My 2017 presentation on Ireland and the Eurozone has been uploaded to YouTube. See the previous post for the link to the full conference, which includes the Q&A.
On Monday of this week I gave a presentation on the current state of the Irish economy at a conference on the future of Europe at the LBJ School of Public Affairs. My presentation begins around the 33 minute mark and is followed by some Q&A. It can be accessed here:
On Tuesday 16th of March 2017 Robert Skidelsky and I launched my book The Reformation in Economics in the Clement Attlee room in the House of Lords. It has taken some time to upload these videos of Robert’s and my comments. Most of the speeches were captured but I have included my own text below (I did not end up sticking to the written comments verbatim).
On a separate note, Brian Romanchuk has written a short review of the book here.
Thanks very much for coming. I’m pleased to see that you’ve shown some interest in the book.
I won’t beat around the bush. I’ll get right to it.
What is the key question that the book seeks to address? It is this.
To what extent is economic theory an ideology and to what extent can it be thought of as a neutral tool which can be used to explore and possibly improve reality?
What I mean by ‘ideology’ is not a political ideology – I’m not concerned with whether you lean Tory or Labour. Nor do I mean a worldview or Weltanschauung – such as Marxism, libertarianism or communitarianism.
What I mean by ‘ideology’ is a mode of thinking that does not seek to attain Truth – but rather seeks to attain Power. A mode of thinking that seeks to justify a certain social or economic order.
The argument that I make in the book is that a good deal of contemporary mainstream or ‘marginalist’ economics is in fact ideology in this sense.
BUT – and I hope you will have some sympathy for this claim – I also argue that there are aspects of economics that are not ideology. That is, there are aspects of economics that do in fact aim at revealing truth – rather than imposing power.
These are the aspects of economics that seek to explain the facts of the world as we see them – and, in the best instances, give us structural explanations why these facts line up in the way that they do.
If we are going to be serious, however, I think that we need to ask firmly: which is which? Which aspects of economics seek Truth and which seek Power? And in order to do this we must inevitably start with some robust epistemological questioning of economic theory.
Until now I think that economists have been somewhat cagey about discussing epistemological issues. To be frank I think that this reluctance is due to the fact that the epistemological foundations of modern economics are a little embarrassing. The “assumptions don’t matter” approach of the marginalists tends to crop up in conversation like an uninvited dinner guest or a weird uncle.
On the other hand, those that have criticised economics for being “too unrealistic” or based on flimsy assertions often seem to find it difficult to lay down clear criteria of evaluation.
This is where I hope that my book fits in. In it I have attempted to do a bit of everything at once. First, I have held what seem to me to be the major tenets of contemporary marginalist economics up to epistemological criticism. I have then – drawing on an old Prussian named Kant – laid out clear methodological and epistemological criteria to judge suitable theoretical replacements. And finally, I have constructed the skeleton of what I think could develop into a suitable alternative.
All that sounds rather grand – perhaps even tipping into the grandiose. But the book is in no way a creation ex nihilo. I have drawn on decades of excellent work by economists that have unfortunately been shunned by the marginalists.
Nor is the book an attempt at a Grand Unified Theory of everything. This is not a Book of Scripture. We have enough of those.
In fact one of the driving forces of the book is the feeling, wisdom, knowledge – call it what you will – that we can’t know everything.
We cannot, in fact, produce large-scale models of economy like the physicists do. It simply doesn’t work. Economies are too complex. They are not written in the elegant, concise prose of the Book of Nature.
If the DSGE modellers are attempting to draw up a detailed map of the economy I am merely trying to provide some directions. I call this method – following Mr Kant – the construction of ‘schema’. These schema work to try to orient us in the world of economic events and provide a firm footing.
I’ll run through the specifics of the book quickly.
Apart from this new approach to economic method the book deals with theories of money and banking; it deals with theories of profits, prices and income distribution; and it deals with theories of finance and investment – that last one I believe makes up the core of economic theory properly understood.
