• Robert Marks [2010]:  A timeline of the Global Financial Crisis. (Initial version published in the Australian Journal of Management, and since updated.)
• Larissa MacFarquhar [2010]:  The deflationist:  How Paul Krugman found politics.  The New Yorker, 2010-03-01, pp. 38-49
• John Lanchester [2009]:  Outsmarted:  high finance vs. human nature.  The New Yorker, 2009-06-01, pp. 83-87.
• Anon [2009]: The other-wordly philosophers.  The Economist, 2009-07-18/24, pp. 70-72.
• Anon [2009]: Efficiency and beyond.  The Economist, 2009-07-18/24, pp. 73-74.
• John Cassidy [2010]:  After the Blow-Up:  Laissez-faire economists do some soul-searching – and finger-pointing.  The New Yorker, 2010-01-11, pp. 28-33.
• Paul Krugman [2009]: How did economists get it so wrong? The New York Times, 2009-09-06.
• J. Doyne Farmer and Duncan Foley [2009]: The economy needs agent-based modeling. Nature, 460, 685-686 (2009-08-06).
• Mark Buchanan [2009]: Economics: meltdown modeling. Nature, 460, 680-682 (2009-08-06).
• Jonathan Jarvis [2009]:  Crisis of Credit (Animation).

An excerpt from Kay’s column:

Both the efficient market hypothesis and DSGE [Dynamic Stochastic General Equilibrium models] are associated with the idea of rational expectations – which might be described as the idea that households and companies make economic decisions as if they had available to them all the information about the world that might be available. If you wonder why such an implausible notion has won wide acceptance, part of the explanation lies in its conservative implications. Under rational expectations, not only do firms and households know already as much as policymakers, but they also anticipate what the government itself will do, so the best thing government can do is to remain predictable. Most economic policy is futile.

So is most interference in free markets. There is no room for the notion that people bought subprime mortgages or securitised products based on them because they knew less than the people who sold them. When the men and women of Goldman Sachs perform “God’s work”, the profits they make come not from information advantages, but from the value of their services. The economic role of government is to keep markets working.

These theories have appeal beyond the ranks of the rich and conservative for a deeper reason. If there were a simple, single, universal theory of economic behaviour, then the suite of arguments comprising rational expectations, efficient markets and DSEG would be that theory. Any other way of describing the world would have to recognise that what people do depends on their fallible beliefs and perceptions, would have to acknowledge uncertainty, and would accommodate the dependence of actions on changing social and cultural norms. Models could not then be universal: they would have to be specific to contexts.

The standard approach has the appearance of science in its ability to generate clear predictions from a small number of axioms. But only the appearance, since these predictions are mostly false. The environment actually faced by investors and economic policymakers is one in which actions do depend on beliefs and perceptions, must deal with uncertainty and are the product of a social context. There is no universal economic theory, and new economic thinking must necessarily be eclectic. That insight is Keynes’s greatest legacy.

Technorati Tags: efficient market hypothesis, Dynamic Stochastic General Equilibrium, rational expectations

My first comment is that the poster misunderstands the axiomatic method in pure mathematics.  It is not the case that “axioms are by assumption true”.  Truth is a bivariant relationship between some language or symbolic expression and the world.  Pure mathematicians using axiomatic methods make no assumptions about the relationship between their symbolic expressions of interest and the world.   Rather they deduce consequences from the axioms, as if those axioms were true, but without assuming that they are.    How do I know they do not assume their axioms to be true?  Because mathematicians often work with competing, mutually-inconsistent, sets of axioms, for example when they consider both Euclidean and non-Euclidean geometries, or when looking at systems which assume the Axiom of Choice and systems which do not.   Indeed, one could view parts of the meta-mathematical theory called Model Theory as being the formal and deductive exploration of multiple, competing sets of axioms.

On the question of economic modeling, the blogger presents the views of Gerard Debreu on why the abstract mathematicization of economics is something to be desired.   One should also point out the very great dangers of this research program, some of which we are suffering now.  The first is that people – both academic researchers and others – can become so intoxicated with the pleasures of mathematical modeling that they mistake the axioms and the models for reality itself.  Arguably the widespread adoption of financial models assuming independent and normally-distributed errors was the main cause of the Global Financial Crisis of 2008, where the errors of complex derivative trades (such as credit default swaps) were neither independent nor as thin-tailed as Normal distributions are.  The GFC led, inexorably, to the Great Recession we are all in now.

