Energy modeling and policy

1. Energy modeling and policy Relationships between modeling and decision-making

2. Different worlds… Analysis • Truth • Verifiability /falsifiability • Accuracy • Complexity • Policy / politics • Legitimacy • Relevance • Trust • Accommodation • Simplification

3. 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.

4. 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?

5. 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