I hear that there is already a myth floating around out there that this is a highly abstract theoretical book with no bearing on the real world. Overuse of words like ‘epistemology’ in what I’ve just said aside, I want to dispel this myth.
Many of the aspects of theory that I discuss in the book are directly tied to key contemporary policy debates that we hear today. This is not a coincidence. I wrote it that way. I tried to avoid the more irrelevant aspects of economic theory and stick to the good stuff.
To run through a few practical examples, the chapter on money and banking has direct bearing on the quantitative easing programs that have been run by the worlds’ central banks recently – and may help to understand why these did or didn’t work as they were supposed to.
There is also a chapter on free trade that takes what seems to me a more realistic approach to the economics of trade.
I’ll briefly conclude by asking: what would economics look like after a reformation? To my mind it would be a lot more pluralistic. There would be an awful lot more debate – and by ‘debate’ I mean debate over deeply held general principles and not over whether the bell looks nicer than the whistle.
I think it would be a lot less dogmatic and people would be more willing to ask challenging questions. I think that economists would be a lot more humble with regards to what they could say with confidence. Basically I think it would look a lot more like the economics of 70 or 80 years ago.
And for that it would be a lot more exciting, a lot more engaging, and a lot more interesting. I also think that economists would develop a healthy allergic reaction to doctrinal method, gatekeepers and taboos.
I’ll leave it there.
Please enjoy drinks and conversation. If anyone has any questions about the book I’d be more than happy to answer them. Thank you for coming and have a good time.
By Philip Pilkington, a macroeconomist working in asset management and author of the new book The Reformation in Economics: A Deconstruction and Reconstruction of Economic Theory. The views expressed in this interview are not those of his employer
Ever since the Enlightenment many societies have moved away from justifying their existence and formulating their aims through recourse to religious language. Gone are the days of the ‘Great Chain of Being’ which justified the natural and social orders all the way from the plants and trees through the commoners, via the nobility and the King all the way up to God the creator. What replaced these ideologies were ideas about ‘Progress’ – how the good society was attained through Progress and what such Progress would look like. Progress, it was said, was to be grounded in the scientific method; what had worked so well to uncover natural processes could also be applied to engineer society.
It was in the 19th century, however, when the ideologies of Progress really began to blossom and flower. One was economics, of which we will have more to say about below. Another was phrenology. Phrenology was a science that claimed that a person’s character – including his capacities and his dispositions – were contained within his skull and could be determined by studying his skull carefully. Today few take this seriously – although many still recognise that phrenology was an early progenitor to so-called ‘neuroscience’. But throughout the 19thcentury these ideas were enormously popular – one popular English work sold more than 300,000 copies!
What made phrenology so popular was what also made economics so popular at the time: it gave a rationale for a society based on Progress and also provided a blueprint for how this could be achieved. The phrenological doctrine, being so vague in its pronouncements, was highly malleable and could be used to justify whatever those in power needed justifying. So, for example, in 19th century England phrenology was used to justify laissez faire economic policies by emphasising unequal natural capacities amongst the population while in early 20thcentury Belgian Rwanda it was used to justify the supposed superiority of the Tutsis over the Hutus.
In my book The Reformation in Economics I take the position that modern economics is more similar to phrenology than it is to, say, physics. This is not at all surprising as it grew up in the same era and out of remarkably similar ideas. But what is surprising is that this is not widely noticed today. What is most tragic, however, is that there is much in economics that can and should be salvaged. While these positive aspects of economics probably do not deserve the title of ‘science’ they at least provide us with a rational toolkit that can be used to improve political and economic governance in our societies.
The Ideology at the Heart of Modern Economics
The curious thing about modern economics is its almost complete insularity. Its proponents appear to have very little notion of how it applies to the real world. This is not the case in normal sciences. Take physics, for example. It is extremely clear how, say, the inverse squares law applies to experienced reality. In the case of gravitation, for example, the inverse squares law makes experimentally testable predictions about the force exerted by, say, the gravitational pull between the sun and the earth.
