Hiding behind complexity

This week on Econtalk, Russ Roberts and John Papola discuss their most recent economics rap video about a fictional  debate between  F.A. Hayek and J.M. Keynes entitled “Fight of the Century”.  You can find the video here.  An earlier one also featuring Hayek and Keynes was called “Fear the Boom and the Bust”.  Both of the videos are entertaining and educational.   If you don’t know anything about Hayek, Keynes or macroeconomics, you can learn quite a bit just from these videos. Although, Roberts and Papola are strong proponents of Hayek, they try their best to represent Keynes fairly.    I will not address the economics arguments directly but rather comment on the philosophy of Hayek that motivates his ideas.

Hayek is a hero of libertarian leaning economists such as Roberts.  His main thesis is that the economy is far too complex to ever be understood by economists so any attempt at economic manipulation by the government such as the stimulus is misguided at best and dangerous at worst.  He claims that it is always better to just let the free market play out.  An Econtalk episode on Hayek can be found here and these ideas are summarized in his Nobel address found here.  In some sense, Hayek was ahead of his time in recognizing the importance  of complex systems as a field into itself.  On the other hand, he takes a very defeatist attitude towards it.   I have argued before (e.g. see here) that a purely reductionist approach is a futile approach to understanding complex phenomenon like biology or economics.  However, that doesn’t imply that some form of systematization or quantification is impossible.  For example, consider water flowing in a pipe.  If the velocity of the water is low then the water will flow smoothly.  However, when the velocity is fast enough the flow will become turbulent.  We can even calculate when the instability transition will occur.  Although it is completely futile to predict the trajectories of water molecules in a turbulent flow, there are statistical invariants that are well behaved.  Hayek claims that even a statistical theory of economics is impossible because economics is comprised of heterogeneous players so there is no natural way to average.  However, he makes such claims without any proof.  It may be true that there are no statistical invariants in economics but that is a question that can at least be studied.  Hayek doesn’t even believe that economics measures like the unemployment rate is of any use because that knowledge cannot be used in any useful way.

My approach to complex systems is based on two observations.  The first is that we can only have some quantitative control of a system if it is smooth enough so that small perturbations generally lead to small changes in the system.  We can handle  instances of where small changes lead to big changes (e.g. bifurcation or critical points where the qualitative behavior of the system changes drastically, like a phase transition between liquid and gas) if they are not too close to each other.  Hence, I only try to model something that behaves relatively nicely.  (I’ve argued before (e.g. see here) that physics could be described as the science of model-able things.)  The second observation is that most functions, no matter how badly behaved, can be made smooth if you integrate over it enough times.  If a system is very complex, I look for integrated or averaged quantities that seem to behave better.  For example, while the dynamics of the molecules in a gas are buzzing around in a haphazard unpredictable way, the temperature of the gas is well defined and can be described quantitatively.  Although human metabolism is highly complex, it still obeys the conservation of energy at a global level and I can use that fact to make quantitative predictions about the response of body weight to changes in food intake. So my take on the stimulus is that it is plausible that increasing spending can increase the velocity  of  money flow in the economy and kick us out of a recession.  While I doubt that we will ever be able to predict exactly how well a stimulus will work I think we can at least make some probabilistic predictions about the effect size.

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6 thoughts on “Hiding behind complexity

  1. I think complexity in economics is far harder to overcome than in physics.

    (1) In economics, reasonable people can and do strongly disagree about the parameter values in the equations being integrated, or even what the equations are. Even in recent years, there have been both very left-wing and very right-wing Nobel laureates.

    (2) Verifying those parameters is difficult in economics because there are so few proper experiments being done, and at the macro scale these experiments are nearly impossible. One interesting question is why we don’t use policy (like the stimulus) to do more economics experiments, via randomization (but see (5)).

    (3) Economic actors adapt to policy, and even to the belief in future policy, in ways that inanimate matter does not; consider inflation expectations as just one example. That makes planning difficult; but it also makes rapidly achieving stable equilibria easier with markets; the physical analogy to central planning would be, in a complex physical system, replacing some fraction of each particle’s ability to spontaneously find its lowest energy state (by simplying following the laws of physics) with the experimenter’s calculated decision about where particles should go. It is difficult to imagine this being an efficient tradeoff in most systems, unless the spontaneously reached low energy states were clearly not the globally lowest (but see (1) and (2)).