Secondly, considered only as a research program, this approach has serious flaws.  If you were planning to construct a realistic model of human economic behaviour in all its diversity and splendour, it would be very odd to start by modeling only that one very particular, and indeed pathological, type of behaviour examplified by homo economicus, so-called rational economic man.   Acting with with infinite mental processing resources and time, with perfect knowledge of the external world, with perfect knowledge of his own capabilities, his own goals, own preferences, and indeed own internal knowledge, with perfect foresight or, if not, then with perfect knowledge of a measure of uncertainty overlaid on a pre-specified sigma-algebra of events, and completely unencumbered with any concern for others, with any knowledge of history, or with any emotions, homo economicus is nowhere to be found on any omnibus to Clapham.  Starting economic theory with such a creature of fiction would be like building a general theory of human personality from a study only of convicted serial killers awaiting execution, or like articulating a general theory of evolution using only a hand-book of British birds.   Homo economicus is not where any reasonable researcher interested in modeling the real world would start from in creating a theory of economic man.

And, even if this starting point were not on its very face ridiculous, the fact that economic systems are complex adaptive systems should give economists great pause.   Such systems are, typically, not continuously dependent on their initial conditions, meaning that a small change in input parameters can result in a large change in output values.   In other words, you could have a model of economic man which was arbitrarily close to, but not identical with, homo economicus, and yet see wildly different behaviours between the two.  Simply removing the assumption of infinite mental processing resources creates a very different economic actor from the assumed one, and consequently very different properties at the level of economic systems.  Faced with such overwhelming non-continuity (and non-linearity), a naive person might expect economists to be humble about making predictions or giving advice to anyone living outside their models.   Instead, we get an entire profession labeling those human behaviours which their models cannot explain as “irrational”.

My anger at The Great Wen of mathematical economics arises because of the immorality this discipline evinces:   such significant and rare mathematical skills deployed, not to help alleviate suffering or to make the world a better place (as those outside Economics might expect the discipline to aspire to), but to explore the deductive consequences of abstract formal systems, systems neither descriptive of any reality, nor even always implementable in a virtual world.

Technorati Tags: economics, economic modelling, homo economicus, mathematical economics

• The problems are not posed in a form amenable to classical decision theory.
  

  Decision theory requires the decision-maker to know what are his or her action-options, what are the consequences of these, what are the uncertain events which may influence these consequences, and what are the probabilities of these uncertain events (and to know all these matters in advance of the decision). Yet, for many real-world decisions, this knowledge is either absent, or may only be known in some vague, intuitive, way. The drug thalidomide, for example, was tested thoroughly before it was sold commercially – on male and female human subjects, adults and children. The only group not to be tested were pregnant women, which were, unfortunately, the main group for which the drug had serious side effects. These side effects were consequences which had not been imagined before the decision to launch was made. Decision theory does not tell us how to identify the possible consequences of some decision, so what use is it in real decision-making?

• There are fundamental domain uncertainties.
  

  None of us knows the future. Even with considerable investment in market research, future demand for new products may not be known because potential customers themselves do not know with any certainty what their future demand will be. Moreover, in many cases, we don’t know the past either. I have had many experiences where participants in a business venture have disagreed profoundly about the causes of failure, or even success, and so have taken very different lessons from the experience.

• Decisions may be unique (non-repeated).
  

  It is hard to draw on past experience when something is being done for the first time. This does not stop people trying, and so decision-making by metaphor or by anecdote is an important feature of real-world decision-making, even though mostly ignored by decision theorists.

• There may be multiple stakeholders and participants to the decision.
  

  In developing a business plan for a global satellite network, for example, a decision-maker would need to take account of the views of a handful of competitors, tens of major investors, scores of minor investors, approximately two hundred national and international telecommunications regulators, a similar number of national company law authorities, scores of upstream suppliers (eg equipment manufacturers), hundreds of employees, hundreds of downstream service wholesalers, thousands of downstream retailers, thousands or millions of shareholders (if listed publicly), and millions of potential customers. To ignore or oppose the views of any of these stakeholders could doom the business to failure. As it happens, Game Theory isn’t much use with this number and complexity of participants. Moreover, despite the view commonly held in academia, most large Western corporations operate with a form of democracy. (If opinions of intelligent, capable staff are regularly over-ridden, these staff will simply leave, so competition ensures democracy. In addition, good managers know that decisions unsupported by their staff will often be executed poorly, so success of a decision may depend on the extent to which staff believe it has been reached fairly.) Accordingly, all major decisions are decided by groups or teams, not at the sole discretion of an individual. Decision theorists, it seems to me, have paid insufficient attention to group decisions: We hear lots about Bayesian decision theory, but where, for example, is the Bayesian theory of combining subjective probability assessments?

• Domain knowledge may be incomplete and distributed across these stakeholders.
• Beliefs, goals and preferences of the stakeholders may be diverse and conflicting.
• Beliefs, goals and preferences of stakeholders, the probabilities of events and the consequences of decisions, may be determined endogenously, as part of the decision process itself.
  