Modern economics – by which I mean neoclassical or marginalist economics which relies on the notion of utility-maximisation as its central pillar – completely lacks this capacity to map itself onto the real world. As philosophers of science like Hans Albert have pointed out, the theory of utility-maximisation rules out such mapping a priori, thus rendering the theory completely untestable. Since the theory is untestable it cannot be falsified and this allows economists to simply assume that it is true.
Once the theory is assumed to be true it can then be applied everywhere and anywhere in an entirely uncritical manner. Anything can then be interpreted in terms of utility-maximisation. This is most obvious in popular publications like Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. Such books read in an almost identical way to the fashionable books of 19th century phrenology. The economists address everything from parenting to crime to the Ku Klux Klan by filtering it through the non-experimental theory of utility-maximisation – a theory that has not and cannot be verified and so the author and reader alike take it entirely on trust.
Such systems of ideas are ideological to the core. They are cooked up independently of the evidence and are then imposed upon the material of experienced reality. We are encouraged to ‘read’ the world through the interpretive lens of economics – and when we ask for evidence that this lens uncovers factually accurate information we are confounded with circular arguments from the economists.
Large-scale public policy is also filtered through this lens. This is done by constraining the study of macroeconomics – that is, GDP growth, unemployment, inflation and so on – by tying it to the theories of utility-maximisation. All macroeconomics today must be ‘microfounded’. This means that it must have microeconomic – read: ‘utility-maximising’ – foundations. In reality, as I show in the book, these foundations are anything by ‘micro’. Rather, what is done is that the entire economy is seen to be dominated by a single uber-utility-maximiser and all the conclusions flow from there.
This may seem like odd stuff but it is built into the theory as a sort of foundational delusion. The arbitrary, non-empirical theory of utility-maximisation assumes primacy to all considerations of actual statistical facts, intuitions about human motivations and even basic assumptions about what should constitute a properly moral view of man. What we end up with is not just a crushing, anti-inquiry ideology but also a lumbering failure of a system of ideas that has no hope in extracting relevant information about the real world.
What Is To Be Done?
Is economics then to be thought of as a failure? Must we scrap economics and try to find other ways to describe and address our economic and political problems? In this regard, my book claims to lay out a new path – albeit one that has been intuitively followed by some economists, most notably those in the heterodox camp. This new path is based on two key interrelated premises.
The first is that we have little insight into what actually motivates human beings. For this reason theories that rest on assumptions about human motivation – like utility-maximisation – must be thrown out and the study of the economy must be undertaken by examining large economic aggregates. In short, micro must be tossed off the throne and the crown must be handed to macro. The second premise is that we must not be overly concerned with highly precise ‘models’ of the economy. Instead we must take what I have come to call a ‘schematic’ approach. A schematic approach involves building tools that can be integrated into how we understand the world around us without assuming that these tools provide us with an exact description of this world. This schematic toolkit – which I begin to lay out in the later chapters of the book – can then be used to approach the study of actual economies.
These may seem like rather simple rules. But when applied to economic theory they generate rather radical results. At the same time they greatly constrain the amount of wisdom that we can assume economists to have; given these premises no book like Freakonomics should ever be taken seriously and should probably even be written in the first place. In that sense, they may appear to militate against Enlightenment optimism. This may well be so, but I would argue that they are arrived at through rational Enlightenment-style inquiry and so should be taken seriously even by proponents of Enlightenment Progress. After all, phrenology eventually fell in the face of rationalistic criticism.
In the book some of the issues around uncertainty and free will are also explored. Implicit in some of the book’s central criticisms is that societies are not to be understood in a deterministic manner. Unlike billiard balls, social forces are not subject to deterministic laws. In one sense this is unfortunate as it means that our understandings of social and economic processes must always be of a contingent and not-too-precise nature. But on the other hand it is optimistic in the sense that it attributes an agency to human beings to create the world around them that mainstream marginalist economics stripped away by imposing the limited utility-maximiser framework on everyone from Mother Theresa to Hitler.
