Social Evolution as an Explanation of Differential Knowledge Distribution

What follows is a description of evolution as knowledge-based, the predictions it makes about socio-economic transformation in the Digital Era, and the views the theory holds on intentionality and human agency, and hence technological determinism and social construction.

It is proposed that the theory of social evolution is able to advance our thinking regarding the role of knowledge in today's Digital Era. Before this proposal can be made, in the form of case studies and a discussion of implications, it is necessary to describe and justify the use of the concept of evolution within the domain of social life. If evolutionary theory applies to areas of life, other than organic gene-based evolution as is traditional and conventional, it must have a common denominator and certain universal principles. The common denominator is upheld by some (Plotkin, 1993; Blackmore, 2002) to be knowledge based, following the logic that knowledge is an entity present in all subsystems of life, making it a suitable substrate on which the universal evolutionary variation-selection-retention algorithm can work.

It is recognized that the field of evolutionary theory, especially when applied to broader areas than genetics, is a field characterized by debate. This is not the place for such debate in that there is not enough room for such discussions. Readers, interested in the positioning and justification of the evolutionary stance within social and cultural life in terms of organizational life and theory, are directed elsewhere (Shepherd, 2002; Weeks & Galunic, 2003). The backbone of the theory is the relationship between the evolutionary definition of knowledge, different speeds of knowledge transformation, and hence different distributions of knowledge, all of which combine to create a system that is interactive and very much 'alive'. The interaction never, however, results in whole system breakdown, as much as subsystems might cease to exist and might be created as the system is forever recreating itself.

As regards knowledge, Plotkin (1993), an evolutionary epistemologist, sees knowledge as never true, just true enough to provide the system with a way of balancing itself, making it dynamically sustainable. This evolutionary definition of knowledge, as an independent entity that becomes, or not, over time embedded in material objects and embodied in human minds and which is generated through social interaction, is not without controversy. It suggests that what should be considered is as much the dynamics that lead to embeddedness and embodiment, as what is embedded and embodied. It suggests that intuition is a less-than-conscious selection of what knowledge should survive, and judgment is a function of past knowledge selection and is subject to a whole range of rationalities from the highly logical to the highly emotional, explaining perhaps why these two forms of knowledge are becoming ever more prevalent in the Digital Era. It suggests information has no relationship with the world and must be contextualized and related to, in order for it to become knowledge. Lastly, the evolutionary perspective suggests that the conduits for knowledge include anything that can copy knowledge, albeit perhaps not perfectly. Humans are unique (save for perhaps birds and dolphins and perhaps in the future computers—see semantic web case below) in their ability to copy social knowledge, as they can semantically interpret others people's meaning.

Evolution is not defined here as it is in colloquial talk, as gradual change, but as it is defined in evolutionary theory. Evolution is the differential survival of knowledge over time. Evolutionary theory seeks to explain those differences by seeking to explain why the knowledge that survives into the next generation is more likely to be copied into the next generation than the knowledge it has replaced.

The knowledge within each of the forms of evolution (if they exist—once again see Shepherd, 2002, for an understanding of this stance) takes on different forms. Within cosmology there are the 'laws' of nature that are thought to change as the universe interacts with other universes. Within geology there are planetary and climatic relationships which alter knowledge. Within organic life there are genes and other forms of material, which upon replication during reproduction, produce variant knowledge. Within immunology there are antibodies that develop as the knowledge of the dangers in the world are incorporated into the immune system. Within social life there are thought to be pieces of social knowledge, called memes (Dawkins, 1976), that are differentially exchanged and interpreted, creating new knowledge. All of these changes allow the system to continue to interact and yet also remain stable by constantly re-relating knowledge and creating new knowledge relative to, and which functions within, new circumstances.

Systemic dynamism, and counter intuitively, sustainability of that same dynamic system, are created as elements of the system interact. Animals consume plants and other animals, altering resource availability. Planetary movements create, for example, past Ice Ages that create new obstacles for humans and result in them becoming more socially cohesive. Humans consume so much fossil fuel, they alter the climate of the planet. Every interaction counts, every interaction contributes to instability and stability, but overall, at the whole system level, dynamic stability reigns.

