[Note on Brand Evolution] This post discusses concepts and methodologies initially developed under the scientific rigor of Shaolin Data Science. All services and executive engagements are now delivered exclusively by Shaolin Data Services, ensuring strategic clarity and commercial application.
Given the modern view and usage of scenario planning, it is easy to feel that it differs from forecasting. However, Koehler & Harvey (2004) emphasized that scenario planning, in its completeness, is a forecasting tool. It offers a compromise between formal probabilistic models and informal conjecture (Koehler & Harvey, 2004, pp. 274–296). Scenario planning begins by identifying current trends and continues by identifying outliers and regions of discontinuity that escape or exceed expected results (Derbyshire & Giovannetti, 2017). In other words, scenario planning aims to analyze the edge cases that do not generate sufficient data for probabilistic models by applying a plausibility argument. To understand this, consider the most commonly used form of scenario planning, called Intuitive Logics (IL).
Understanding Intuitive Logics
The core of Intuitive Logics requires identifying existing trends and considering them alongside causal components (Derbyshire & Giovannetti, 2017). As a subset of scenario planning, IL’s mechanism is also path-dependent, recognizing aspects of the future that it identifies as convergent or divergent. Specifically, it describes a future state in a horizon year and must be a plausible, internally consistent, and purposeful story of how the future could unfold (Derbyshire & Wright, 2017). This internal consistency mandates a logically sequential flow from one event to the next. The dynamism of the contained story’s predetermined elements describes interrelationships and reveals logical consequences.
The revelation of logical consequences implies that the stories and interrelationships are functional systems describable in terms of Newtonian mechanics (Derbyshire & Wright, 2017). Given sufficient data, this descriptive capability implies that each system can be mathematically detailed with a probability distribution. However, outliers and events with infinitesimal probabilities typically do not generate enough data to construct such a distribution. The systems that IL describes are still aligned with Newtonian perspectives, even if they lack sufficient data for traditional modeling.
The Process of Intuitive Logics
The final stop along this exploration is the steps of Intuitive Logics. The second step of IL involves identifying existing trends and making projections using probabilistic analytics (Derbyshire & Wright, 2017). These projections are models based on the predetermined elements’ data and describe future trend continuity implications. However, because IL is a scenario-planning derivative, it must acknowledge potential implications instead of likelihoods. Nevertheless, the second step still calls for using forecasting techniques for convergent identification (Derbyshire & Wright, 2017). As mentioned, the divergent cases need to generate more data for such techniques to be effective. Thus, while IL and scenario planning do not give a standard prediction, each result must be plausible.
References:
Derbyshire, J., & Giovannetti, E. (2017). Understanding the failure to understand New Product Development failures: Mitigating the uncertainty associated with innovating new products by combining scenario planning and forecasting. Technological Forecasting and Social Change, 125, 334–344. https://doi.org/10.1016/j.techfore.2017.02.007
Derbyshire, J., & Wright, G. (2017). Augmenting the intuitive logics scenario planning method for a more comprehensive analysis of causation. International Journal of Forecasting, 33(1), 254–266. https://doi.org/10.1016/j.ijforecast.2016.01.004
Koehler, D. J., & Harvey, N. (Eds.). (2004). Blackwell handbook of judgment and decision making (1st ed). Blackwell Pub.
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