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Draft, January 2010

Economic exchange as a transmission channel in human societies

Bertin Martens1

Abstract: This paper argues that the (epi)genetic, cultural, symbolic and environmental transmission channels are

insufficient to explain the structure of modern human societies. Economic exchange of knowledge embodied in goods

and services constitutes an additional tranmission channel that makes more efficient use of limited human cognitive

capacity. Economic exchange results in a gradual shift in societies from task-based division of labour to cognitive

specialisation among individuals. Cognitive specialisation shifts scarce cognitive resources away from production and

into learning. It accelerates learning and reinforces the drive towards specialisation. Cognitive specialisation may

constitute another “major transition” towards a higher level of aggregation in human societies, with properties that

differ from symbolic transmission. Collective control of individual market-based exchange is ensured by means of

economic institutions that put a constraint on individual behaviour.

1. Background and objectives

This paper aims to build a bridge between evolutionary biology and the evolution of human

societies, including the evolution of economic systems. The common theme that links them is the

evolution of cognition. The idea that all biological evolution is a form of cognitive evolution is not

new and dates back to Konrad Lorentz and the Evolutionary Epistemology school of thought

(Callebaut & Pinxten, 1987). This paper extends the evolutionary epistemology hypothesis to the

evolution of human societies and their economic systems.

1 This paper is a contribution to an edited volume of a workshop at the Konrad Lorentz Institute (Altenberg) on "Models of Man for Evolutionary Economics" (September 2009). The author can be reached at bertin.martens AT ec.europa.eu

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Evolutionary biology recognizes several transmission channels for knowledge and acquired

behaviours. Apart from genetic and epigenetic transmission, the modern “new synthesis” (Jablonka

& Lamb, 2006) also includes behavioural imitation (Boyd & Richerson, 1985), environmental

“niches” (Odling-Smee et.al., 2004) and symbolic transmission (Deacon, 1997). The latter is

essentially limited to human societies with elaborate language skills. This paper aims to demonstrate

that economic exchange constitutes another cognitive transmission channel unique to human

societies.

Maynard-Smith & Szathmary (1995) suggested that biological evolution evolves in qualitative jumps

or “major transitions” whereby individual organisms are aggregated into a larger organism with a

division of labour between the aggregated entities. This scaling process delivers more complex

organisational forms that can better adapt to their environments because of more cognitive and

behavioural flexibility. Large human societies constitute such a major transition that has been

enabled by the emergence of symbolic communication, mostly by means of language. This paper

aims to demonstrate that another major transition is on its way in human societies, driven by

economic exchange and the emergence of distributed cognition and cognitive specialisation.

This paper has only weak connections with modern evolutionary economics. Modern evolutionary

economics is a large tree with many branches, ranging from the direct application of the principles

of Darwinian genetic evolution to technological innovation and economic development (Nelson &

Winter, 1982), to evolutionary institutional economics and more normative political economy

approaches. For an overview, see Witt (2003, 2008), Harappi & Elsner (2008), Hodgson (2002). In

stead, this paper follows a route that revolves around the role of cognition in evolution. It starts

from the assumption that all organisms, including humans, face cognitive capacity constraints2 that

induce cognitive opportunity costs. That assumption gives economics a central role in the study of

the evolution of cognition: economics is all about the search for efficiency in the presence of scarce

resources. In that sense, this paper gives a new interpretation to the label “evolutionary economics”:

the economic study of the evolution of cognition, how evolution found more efficient ways to

overcome cognitive capacity constraints, including in human societies where ecomic exchange

constitutes such an efficient solution. It also implies that changes in cognitive systems become a key

2 Brain capacity is limited and our sensory channels (sound, vision) also have limited capacity. It has been estimated that human information input capacity does not exceed 120 bytes per second (ref).

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explanatory factor for long-term structural changes in the functioning and organisation of human

societies. This paper introduces a number of economic concepts in the evolution of cognition.

I argue in this paper that the emergence of economic exchange is a defining cognitive characteristic

of human societies. Economic exchange enables the transfer of knowledge embodied in goods

rather than in the brain. Inherent limits to human information processing capacity, and relative

scarcity of human cognitive capacity, implies that any cognitive activity entails an opportunity cost.

Any form of information storage, processing and communication that reduces this opportunity cost,

such as economic exchange of knowledge embodied in goods, constitutes an advantage. Economic

exchange induces economies of scale and scope that further reduce these opportunity costs and

result in the emergence of a cognitive division of labour. Individual agents move away from

autarchy and become specialised producers at a higher level of economic integration. Individual

needs are no longer satisfied through individual production but through exchange between

specialised producers. Furthermore, the emergence of a division of labour satifies the three

characteristics of a “major transition” towards a higher level of aggregation, as defined by Maynard-

Smith & Szathmary (1995): similarity between the constituent parts, contingent irriversibility of the

integration, and some degree of central control. This paper demonstrates how these three

characteristics apply to modern human societies.

