Saturday, August 12, 2017

on the intersection between economics and climate science

A helpful article by Vincent Randall on its own merits... 

Of great interest to me, other economists, and non-fundies in the climate science (CS) debates: the author's link between the difficulties of CS and (macro) economics-- with theoretical and empirical modeling in low-information, high-uncertainty contexts...

As the author notes, economists have wrestled with information problems and uncertainty for a long time. In Micro, it's a common feature of its models-- and an important discussion when we relax the assumptions in those models to get closer to many elements in the real world. In Macro, it's a necessary part of the deal (when you're trying to model something so amazingly complex). And the "information problem" is a particular point of emphasis in the field of "Austrian Economics"-- where they grapple with how economic markets do an amazing job (particularly with tacit information) and how much political markets necessarily struggle with the same problem.

Rnadall's intro: In this essay I am going to try to introduce non-economists who work in fields where they are first coming into contact with the ‘uncertainty monster’...what some economists have learned from their encounter with it. First I will try to explain why economists encountered the monster before others working in different disciplines. Then I will try to give the reader an overview of what different economists have said about it. Then finally I will briefly consider the differences and similarities between how economists are confronted with the uncertainty monster and how those working in ‘harder’ sciences, like climate science, are confronted with the uncertainty monster. There are definite differences and definite similarities.

Dealing with his first point, Randall explains: The questions raised by uncertainty seem to have been addressed in more depth and with more clarity in the discipline of economics than they have elsewhere. It seems that this is because they were encountered in economics more forcefully than in other disciplines that lent themselves to mathematical modelling and statistical hypothesis testing. The reason that they were encountered so much more forcefully is that economics deals with human behavior – and humans are constantly faced with an uncertain future. For this reason all human behavior is undertaken in the face of uncertainty. 

Randall then talks to the preeminent example in Micro: the role, the challenge, and the opportunity of the entrepreneur-- and the need to do it over and over, for years. He notes the debate between Myrdal, Keynes and others in the 1920s and 1930s. For example, Keynes "concluded that this means that a lot of economic activity is determined not by calculation of probabilities or anything like it. Rather it is determined by the state of confidence."

The implications for strict economic modeling are devastating-- for micro and esp. for macro (and its cousin, "economic development"): "Once this Pandora’s Box was opened up it started eating economic theory from the inside out. The whole theory was based on decisions made in the face of calculable certainty. But once we admitted that the future is properly uncertain the theory started to unravel. Within a few years the economists have put the ‘uncertainty monster’ back in the box. From where I’m standing this rendered their theories pretty much useless and I’m sure that many readers can make the connection between this fundamental epistemological error and the inability of economists to see the Great Financial Crisis coming..."
He has a great quote from GLS Shackle (previously an unknown to me): 

To be uncertain is to entertain many rival hypotheses. The hypotheses are rivals of each other in the sense that they all refer to the same question, and that only one of them can prove true in the event. Will it, then, make sense to average these suggested mutually exclusive outcomes? There is something to be said for it...'The golden mean’ has been a precept from antiquity, and in this situation it will ensure that, since the mass of hypotheses will still be in disagreement with the answer which is thus chosen, they shall be divided amongst themselves and pulling in opposite directions. Moreover, the average can be a weighed one, if appropriate weights can be discovered. But what is to be their source? We have argued that statistical probabilities are knowledge. They are, however, knowledge in regard to the wrong sort of question, when our need it for weights to assign for rival answers. If we have knowledge, we are not the answer to a question about a single trial, the frequency-ratios are not knowledge. They are only the racing tipster’s suggestion about which horse to back. His suggestions are based on subtle consideration of many sorts of data, including statistical data, but they are not knowledge.

Randall quotes Shackle "at length to give the reader a sense of how reading his work might be a useful guide to making certain decisions that are encountered with some regularity in climate science." 

Next, he cites Paul Davidson on ‘non-ergodic’ science-- the future does not necessarily mirror the past; just because x happened in the past does not mean that x will happen in the future: "the assumption of ergodic stochastic economic processes permits the analyst to assert that the outcome at any future date is the statistical shadow of past and current market data." He also makes the case – and this is of interest to those in other sciences – that non-ergodicity may apply to systems that are very sensitive to initial conditions. That is, systems which are commonly referred to as ‘chaotic’ today."

Next up is Tony Lawson: Randall says "He makes the case that recognizing uncertainty requires the economist/scientist to occupy an entirely different ontological position – that is, they have to view the world in an inherently different way to the way their uncertainty-free colleagues do...only ‘closed systems’ – that is, systems that are both deterministic and in which we fully understand the determinates driving the system – can be mathematically modeled in any serious way." 

Lawson: "The first thing to note is that all these mathematical methods that economists use presuppose event regularities or correlations. This makes modern economics a form of deductivism...formulate theories in terms of isolated atoms." 

Lawson then gets back to what is usually a key assumption in Micro: "Notice too that most debates about the nature of rationality are beside the point. Mainstream modellers just need to fix the actions of the individual of their analyses to render them atomistic, i.e., to fix their responses to given conditions. It is this implausible fixing of actions that tends to be expressed though, or is the task of, any rationality axiom. But in truth any old specification will do...It is easy to show that this ontology of closed systems of isolated atoms characterizes all of the substantive theorizing of mainstream economists. It is also easy enough to show that the real world, the social reality in which we actually live, is of a nature that is anything but a set of closed systems of isolated atoms."

Randall closes with a compare/contrast between econ and climate science: CS studies natural processes, so that's good news. But the climate is extremely complex-- in ways that we don't understand-- and so we're prone to fall prey to unknown unknowns. Empirical work is notoriously difficult in both-- and is often assumed, naively, to be a lot easier and more accurate than is reasonable to have confidence about. In any case, Randall hopes that CS will learn a lot and learn humility from economists who have wrestled with these issues.


Post a Comment

Subscribe to Post Comments [Atom]

<< Home