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Energy modeling and policy. Relationships between modeling and decision-making. Different worlds…. Analysis Truth Verifiability /falsifiability Accuracy C omplexity. Policy / politics Legitimacy Relevance Trust Accommodation Simplification. Decision-making in policy processes.

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energy modeling and policy

Energy modeling and policy

Relationships between modeling and decision-making

different worlds
Different worlds…


  • Truth
  • Verifiability /falsifiability
  • Accuracy
  • Complexity
  • Policy / politics
  • Legitimacy
  • Relevance
  • Trust
  • Accommodation
  • Simplification
decision making in policy processes
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
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?
  • 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