  For instance, economists use the term network goods to refer to a good where one person’s utility depends on the utility of others. A fax machine is an example, since being the sole owner of fax is of little value to a consumer. Thus, a rational consumer would determine his or her preferences for such a good only AFTER learning the preferences of others. In other words, rational preferences are determined only in the course of the decision process, not beforehand.Having considerable experience in marketing, I contend that ALL goods and services have a network-good component. Even so-called commodities, such as natural resources or telecommunications bandwidth, have demand which is subject to fashion and peer pressure. You can’t get fired for buying IBM, was the old saying. And an important function of advertising is to allow potential consumers to infer the likely preferences of other consumers, so that they can then determine their own preferences. If the advertisement appeals to people like me, or people to whom I aspire to be like, then I can infer that those others are likely to prefer the product being advertized, and thus I can determine my own preferences for it. Similarly, if the advertisement appeals to people I don’t aspire to be like, then I can infer that I won’t be subject to peer pressure or fashion trends, and can determine my preferences accordingly.

  This is commonsense to marketers, even if heretical to many economists.

• The decision-maker may not fully understand what actions are possible until he or she begins to execute.
• Some actions may change the decision-making landscape, particularly in domains where there are many interacting participants.
  

  A bold announcement by a company to launch a new product, for example, may induce competitors to follow and so increase (or decrease) the chances of success. For many goods, an ecosystem of critical size may be required for success, and bold initiatives may act to create (or destroy) such ecosystems.

• Measures of success may be absent, conflicting or vague.
• The consequences of actions, including their success or failure, may depend on the quality of execution, which in turn may depend on attitudes and actions of people not making the decision.
  

  Most business strategies are executed by people other than those who developed or decided the strategy. If the people undertaking the execution are not fully committed to the strategy, they generally have many ways to undermine or subvert it. In military domains, the so-called Powell Doctrine, named after former US Secretary of State Colin Powell, says that foreign military actions undertaken by a democracy may only be successful if these actions have majority public support. (I have written on this topic before.)

• As a corollary of the previous feature, success of an action may require extensive and continuing dialog with relevant stakeholders, before, during and after its execution.
  

  This is not news to anyone in business.

• Success may require pre-commitments before a decision is finally taken.
  

  In the 1990s, many telecommunications companies bid for national telecoms licences in foreign countries. Often, an important criterion used by the Governments awarding these licences was how quickly each potential operator could launch commercial service. To ensure that they could launch service quickly, some bidders resorted to making purchase commitments with suppliers and even installing equipment ahead of knowing the outcome of a bid, and even ahead, in at least one case I know, of deciding whether or not to bid.

• The consequences of decisions may be slow to realize.
  

  Satellite mobile communications networks have typically taken ten years from serious inception to launch of service.  The oil industry usually works on 50+ year cycles for major investment projects.  BP is currently suffering the consequence in the Gulf of Mexico of what appears to be a decades-long culture which de-emphasized safety and adequate contingency planning.

• Decision-makers may influence the consequences of decisions and/or the measures of success.
• Intelligent participants may model each other in reaching a decision, what I term reflexivity.
  

  As a consequence, participants are not only reacting to events in their environment, they are anticipating events and the reactions and anticipations of other participants, and acting proactively to these anticipated events and reactions. Traditional decision theory ignores this. Following Nash, traditional game theory has modeled the outcomes of one such reasoning process, but not the processes themselves. Evolutionary game theory may prove useful for modeling these reasoning processes, although assuming a sequence of identical, repeated interactions does not strike me as an immediate way to model a process of reflexivity. This problem still awaits its Nash.

In my experience, classical decision theory and game theory do not handle these features very well; in some cases, indeed, not at all.  I contend that a new theory of complex decisions is necessary to cope with decision domains having these features.

Technorati Tags: game theory, decision theory, network goods, reflexivity

• A successful single European currency requires a single European monetary policy.
• A successful single European monetary policy requires a single European fiscal policy.
• A successful single European fiscal policy requires fiscal transfers from one part of the European Union to another.
• Fiscal transfers from one part of the European Union to another can only be undertaken over the long term by European institutions having democratic legitimacy.
• To achieve democratic legitimacy for European institutions, the nations of Europe will require full political union.

This is not a new argument.  I first heard it put by Zambian economist Chiselebwe Ng’andwe in a paper read to a meeting of the African Association of Political Science in Salisbury (later Harare), Zimbabwe, in 1981, talking about regional economic unions in Africa.   In today’s Guardian, Simon Jenkins refers back to a book about European integration by Larry Seidentop, published in 2000, which apparently makes a similar case about Europe.

Why is this argument not, then, widely understood?  Is it that some ideas are too comprehensible – in other words, apparently lacking in complexity or subtlety – to be understood by intelligent people? Or is that the political forces which benefit from the non-democratic European status quo are so strong as to prevent the adoption of democratic structures, and to muzzle the arguments for them?  As I recall, Ng’andwe’s talk was received very coldly by his audience, most of whom were keen on economic unions (between African countries), while maintaining national sovereignty in all other respects.

References:

Chiselebwe Ng’andwe [1981]:  Problems of Economic Integration in Africa.  Paper presented to the Fourth Bi-annual Meeting of the African Association of Political Science (AAPS 1981).  Salisbury, Zimbabwe:  May 1981.