    (4) Many of the variables that are theoretically under our control are in fact endogenous to the system; for example, the federal government cannot decide how state governments respond to federal fiscal policy (do they offset it with spending cuts?), or how monetary authorities respond (does the fed raise interest rates sooner than they would have in response to the stimulus, offsetting the stimulus, to achieve their own policy goals?) Worse yet, combining (3) and (4), do markets know this, nullifying the expansionary expectations of stimulus?

    (5) Even if (1)-(4) were overcome, politicians in charge of fiscal policy have other interests besides actually enacting sensible fiscal policy, whatever that might be. So the specific argument of “what should we (as economic policymakers) do” is different from the “what political and economic institutions should we (as citizens and voters) support”.

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  2. As I have argued before, I agree that economics is far harder than physics. My argument is simply that there is a difference between hard and impossible. This is a question about complexity classes and can be addressed. Now even if economics is in a very difficult complexity class or even if it is undecidable, you might still be able to find approximate solutions for some circumstances or make probabilistic statements. A second point that I didn’t get to in my post was that even if you think that government interventions are bad, how do you know that free markets are better? If you had a proof that markets were optimal then you could say something about the response to perturbations to the free market. As for your last point, I agree completely but policy makers shouldn’t use Hayek’s argument as the reason for not acting.

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  3. Impossible in theory or in practice? Even if macroeconomics is in a very simple complexity class, it still might not be possible to collect the data required to fit the solution, and if it is possible, the parameters obtained might be out of date by the time any policy was implemented. Knowing how those parameters change over time would be tantamount to knowing how both human cultural preferences and technology utilization change over time, which is tantamount to solving sociology, psychology, and physics. In short, you’re in Hari Seldon territory.

    Also, the statistical invariants and probabilistic statements you hope to obtain would only be useful if the microstates were traversed rapidly compared to the duration of measurement, so that something like a steady-state distribution would even make sense. But economic fundamentals might change too quickly compared to theory and policy for this assumption to be reasonable.

    As for the affirmative argument for free markets, I don’t think they can be asserted to be optimal for the same reasons above, but as Hayek would say the decisions of each economic actor (individuals and firms) reflects specific domain knowledge about current and expected future market conditions that cannot be aggregated efficiently except via the price mechanism. Obviously there will be many exceptions, like when the price does not reflect externalities, or goods are non-rivalrous or non-excludable, or prices and quantities are not determined by competitive markets; these exceptions carve out a substantial role for government in specific sectors, but this doesn’t make attempting to set the path for all sectors (i.e. macroeconomic planning) any more feasible, at least with fiscal policy.

    Monetary policy is different, because since the government is already explicitly providing the medium of account, i.e. the quantity of money, they have to choose *some* path for that variable. Even 0% growth is an explicit choice for a path. So they might as well choose the one that seems the best, according to provisional theories, since that’s the best they can do. And if they continually revise that path in order to target some nominal variable, all the better.

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  4. This is where I wonder how much data is really needed. Perhaps just a few macroeconomic markers would be sufficient to give some clue as to what to do. I agree that it is pointless to try to understand the microscopic interactions but I question if that is necessary. I’m not arguing that we can have full control of the economy, just that we shouldn’t just throw our hands in the air and give up. Monetary policy is a perfect example. Sure there can always be exogenous shocks and black swans you can’t predict. However, given the information we have, I think decisions can be made on interest rates to make things better.

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  5. When it comes to the future of science (here, can we predict economy?), I think we should first keep away from bold statements such as “it is impossible to predict anything because the system is far too complex”. What Rick is saying here is akin to an anti-Laplace demon. Laplace’s statement was certainly too strong and turned out to be wrong a century later.

    There are currently numbers of papers trying to work out simple socio-economic systems through multi-agent modelling and links with statistical physics. Though it is still starting, there are already some very interesting results. For example, on the Schelling (Nobel prize 2005) segregation model, phase diagrams of the outcome of the model have been built by making analogies to spin systems (Kirman and Vinkovic 2006, Gauvin et al 2009, 2010). Solutions to point 3 have also been proposed (Grauwin et al 2009) by compromising usual physical systems with multi-agent models.

    Though this is a start, it is a proof of principle that we can perform systematic analyses of some complex socio-economic systems.

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