An evening of a long and busy day. Day 1 of INEM 2017 is over, and for it is raining cats and dogs at the moment, I can spend some time writing this post. After having a wonderful breakfast I was heading to the conference centre. San Sebastian is situated in the Western area of the time zone, so at 6 AM when I usully wake up it is pitch black, while at 8 PM you can go for a walk in broad daylight. Funny. But anyway, the Palace is within easy reach, it takes only 15 minutes to get there. A light walk on the shore… an ideal start for the day. I love to watch animals walking up and down next to me. I can still picture myself at Duke University watching the large squirrel population. I named one of the squirrels ‘Brian’… here I met some seagulls. They are the local Bryans.
I need to remind myself about not forgetting about the conference… The day started at 10:30 with Julian Reiss’ talk. First, he gave us some practical sugesstions… where to eat, what to see, where to stay.. and the like. Then he had his presidental address under the title ‘Against epistocracy’. In this talk, Julian responded to some recent books (one of them is Jason Brennan’s ‘Against democracy’) about the challenges modern democracies face. In particular, the social role of scientists and experts should be strengthened, say the books. Why…? Laymen’s decisions are troublesome. So, according to Brennan and others, it is time for us to switch to epistocracy as epistocracy is likely to result in better decisions. This is simple instrumentalism. Epistocracy is better for it leads to better decisions.
We have some implicit assumptions at this point. There exists higher-order political knowledge that entails higher-order knowledge of the society and, what is more, this higher-order social knowledge is objective. This is not a necessity, however. There are values in the game with complex trade-offs among them. It is, too, very dubious to apply values to some situations. But the most fundamental question regards the alleged irrationality of the voters. According to Brennan, voters are ignorant, arrogant, misinformed nationalists (don’t dell them, please… they may feel offended). However, this portray is dependent on the notion of rationality we apply. The point is science, social science, does not necessarily lead to a consensus – or if so, this is only ‘accidentally’, resulting from some common biases of the sciences. Seeking a consensus is not the best way of solving a social problem. Maybe it is a wrong theory or a wrong methodology or some selective biases or even wrong social values that underlie a consensus. However, this answer seems as problematic as the basic question itself. One can reach such a conclusion only by depriving science of the ability of self-reflection. I think science can correct its own mistakes. But for Julian, there is no need for us to prefer science to non-scientific decision-making processes. He takes a sceptical view.
Brian Albrecht and Brian Kogelmann regarded models as foils. It is an interesting new way of addressing an age-old problem. We have descriptive models for sure, but we have models the best use of which is making contrast. They serve as the basis for comparison, so it is a virtue of them if they are NOT like reality. Some examples from literature were listed: foils are often applied in novels in order to highlight some characteristics of a protagonist. But I think something is still missing from this account. Is this enough to set up a model that is different from reality on purpuse…? I don’t think so. Suppose, I have a model according to which it is the storks that bring the new-born babies. Is such a model different from reality? Naturally. How can it help us understand reality? By making a contrast, we can know for sure that it is NOT the storks that bring the babies: reality is necessarily different from our model as we failed to use reality as the matter for our bricks to build models. There is a difference between unrealisticness and unrealisticness. Cooked-up assumptions are useless, they can have instrumentalist benefits at best.
Then Melissa Vergara Fernández talked about what philosophical theories say about model failure. Her point is interesting: even if we know the criteria for model success, these criteria cannot be used as a measure of theory fail. We need further criteria, for example expectations. It is expectations that make the difference between success and failure.
Matti Heinonen gave a talk about evidential looping effect which is a special form of feedback effects. However, there are serious differences. The way one classifies something will have an effect on the behaviour of the classified thing. For instance, if GDP-dynamics is regarded as of recession, it will have an effect on the investment decisions of private companies. On financial markets similar feedback effects are in the game: events are brought about individual agents with intentions and there are concepts the application of which have effects on behaviour and outcomes. For instance, a financial institution will change its behaviour in case it is reported as insolvent. In other words, using models and making predictions have effects on the behaviour of the analysed thing. I think this framework is an interesting new way of addressing the problem of self-fulfilling prophecies and spirals: if a recession is reported and the central bank cuts the rate of interest, private sector agents may regard this as a symptom of recession and will act accordingly by cutting investment activity. This may result in a spiral.
One more lecture deserves attention. It is Olga Koshovets and Taras Varkhotov’s ‘Neuroeconomics’. It is a very recent attempt to reconceptualize economics in biological terms. Maybe our very concepts can be replaced by biological notions. Maybe talking about neurotransmitters and biochemistry may be an effective framework to describe economic processes. For me, the most strinking thing was the consequence of this reasoning: economics will be a natural science. So, what proved imposissible to complete on physical grounds may be completed on biological grounds. Shocking. Neuroeconomics makes it possible to trace subjectivity back to neurophysiological predeterminacy. On these grounds neuromarketing has emerged very recently. Neuromarketing aims to understand the neurophysiological reactions of customers on the basis of their eye-movements or even mimicry. Filmmaking has already benefited from these striking results. Neuroeconomics is so recent that neuroeconomists now prefer publishing in biological-medical-biochemical journals for physically grounded economics may be averse to such achievements and theoretical developments.
I cannot resist to mention Margaret Schabas’ talk on Hume’s proto-statistical methods. In this lecture, Margaret made some really interesting points. For instance, she called attention to how extensively Hume used statistical data and how close he was to some modern concepts such as variance or regression.
It was day 1. It is time to go to sleep for tomorrow’s program is heavier than today’s schedule. Moreover, I am leaving the hotel on Thursday morning (my flight departs at 7:30) and it is problematic to get to the airport that ‘early’. Nonsense. I need to plan my way home… that I intended to be a light process. It’s a pity… Good night!