Summary of points made in this paper:

1. The (epi)genetic, cultural (imitation and symbolic) and environmental (niche) transmission

channels cannot explain the structure of modern human societies. Adding economic exchange as

an additional channel explains the emergence of cognitive specialisation. Moreover, economic

exchange makes more efficient use of limited human cognitive capacity than the other channels.

2. Economic exchange results in a gradual shift in societies from task-based division of labour to

cognitive specialisation among individuals. At the same time, knowledge accumulation and

learning accelerate with the degree of specialisation. The more acceleration, the more scarce

cognitive resources are shifted away from production and into learning, thereyby reinforcing the

drive towards specialisation.

3. Specialisation is hard to explain in a Darwinian model with individual selection; group selection

is more appropriate. Specialisation becomes conditionally irriversible in human societies.

Collective control systems of specialised societies take the form of institutions that put a

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constraint on individual behaviour and property rights but leave enough freedom to give

individuals an incentive to invest in further learning. These characteristics indicate that cognitive

specialisation may constitute another major transition in human societies, with properties that

differ from purely symbolic (language) exchange.

2. Economic exchange as a more efficient communication channel

Modern evolutionary biology in its “new synthesis” version has identified several transmission

channels for acquired knowledge and behaviours: genetic inheritance (the Darwinian model),

epigenetic transmission of acquired behaviours (Jablonka & Lamb, 2006), “cultural” transmission of

observable behaviours (Boyd & Richerson, 1985), transmission via constructed environmental

“niches” (Odling-Smee et.al., 2004), and symbolic transmission (Deacon, 1997). In this section I

argue that economic exchange constitutes another, and a more efficient, transmission channel from

a cognitive point of view.

The original transmission channel in biological evolution is genetic inheritance. Random genetic

variation and selective retention is the underlying mechanism of Darwinian evolution. However,

spreading genetic change in a population takes many generations. Genetic evolution is too slow to

explain much of cognitive evolution of human societies in the last millennia (Adenzato, 2000)

though that is still a somewhat controversial issue (Odling-Smee, 2004, pp 248-249) (Kingsolver,

2001)3. A key assumption of the neo-Darwinian paradigm is that behaviours acquired during the

lifetime of an organism cannot be transmitted to the next generation. Molecular biology has

demonstrated however that this assumption cannot be maintained. Mechanisms such as methylation

enable the transmission of acquired behaviours (Maynard-Smith & Szathmary, 1995; Jablonka &

Lamb, 2006). This adds a second biological inheritance channel: the vertical transmission of

epigenetic traits. A degree of Lamarckism is back in vogue in modern evolutionary biology. Both

genetic and epigenetic transmission take place outside the brain and are therefore not subject to

cognitive constraints.

3 Any evidence to the contrary remains very weak. Only small and statistically unreliable samples show high evolution gradients, large sample studies point to very slow gradients.

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A “cultural” transmission channel, proposed by Boyd & Richerson (1985), allows transmission of

acquired behaviour via imitation. This has at least two advantages over genetic transmission: it

allows (a) intra-generational change in behaviour rather than having to wait for next generations and

(b) horizontal transmission rather than vertical transmission only. That accelerates the speed of

change and the spreading of acquired behaviours. But does it increase fitness? B&R (1985) argue

that a simple social learning mechanism (imitation) provides an evolutionary advantage. Rogers

(1988) demonstrates that this is not the case: in equilibrium, imitation does not increase the fitness

of a population. B&R (1995) add selective imitation and cumulative learning to their original model

and thereby restore the validity of their argument, albeit under restrictive conditions. Imitation is a

very narrow communication channel because it applies only to situations where all relevant

cognition is revealed in externally observable behaviour. Very simple activities like painting with a

brush or tying shoelaces can be successfully imitated. But imagine imitating a car mechanic, or a

medical doctor: copying their movements without understanding the underlying reasoning would

produce disastrous results. More complex communication is required to explain the underlying

knowledge algorithms. That is where symbolic communication comes in.

Since language is part of “culture”, the “cultural” transmission channel is often assumed to include

symbolic transmission as well. However, the cognitive properties and impact of symbolic

communication differ significantly from the properties of behavioural imitation. The B&R model

cannot simply be transposed to symbolic transmission4. Both symbolic transmission and

behavioural imitation affect cognitive processes in the brain, though in different ways. Contrary to

genetic and epigenetic transmission, behavioural and symbolic transmission work through the brain

and are thus subject to the cognitive capacity constraints of the human brain. However, symbols are

versatile and potent transmitters. Symbols can express latent knowledge not activated in behaviour;

imitation can only do the latter (Jablonska & Lamb, 2006). Symbols can act as an external storage

medium (external memory), they can facilitate the thinking process through external connections

that do not exist yet in the brain, they can facilitate the communication of complex thought 4 The B&R behavioural imitation model does not explicitly model learning costs. There is an implicit learning cost in the sense that agents can imitate wrong or at least suboptimal behaviours and thereby incur the cost of missed opportunities. But there is no scarcity of cognitive processing capacity and social learners are not restricted in how much they can usefully learn. Cognition is not constrained by economic considerations in the B&R model. Moreover, learning remains an accidental event in the B&R model. There is no systemic incentive for agents to invest in learning and the accumulation of knowledge; they might as well copy behaviours from others. Even if they do improve on existing behaviours and acquire new knowledge, others can free-ride on this to increase their fitness. Since human cognitive capacity is inherently limited and has an opportunity cost, there is no incentive to invest scarce cognitive capacity in the improvement of knowledge that would become a free public goods, for others to free-ride on. This stands at odds with a key characteristic of modern societies where there is substantial private investment in the acquisition of knowledge.

