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 The basis of traditional economics seems as outdated as the Cuban cars.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.

Evolution of institutions in human society

Institutions in human society can evolve in two ways.

For one, transactions which start to repeat get recognized by the relevant actors as conventions. Over time these conventions may give rise to more rigid institutions. This is emergent variety of the evolution of institutions.

Secondly, someone with coercive power (generally a state but in a narrower settings can be corporate head office or military general) imposes set of rules. These rules are either accepted by the recipients or rejected. When accepted they become institutionalized. (Even when rejected there might be a counter-institution that may develop in some cases.) The question of whether these rules are imposed on account of soft paternalism or vested interests or general lunacy (or idiosyncrasy) is immaterial. The common thread is that they are imposed from outside. They may be ‘sold’ to the recipients by their proponents – and if they are, it may be with the true intent or with a façade or some combination. It is also possible that the proponents are not all on the same page regarding the intent and that fact itself drives how the rules are sold.

In either case, once an institution evolves into more rigid form, it starts to guide/restrict behavior (that is whole purpose of any institution.) Since it is the transactions arising out of the behavior that gives rise to institutions in the first place, the evolution of subsequent institutions is then affected by the combination of transactions and current institutions. This also includes the modifications in the institutions.

Thus we come to the reflexive relationship between transactions of actors and the institutions that evolve out of them and guide their subsequent evolution.

It can be likened to the interrelationship between water and the ground shaped by it. The water has some tendencies, which are inherent to it. The ground has some starting structure. The movement of water then alters the ground in some ways. The evolved structure of the ground itself starts to affect subsequent behavior of water (with the same tendencies). The structure of the ground at any given point of time is hence an outcome of a complex process of interaction between the ground and the water.

In general self-emergent institutions tend to be solving some felt need. The imposed institutions may not necessarily do so. The ideological variety on paper at least aspires to address some need. The vested interests driven one will typically find a façade of a need to address.

How real the need is and how long it lasts will have an important bearing on the success or failure of the institution. However, the strength of the sponsor of the institution also has an equally important bearing on the success. It is entirely possible that a very naturally emergent institution was so strongly opposed by a powerful opponent that it failed to evolve. Its time having gone and it being replaced by something else, it may never evolve again. (some standards in the internet space are an example). On the other hand, it is possible that some institution exists primarily because its sponsor was so powerful and purposeful. (the continued presence of autocratic governments in middle east are an example). Most of the cases will of course be of an intermediate variety. Here the success of an institution will be a combination of inherent coherence of the institution with its context, its appeal to the audience and the strength of its sponsor.

Path of evolution

The other noteworthy aspect of this phenomenon is that the path of evolution of the institutions is not unique. Since the stimuli from transactions are partially stable and partially random, it is hard to imagine that the institutional evolution is unique and will flow from the starting point of the society and the tendencies of people.

Who builds them then?

Also noteworthy is the inference that the institution building is open to vested interests, soft paternalists/ideologues and the society itself. At all times some combination of these are trying to build institutions to their ends. The vested interests have selfish ends, the ideologues/soft paternalists have ideological ends and the society itself has no stated and coordinated goals (its behavior as a collective is emergent).

Who succeeds at what times is not a foregone conclusion. In fact these forces may be allying with each other as also opposing each other from time to time.

E.g. the "Nudge" theorists are trying to work against the natural dispositions of people which are supposed to be in their bad interest. Sometimes vested interests may have a common agenda in exploiting these natural dispositions (case in point is fatty food). So we have the ideologues vs vested interests plus society. At other times, some other combination may be at loggerheads. Sometimes it might boil down to just two out of these three participants.

The idea of emergent phenomena and downward causation

Recently I came across some things written on emergent phenomena. While I was searching for “emergence” as a theme and thus was not enlightened about emergence itself, I did come across something interesting in the same domain. This is called downward causation.

Downward causation is when the higher order phenomenon exerts influence on its constituents. This effect, if it can be inferred from the structure of the system is called weak downward causation and if not, strong downward causation. The weak downward causation is relatively simple idea – the parts make the whole and the whole in turn affects the parts; and that the effects of whole on parts are integral to the coming together of parts. The strong downward causation is a much more powerful idea – since in this case the parts do make the whole but it starts to influence parts in ways not foreseeable from the parts alone. A set of new phenomena hence emerges at the level of the whole.

Let us work with an example. If a lot of people walking on a rope bridge decide for some reason to move towards its one side, the rope bridge will tilt to that side and could potentially trip. The whole in this case is influenced by its parts and influences the parts in turn. The fact that the rope bridge turns over is neither unpredictable nor surprising. On the other hand, in equity markets, the stocks constituting an index start to move in a similar direction, they make the index move in that direction. Now some market participants start to get influenced by the overall movement of the index in one direction and that guides their actions regarding the constituents of the index. In this case, the parts move the whole, the whole starts to influence the parts in unpredictable ways.

Another example is the economy. Let us specifically refer to what is called the paradox of thrift. While individually saving is considered a virtue, if everyone starts to save more in an economy, the total and per capita income is bound to fall. In a mathematical identity sense, this is weak form of downward causation since the behavior of the system can be predicted from the parts and their interconnections. Where it may start to become strong form downward causation is if the very act of falling incomes prompts people to save even more thus creating a vicious circle of greater savings and lesser incomes.

The presence of downward causation raises a fundamental epistemological question as regard our methods of enquiry in sciences hard and soft. The reductionist and analytical approach is likely to hit its bounds when dealing with real life due to presence of emergence and downward causation. Stated simply, knowing how electrons and molecules behave will still not tell us how systems like whether evolve and knowing how individuals react to economic incentives will not tell us how the economy will evolve. Granted that the study of parts is a crucial first step in most enquiries. The almost exclusive focus on reductionist methods is quite a limiting feature of our knowledge building endeavour though. In general the reductionist conclusions are aggregated clumsily into larger wholes (as in economics) or left to statistical techniques (e.g. thermodynamics and gas theory) or simply left alone (particle physics vs real objects).

Fritjof Capra draws attention to this in his emphasis on synthetic thinking besides analytical thinking in the pursuit of knowledge. As we grapple with the complexities of real life and real system, and armed with the computational power unheard of just 20 years ago, I suppose we can start to build models of emergent phenomena and downward causation.