This also creates an opening for a proper discussion of ethics and morality. Although this is not dealt with directly in the book – it would surely require another ten volumes – the framework does reopen awkward questions surrounding morality and ethics. Some self-professed social scientists, nervous that these questions have been passed to us from the world religions, would prefer to do away with any moral and ethical questions. But this was always a fantasy – even the most hardened anti-ethicist, unless they are serving life for serial-killing, has a system by which they determine right from wrong.
All that I have said here is rather abstract. But a good portion of the book is not and I do not want to give that impression. It contains chapters that deal with inflation, profits, income distribution, income determination, financial markets, interest rates, investment and employment. It is not simply a book of methodology but rather one that tries to also provide the basic building blocks of a theory that can be applied to understand really-existing economies. In this sense, I hope that it is again more optimistic than many mainstream economics books that leave the reader without any capacity to apply the supposed ideas that they have absorbed by reading them beyond mere chest-puffing at dinner parties and moral condemnations of the social safety net.
Why the Pollsters Totally Failed to Call a Trump Victory, Why I (Sort Of) Succeeded – and Why You Should Listen to Neither of Us
The views expressed in this article are the author’s own and do not reflect the views of his employer.
The election of Donald Trump as president of the United States will likely go down in history for any number of reasons. But let us leave this to one side for a moment and survey some of the collateral damage generated by the election. I am thinking of the pollsters. By all accounts these pollsters – specifically the pollster-cum-pundits – failed miserably in this election. Let us give some thought as to why – because it is a big question with large social and political ramifications.
Some may say that the polls were simply wrong this election. There is an element of truth to this notion. The day of the election the RCP poll average put Clinton some three points ahead of Trump which certainly did not conform to the victory that Trump actually won. But I followed the polls throughout the election and did some analysis of my own and I do not think that this explanation goes deep enough.
I have a very different explanation of why the pollsters got it so wrong. My argument is based on two statements which I hope to convince you of:
- That the pollsters were not actually using anything resembling scientific methodology when investigating the polls. Rather they were simply tracking the trends and calibrating their commentary in line with them. Not only did this not give us a correct understanding of what was going on but it also gave us no real new information other than what the polls themselves were telling us. I call this the redundancy argument.
- That the pollsters were committing a massive logical fallacy in extracting probability estimates from the polls (and whatever else they threw into their witches’ brew models). In fact they were dealing with a singular event (the election) and singular events cannot be assigned probability estimates in any non-arbitrary sense. I call this the logical fallacy argument.
Let us turn to the redundancy argument first. In order to explore the redundancy argument I will lay out briefly the type of analysis that I did on the polls during the election. I can then contrast this with the type of analysis done by pollsters. As we will see, the type of analysis that I was advocating produced new information while the type of approach followed by the pollsters did not. While I do not claim that my analysis actually predicted the election, in retrospect it certainly helps explain the result – while, on the other hand, the pollsters failed miserably.
Why I (Sort Of) Called The Election
My scepticism of the US election polling and commentary this year was generated by my analysis of the polls during the run-up to the Brexit referendum. All the pollsters claimed that there was no way that Brexit could go through. I totally disagreed with this assessment because I noticed that the Remain campaign’s numbers remained relatively static while the Leave campaign’s numbers tended to drift around. What is more, when the Leave campaign’s poll numbers rose the number of undecided voters fell. This suggested to me that all of those that were going to vote Remain had decided early on and the voters that decided later and closer to the election date were going to vote Leave. My analysis bore out in the election but I did not keep any solid, time-stamped proof that I had done such an analysis. So when the US election started not only did I want to see if a similar dynamic could be detected but I wanted to record its discovery in real time.
When I examined the polls I could not find the same phenomenon. But I then realised that (a) it was too far away from the election day and (b) this was a very different type of election than the Brexit vote and because of this the polls were more volatile. The reason for (b) is because the Brexit vote was not about candidates so there could be no scandal. When people thought about Brexit they were swung either way based on the issue and the arguments. If one of the proponents of Brexit had engaged in some scandal it would be irrelevant to their decision. But in the US election a scandal could cause swings in the polls. Realising this I knew that I would not get a straight-forward ‘drift’ in the polls and I decided that another technique would be needed.