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processes between persons. More importantly, symbols can compress complex ideas and thought

processes in relatively simple symbolic expressions (Zhang, 1997, 2000; Deacon, 1997). Because of

the virtually unlimited number of possible symbols and combinations of symbols, symbols are

unlimited replicators (Maynard-Smith & Szathmary, 1995). They can convey the outcome of a long

and complex thought process to a recipient, without the recipient having to go through the entire

process again, though he still needs to integrate the connections between categorizations in his own

brain. The connections between various categorizations are embedded in a string of symbols. By

acquiring these symbols, the recipient acquires these links and thereby shortcuts a long learning

process. Learning is an absorption process; its speed depends on the extent to which emitter and

recipient already share ideas. As such, symbolic representations can leverage limited human

cognitive processing capacity; they economize on scarce cognitive capacity.

Moreover, symbolic transmission can induce significant cognitive opportunity costs. In formal

learning situations where students have to take time off from productive activities in order to focus

their scarce processing capacity on decoding (understanding) and encoding (writing, speaking)

symbolic messages, the economic limits to symbolic transmission can quickly be reached.

Because of these limits symbolic communication cannot account for a number of features of

modern societies. Imagine what life would be like in a society that can only transmit knowledge

through symbolic communication. We would have to learn the skills of an engineer to build our

own cars, washing machines and computers. Most knowledge needed for modern life could only be

acquired through learning, at a very high cognitive opportunity cost. For example, in order to

acquire a PC, I would first have to learn (through symbolic learning) all the embedded knowledge

that goes into the construction of a PC and build one myself, including every part that goes into a

PC: the chips, microprocessor, hard drive, software, screen, etc. That goal would probably be

beyond the reach of a human lifetime. The opportunity cost of the activities that I would have to

drop in order to concentrate my limited cognitive capacities on learning how to build a PC would be

so high that it would not be worthwhile to build that PC.

Odling-Smee at.al. (2004) introduces a fifth transmission channel: the environment. The

environment is no longer considered as an exogenous factor that uni-directionally shapes agents’

adaptation. Agents can change the environment and construct “niches” that are adapted to their

needs. For example, birds build nests, termites build mounds and beavers build dams. Others can

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fit into this constructed environment too, and can mutually affect each other’s constructed

environments. Agents and their environment become mutually dependent. In this model

knowledge can be transmitted by means of “imprinting” knowledge on the environment. Several

forms of niche construction may be considered (Boulding, 1966)5:

- The most literal form of imprinting knowledge is the printing of symbolic language in written

form on a material carrier: writing and printing press. These imprints serve as an external

storage device for knowledge that can be retrieved by other agents. If interpreted narrowly, this

is just an annotation to the cultural transmission channel: rather than direct here-and-now

symbolic transmission between agents, temporary symbolic storage may defer transmission in

time and space, without necessarily changing the underlying cultural transmission model.

- A more general and more important form of imprinting is to shape (a piece of) the material

environment in accordance with an agent’s knowledge. This requires the use of energy. Local

entropy decreases (and the local information content increases), at the expense of an overall

entropy increase (loss of potential energy) in the environment (Georgescu-Roegen, 1971).

Niche construction constitutes the foundation stone for another knowledge transmission channel:

economic exchange. Economics is all about the production and exchange of shaped pieces of the

material environment. Contrary to “cultural” transmission, economic exchange enables agents to

transfer knowledge without actually having to learn or acquire all the knowledge embodied in the

good that is being exchanged. Take the example of a personal computer. The production of a PC

requires the “imprinting” of vast sets of knowledge accumulated in the brains of engineers,

managers and productions workers onto the materials that constitute a PC: the production of silicon

chips, hard disks, screens, casings, plastics, wiring, writing software, organizing production, logistics

and marketing, etc. As a buyer of that PC, I have no need to learn all the knowledge that goes into

this PC before it arrives on my desk. Suffice for me to learn how to operate the software in order to

enjoy the full benefits that the machine can deliver. I only need a small overlap in knowledge (how

to use the software) with the many minds that contributed to its production. Apart from this shared

software interface, buyer and seller can keep their knowledge sets far apart and specialized.

5 It is not entirely clear whether niche construction is a channel in its own right. Boulding’s distinction between printing symbols on a material carrier and more general imprinting of “products” on the material environment suggests that niche construction is just a combination of the symbolic and economic channels, both of which require a material carrier.

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Compare this with the insurmountable cognitive resource costs of purely symbolic transmission

discussed above.