The interactions create ever-changing contexts and environments. Cumulatively the interactions create environments and hence knowledge bases that change faster than others. The Galapagos and the Amazon are examples of stable environments, at least until recent times. They are relatively stable because of environmental isolation and because of the green, ecological meme, which states that man should not interfere with such environments. If, or rather when they change, they do so because the system is perturbed by a new element appearing in the system. In both cases of the Galapagos and Amazon, the biggest effect on stability has been humans, as they are able, because of their mobility and intelligence, to introduce new knowledge (whether genetic in the form of the introduction of goats in the Galapagos or geo-memetic in the form of oil exploration in the Amazon), which can rapidly unbalance many years of stability as new knowledge is suddenly introduced. Equally very unstable environments, such as those associated with the Digital Era, exist. In these times social knowledge is being endlessly renewed. Specific to the digital age is the phenomenon of knowledge being distributed as a function of the knowledge being associated with highly 'irrational' emotions and exuberance (the dot.com boom and bust is the example mentioned before).

Importantly for this chapter and book, evolutionary theory explains the advent of ICT (Blackmore, 1999) and predicts that it will become more able to move more sophisticated forms of knowledge in and out of people's minds, whereas today it tends to move data—or at best contextualized data—and therefore information. This is because on average evolution has become faster. Evolution, on average, has become faster because the faster a knowledge base is renewed, the better able it is to compete with other knowledge that is also changing rapidly. For example, memes, the knowledge base of social life, are thought to have evolved because such knowledge could be renewed within the lifespan of a human, countering the effect of genes taking a whole human genetic generation (and hence many years) to be renewed. Equally ICT and the Digital Era are seen within this evolutionary perspective as making memes move faster within larger populations of minds and in different ways, and making memes independent of human minds.

As regards intentionality and human agency, strict memeticists see man as a 'meme machine' to the extent that man is a gene machine, or product of his or her genes (Blackmore, 1999). Evolutionary theory relies on the assumption that sustainable systems are 'designed' to have no foresight. Instead, they incorporate the capability of producing infinite knowledge, which is retained only if it creates stability at that moment in time. That said, taking the lack of design to its extreme, it can be thought that many systems might have existed with and without foresight, with the system(s) without foresight surviving as they are sustainable over a longer period of time. Agency lies therefore in the system, or rather, any system that survives is one that maintains itself without foresight. It produces an infinite amount of new knowledge (variety), some of which brings the system back into balance and hence is retained in the system. Human agency would interfere with this process, or rather if it exists, and if it interferes with that process, it (and therefore humans) is unlikely to survive. This is the aspect of evolutionary theory that makes the technological determinism versus social construction debate an unproductive one. What is relevant to evolution is whether the interaction between technology and humans reduces memetic variety, and whether technology interacting with technology might result in the future in a source of memetic variety.

What exactly intentionality, rationality, and lack of foresight mean is a topic of hot debate (see Jahoda, 2002a, 2002b; Blackmore, 2002). What makes a meme (or piece of social knowledge that has some independent meaning that can be transferred, communicated, and possibly exchanged with others during social interaction) more or less likely to be copied depends on its 'strategy', according to Blackmore. Hence computer viruses consisting of e-mails with 'I love you' attachments are successful, as they involve a meme strategy that has universal appeal, in that we all love to be loved. The extra 'I love you' makes the core knowledge more likely to be attractive to more minds despite the reality of the destructiveness of the virus. In contrast, different communities differ according to what knowledge they are 'naturally' attracted to. Scientists are, for example, attracted towards rational 'objective' knowledge (Shepherd, 2002). The meme strategy here is to have no strategy, that is to say no extra bits of knowledge are present or necessary to make the core knowledge more likely to enter the minds of scientists. An ecology of memes with different knowledge content and different meme strategies is therefore created as they are exchanged, altered during that exchange to produce new knowledge, and differentially retained in communities of interest as people interact on an everyday basis. No meme consciously adopts a strategy, it just is what it is, and what it is determines whether—in the environment it happens to be exchanged in—it survives or not over time. Memetics suggests that the concept of self is an illusion, as we are just a bunch of memes whose existence within our brains are a function of processes that are so complex (who meets who and what emerges as a result, as well as the interaction of social evolution with all other forms of evolution) that to say we are in control of our mind is inaccurate.

Above all, what evolutionary theory and the evolutionary perspective on the Digital Era questions is the extent to which man is at the center of the activity that makes up the system he or she lives within. What follows are cases of activity that we appear to be in control of, that are knowledge based, that exist at the interface of the economy and society in the Digital Era, that are managed in formal and informal ways, and that raise fundamental questions about the control of socio-economic transformation because they are part of a broader system—universal evolution. These cases serve to illustrate the potential of evolutionary thinking to explain the Digital Era.