Then along came the Republican and Democratic conventions in July. These were a godsend. They allowed for a massive experiment. That experiment can be summarised as a hypothesis that could be tested. The hypothesis was as follows: assume that there are large numbers of people who take very little interest in the election until it completely dominates the television and assume that these same people will ultimately carry the election but they will not make up their minds until election day; now assume that these same people will briefly catch a glimpse of the candidates during the conventions due to the press coverage. If this hypothesis proved true then any bounce that we saw in the polls during the conventions should give us an indication of where these undecided voters would go on polling day. I could then measure the relative sizes of the bounces and infer what these voters might do on election day. Here are those bounces in a chart that I put together at the time:
Obviously Trump’s bounce was far larger than Clinton’s. While it may appear that Clinton’s lasted longer this is only because the Democratic convention was on five days after the Republican convention so it stole the limelight from Trump and focused it on Clinton. This led to his bump falling prematurely. It is clear that the Trump bounce was much more significant. This led me to believe that undecided voters would be far more likely to vote Trump than Clinton on election day – and it seems that I was correct.
In addition to this it appeared that Trump was pulling in undecideds while Clinton had to pull votes away from Trump. We can see this in the scatterplot below.
What this shows is that during the Republican National Convention (RNC) Trump’s support rose without much impacting Clinton’s support – if we examine it closely it even seems that Clinton’s poll numbers went up during this period. This tells us that Trump was pulling in new voters that had either not decided or had until now supported a third party candidate. The story for Clinton was very different. During the Democratic National Convention (DNC) Clinton’s support rose at the same time as Trump’s support fell. This suggests that Clinton had to pull voters away from Trump in order to buttress her polls numbers. I reasoned that it was far more difficult to try to convince voters that liked the other guy to join your side than it is to convince enthusiastic new voters. You had to fight for these swing voters and convince them not to support the other guy. But the new voters seemed to be attracted to Trump simply by hearing his message. That looked to me like advantage Trump.
“Aha!” you might think, “maybe you’re just faking it. How do I know that you didn’t just create that chart after the election?” Well, this is why I time-stamped my results this time around. Here are the results of my findings summarised on a piece of paper next to a Bloomberg terminal on August 9th.
I also sent this analysis to some of the editors that are handling this piece. So they have this analysis in their email archives and can check to see that I’m not just making this up.
The reader may note that I criticise Nate Silver’s analysis in the text in the picture. I was referring to his post-convention bounce analysis in which he used the spread between the two candidates to gauge it – this was an incorrect methodology because as we have already seen the Democratic convention ate away at the Trump bounce because it came during the Trump bounce and this artificially inflated Clinton’s bounce in spread terms. The correct methodology was to consider the two bounces independently of one another while keeping in mind that the DNC stole the limelight from Trump five days after his bounce started and thereby brought that bounce to a premature halt.
This was a bad analytical error on Silver’s part but it is not actually what really damaged his analysis. What damaged his analysis significantly is that he did not pay more attention to this ‘natural experiment’ that was thrown up by the convention. Rather he went back to using his tweaked average-tracking polls. This meant that while he was following trends I was looking for natural experiments that generated additional information to that which I had from the polls. This led to Silver and other pollsters becoming trapped in the polls. That is, they provided no real additional information than that contained in the polls.
After this little experiment, as the polls wound this way and that based on whatever was in the news cycle I constantly fell back on my analysis. What was so fascinating to me was that because the pollsters simply tracked this news cycle through their models their estimates were pretty meaningless. All they were seeing was the surface phenomenon of a tumultuous and scandal-ridden race. But my little experiment had allowed me a glimpse into the heart of the voter who would only make up their mind on voting day – and they seemed to favour Trump.