Larry Seidentop [2000]:  Democracy in Europe.  London, UK: Penguin.

Technorati Tags: Larry Seidentop, Chiselebwe Ng’andwe

So, one more time: here’s an attempt to put together some key arguments about why the rush to fiscal austerity is deeply misguided.

Let me start with the budget arithmetic, borrowing an approach from Brad DeLong. Consider the long-run budget implications for the United States of spending $1 trillion on stimulus at a time when the economy is suffering from severe unemployment.

That sounds like a lot of money. But the US Treasury can currently issue long-term inflation-protected securities at an interest rate of 1.75%. So the long-term cost of servicing an extra trillion dollars of borrowing is $17.5 billion, or around 0.13 percent of GDP.

And bear in mind that additional stimulus would lead to at least a somewhat stronger economy, and hence higher revenues. Almost surely, the true budget cost of $1 trillion in stimulus would be less than one-tenth of one percent of GDP – not much cost to pay for generating jobs when they’re badly needed and avoiding disastrous cuts in government services.

But we can’t afford it, say the advocates of austerity. Why? Because we must impose pain to appease the markets.

There are three problems with this claim.

First, it assumes that markets are irrational – that they will be spooked by stimulus spending and/or encouraged by austerity even though the long-run budget implications of such spending and/or austerity are trivial.

Second, we’re talking about punishing the real economy to satisfy demands that markets are not, in fact, making. It’s truly amazing to see so many people urging immediate infliction of pain when the US government remains able to borrow at remarkably low interest rates, simply because Very Serious People believe, in their wisdom, that the markets might change their mind any day now.

Third, all this presumes that if the markets were to lose faith in the US government, they would be reassured by short-term fiscal austerity. The available facts suggest otherwise: markets continue to treat Ireland, which has accepted savage austerity with little resistance, as being somewhat riskier than Spain, which has accepted austerity slowly and reluctantly.

In short: the demand for immediate austerity is based on the assertion that markets will demand such austerity in the future, even though they shouldn’t, and show no sign of making any such demand now; and that if markets do lose faith in us, self-flagellation would restore that faith, even though that hasn’t actually worked anywhere else.

• The discipline of Computer Science has usually developed first through practice, and only later through theory.
  

  The first calculating machine was invented by Leibniz in 1671, while the first mathematical theory of computing machines was that of Turing in 1937. The first widespread programmable device was Jacquard’s Loom, invented in 1801, while the first mathematical theory of programme languages did not appear until the 1960s. The Internet has been operating (first as Arpanet) since 1969, yet we still lack a formal theory of interaction. The first online friendships between people who never met were between telegraph operators, and later between telephonists, in the 19th century. The first e-commerce network was the Florists Telegraph Delivery Association, created in the USA in 1910.

• It is therefore a profound mis-understanding to consider Computer Science to be a branch of Pure Mathematics.

The scope (the content) of the discipline of Computer Science comprises the behaviors of human artefacts of a certain sort, along with some natural phenomena. Without the artefacts, we would likely not have the theory, or at least not yet (and perhaps not for a long time). Without the theory, we would not fully understand the performance of the artefacts, so both are needed. But the artefacts came first, and practice should dominate. If the theory dominates, our discipline will shrivel and die, becoming as dessicated and as useless as mathematical economics.

• Artificial Intelligence (AI) is the study of thinking about ways of knowing and ways of acting.
  

  This statement updates a statement of Seymour Papert (1988, p.3), who considered only ways of knowing.

  Seymour Papert [1988]: One AI or Many? Daedalus, 117 (1) (Winter 1988): 1-14.

• Not all ways of thinking are equally effective in all situations.
  

  In particular, some means of representing knowledge are more effective than other representations for some purposes. For instance, non-probabilistic formalisms for representing uncertainty, such as Dempster-Shafer Theory and Possibility Theory, are more effective than Probability Theory for domains where knowledge may be inconsistent or incomplete (ie, domains where the Law of the Excluded Middle cannot be presumed to hold, such as in medical diagnosis and in criminal forensics). The statement in the previous sentence remains true even though many such non-probabilistic formalisms can be shown to be equivalent to second- or higher-order nested probabilistic formalisms. This equivalence is a quaint mathematical result; non-probabilisitic formalisms are often easier for ordinary humans to understand than nested probabilistic formalisms. This is why the statement in the second sentence in this paragraph is an instance of the statement in the first sentence.

• Corollary: It behooves no one in AI to be dogmatic about ways of thinking.
  

  This is one reason why I am not a Bayesian.

• Deductive reasoning over an abstract mathematical model will only provide information about the real world to the extent that the relationship between the model and reality is continuously dependent on the initial assumptions.
  