Obviously, not all knowledge can be transmitted in embodied form. Engineers cannot transmit their

knowledge to a new generation of engineers in embodied form. It is not enough for young aspiring

engineers to look at the products of their predecessors in order to reproduce these same products.

They need to go through an extensive symbolic learning process to acquire this knowledge before

they can apply it and possibly build on it to create new knowledge. Likewise, I cannot learn how to

use the operating system and software of my computer simply by buying these software packages; I

need to go through a symbolic learning process. Agents in a group can never fully specialize. In

order to function they need some degree of common knowledge that enables them to “understand”

the specialized products that are exchanged and share a symbolic communication channel (a

language, other symbolic markers) that permits the transmission of non-embodied knowledge where

necessary. As such, symbolic “cultural” transmission and economic transmission are separate but

complementary channels.

Greenfield (1991) finds that the linguistic skills and tool use run in parallel in early child brain

development. Both skills use a common neural area (Broca’s area) and rely on similar hierarchical

organization of that area. This finding is supported by research on chimpanzees that face the same

constraints in the manipulation of tools and symbols. She extrapolates this hypothesis to early

hominid development where linguistic skills could have provided a substrate for tool use.

Intuitively, it is easy to see that language and manipulation of the material environment go hand in

hand: we need to be able to categorize objects before we can establish an action that links the two.

Hierarchical categorization is a brain skill that creates the platform for and conditions the

development of both language and manipulation of the material environment. The latter then gave

rise to material production and laid the foundation stone for economic exchange as a new

transmission channel.

Economic transmission creates more potential for savings on scarce human cognitive capacity than

symbolic transmission can achieve. Take again the example of the PC. The cognitive investment

required to learn how to use the software interface is marginal compared to the symbolic learning

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effort to acquire all the knowledge that goes into building my own PC. Economic exchange is the

most efficient transmission channel available so far in evolution.

One could argue that economic transmission is a special case of the environmental “niche”

transmission channel. Indeed, economic exchange consists of “imprinting” knowledge on a material

good, carved out of the material environment, in exchange for other goods. However, there are

several reasons to consider it as a separate transmission channel. First, economic exchange is almost

exclusively limited to human societies. Animals construct niches but do not exchange them in an

economic system. Birds build nests and beavers build dams, but neither are known to operate real

estate agencies or construction companies. There are very few examples of insects or animals that

operate something resembling an economic exchange. One of the best-known examples is bees,

one of the few species with an explicit division of labour. Beehives could be considered as a simple

exchange system whereby specialized bees exchange services within the group. However, there is no

market-based exchange and no cognitive division of labour. Only humans exchange the products of

their labour. Second, economic exchange has fundamentally modified the cognitive dynamics of

human societies. As will be demonstrated below, it has resulted in a cognitive division of labour and

an acceleration of the pace of knowledge accumulation, far beyond the division of labour in bee

societies. Third, and more importantly, I will argue that the emergence of economic exchange, and

the resulting cognitive division of labour, provided a platform for a major transition that is unique to

human societies.

3. Specialisation and the acceleration of learning

3.1. Division of labour and division of knowledge in society

Before we go into a discussion of specialization and the division of labour, we first need to define a

few basic concepts. In economics, the concept of division of labour is intuitively clear, ever since

Adam Smith’s famous example of the pin factory where the production process is split in tasks that

are distributed among workers: “One man draws the wire, another straights it, a third cuts it, a

fourth points it, a fifth grinds it a the top for receiving a head …” (Smith 1776/1993, p12). All these

tasks are fairly simple and can be carried out by low-skilled workers after just a few hours of training.

Workers can further increase their productivity at these tasks through learning-by-doing but basically

these workers are easily substitutable with very little learning costs. There is little cognitive

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differentiation between these workers. Despite a strong task-based division of labour there is hardly

any distribution of cognition.

Another example involves true distributed cognition. Take an economist, a computer programmer

and a lawyer working together as a team on a research project. The team members have essentially

different knowledge sets. They need to communicate some parts of their specialized knowledge to

the other team members to make themselves understood and create an minimal shared knowledge

set, but they keep most of their specialized knowledge to themselves. It would anyway take a great

communication and learning effort to share all their relevant knowledge with each other. The

member of this team can only be substituted at a high learning cost. They have complementary

rather than substitutable knowledge sets. They have been put together in this team precisely because

complementary knowledge gives the entire team access to a much larger knowledge set than each of

them on their own could master.

The corollary of high substitution costs between team members is that distributed knowledge

systems hold more knowledge than individual team members have. The less overlap in knowledge

between members, the higher the total volume of knowledge available to the team (for a given

volume of knowledge for each team member). Simple distributed tasks systems such as Adam

Smith’s pin factory do not have that important property and therefore do not contribute to

overcoming the cognitive limits of individual agents. They may help to overcome space-time

constraints – Adam Smith refers to “time savings” in the pin factory because it avoids time losses in

switching from one task to another – but not cognitive capacity constraints.

