Economics with Fewer Equations

The theme that economics is less like physics and more like biology is not new. However it is more a fringe view than a mainstream one. Even now, I would guess that the general expectation of an economic journal in publishing a paper is what mathematical basis it has and how robust the Econometrics is. Several recent things have pointed to a better direction.

• The economist article on agent based models
• The book – growing artificial societies
• The book – origin of wealth, especially the early sections
• One recent article in Bloomberg by a theoretical physicist 

The essential argument is same. We can’t draw up pretty equations to predict people’s behavior. Generally it is argued that these equations are trying to predict the behavior of only the “model” or average individual or the behavior of the collective. The implicit assumption in the usefulness of this approach is that this “average” study is genuinely the median behavior and the deviation around this median is noisy but controlled. Also it is assumed that the noise is averaging to zero and has no impact on the overall behavior of the economy. 

It is not very different from saying that a ball of steel has electrons inside it moving in all directions. However, at no point is the ball moving anywhere on account of this movement. The diverse directions of movement of the electrons are random and thus cancel each other out at the aggregated level. Similarly it is argued implicitly that in equationalizing economics, individual differences of behavior around the “mean” are random and they cancel each other out. As it turns out, more often than not, they do not.

Some systems in economics may indeed be amenable to a linear model of this sort. However most useful phenomena are non-linear and dynamic and thus do not readily lend themselves to this approach. Imposing this approach on those systems then is bound to yield erroneous forecasts. I have elsewhere argued with the example of the billiards table where the physicist refuses to predict where each ball would be after the first strike. Physics concerns itself with questions like conservation of momentum in each interaction and the inertia and friction and so on. No physicist would try to build a model of the average ball and then hope that some contained linear variation around it would be a good way to explain how the strike leads to the evolution of the table.

Conventional economics is routinely trying to do this. I don’t know how it came to be here. The book 'Origin of Wealth' offers some explanations. Historically Walras and his contemporaries were quite enamoured by the success of physics with equations and tried to use the same in their work. Since then, almost as a historical accident, economics has continued to progress in that direction. The author of origin of wealth even goes ahead and calls this a century long detour. Audacious maybe? But most likely quite accurate description of what has gone on since.

The beer game example in origin of wealth is quite illuminating. (http://en.wikipedia.org/wiki/Beer_distribution_game)

A simple trigger at the customer demand end causes all sorts of fluctuations in the supply chain although after the initial reaction, the variation in customer demand is taken out. It goes to show that in absence of perfect information and strategic gameplay between transacting parties, the supply chain can exhibit very dynamic patterns – which are far from equilibrium. The Growing artificial societies authors call it far from equilibrium economics.

The opposition to the equationalizing of economics earlier was countered with TINA. What do you propose, the proponents would ask. Since the opponents never really had much of a proposal, the conventional equilibrium economics continued. Now in computational economics, complex adaptive systems and agent based modeling, there might be a genuine alternative.

This approach can open up new areas of dynamic modeling which were intractable for analytical solutions. This can also help learn emergent phenomena which are otherwise blackbox to top down modelers.

What are the limitations?

One needs to start somewhere. Where one starts can significantly impact the answers one gets. Hence the approach is somewhat prone to curve fitting. Some intellectual discipline and robustness inducing techniques are required in this case.

The micro leading to macro is an interesting theme and a long cherished dream of economists. However, conventionally the two have stayed separate. The agent based modeling with inclusion of complexity approach can start to make this reality.

Bitcoins and alternate currencies : some speculation

Greenspan seems to say that bitcoins are a bubble. Insofar as he is referring to them being prone to Tulip-Mania like irrationally high price rise and subsequent price fall, he is probably right. However, in claiming that they have no ‘basis’ and are hence a bubble is conceptually incorrect. This is because implicit in such a claim is another – that the ‘real’ currencies are not like this and have a more solid ‘basis’. In fact, they don’t. All modern currencies are fiat currencies. They do in fact have a stronger basis in the central bank, banking system and the home economy. However, conceptually they are no different than a widely accepted form of payment.