  In other words, the fact that the assumptions of a model are close to some real phenomenon tells us nothing about whether the outputs of the model are close to those of the real phenomenon if the relationship between model and reality is not continuous. If you derive some result from an assumption that participants in some interaction have infinite processing capabilities, for example, then it does not necessarily follow that the same or close result holds if their real processing capabilities are finite, even if very large. Economics has always suffered from forgetting this truth, but most mainstream economists only seem to have realized it following the Great Global Economic Crisis of 2007. Indeed, some of the so-called freshwater economists have still not realized it, alleging that their models are still good predictors.

• In domains with intelligent participants (such as economics and computer science), models may be performative.
  

  In other words, participants may decide their modes of behaviour based on what modelers have suggested, so that modeling becomes, in effect, a form of self-fulfilling prophecy.

  In Economics, for example, the Black-Scholes model of options pricing allowed traders to price options rigorously. To do so, traders adopted the assumptions made by the modelers (eg, that errors are normally distributed, that decision-makers maximize expected utility, etc).

  Philip Mirowski [2002]: Machine Dreams: Economics Becomes a Cyborg Science. Cambridge, UK: Cambridge University Press.

  For doctrines of nuclear warfare, decision-makers adopted the modes of analysis, assumptions, and decision-options suggested to them by game theorists. Some of these assumptions were questionable during the Cold War – for example, that all participants know and agree on the game they are playing. As a consequence, the US Government appears to have embarked in the late 1950s on a mission to ensure the leaders of the USSR were also using game theory (mainly by issuing high-level public statements asserting that game theory was of no use in military applications).

• We have entered an era when the prevailing paradigm for the notion of computation is computing-as-interaction.

This paradigm follows earlier paradigms of computation as calculation (c. 1600 – 1965), of computation as information-processing (1965 – 1980), and computation as cognition (1980 – 1995). The new paradigm changes everything. In particular, an abstract model of computers based on movie projectors (ie, Turing Machines) is woefully inadequate for computing where outputs may be needed before all the inputs arrive, where there are multiple threads of control, where programs may be created, composed with one another, and compiled when invoked (ie, at run-time), and where computational devices and software exist together in ever-on, dynamic ecologies. We await an adequate formal, mathematical account of computing-as-interaction, and the game semantics of Samson Abramsky et al, and the bigraphs of Robin Milner are possible candidates.

M. Luck, P. McBurney, S. Willmott and O. Shehory [2005]: The AgentLink III Agent Technology Roadmap. AgentLink III, the European Co-ordination Action for Agent-Based Computing, Southampton, UK.

• As a consequence, Computer Science has a lot to learn from disciplines that have studied interaction.
  

  Disciplines such as Argumentation Theory, the Philosophy of Language, Linguistics, Economics, Social Psychology, Sociology, Anthropology, Political Science, Marketing, Epidemiology, Ecology, and Biology.Hopefully, the learning will be in both directions. For example, it is possible (although, in my personal opinion, unwise and immoral) for economists to assume that all economic actors always act in their own self-interest, maximizing their perceived expected utility. No rational computer scientist could make this assumption, however (except pro tem), since we all know the prevalence of buggy code: such code means that software entities may act against their own-self interest, or the interests of their principals. Creating a computational theory of interacting economic actors which does not make such false and unfalsifiable assumptions will surely benefit Economics, as well as Computer Science.

  The two-way interplay of Computer Science with these other disciplines of interaction provides further evidence that Computer Science is not a branch of Mathematics.

• Conflict and disagreement is inevitable in open computer systems.
  

  It therefore seems absurd to use formal models in which conflict is not permitted. Instead, it behooves us to consider computational models which enable conflict to be identified, managed, mitigated, and possibly resolved. Argumentation theory, not classical logic, is appropriate here.

• The Killer App for multi-agent systems is Distributed Computing.
  

  I have lost count of the number of times I have been asked by people outside the agents community, particularly other computer scientists, to name the killer app for agent technologies. Look about! It is all around you!Agent methodologies and technologies enable us to model and simulate complex adaptive systems, such as distributed computer systems. They also enable us to engineer (to specify, to design, and to create) such systems. And they enable us to study the properties of such systems, and hence to manage and control them.

• Agents are not objects.
  

  Objects always execute when invoked, and execute as expected. Agents may not. Objects maintain persistent relationships between one another. Agent relationships may be dynamic.

But then, once the flirtation and seduction are over, her flaws become evident. I have been thinking about these flaws again, having just read Deirdre McCloskey’s superb 2002 pamphlet, The Secret Sins of Economics.  Many of McCloskey’s criticisms are ones I (and many others) have made before, but some are new.   I decided, for comparison, to list here my chief complaints with this blemished beauty, this feline seductress, Our Lady of the Catallacts.  Date her if you wish, but you should read these accounts by her ex-lovers before you do.