Historically, the earliest forms of specialization and economic exchange in human societies emerged

between clans and tribes that held geographical monopolies on particular goods, linked to

geographical and environmental factors. Inland hunters traded their goods with fishers living closer

to the sea. Miners traded the minerals from mines in their territory with tribes that did not have

access to these mines. Even David Ricardo’s (1806) well-known example of trading British wheat

versus Portugese wine was linked to the fact that the British climate was not conducive to the

production of vineyards and wines. Other types of task distribution in human societies were based

on caste systems that preserved rents related to ethnic, religious, family and socio-economic

segmentation in society. Somewhat more open professional guild systems emerged in Europe in the

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Middle Ages. As society progressed and knowledge accumulated, cognitive capacity constraints

became more binding. The Encyclopedists of the 18th century could still claim to put all the world’s

knowledge into a series of books – though already at that stage in human history their claim was

probably not very credible anymore. Closed professional caste and guild systems were replaced by

fully open access to profession (North, Wallis & Weingast, 2009). As the volume of available

knowledge in society increases significantly and beyond the scope of an individual to acquire,

emphasis gradually started to shift to innovation and knowledge-based competition. That implies a

cognitive division of labour rather than a simple task-based division of labour. Cognitive capacity

constraints are a fact of life and we have to make decisions about a hopefully efficient allocation of

our scarce cognitive resources. Cognitive specialization is unavoidable in modern society.

3.2. Incentives for specialization

3.2.1. From an evolutionary biology point of view, the factors that drive a species towards

specialization should not be sought in rational decision-making by individual agents but in blind

Darwinian selection mechanisms that affect the fitness and resource availability for these agents. We

can cast the question of cognitive specialization in a Darwinian evolutionary economics framework

by examining the optimal allocation of scarce cognitive capacity for an agent living in cognitive

autarky and examine under which conditions specialization could improve his resource availability.

Let us assume that the volume of available information in a given environment is, for all practical

purposes, infinite and can be plotted along two dimensions: the degree of accuracy or detail of

knowledge and the range of variety in events or situations that this knowledge covers. Faced with

an overall cognitive capacity constraint, an agent allocates this scarce capacity between accuracy and

variety. The optimal allocation that maximizes his chances of an appropriate response to any

opportunity or challenge that may emerge in his environment is presented in Figure 1. That optimal

allocation maximizes his resources and could thus be considered as fitness-maximizing in a

Darwinian evolutionary selection context. Moving towards cognitive specialisation would reduce

the variety of events to which he can respond but increase the accuracy of his response for specific

events. That moves the agent away from his maximum fitness and optimal composition of

knowledge. The probability that he will be able to respond appropriately to an event in his

environment declines. At first sight, this is not a rational move.

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When an agent becomes a specialized cognitive entity in a group he can focus his limited cognitive

resources on acquiring knowledge to respond to a sub-set of all possible events in an environment.

Other agents will specialize in other subsets; together, they still cover a wide range of possible

events. In order to make this distributed cognition work, the group needs to make total available

knowledge accessible to all members, and minimize the cognitive costs associated with that

availability. With economic exchange, that is achieved by means of exchange of knowledge

embodied in goods and services. As a result, an agent’s resources do no longer depend on his

fitness in the overall environment but on his fitness in the “niche” in which he operates. A medical

doctor’s income depends on the quality of his medical skills, not his other skills. The “exchange

rate”, or the relative value of his production of resources compared to the value of the resources he

receives through the market, depends on fluctuations in the market price. There is no guarantee that

his specialized income will be sufficient to cover his needs. Full specialization therefore entails

significant resource risks.

3.2.2. The above reasoning focused on individual fitness. We should also examine how

specialization affects group fitness. Cognitive specialization affects group resource availability in

two ways. Statically, it increases total knowledge available in the group to respond to events in the

environment. Dynamically, it accelerates the rate of learning or knowledge accumulation for the

group.

Total knowledge in a group of agents increases with the degree of cognitive specialisation in that

group. Assume a society with two agents, i and j, with given sets of acquired knowledge Hi and Hj6.

When both hold indentical knowledge, that is when Hi = Hj, then total knowledge H in that group

of two equals the knowledge of one, or H = Hi = Hj. When both have completely different

knowledge, that is when Hi ≠ Hj, then total knowledge H = n x Hi. “Completely different”

knowledge implies complete specialisation, without any overlapping knowledge at all between

individuals. This is an extreme situation. In reality, some degree of overlap will be necessary

because agents need a common language and common understanding of basic issues in order to

6 It is assumed here that knowledge can be measured. Graph theory is a frequently used mathematical tool to measure the complexity of networks, including cognitive networks, with k nodes and n links between these nodes. See for instance, Harsanyi.

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form a group. Consequently, the degree of specialisation S will be somewhere between 0<S<1 and

total group knowledge somewhere in between Hi and nHi7.