Before I move on, some becoming modesty is necessary: I do not believe that I actually predicted the outcome of the election. I do not think that anyone can predict the outcome of any election in any manner that guarantees scientific precision or certainty (unless they rigged it themselves!). But what I believe I have shown is that if we can detect natural experiments in the polls we can extract new information from those polls. And what I also believe I have shown is that the pollsters do not generally do this. They just track the polls. And if they just track the polls then instead of listening to them you can simply track the polls yourself as the pollsters give you no new information. In informational terms pollsters are… simply redundant. That is the redundancy argument.
Why the Pollsters’ Estimates Are So Misleading
Note the fact that while my little experiment gave me some confidence that I had some insight into the minds of the undecided voter – more than the other guy, anyway – I did not express this in probabilistic terms. I did not say: “Well, given the polls are at x and given the results of my experiment then the chance of a Trump victory must be y”. I did not do this because it is impossible. Yet despite the fact that it is impossible the pollsters do indeed give such probabilities – and this is where I think that they are being utterly misleading.
Probability theory requires that in order for a probability to be assigned an event must be repeated over and over again – ideally as many times as possible. Let’s say that I hand you a coin. You have no idea whether the coin is balanced or not and so you do not know the probability that it will turn up heads. In order to discover whether the coin is balanced or skewed you have to toss it a bunch of times. Let’s say that you toss it 1000 times and find that 900 times it turns up heads. Well, now you can be fairly confident that the coin is skewed towards heads. So if I now ask you what the probability of the coin turning up heads on the next flip you can tell me with some confidence that it is 9 out of 10 (900/1000) or 90%.
Elections are not like this because they only happen once. Yes, there are multiple elections every year and there are many years but these are all unique events. Every election is completely unique and cannot be compared to another – at least, not in the mathematical space of probabilities. If we wanted to assign a real mathematical probability to the 2016 election we would have to run the election over and over again – maybe 1000 times – in different parallel universes. We could then assign a probability that Trump would win based on these other universes. This is silly stuff, of course, and so it is best left alone.
So where do the pollsters get their probability estimates? Do they have access to an interdimensional gateway? Of course they do not. Rather what they are doing is taking the polls, plugging them into models and generating numbers. But these numbers are not probabilities. They cannot be. They are simply model outputs representing a certain interpretation of the polls. Boil it right down and they are just the poll numbers themselves recast as a fake probability estimate. Think of it this way: do the odds on a horse at a horse race tell you the probability that this horse will win? Of course not! They simply tell you what people think will happen in the upcoming race. No one knows the actual odds that the horse will win. That is what makes gambling fun. Polls are not quite the same – they try to give you a snap shot of what people are thinking about how they will vote in the election at any given point in time – but the two are more similar than not. I personally think that this tendency for pollsters to give fake probability estimates is enormously misleading and the practice should be stopped immediately. It is pretty much equivalent to someone standing outside a betting shop and, having converted all the odds on the board into fake probabilities, telling you that he can tell you the likelihood of each horse winning the race.
There are other probability tricks that I noticed these pollsters doing too. Take this tweet from Nate Silver the day before the election. (I don’t mean to pick on Silver; he’s actually one of the better analysts but he gives me the best material precisely because of this).
Now this is really interesting. Ask yourself: Which scenarios are missing from this? Simple:
- Epic Trump blowout
- Solid Trump win
Note that I am taking (c) to mean that if the election is close or tied Silver can claim victory due to his statement of ‘*probably*’.
Now check this out. We can actually assign these various outcomes probabilities using the principle of indifference. What we do is we simply assign them equal probabilities. That means each has a 20% chance of winning. Do you see something awry here? You should. Silver has really covered his bases, hasn’t he? Applying the principle of indifference we can see that Silver has marked out 3 of the 5 possible scenarios. That means that even if we have no other information we can say that Silver has a 60% chance of ‘predicting the election’ using this statement. Not bad odds!
