Energy modeling and policy

Relationships between modeling and decision-making

Different worlds…

Analysis• Truth• Verifiability

/falsifiability• Accuracy• Complexity

Policy / politics• Legitimacy• Relevance• Trust• Accommodation• Simplification

Decision-making in policy processes• "The essence of ultimate decision remains impenetrable to the

observer - often, indeed, to the decider himself.” JF Kennedy, after the Cuban missile crisis

• Decision-making involves the exercise of judgement – almost all policy decisions respond to a number of different goals and interests, which are usually not comparable. Tradeoffs are almost always involved.

• Only very low-level decisions can be taken solely on the basis of analysis. Analysis cannot replace judgement.

• For the rest – so-called “wicked problems” (Rittel and Webber), analysis informs and enhances judgement – more complex accommodations and tradeoffs are possible

• The key interface for policy and analysis consists of indicators. These are a quantitative output of modeling processes, and are proxies for non-quantified / non-operational policy goals.

Specific challenges for modeling• The “black box” problem – raises problems of

legitimacy for decision-makers / stakeholders• What do the results mean (given that these

are all counterfactuals)? Do they help?• Complexity – sophistication vs usefulness• Judgements re data and assumptions – who

makes these?• Where do the boundaries lie between

modelers and decision-makers?

Ideas• Reframing – from a focus on results to a focus on learning re the tradeoffs

in a complex system – stakeholders may not agree on which outcomes are valued, but modeling provides an avenue for reaching agreement on what tradeoffs need to be made

• Indicator-driven – modeling processes should start with indicators, which come out of interactions with decision-makers

• Opening the “black box” – apply same standards to modeling as to research: results should be replicable and peer-reviewed, which means the complete public documentation of the modeling process, including all data and assumptions.

• Qualify results – more model runs for each result• Apply Occam’s Razor – models should not be more complex than

necessary, and not more complex than the data can support• Several scales of models – from simple to complex – part of the shift to a

process-oriented approach• Comparison of results across methodologies – why are these different?

Or more troubling, why are these the same?• Communication – innovative ways of communicating results, key indicators