First, she is blinkered, often unable to see what is obvious to anyone else – that we are all shaped by social and cultural forces, and peer pressures.   Instead, Catwoman and her acolytes invariably assume an individualist explanation for any economic or social phenomenon, and then seek to demonstrate it.  McCloskey calls this a focus on the P-variables (price, individual prudence, profit, the profane) as distinct from the S-variables (solidarity, speech, stories, shame) which Anthropology, that Indiana Jones of academic disciplines – creative, unruly, a thorn in everyone else’s side – has focused on.   A classic example is Levitt and Dubner’s Freakonomics.

Because of her blindness to the social, Cat Lady mostly ignored (until recently) major aspects of society, such as Institutions, legal frameworks, norms, and power relationships, aspects which can make or fail the marketplaces she says she studies.   She can’t claim that no one mentioned these to her, since 19th-century economists such as Karl Marx made the study of these aspects the work of a lifetime, and their study has continued to the present by sociologists and anthropologists and political scientists.

She has also been blind to anything historical or temporal, as if all her work stood outside the mundane and messy world in which we live.  This blindness manifests itself most strongly in the complete disregard (until recently) for endowments:  how did we get to where we are?  So, for example, free trade theory says that if England produces textiles more cheaply than Portugal, and Portugal produces wine more cheaply then England, the two should trade textiles for wine, and wine for textiles.   And the choice of these products is a subtly clever one, obfuscating much, since wine needs sunshine and not too much rain, while textiles (in the 18th and early 19th centuries) needed lots of rain, in order that the damp air would ensure cotton threads did not break when woven by machines.   So, Portugal’s sunshine and Northern England’s rain, being part of the God-given climate, were natural advantages, beyond the control or manipulation of any temporal human powers.  Free trade seems to have been ordained by the Almighty. But why consider only England’s textiles and not Ireland’s?    The answer is that Ireland had no textile industry to speak of.  And just why is that?  After all, much of Ireland is as damp as the valleys of Lancashire.   The reason is that the owners of northern English textile factories lobbied the British authorities to exclude Irish-made textiles from entering England.  When Ireland lost its own Parliament in a hostile takeover by Westminster, this protectionism for English textiles was entrenched, and the growing British Empire provided the critical masses of customers to ensure bonuses in Bury and Bolton and Burnley.     (Is it any wonder that people in Ireland and India and elsewhere sought Independence, when colonialism so powerfully stifled economic aspirations.)  Northern England has no natural comparative advantage in textile production, at least, not when compared to Ireland, but an artificial, man-made advantage.  The same type of advantage, in fact, that South Korea today has in ship-building, or the USA in most computer and aerospace technologies.   Where, in the mainstream theory of free trade, are these aspects studied, or even mentioned?

And when, angered by these failings, you face her with them, the wench promises you that that was all in the past, and she will be different from now on.  Path dependence and network goods and institutional economics are all the rage, she says.   But then you find, she’s still up to her old tricks:  She says she’s building models of economic phenomena in order to understand, predict and control, just like physicists do.  But, although it looks like that’s what she’s doing, in fact her models are not models of real phenomena, but models of stylized abstractions of phenomena.  Her acolytes even use that very word – stylized – to describe the “facts” which they use to calibrate or test their models.

Of course, she will say, physicists do this too.  Newton famously assumed the planets were perfect spheres in order to predict their relative movements using his theory of gravitation.   But physicists later relax their assumptions, in order to build revised models, in a process that has continued since Newton to the present day.  Physicists also allow their models to be falsified by the data they collect, even when that data too is stylized, and overturned.     Instead, Catwoman is still assuming that people are maximizers of individual utility, with perfect foresight and unlimited processing capabilities, obeying the axiom of the irrelevance of independent alternatives, when all these assumptions have been shown to be false about us.   When was the last time a mainstream economic model was overturned?

Indeed, here is another of her flaws:  her loose grasp of reality.  She says we are always, all of us, acting in our own self-interest.  When you quiz this, pointing out (say) a friend who donated money to a charity, she replies that he is making himself feel better by doing something he thinks virtuous, and thus is maximizing his own self-interest.  Her assumption, it turns out, is unfalsifiable.   It is also naive and morally repugnant – and false!  Anyone with any experience of the world sees through this assumption straight away, which is why I think our feline friend is borderline autistic.   She just does not know much about real people and how they interact and live in the word. Who would want to step out with someone having such views, and unable to reconstruct them in the light of experience?

And, despite her claims to be grounded in the material world (Paul Samuelson:  “Economics is the study of how people and society end up choosing, with or without the use of money, to employ scarce productive resources that would have alternative uses,  . . .”), she sure is fond of metaphysical entities for which no hard evidence exists:  invisible hands, equilibria, perfect competition, free trade, commodities, in fact, the whole shebang.   As marketers say, the existence of a true commodity is evidence that a marketing manager is not doing his or her job.  In comparison, Richard Dawkins with his memes is a mere amateur in this creation of imaginary objects for religious veneration.