What is more, we can actually add in some fairly uncontroversial information. When this tweet was sent out the polls showed the candidates neck-and-neck. Surely this meant that a simple reading of the polls would tell us that it was likely to be a close call. Well, I don’t think it would be unfair to then weight the probability of (c) accordingly. Let’s say that the chance of a close call, based on the polls, was 50%. The rest of the possibilities then get assigned the rest of the probability equally – they get 12.5% each. Now Silver really has his bases covered. Without any other information he has a 75% chance of calling the election based on pure chance.
The irony is, of course, he got unlucky. Yes, I mean ‘unlucky’. He rolled the dice and the wrong number came up. Though he lost the popular vote, Trump won the electoral votes needed by a comfortable margin. But that is not the key point here. The key point here is that something else entirely is going on in this election forecasting business than what many people think is happening. What really appears to be going on is that (i) pundits are converting polls numbers into fake probability estimates arbitrarily and (ii) these same pundits are making predictive statements that are heavily weighted to being ‘probably’ correct – even if they are not conscious that they are doing this. This is really not much better than reading goat entrails or cold reading. Personally, I am more impressed by a good cold reader. The whole thing is based on probabilistic jiggery-pokery. That is the logical fallacy argument.
And Why You Should Listen to Neither of Us
Are you convinced? I hope so – because then you are being rational. But what is my point? My very general point is that we are bamboozling ourselves with numbers. Polls are polls. They say what they say. Sometimes they prove prescient; sometimes they do not. If we are thoughtful we can extract more information by analysing these polls carefully, as I did with my little experiment. But beyond this we can do little. Polls do not predict the future – they are simply a piece of information, a data point – and they cannot be turned into probability estimates. They are just polls. And they will always be ‘just polls’ no matter what we tell ourselves.
But beyond this we should stop fetishizing this idea that we can predict the future. It is a powerful and intoxicating myth – but it is a dangerous one. Today we laugh at the obsession of many Christian Churches with magic and witchcraft but actually what these institutions were counselling against is precisely this type of otherworldly prediction:
The Catechism of the Catholic Church in discussing the first commandment repeats the condemnation of divination: “All forms of divination are to be rejected: recourse to Satan or demons, conjuring up the dead or other practices falsely supposed to ‘unveil’ the future. Consulting horoscopes, astrology, palm reading, interpretation of omens and lots, the phenomena of clairvoyance, and recourse to mediums all conceal a desire for power over time, history, and, in the last analysis, other human beings, as well as a wish to conciliate hidden powers. These practices are generally considered mortal sins.
Of course I am not here to convert the reader to the Catholic Church. I am just making the point that many institutions in the past have seen the folly in trying to predict the future and have warned people against it. Today all we need say is that it is rather silly. Although we would also not go far wrong by saying, with the Church, that “recourse to mediums all conceal a desire for power over time, history, and, in the last analysis, other human beings”. That is a perfectly good secular lesson.
I would go further still. The cult of prediction plays into another cult: the cult of supposedly detached technocratic elitism. I refer here, for example, to the cult of mainstream economics with their ever mysterious ‘models’. This sort of enterprise is part and parcel of the cult of divination that we have fallen prey to but I will not digress too much on it here as it is the subject of a book that I will be publishing in mid-December 2016 – an overview of which can be found here. What knowledge-seeking people should be pursuing are tools of analysis that can help them better understand the world around us – and maybe even improve it – not goat entrails in which we can read future events. We live in tumultuous times; now is not the time to be worshipping false idols.
Hi everyone – or, at least, whoever is left out there. As you probably know, this blog has been shut down since October 2014 and I have pretty much fallen off the face of the planet. Actually I’ve been working in investment where I’ve found a job that allows me to pursue non-mainstream economic research.
Some of you may recall that I was writing a book during the last days of this blog. I’m happy to say that this book is now fully completed and has been accepted for publication by Palgrave Macmillan. The provisional publication date for the book will be October 2016 and the price will be around £19.50. The book’s title will be: ‘The Reformation in Economics: A Deconstruction and Reconstruction of Economic Theory’.