That is not to say that bitcoins are on their way to become an alternate currency anytime soon. However, that does bring forth an important question. How does something become a currency? Or less ambitiously, how can something like bitcoins in the modern world become a currency? (The former is too general a question and is very hard to answer. The latter is easier due to the particularization afforded by placing it in the current context.)

It boils down to development of a critical mass of individuals and organizations accepting something as a means of payment. Why they would do so is of course the next question. That has varied answers. Broadly though, I can think of the following three.

1. The (perception of) failure of the currently accepted currencies in some way – massive inflation worries is one possibility, difficulty in online payments is probably another, perception of over-regulation, lack of faith in banking system which backs most of the currency besides the central bank.

2. The opportunity to carry out some project not feasible in the current context.

3. Speculation over the wider acceptance of the new currency

The first and second motives are based in themselves. The third is a transient motive. However, the interplay among these has implications for whether something becomes a bubble and bursts or gets accepted as a real currency.

The question of critical mass is crucial. Currencies have tremendous network effect. If more and more people start accepting a currency, that in itself becomes a strong reason for even more to accept. The reverse also holds. One place where this is being played out even now is the status of Dollar as the reserve currency and the currency of choice for international trade, especially in oil. Below the critical mass, the people trading into the currency for the third motive above will quickly abandon it. With values falling, those in first two motives will also be forced to stop dealing in it.

This is where the institutional framework backing a currency and the economic interests served by it become crucial. If the currency manages to do something very useful for a sufficiently large group of people and organizations, they would try to find a way to make it work. In parallel, if the institutional framework for a currency is well developed and has a sponsor in a suitably empowered entity, it can grow.

Probably the most important element of the institutional set up is that of seigniorage. This is the privilege that comes to the ‘issuer’ of a currency, if there is one. In the gold standard currencies, there is no ‘issuer’ since everything is backed by equal value of gold. In the fiat currencies’ case, the central banks and hence their respective governments are the issuers. (Tangent: Rothbard describes brilliantly the evolution of the federal reserve in the US and the interaction between governments and central banks and monetization deficit etc.)

If the issuers get greedy, inflation ensues and can quickly become hyperinflation (Germany, Poland, Zimbabwe). I don’t know what the issuing mechanism for bitcoins is. I am sure something this obvious has already been dealt with and seigniorage hopefully taken out or made transparent. However, if and when the stature and acceptance of the currency grows, these worries might start haunting the users. It is interesting to draw a parallel with the growth of modern currencies and their convertibility into gold. Till as late as second world war, there was a constant tension between users and issuers of the currencies – the users routinely converting their currency holdings into ‘species’ (a term used for gold and silver) and issuers suspending ‘specie payment’ every now and then. 

For bitcoins, Dollar is the ‘specie’. As the users of bitcoins try to conduct transactions outside the regular users of bitcoins, they will need payment in ‘specie’. This is a crucial juncture. If the non-users of bitcoins keep insisting on payment in ‘specie’, the critical mass of bitcoin users may not grow much – although the legitimacy afforded by the notional acceptance of the bitcoins in the outside world is quite comforting for the present users to stay put. On the other hand if the non-users start to keep the bitcoins and postpone conversion, that could really provide a strong boost to the growth of bitcoins. The non-users thus become users or at least part-time users (they don’t actively transact but store bitcoins for speculative reasons.)

The case of a car dealership accepting bitcoins for payment is thus interesting. Equally interesting is the ban imposed by China onpayments using bitcoins.

The growth of capitalism and government spending in 19^th century and early 20^th century created the basis for the central banking and modern banking system that we know today – along with the fiat currencies that they support. The growth of internet in 21^st century might be able to support a new form of currency – which is as unlike the current currencies as these current currencies were to gold back then! 