One could perhaps accept the scented candles and the imaginary friends if she was a little more humble and tolerant of the opinions of others.  But no, the feline femme fatale and her acolytes are among the most arrogant and condescending of any academic disciplines.  Read the recovering Chicago economist McCloskey for an account of this, if you don’t believe me.   McCloskey’s anecdotes and experiences were very familiar to me, especially that sneer from an economist who thinks you’ve not acted in your own self-interest – for example, by helping your colleagues or employer with something you are not legally required to do.  Indeed, the theft by economists from philosophers of the word “rational” to describe a very particular, narrow, autistic behavior is the best example of this.   Anyone whose behavior does not fit the models of mainstream economics can be thus be labeled irrational, and dismissed from further consideration as if insane.

Date her at your peril!  You have been warned!

If business were truly the self-interested, take-no-prisoners world of competition described in economics textbooks and assumed in mainstream economics, our lives would all be very different.  Fortunately, our world is mostly not like this.   One example is in telecommunications where companies compete and collaborate with each other at the same time, and often through the same business units.  For instance, British Telecommunications and Vodafone are competitors (both directly in the same product categories and indirectly through partial substitutes such as fixed and mobile services), and collaborators, through the legally-required and commercially-sensible inter-connections of their respective networks.  Indeed, for many years, each company was the other company’s largest customer, since the inter-connection of their networks means each company completes calls that originate on the other’s network; thus each company receives payments from the other.  Do you seek to drive your main competitor out of business when that competitor is also your largest customer?   Would you do this, as stupid as it seems, knowing that your competitor could retaliate (perhaps pre-emptively!) by disconnecting your network or reducing the quality of your calls that interconnect?  No rational business manager would do this, although perhaps an economist might. 

Nor would you destroy your competitors when you and they are sharing physical infrastructure  – co-locating switches in each other’s buildings, for example, or sharing rural cellular base stations, both of which are common in telecommunications.   And, to complicate matters, large corporate customers of telecommunications companies increasingly want direct access to the telco’s own switches, leading to very porous boundaries between companies and their suppliers.   Doctrines of nuclear warfare, such as mutually-assured destruction or iterated prisoners’ dilemma, are better models for this marketplace than the mainstream one-shot utility-maximizing models, in my opinion.

You might protest that telecommunications is a special case, since the product is a networked good – that is, one where a customer’s utility from a particular service may depend on the numbers of other customers also using the service.    However, even for non-networked goods, the fact that business usually involves repeated interactions with the same group of people (and is decidely not a one-shot interaction) leads to more co-operation than is found in an economist’s philosophy.   The empirical studies of hedge funds undertaken by sociologist Donald MacKenzie, for example, showed the great extent to which hedge fund managers rely in their investment decisions on information they receive from their competitors.  Because everyone hopes to come to work tomorrow and the day after, as well as today, there are strong incentives on people not to  mis-use these networks through, for instance, disseminating false or explicitly-self-serving information.  

It’s a dog-help-dog world out there!

Reference:

Iain Hardie and Donald MacKenzie [2007]:  Assembling an economic actor: the agencement of a hedge fund. The Sociological Review, 55 (1): 57-80.

Technorati Tags: economics, competition, telecommunications, mutually-assured destruction, iterated prisoners’ dilemma

If Akerlof and Shiller’s grip on the history of economic thought is shaky, they also fail to grasp why Keynes rejected the idea that markets are self-stabilising. Throughout Animal Spirits they portray him as reintegrating psychology with economic theory. No doubt this was one of Keynes’s goals, but it is not his most fundamental revision of economic orthodoxy. Among his other accomplishments he was the author of A Treatise on Probability (1921), in which he tried to develop a theory of ‘rational degrees of belief’. By his own account he failed, and in his canonical General Theory of Employment, Interest and Money (1936) he concluded that there was no way anyone could make forecasts. Future interest rates and prices, new inventions and the likelihood of a European war cannot be predicted: there is no ‘basis on which to form any calculable probability whatever. We simply do not know!’ For Keynes, markets are unstable less because they are driven by emotion than because the future is unknowable. To suggest that the source of market volatility is unreason is to imply that if people were fully rational markets could be stable. But even if people were affectless calculating machines they would still be ignorant of the future, and markets would still be volatile. The root cause of market instability is the insuperable limitation of human knowledge.

Later economists have made much of a distinction between risk, which can be assessed in terms of quantifiable likelihood, and uncertainty, where probabilities cannot be attached to possible outcomes. The trouble is that when attempting to forecast the course of the economy we often cannot confidently distinguish between the two. Even our list of possible outcomes may turn out to have omitted the ones that are most important in shaping events. Such an omission was one of the factors that led Long-Term Capital Management, a highly leveraged hedge fund set up by two Nobel Prize winning economists, to fail in 1998-2000. The information used in applying the formula did not include the possibility of such events as the Asian financial crisis and Russia’s default on its sovereign debt, which destabilised global financial markets and helped destroy the fund. The orthodoxy that came unstuck with the collapse of LTCM was not faulty because it neglected the vagaries of human moods; its mistake was to think that the unknown future could be turned into a set of calculable risks and, in effect, conjured out of existence, which was impossible. Several centuries earlier, Pascal – one of the founders of probability theory – had come to the same conclusion, when in the Pensées he asks ironically: ‘Is it probable that probability brings certainty?’