The book will not be a rehash of material that is available on this blog. I consciously avoided this as I thought that it would be rather boring. So the book is all brand new material. Some of the ideas were thrown around on this blog in more primitive form but I have tried to develop them properly in the book.
The idea for the book is to go right back to first principles. The more I engaged with economic theory the more I found two things.
First of all, much economic theory is in fact ideology. By ‘ideology’ I do not mean something resembling a political ideology – I do not mean ‘socialism’ versus ‘libertarianism’ or ‘left-wing’ versus ‘right-wing’ or anything like that. Rather I mean a manner of structuring how we view the world around us – how we frame things to ourselves and how we understand what is and what is not possible to accomplish. A good example of an ideology is the idea that the world is flat and that if you sail out beyond a certain point you will be devoured by monsters. These sorts of ideas provide a sort of semi-conscious map that we use to engage with the world that has no basis in rationalistic or scientific inquiry.
Secondly, most of the problems with economic theory are actually buried in its very foundations. In the book I argue that much of economic theory is not actually aimed at being applied to the real world and that most economists have never actually thought through how their theory applies to reality. A physicist, for example, will typically have some real-world object that they are trying to understand – say, a ball falling from a building – and they know exactly how to fit their abstract theory onto the phenomenon that they are studying. I do not think that the vast majority economists have a clear conception of the real-world object that they are trying to approach and I do not think that they have the faintest clue of how they should apply their theory to the real-world. In this they typically fall back on institutionalised norms such as econometric testing which they do not really understand.
The aim of the first half of book is to interrogate these foundations – this is the act of deconstruction alluded to in the title. When we interrogate these foundations much of mainstream economic theory is shown to be entirely irrelevant – nothing more than a series of floating symbols that have no parallel existence in the real world. By understanding this this we also form a clear conception of what a good theory that is actually oriented to the real-world would look like.
The second half of the book attempts a reconstruction of what I call ‘stripped-down macroeconomics’. The first half of the book argues that any theoretical edifice that is overly precise or unwieldy will not function when applied to the real world. For this reason, economic theory is much better served by using very simple and clearly understood ideas. These ideas are then thought to serve as schemata – that is, “an organized pattern of thought or behavior that organizes categories of information and the relationships among them” – which can be mapped onto empirical material in order to gain an understanding of the world around us.
There is much else in the book that is dealt with along the way: critiques of the ISLM model; an examination of the different conceptions of equilibrium applied in economic theory; a critique of the EMH view of financial markets; reflections on the use of mathematics in economics; and much more. Although the book attempts to tackle the foundations of economic theory I had no interest in turning it into a dry, abstract tome full of needlessly big words and short on examples.
Anyway, I will be doing some media for the book in the coming weeks and months. I will update this blog post whenever new media appears. If you are interested in following this I would suggest that you should just check back here from time to time. Of course, when the book comes out I will also provide links to purchase it on this blog. I have also included the table of contents for the book.
- Philip Pilkington on Determinism and the Reformation in Economics. An interview with Frank Conway on the Economic Rockstar podcast.
- INET YSI Seminar Based on a Draft Version of Chapter 4 ‘Methodology, Modelling and Bias’.
The Reformation in Economics: A Deconstruction and Reconstruction of Economic Theory
By Philip Pilkington
Section I: Ideology and Foundations
- Economics: Ideology or Rationalistic Inquiry?
- The Limiting Principle: A Short History of Ideology in 20th Century Economics
- Deconstructing Marginalist Microeconomics
- Methodology, Modelling and Bias
- Differing Conceptions of Equilibrium
Section II: Stripped-Down Macroeconomics
- Theories of Money and Prices
- Profits, Prices, Distribution and Demand
- Finance and Investment
Section III: Approaching the Real-World
- Uncertainty and Probability
- Non-Dogmatic Approaches to the Economics of Trade
Conclusion and Appendices
- Philosophical and Psychological Appendices
- Determinism and Free Will in Economics
- Between Personal Responsibility and Poor Theory
- Economic Modelling: A Psychologistic Explanation