Similarly, the rate of learning in society increases with the degree of specialisation. To demonstrate

this, assume that all individuals have identical and limited cognitive absorption or learning capacity h

= dHi/dt. This means that their intake of information (in terms of bytes per second or some

equivalent measure) is constrained. If specialisation S=0 in the group, than the ceiling on total

knowledge accumulation in the group equals the rate of individual learning or dHT/dt = dHi/dt =

h. When specialisation S=1, dHT/dt = n dHi/dt = n h > h if n>1. For any value 0<S<1, the rate

of group learning will be in between these extremes.

Contary to genetic and cultural transmission, where the speed and extent of spreading of newly

acquired behaviours in a population are a measure of succes, the succes or cognitive efficiency of

economic exchange can be measured by the extent to which knowledge is prevented from spreading

to the minds of many agents and remains localised in one or a few specialised minds only.

Knowledge should be circulated in materially embodied and exchangeable forms only, in order to

circumvent human cognitive capacity constraints. By implication, that reduction in spreading

knowledge to other minds results in cognitive specialisation in society: not all agents share the same

knowledge set. Cognitive specialisation is a means to overcome cognitive capacity constraints in the

human mind; knowledge circulation by-passes the human mind.

3.2.3. Apart from the evolutionary biology angle that looks at the impact of specialization on the

fitness of agents and the group, we can also examine the question from the angle of individual

rational decision-making (or methodological individualism) that is so fundamental to economics:

what are the incentives for an agent to learn specialised knowledge, and how specialized would he

want to become?

There are several incentives and disincentives for learning at work here. In a simple learning-by-

doing model (Arrow, 1962) there is no opportunity cost to learning: agents learn while they apply

behavioural algorithms. Learning can go on forever. In formal learning situations (Becker, 1962)

however, agents cannot combine learning and doing and need to take time off from productive

behaviour to acquire knowledge. This is especially the case for symbolic learning that takes time to

7 See Martens (2004) p 106 for a complete formula to calculate H when 0<S<1.

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acquire and decypher a set of symbols, and transpose these into behaviour. This generates an

opportunity cost of learning that rises with the volume of already acquired knowledge. The more an

agent knows, the higher his productivity and the higher the opportunity cost of further learning and

not producing. This rising cost slows down learning and eventually stops it when the marginal cost

of learning equals the marginal benefit.

The speed at which the environment changes also affects learning. In stable environments where

acquired knowledge remains indefinetly valid, learning may soon stop. In fast changing

environments on the other hand, agents may not reach their economic limit to learning and may

continue forever (see Fig 3). With economic “niche” construction changing very fast in modern

societies, the latter may be closer to the real situation for many workers.

This approach determines the economic limits to learning and the limits to the volume of knowledge

acquired by an individual agent. The economic literature on the limits to specialisation shows that

there are two factors involved: The size of the market for specialised knowledge (Adam Smith,

1779; Baumgardner, 1988) and economies of scope in learning (Rosen, 1978, 1983).

Rosen (1983) distinguishes between separable and non-separable knowledge sets. Two knowledge

sets A and B are separable if the opportunity cost of learning A does not reduce the cost of learning

B; if it does affect the cost of learning B than A and B are non-separable. For example, learning law

and learning computer engineering are separable because there is unlikely to be much overlap

between the two. However, learning general engineering and then moving to computer engineering

are non-separable because once you know the first it is less costly to acquire the second. Rosen

attributes this decline in learning costs to economies of scope. For Edwards & Starr (1987),

economies of scope are a special case of economies of scale: the cost of an additional “unit” of

learning declines with the volume of knowledge already acquired in a specific domain of knowledge.

Adam Smith already understood that it is enough to learn one profession; learning both law and

engineering will not increase your income - because you can only exercise one of the two

professions at the time – while it doubles your learning costs and there are no economies of scope in

switching from one to the other: having learned one does not make it easier to learn the other. By

contrast, once you have learned law, it may well make sense to move on to the more specialised

domain of criminal law. To the extent that any knowledge set has infinite potential for further

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learning (there is no end to what you can learn about computer engineering) economies of scope can

motivate an agent to continue learning and overcome increasing opportunity costs.

Adam Smith suggested that the degree of specialisation is limited by the extent of the market. How

much demand is there for a computer engineer? Quite a lot actually. Computer engineering is a

fast-moving technology which makes yesterday’s knowledge about the subject quickly out-of-date

and forces agents to continue learning about this subject. The higher the rate of technological

change, the stronger the incentive to continue learning and the more scarce cognitive resources will

be shifted into learning – away from production.

In conclusion:

Early human societies have little cognitive division of labour. At best, there is a division of tasks

between easily substitutable individuals, sometimes related to castes that have socio-economic rather

than cognitive origins. However, as the volume of knowledge available in societies increases, for

instance through learning-by-doing, economic incentives gradually drive individuals into cognitive

specialisation. Their knowledge sets become less substitutable and more complementary. Cognitive

opportunity costs, induced by scarce human cognitive capacity, play a central role in the extent of

specialisations and the choice of domains of specialised knowledge. As a result of increasing

specialisation, the rate of knowledge accumulation in society accelerates.

