Uncertainty & futures

What are known knowns?

• universally accepted knowledge

• universally accepted relationships

• often in biophysical disciplines

• often related to short-term futures

What are unknown knowns?

• state-of-the-art research

• not generally accepted yet

• gaps in knowledge that we are aware of

• knowns, but only by specialists and experts

Remember

(not related to uncertainty)

even for the most uncertain topics, there are aspects that are known!

what are known unknowns?

• things we know will happen, but er don’t know where, when, how strong, in which form, what are the consequences etc.

• Aspects of systems that we know exist and are accepted to be important, but only in general terms

• examples: extreme events, new inventions

What are unknown unknowns?

• things we know will happens, but we don’t know what they are and we don’t know that we don’t know them

• total surprises; outside the realm f imagination

Remember (2)

(related to uncertainty)

most if not all research related to ‘uncertainty’ relates to the known unknowns.

What is uncertainty?

not knowing or having a doubt about a particular situation

What are the 3 types of uncertainty?

• Epistemic uncertainty

• Aleatory uncertainty

• Ontological uncertainty

What is Epistemic uncertainty with examples?

• Arises from lack of knowledge about a system or phenomenon.

• often reducible by gathering more data or improving models.

• relates to known unknowns.

• Examples: round-off errors, measurement systematic errors, lack of data, lack of information about dependency, conflicting beliefs, conflicting information etc.

• Groene hart example: the exact mathematical relationship between specific groundwater table levels and the precise rate of peat oxidation/soil subsidence.

What is Aleatory uncertainty with examples?

• represents inherent, natural variability in a process

• unpredictable and cannot be reduced

• relates to known unknowns

• groene hart example: the exact timing, frequency, and intensity f extreme rainfall events or severe summer droughts over the next decade.

What is ontological uncertainty?

• occurs when the system’s structure or potential behaviours are unknown

• a fundamental lack of clarity rather than just limited knowledge about them

• the very components defining a situation are unknown, ambiguous or shifting

• relates to unknown unknowns

• groene hart example: how local dairy farms will strategically adapt ot stricter nitrogen regulations and agricultural trasition policies

Explain what are the 2 very important tools and one method?

• Scenarios: representations of possible futures for one or more components of a system

• Models: qualitative or quantitative description of key components of a system and of relationships between these components

• Story-and-simulation (SAS) approach: An integrated methodology that combines storylines(scenarios) with quantitative model runs to bridge qualitative stakeholder input and technical analysis

Describe the futures cone.

• preposterous futures: (impossible, won’t ever happen)

• possible futures: (might happen)

• plausible futures: (could happen)

• the projected future: (default, extrapolated baseline, business as usual)

• probable futures: (likely to happen)

• preferable futures: (want to happen, should happen)

What is the scenario uncertainty?

• the future is fundamentally unknown and unpredictable

• there are plural futures - possible, plausible, probable etc

• scenarios help structuring this uncertainty of future states

• scenarios include assumptions of those aspects that are known (drivers of change), but their future state is unknown

• Scenario uncertainty cannot be reduced & relates to aleatory uncertainty

what are the 4 curves in a graph of uncertainty vs model complexity?

what is model uncertainty?

• models structure knowledge in sets of internally consistent relationships, based on today’s system understanding

• multiple models based in different data sets or different assumptions

• models help structuring the uncertainty os system understanding

• this uncertainty can be reduced and relates to epistemic uncertainty

Why can uncertainty in socio-environmental systems not always be resolved by more data?

Because of the inherent nature of certain types of uncertainty.

• epistemic uncertainty which arises from a lack of knowledge, is reducible through better data or improved modes

• aleatory uncertainty represent inherent, natural variability in a process which is fundamentally unpredictable and irreducible, regardless of how much data is collected. For example the inherent randomness of future events

• also scenario uncertainty

• Ontological uncertainty which refers to a fundamental lack of clarity regarding system structure or behaviour also limits the effectiveness of more data.