaging as they resist the creation of scientific theory on futures and foresight practices and processes in organizations. Drawing from the tradition of management and organization sciences, we describe what scientific theory in the context of organizations is and is not, and how theory development contributes to the incremental progress of scientific fields. We then unpack the crux of the problem, deconstructing the resistance to scientific theory within our field into nine, closely related reasons. We offer solutions to the problem in the form of three sets of recommendations: for authors, journal editors, and practitioners. We conclude by responding to likely misunderstandings in advance.

Schoemaker's paper “How Historical Analysis Can Enrich Scenario Planning” expertly portrays the differences and similarities between historical analysis and scenario planning. While both fields study developments over time, historians are focused on looking backward while scenario planners look forward. Examining the parallels, Schoemaker gives an informative analysis of how both fields operate and illustrates the challenges with a 1992 scenario planning exercise on South Africa's post-apartheid future. He concludes with the notion that “..historians and scenario planners can very much learn from each other” with the qualifier that this will be challenging, as both disciplines are still developing learning and research methods within their own respective fields.

The paper is extremely timely as academics and practitioners are trying to make sense of (and learn from) the unexpected developments perturbing world platforms via Covid-19. Ironically, many countries and organizations have had scenarios for pandemic outbreaks for years. Still, COVID-19 came as a surprise. It appears that no effective planning was done, no proactive measures were taken and governments were overwhelmed while experts warned for similarities to historical outbreaks and drew attention to lessons learned from past epidemics (Snowden, 2020). The turbulence surrounding Covid-19 presents a productive living and learning laboratory that confirms the importance of how historical analysis can enrich scenario planning as suggested by Schoemaker; and it also highlights how constructing scenarios are not sufficient if they do not translate to forecasts and actions.

Our efforts to better understand how historical analysis-scenario planning dynamics could lead to improved forecasts and decisions will need to include studies on biases and informational asymmetries that permeate past–future synergies. Biases are systematic deviations from norm or rationality in judgment, influenced by the context and framing of information (Hasselton, Nettle, & Andrew, 2005). The literature on biases is extensive and reaches back to Amos Tversky and Daniel Kahneman's seminal work (Tversky & Kahneman, 1974). Biases play a key role in the context of foresight and scenario processes (Bradfield, 2008; Schirrmeister, Göhring, & Warnke, 2019; Schoemaker, 1993; Wack, 1985), while also affecting the way we view historical events (Mccullagh, 2002; Mukharji & Zeckhauser, 2019).

The work on biases can provide an additional perspective to Schoemaker's portrayal of similarities and differences between the two fields. One crucial lesson learned from this paper is that we will be better in looking forward (scenario planning) if we learn how to look back (historical analysis of information). While this is a valid point, historical analysis can be subject to misinformati