4. Does the emergence of cognitive specialisation constitute another major transition?

It is widely recognized that the history of life shows a trend towards hierarchical organization,

revealed by the successive emergence of organisms with more levels of nesting and greater

individuation at the highest level (McShea, 2001). Maynard-Smith & Szathmary (1995) describe six

“major transitions” in levels of hierarchical organization of life, the most recent in their view being

the emergence of large-scale human societies, enabled by symbolic communication through

language. Goody (1986) goes a step further and uses the archeological record to show that written

language is a necessary condition for the organization of large human societies. I argued above that

language and symbolic communication in general are not a sufficient condition to explain the

emergence of cognitive specialization that is so characteristic of contemporary human societies. To

explain that, another transmission channel of knowledge embodied in goods and services, or

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economic exchange that short-cuts cognitive capacity constraints, has to be added on top of

symbolic exchange. The resulting cognitive specialization causes a gradual shift from easily

substitutable individuals to strong complementarity between individual knowledge sets. I will argue

below that this trend is conditionally irreversible and requires a change in the organizational

architecture of human societies, i.e. the design of appropriate institutions that can coordinate this

market-based exchange of knowledge. In short, the transition from symbolic communication to

economic exchange and cognitive specialization appears to fulfill Maynard-Smith & Szathmary’s

characteristics of a major transition.

4.1. Irreversibility:

One of Maynard-Smith & Szathmary’s (1995) defining characteristics of major transitions is

contingent irreversibility. A transition implies that lower-level participating organisms can no longer

reproduce on their own and depend on the higher-level aggregation or group for their survival. This

shifts the pressures of Darwinian selection from individuals to the group level. In biology, the

reproductive mechanism is linked to genes. When biological organisms aggregate into a new

organism, they can no longer survive or reproduce on their own. This is the case for multi-cellular

organisms where cells cannot reproduce on their own and the reproduction function for the entire

organism is assigned to specifically designed parts of the body. In some insect societies, such as

termites, ants and honey bees, a similar division of labour occurs that leaves reproduction to a few

specialized individuals while the majority becomes functional caretakers for these reproducers. This

functional specialization is genetically programmed and regulated through hormonal systems. For

honey bees for instance, hormonal activity varies with age and results in different types of activities.

Because of this biologically programmed functional specialization, some individuals become better at

some activities than others; they drive others out of that activity and establish occupational castes.

Some human societies also have a caste-based occupational division of labour, though that is not

genetically or hormonally determined. Ethnic, racial, family and socio-political factors play a

determining role here. Moreover, occupational castes in human societies are often highly regulated,

behaviour is ritualized and lacks incentives for innovation and further knowledge accumulation.

From a purely biological point of view, humans can survive and reproduce outside their professional

caste although it would probably be very costly to do so. In other words, in traditional human

societies aggregation is not entirely irreversible but very costly and therefore unlikely.

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It may even be harder in modern highly specialized societies with a high degree of cognitive division

of labour. Cognitively very specialized individuals do not have all the skills required to survive and

reproduce on their own. As a professional economist I know very little about growing my own food

or hunting for food, I don’t know how to make basic clothing and wouldn’t know how to light a fire

without the help of matches. If I would suddenly be put in a situation that requires complete

autarky, I would probably die before I would have learned all the necessary skills to survive. Even if

I would be given time to learn these basic skills, a return to cognitive autarky would result in a very

dramatic reduction in my resources, bringing me close to starvation. Irreversibility in modern

societies is not as absolute as in the major biological transitions but certainly extremely costly and

therefore unlikely.

4.2. Collective constraints on individual behaviour

Maynard-Smith & Szathmary (1995: 9-10) mention “central control” as a condition for a major

transition. What they mean is not a single central entity that controls everything but rather some

complex form of interactive control systems that ensure the sustainability of a higher aggregation of

individual cells, organisms or agents. In biological terms, this could be suppression mechanisms like

the immune system that contain selfish mutations that do not contribute to the survival of the

whole. In insect and animal societies, as well as in human societies, force may be an effective

instrument to eliminate selfish free-riding behaviour and maintain the integrity of the group. In

contemporary human societies, economic and institutional systems fulfill that coordination role.

The debate on the role of the market as a coordination mechanism in society is probably as old as

economic science itself. Adam Smith (1779), who was to first to elaborate on the concept of

division of labour in economics, was convinced that the free market could not be left to itself.

Hayek (1945) thought that the market was the best coordination mechanism available. In his view,

the market price of a good reflects all relevant qualities of that good. Price signals are the most

efficiency information system to result in an efficient allocations of production factors and goods.

This view was formalised in the Arrow-Debreu (1954) general equilibrium paradigm. Contemporary

economics has moved beyond this free market paradigm. The rapid transtion from communist

central planning to a market-based economy in Central and Eastern Europe in the 1990s showed

that markets cannot function properly without appropriate institutions that allocate enforceable

property rights and put constraints on externalities and risks taken by producers and consumers.

