Cross-fertilising scenario planning and business history by process-tracing historical developments: aiding counterfactual reasoning and uncovering history to come

Scenario planning is a tool for considering alternative futures and their potential impact. The paper firstly addresses the paucity of history on management tools by discussing several important lineages in scenario planning’s evolution over time, and the emphasis placed on historical analysis by some specific variants therein. Secondly, it describes how causal analysis can be enhanced in scenario planning by process-tracing important historical developments. Thirdly, it outlines how a scenario planning that incorporates history in this way can assist historians to identify counterfactuals and understand the relative importance of alternative causes, thus enriching historical accounts. It can also enable business historians’ research on the relationship between businesses and their external environments, and on management decision-making. In concluding, scholars of scenario planning and business history are urged to open a mutually-beneficial dialogue. The paper initiates this by setting out some ways in which they can cross-fertilise each other

The siren call of probability: dangers associated with using probability for consideration of the future

Many tools for thinking about the future employ probability. For example, Delphi studies often ask expert participants to assign probabilities to particular future outcomes. Similarly, while some scenario planners reject probability, others insist that assigning probabilities to scenarios is required to make them meaningful. Formal modelling and forecasting methods often also employ probability in one way or another. The paper questions this widespread use of probability as a device for considering the future, firstly showing that objective probability, based on empirically-observed frequencies, has some well-known drawbacks when used for this purpose. However, what is less-widely acknowledged is that this is also true of the subjective probability used in, for example, Delphi. Subjective probability is less distinct from objective probability than proponents of its use might imply, meaning it therefore suffers from similar problems. The paper draws on the foundations of probability theory as set out by Kolmogorov, as-well-as the work of Keynes, Shackle, Aumann, Tversky and Kahneman, and others, to reassert the essential distinction between risk and uncertainty, and to warn about the dangers of inappropriate use of probability for considering the future. The paper sets out some criteria for appropriate use

The implications, challenges and benefits of a complexity-orientated Futures Studies

Complexity science is increasingly cited as an essential component of a Futures Studies (FS) capable of assisting with the wide-ranging and complex societal problems of the 21st century. Yet, the exact implications of complexity science for FS remain somewhat opaque. This paper explicitly sets out the challenges for FS that arise from six complexity science concepts: 1) irreversibility of time 2) path dependence 3) sensitivity to initial conditions 4) emergence and systemness 5) attractor states 6) complex causation. The discussion highlights the implications of these challenges for FS tools such as horizon scanning and weak signals, and sets out the benefits of overcoming the challenges to create an explicitly complexity orientated FS. The discussion concludes with a set of questions summarising the challenge for FS from complexity science with the aim of stimulating a discussion as to how they can be met. The concluding remarks make some initial suggestions in this regard

Answers to questions on uncertainty in geography: old lessons and new scenario tools

In many domains, including geography, there can be the implicit assumption that improved data-analysis and statistical modelling must lead to improved policymaking, and its perceived failure to do so can be disconcerting. Yet, this assumption overlooks the fundamental distinction between epistemological and ontological uncertainty, as discussed herein. Epistemological uncertainty describes the known and bounded inaccuracy of our knowledge about the world as now. Whereas ontological uncertainty describes the rendering completely obsolete of this present knowledge by surprises in the form of currently unknown future events, and by cascading changes to beliefs, attitudes and behaviours made by diverse actors in response to - and in anticipation of others’ responses to - new developments. This paper does the following: 1) shows that, because of ontological uncertainty, improved data-analysis and statistical modelling can never lead straightforwardly to improved policymaking, no matter how well implemented; 2) outlines how probability-based tools offer little assistance with ontological uncertainty because they are based on present perceptions of future possibilities; 3) urges geographers to reconcile with ontological uncertainty as a source of potentially transformational change, rather than viewing it as a problem to be overcome, or something to be defended against; 4) reviews a range of new, non-probabilistic scenario tools that, when used in combination, can assist in harnessing ontological uncertainty for transformational purposes by surfacing what is to be gained and by whom from enabling, blocking or altering intended policy outcomes, and by searching for future possibilities unconstrained by the present

Review of Mario Morroni, 'What is the truth about the Great Recession and increasing inequality? Dialogues on disputed issues and conflicting theories', Cham: Springer, 2018 [Book review]

