Friday, November 25, 2016

Expert complexity

A new tool rolled out by a research institution (1), allows decision-makers reach better decisions by surveying experts. Specifically, a related paper published in Nature Energy concludes that wind energy costs will fall by 24-30% by 2030 (2). To understand what method of alternate energy production is most viable, one has to disaggregate the problem into two distinct questions:

1. What's the status-quo economics - i.e what is the total cost of production per unit of energy for all available modalities - including, solar, wind, tidal, geothermal, nuclear, fossil and others? And, it is important to understand total costs, however "green," the manufacturers of the turbines and solar cells claim to be.

2. How is this cost likely to decline by scale or newer technologies or both?

The first question has significant available data and does not require any expert opinion. The second question has two parts to it - scale based decline in cost and the probability of the arrival of a technology discontinuity. The former is also an empirical and engineering question that does not require experts to open up their infinite wisdom and the latter is mere speculation that experts could certainly contribute to.

More generally, alternative energy production techniques have comparable status-quo metrics that informs which method is dominant. The idea that "we should try everything," is fundamentally faulty as there is only one best design. The scale question requires more analysis and thought - part of this is related to total available quantity (i.e. how much energy could the world produce if it were to harness the source with 100% efficiency) and the other is related to efficiency gains that will accrue due to scale and learning effects. Both of these questions are well explored in many fields and thus we can easily create forecasted metrics of cost per unit of production across production modalities, without troubling the experts.

Scientists and policy-makers have a tendency to complicate simple problems. But it typically does not add much value, even with software, experts or research. Numbers are more reliable predictors of the performance of engineering systems than experts.