Future predictions, uncertainty and Unpredictability

Future predictions, uncertainty and Unpredictability

Aug 20th, 2015

Future predictions, uncertainty and Unpredictability

Some authors state that the key difference between human intelligence and animal intelligence is that human perform “conscious and not infrequent planning for the future”. Maybe that started with a “where do we search for food today” question.

Ancient fresco of dice players in Pompei.

Ancient fresco of dice players in Pompei.

As we often discuss in this blog, planning in the face of uncertainty requires some notion of what is likely or unlikely to happen. From here, it only seems “natural” that we must all have some intuitive notion related to likelihood, most likely based on a frequentist approach.

When we think about something being “likely” or “unlikely”, we are consciously recognizing its opposite, i.e. unpredictability or uncertainty. In many instances we think only qualitatively to likelihood, maybe adding some rough scale to the words (little, quite, very, extremely, etc.).

What happens when we hear replies to a question in terms like:

  • “I’m going to ….” or “I prefer to stay home” or “maybe ….” or “probably…” or “seems difficult…”? What we do is that deep inside we attribute a likelihood scale to those replies which then is used to formulate our reaction/action plan. Some could argue that at this level likelihood and uncertainty are almost “synonyms” and can be used indifferently.

The Intergovernmental Panel on Climate Change (IPCC) issues authoritative analyses of scientific understanding of climate change. Future predictions involve uncertainty, and the panel wants the authors to be consistent in how they qualify uncertainty, so it provides a technical Guidance Notes for Lead Authors from which the table below was extracted.

Type Indicative examples of sources Typical approaches or considerations
Unpredictability Projections of human behavior not easily amenable to prediction (e.g. evolution of political systems). Chaotic components of complex systems. Use of scenarios spanning a plausible range, clearly stating assumptions, limits considered, and subjective judgments. Ranges from ensembles of model runs.
Structural uncertainty Inadequate models, incomplete or competing conceptual frameworks, lack of agreement on model structure, ambiguous system boundaries or definitions, significant processes or relationships wrongly specified or not considered. Specify assumptions and system definitions clearly, compare models with observations for a range of conditions, assess maturity of the underlying science and degree to which understanding is based on fundamental concepts tested in other areas.
Value uncertainty Missing, inaccurate or non-representative data, inappropriate spatial or temporal resolution, poorly known or changing model parameters. Analysis of statistical properties of sets of values (observations, model ensemble results, etc); bootstrap and hierarchical statistical tests; comparison of models with observations.

This table addresses the issue of uncertainty and mathematical modeling. It shows that, within a complex setting (such as future climate change), any asserted numerical probability is (at best) an output from some complicated model in which all these different kinds of uncertainty are present. This point is obvious, but quite far away from  mathematics or probability text books!

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Category: Probabilities, Risk analysis, Risk management

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