Results published in the journal Risk Analysis showed the team found simulations that inform energy policy had unreliable assumptions built into them and that they need more transparency about their limitations. To improve this they recommend new ways to test simulations and be upfront about their uncertainties. This includes methods like ‘sensitivity auditing’, which evaluates model assumptions. The goal is to improve modeling and open up decision-making.
The widespread adoption of nuclear power was predicted by computer simulations more than four decades ago. But a new study has shown the continued reliance on fossil fuels for energy need improvement.
In order to assess the efficacy of energy policies implemented today, a team of researchers looked back at the influential 1980s model that predicted nuclear power would expand dramatically. Energy policies shapes how we produce and use energy, impacting jobs, costs, climate, and security. These policies are generated using simulations (also known as mathematical models) which forecast things like electricity demand and technology costs. But forecasts may miss the point altogether.
Lead researcher Dr Samuele Lo Piano, of the University of Reading, said, “Energy policy affects everybody, so it’s worrying when decisions rely on just a few models without questioning their limits. By questioning assumptions and exploring what we don’t know, we can get better decision making. We have to acknowledge that no model can perfectly predict the future. But by being upfront about model limitations, democratic debate on energy policy will improve.”
A chapter of a new book, The politics of modelling (published on November 20), written by lead author Dr Lo Piano, highlights why the research matters for all the fields where mathematical models are used to inform decision and policy-making. The chapter considers the inherent complexities and uncertainties posed by human-caused socio-economic and environmental changes.
Entitled ‘Sensitivity auditing — A practical checklist for auditing decision-relevant models‘, the chapter presents four real-world applications of sensitivity auditing in public health, education, human-water systems, and food provision systems.
This post isn’t a book review. But the headlining about the study is entrancing. And better yet, at posting, the paper is Not behind a paywall. It is a very refreshing read. Even if you know very little about modeling you will understand it much better when finished.
With two days earlier this week written on the hydrogen field using fuel cells and the government’s news to limit home heating choices to heat pumps and other appliances getting axed the consumer needs to have a better feel for just what all this modeling actually offers.
The fact about modeling is that its just sophisticated speculation. Way sophisticated. Just like older folks who rely on experience to make judgments some folks are better at it than others.
Models act like the systems they represent. Coding draws in the components of the systems. The very good ones like weather forecasts are based and extend the known facts and relationships into the future and they can be very good indeed. Others that portend to explore an unknown are maybe – educated guesses. And unless the model starts with known facts the starting points are assumptions that can be way off the mark and blow up the whole process. That’s known as the garbage in garbage out situation.
So your humble writer has good to high confidence that tonight’s weather forecast about tomorrow is really close. And near zero confidence that energy and fuel models months years and decades out have an economic value at all.
But they are worthwhile as they make folks think about the systems, relationships and inputs made to make predictions. Actually there not enough of them and there’s not one looking for trouble for every one looking to success.
By Brian Westenhaus via New Energy and Fuel
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