Origin of Wealth - review of part I

The title of the book - Origin of Wealth - is misleading. And that is a good thing. The origin of wealth could easily have been a history of money and wealth (not different from say ‘Ascent of Money’ – a great book but more descriptive than imaginative.) Instead it is precisely what the subtitle says – Evolution, Complexity and the Radical Remaking of Economics.

The book’s introduction talks in great detail about the failure of traditional economics and the challenges that has posed. The first chapter covers general question of how is wealth created and quickly moves on to describe economy as a complex system. The second chapter dwells on the traditional economics and its emphasis on equilibrium systems. So far nothing revolutionary and new happens. The fun starts with the third chapter entitled A Critique – Chaos and Cuban Cars. The similie is quite accurate. The basis of traditional economics seems as outdated as the Cuban cars. The experience of the Santa Fe institute dialogue is also very enlightening. It describes how natural scientists were nearly aghast at the assumption-making and theorizing of economists. The chapter goes through several “laws” of economics and describes how they don’t quite hold. It also goes on to describe why economics might have taken the ‘century long wrong turn’ by tracing the origins of this ill-fitting approach to Walras’ emphasis on using equilibrium models from half-baked theories of then available physics. The chapter ends with the coverage of what the author calls ‘misclassification of the economy’. That is apt. The Walrasian classification of an economy as a stable and equilibrium system is a gross oversimplification and fundamentally incorrect. An economy is a dynamic and non-linear and thus complex. This sets the stage of next section.

Chapter 4 is highly fascinating. It starts to get into the real complexity modeling. Although it covers a relatively simple model, it is quite illuminating. The sugarscape described in the chapter is quite an eye-opener. It talks of how markets, inequality, banking and so on emerge as properties of the system when modeled like a agent-based-system without specifying any of these things. Some steps seem like flights of fancy. However, the general tone is quite serious, believable and most importantly reproducible to anyone who bothers enough to model the sugarscape. I of course feel special affinity towards this approach since it gels well with my thinking about using agent based models and simulations to observe emergent properties rather than abstract those from intuition and black-box-like observation of the system as a whole.

Chapter 5 gets more general and still stays quite interesting. It talks about dynamics. The primary coverage is of static systems vs nonlinear systems. More importantly the idea of oscillating equillibria is discussed. Subsequently the chapter goes into the discussion of using nonlinear systems to explain economic phenomena such as business cycles. He exemplifies with the widget production case – which is itself quite interesting and hits home with the very real scenarios. The chapter also describes John Sterman’s attempt at nonlinear modeling of business cycles across industries.

Chapter 6 focuses on agents. This chapter also describes deductive learnings vs inductive learning and classifies the computer’s methodology as deductive and human ones as inductive. That is an interesting though known distinction. It still is brought home beautifully when the author notes that while Deep Blue can play chess as well as Gary Kasparov, the latter can also tie his shoelaces unlike the former. There are some things or skills which are very easily accessible to inductive learning but are very difficult for deductive thinking. Pattern recognition is a prime example. Human beings can reasonably read decently written hand-writing without much error and difficulty. Computers find it extremely hard to do so and have to go through a laborious process to get there – and still with errors.

Traditional economics assumes that human beings possess infinite deductive capacity and do not need inductive learning since they are already perfect in their decision making. The author then proposes that complexity economics would take the reverse view and try to model individuals as agents with inductive machinery and limited deductive powers – but a decent learning program. The frog example is quite illustrative in this regard. Subsequently the chapter goes through a more detailed agent based modeling of stock markets and describes how the simulation at Santa Fe institute led to indicating a close to real life stock market with the attendant volatility, booms and busts and so on. It boils down not to random noise but competing beliefs in the actors’ minds – different hypotheses about what makes money. The economy by extension can also be modeled using boundedly rational agent with inductive skills and competing hypotheses about how to achieve their goals.

The subsequent chapters cover emergence and evolution and are equally fascinating. I will cover them in another blog. The second part on evolution of physical and social technologies starts to look lot less exhilirating conceptually - so i might cover it briefly later.