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North (1990) defined institutions as the collectively agreed rules that put constraints on individual

behaviour. Market-based exchange and collectively agreed institutional constraints on free markets

are two inseparable sides of the same economic coin. Their co-evolution determines the efficiency

of an economic system and its capacity to deliver economic welfare to human societies. Institutions

are the necessary collective control system that contains damaging free-riding and keeps economic

systems sustainable. However, there are many variations in institutional architecture and many

differences in economic systems across the world. Some are more efficient than others in producing

economic growth and welfare. As such, the institutional control system is not perfect and subject to

ocassional failure. Individual agents should have sufficient incentives to invest in learning and

production without running the risk of expropriation. An appropriate balance between the use of

force to ensure the respect of collective rules and guaranteeing individual appropriation of benefits is

necessary.

The origins of collective rules and limits on individual behaviour that are necessary to ensure

coordination in economic exchange systems have been studied extensively. Most of that literature

goes back to Ulmann-Margalit (1979) and Axelrod (1984) who use repeated Prisoner Dilemma

games to demonstrate the emergence of collective behaviour. However, PD games have only weak

explanatory powers, especially in the presence of asymmetric distribution of power and

technological know-how. Skaperdas (1991, 1992) shows has a risk minimizing Nash equilibrium

emerges between two or more agents equipped with both production and conflict technologies.

Depending on the co-evolution between these technologies, various types of societal organisation

can emerge and succeed each other, ranging from slavery (all residual surplus accrues to the ruler) to

autocratic societies (some sharing surplus between rulers and subjects) and liberal democracies

(where rulers receive only a fixed wage and residual surpluses accrue to his subjects). Martens (2004)

shows how the extent of division of knowledge between agents plays a key role in the political and

institutional architecture of society. Institutions in human societies thus play the same role as

immune systems and suppressing mutations in biological systems. They put a limit on unacceptable

free-riding behaviours that endanger the sustainability of society and avoid the use of violence for

individual benefits, limiting it to the enforcement of collective rules only. In this respect, the

institutions of human society could be considered as the equivalent of a collective control system on

economic exchange

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In summary, the emergence of an economic transmission channel for knowledge, leading to

cognitive specialisation, combined with the concomittant evolution of institutions as a coordination

mechanism, can be considered as a “major transition”.

5. Conclusions

The last major transition in Maynard-Smith & Szathmary’s (1995) list is language in human societies

– the most important form of symbolic transmission. He argues that language has enabled the

aggregation of individuals and small kin groups of humans in larger societies with a high degree of

coordination and division of labour between individuals. I have argued in this paper that symbolic

transmission, or language, is not a sufficient condition to explain the characteristics of modern

societies. We need to add economic transmission of knowledge embodied in material goods to

explain the high degree of cognitive division of labour and the acceleration in knowledge

accumulation. Symbolic and economic transmission are complementary however and have evolved

in parallel (Greenfield, 1991). Of these two, economic transmission has potentially the highest

leverage effect on scarce human cognitive capacity. It certainly has a defining impact on incentives

for learning and knowledge accumulation and on the organisation of human societies, stronger than

the incentives and impact of symbolic transmission. Symbolic transmission permits effective

coordination of behaviour between cognitively substitutable individuals while economic

transmission permits true cognitive specialisation. That, in turn, results in higher knowledge

absorption and faster knowledge accumulation in society.

The emergence of economic exchange and cognitive specialisation qualify for the label “major

transition”. It marks another scaling of the “right wall” (McShea, 2001) to a higher level of

integration of its constituent cognitive parts. It corresponds to Maynard-Smith & Szathmary’s three

criteria: genetically similar humans move to a higher level of integration in larger groups (this

movement is still on-going: think globalisation) with a cognitive division of labour between them

(economic specialisation) and an information system that ensures coordination (economic exchange

as a communication system), partially via free exchange of property rights (market mechanism) and

partially via institutional constraints on property rights (some degree of collective control).

The economic evolution of human societies is spread out over millenia, a relatively short period of

time compared to the millions of years since the emergence of modern man. The first economic

exchanges date back at least a dozen millenia or so. Specialisation emerged in its wake but true

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cognitive specialisation is probably a relatively recent phenomenon in human history dating back no

more than a few centuries ago, no much earlier than the time when Adam Smith started writing on

the subject. One can safely assume that cognitive evolution in human societies is not going to slow

down. Quite to the contrary, as argued above, specialisation will only accelerate the rate of

knowledge accumulation and technological progress. More fundamentally, it will also accelerate the

rate of cognitive change towards further cognitive transitions. Scalings of the right wall may happen

in sudden transitions. From an evolutionary time perspective however, a few centuries may be

sudden. And it may well be that time periods between transition walls get shorter, as reflected in

Maynard-Smith & Szathmary’s (1995) five major transitions. The next wall may be much nearer

than we can currently envisage. We don’t know where current trends, for instance in external

information storage and processing, or in cognitive enhancement tools and genetic modifications for

humans, are taking us.

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Figures in Annex :

Figure 1: Accuracy – Variety – Specialisation

Figure 2: Cost of learning by doing versus formal learning

Figure 3: Learning in a changing environment

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