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Intuitively speaking, predictability is the antonym of uncertainty. If predictability is high, uncertainty is low and vice versa. However, each scientific disciplines has its own understanding what is considered to be predictable. Specifically, when natural and social scientists talk about predictability, they usually don't mean the same thing. While in many quantitative natural and social sciences it is common practice to explicitly characterize uncertainties for any measured quantity, the predictability is treated more implicitly in more qualitative oriented social science disciplines. But even if the uncertainty in measure quantities is low, predictability via numerical models can be be limited, because even a small degree of uncertainty can lead to unpredictable outcomes in complex systems.
In all cases, an adequate characterization of what is predictable and what is not is the basis for meaningful policy recommendations. Hence, a basic understanding of different disciplinary viewpoints on predictability and the time scales involved is essential for interdisciplinary collaboration. For this reason, the goal of this workshop is to clarify how predictability is defined in different research fields and what this implies for the communication of interdisciplinary research results.
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