The central flaw of the economic orthodoxy against which Keynes fought in the 1930s was to imagine that an insoluble problem – human ignorance of the future – had been solved. The error was repeated in the 1990s, when economists came to believe that complex mathematical formulae could tame uncertainty in the murky world of derivatives. Steeped in history as they were, this was a delusion that none of the classical economists entertained. It began to shape economics only towards the end of the 19th century, with the rise of Positivism, according to which the natural sciences are the only legitimate repository of human knowledge. It was the formative influence of this philosophy on the Chicago School that enabled the orthodoxy of the 1930s to re-emerge triumphant, and the result was an immense boost to the prestige of economics as a discipline. Economists could claim to be scientists, who with the aid of their mathematical magic could pierce the veil that conceals the future.

The hegemony of Positivism in economics obscured Keynes’s scepticism about probabilistic knowledge, his most important contribution to the discipline. G.L.S. Shackle set Keynes’s argument out systematically in his neglected masterpiece Epistemics and Economics: A Critique of Economic Doctrines (1972). Shackle is probably the only significant economist to have been influenced both by Keynes and by his arch-rival, F.A. Hayek. He knew both of them well, but argued that neither had digested the full implications for economics of our ignorance of the future. Hayek said that governments could never know enough to plan the economy successfully – a claim vindicated by the miserable record of central planning in Communist countries. At the same time, he attributed near omniscience to markets, and never doubted that if left to its own devices the economy would liquidate mistaken investments and return to equilibrium. Against this, Keynes had shown that there is no market mechanism that ensures revival; economic contraction can be self-reinforcing, and only government action can then create a way out.

Shackle took Keynes’s argument a step further, and showed that no economic policy can ensure economic stability indefinitely. ‘Keynesian’ policies are no exception to this rule. Deficit financing and monetary expansion may have worked well in the conditions that existed after the Second World War. It is not clear that they will be so effective today, when globalisation has brought a freedom of capital movements that did not exist then. The lesson of Shackle is that we must be resourceful in devising new remedies, while not losing sight of the fact that none of them works for long.

Akerlof and Shiller claim that their account of the role of psychology helps to explain the financial crisis. ‘Our theory of animal spirits,’ they say, ‘provides an answer to a conundrum: why did most of us utterly fail to foresee the current economic crisis? How can we understand this crisis when it seems to have come out of the blue with no cause?’ They are right that part of the answer lies in an intellectual default within economics, but they seem oblivious of the role of ideology in producing this default. The deformation of economics was not the result only of factors internal to the discipline, it was also part of the short-lived Western triumphalism that followed the end of the Cold War.

. . .

Keynes and the classical economists before him knew that there is no realm of market exchange that obeys laws of the kind that can be formulated in the natural sciences. Economics and politics are not separate branches of human activity, and economic life cannot be studied independently of social divisions and political conflicts among populations, along with their cultures and religions. Familiar to Keynes and most of the economists of his generation, these truisms have been forgotten, or rejected, by many economists today. The result is an economic imperialism that tries to explain every human activity in terms of a conception of rational action that does not work even when applied to the behaviour of markets.

Of course, there is a standard response to these observations, which is that unrealism in economic theories doesn’t matter. As developed by Milton Friedman, among others, this is in effect a version of instrumentalism, a tenable position in the philosophy of science. For instrumentalists, the goal of science is not a true representation of the world; it is to organise our observations into a theoretical framework that serves practical goals, such as prediction and control. But what practical goals have been served by the type of economics dominant over the past two decades? It has been useful neither in making predictions nor in responding to unforeseen developments.

Akerlof and Shiller intend their analysis to contribute to an intellectual reformation in economics, as a consequence of which the discipline will become more useful to policy-makers. It must be doubted, though, that the authors will succeed in persuading economists of the inadequacy of the conception of rational action. The profession is one of the few areas of human activity in which that conception is applicable. In its intra-academic varieties, at any rate, economics is insulated from the world not only by its narrow explanatory methodology but also because it rewards the mathematical modelling that resulted in nearly all of its members failing to anticipate the financial crisis. As institutionalised in universities, the notion of rational decision-making is self-perpetuating. Economics as currently practised may have only a slight grip on market behaviour, but it seems to be powerfully predictive of the behaviour of economists.”

Reference:

John Gray [2009]: We Simply Do Not Know!  London Review of Books,  31 (22): 13-14, 19 November 2009.  Review of: Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, by George Akerlof and Robert Shiller. (Princeton).

Technorati Tags: George Shackle, John Gray, George Akerlof, Robert Shiller