Mario Morroni’s 2016 book on the Great Recession has now been translated from its original Italian and published in English by Springer. It makes accessible to the general reader, and those just embarking on economic studies, discussions and debates on the causes and effects of the Great Recession, the impact from which we still suffer more than a decade on. Nine dialogues are presented in the form of chapters, each of which focuses on a key theme: Increasing inequality; The failure to predict the Great Recession; Why fiscal austerity?; Rolling back the welfare state; The state and the market; The gigantic German trade surplus and the euro; Crisis policy; Environmental degradation; Industrial policy

Use of scenario planning as a theory-driven evaluation tool

Theory-driven evaluation (TDE) is an approach for prescribing an evaluation’s purposes, users and uses, general activities, strategies and methods in the form of a ‘program theory’. While widely-used, the literature highlights a number of common deficiencies in TDEs, among which is the tendency for underdeveloped program theories because of a lack of specificity on the theory-creation process, and because the emergent nature of change renders it difficult to identify relevant theory a priori, leading to uncertainty. Theoretical underdevelopment may reduce the effectiveness of change initiatives and make their evaluation problematic due to a lack of clarity regarding what the program was originally expected to achieve, and how. The paper addresses this issue by showing that scenario planning can assist TDE by 1) making explicit initial causal logic and theory 2) facilitating useful debate and discussion among multiple stakeholders 3) facilitating consideration of how contingent and complex causation may lead to unexpected outcomes, allowing for consideration of adaptations that may be needed as a program unfolds. The paper shows that scenario planning is highly congruent with a complex-realist understanding of evaluation that emphasises causal indeterminism. In sum, we show how scenario planning can be used as a theory-driven evaluation tool

Potential surprise theory as a theoretical foundation for scenario planning

Despite some recent progress, scenario planning’s development as an academic discipline remains constrained by the perception it is solely a practical tool for thinking about the future, with limited theoretical foundations. The paper addresses this issue by showing that G. L. S. Shackle’s ‘Potential Surprise Theory’ (PST) contains much that can lend theoretical support to scenario planning - especially its use of plausibility rather than probability, and its focus on potential extreme outcomes. Moreover, PST and scenario planning share the same ontology, viewing the future as constructed by the imagination of individuals. Yet, under PST, while the future is imagined and, therefore, subjective, individuals nevertheless seek to identify the ‘best’ option through a deductive process of elimination. PST therefore assists in overcoming the divide between the constructivist and deductivist perspectives in scenario planning as it employs both. Finally, the paper shows that theoretically underpinning scenario planning with PST would place it at the heart of contemporary debates on decision making under uncertainty taking place in economics and other fields, enhancing its status and profile as a discipline

The impact of ambidexterity on enterprise performance: evidence from 15 countries and 14 sectors

The assumption made by research on ambidexterity is that enterprises operating ambidextrously perform better as a result. Similarly, the beneficial effects of ambidexterity are often assumed to be invariant across different contexts, such as sector. However, as is widely acknowledged in the literature, there is a paucity of evidence on which to base these assumptions. To address this issue, in this note we examine evidence from the Community Innovation Survey covering 15 countries and 45,113 enterprises. The paper shows a strong, positive effect on growth in sales turnover from ambidexterity in the manufacturing and the scientific and technical services sectors

Is seeking certainty in climate sensitivity measures counterproductive in the context of climate emergency? The case for scenario planning

Climate emergency is fast becoming the overriding problem of our times and rapid reductions in carbon emissions a primary policy focus that is liable to affect all aspects of society and economy. A key component in climate science is the “climate sensitivity” measure and there has been a recent attempt using Bayesian updating to narrow this measure in the interests of “firming up the science”. We explore a two-stage argument in this regard. First, despite good intentions, use of Bayes sits awkwardly with uncertainty in the form of known unknowns and surprise. Second, narrowing the range may have counterproductive consequences, since the problem is anthropogenic climate change, and there are asymmetric effects from under-response in the face of irreversible and ampliative effects. As such, narrowing the range using Bayes may inadvertently violate the precautionary principle. We take from this that there is a case to be made for scenario focused decision frameworks