Weak Emergence
Mainly predictable according to scientific laws, allowing for randomness.
Sand dunes
Formed by the interactions of air flow (wind) and gravity on wind-blown sand particles. A dune moves as sand is removed from one side and deposited on the other and it may initiate the downwind formation of other dunes. This can create complex patterns over large areas. Something similar is seen in ripples on a beach, caused by water flow. There is no intrinsic information in the dune that determines its shape and those shapes can be explained by physical laws. Information about a dune’s form is in the overall shape of the dune. No part of a dune’s carries information that could replicate the whole.
Crystals
Parts of crystals do carry information that not only allow it to grow with a unique structure in certain environments but can seed separate growth (replication) of a similar structure elsewhere. This replication and growth process and the creation of patterned structure is still labelled as weak emergence because it can be explained by scientific laws and can be predicted.
Strong Emergence
Complexity that cannot be explained or forecast is labelled strong. So far as is known, it is exclusively found in biological systems. There are three types of system that exhibit strong emergence.
Genetic
Nucleic acids (DNA/RNA) carry genetic information that determines the form of the phenotype. These discrete packets of information shape the system in which they exist. If changes occur in the nucleic acid and hence in the information they carry, these may be passed on during replication. If the changes allow the nucleic acids to exploit their environment and replicate more effectively, the resulting DNA/RNA will displace their parents and other relatives not carrying the advantageous change. After Charles Darwin, we know this as Darwinian evolution. However, after Richard Dawkins, we must think of the hereditary units as genes (nucleic acids) or genotype, and not as phenotypic individuals.
The point of this is that evolution is not a designed or rule-bound process with goals. Immensely complex systems and systems-of-systems evolve in ways that have no scientifically predictable outcome and these systems can be stable, self-sustaining, and self-replicating. They continue moreover to evolve increasing levels of complexity.
It is possible that the equivalents of genetic information may be found at quantum level or in black holes, and that these may be shaping our universe in ways we have not begun to suspect. For now, however, DNA/RNA are the only discrete, physical packages of information.
Memetic
Memes are abstract, not physical; like virtual genes. They are frequently thought of as behaviours but include ideas, stories, technology and art and any combination of these in what we call culture. This definition of memes is from Richard Dawkins and refers to a unit of cultural transmission or imitation. Memes replicate (by definition) and as they do so they change.
Any consideration of communication and, to a large extent, of all behaviour must conclude that what characterises it is the interaction of memes.
Charles Darwin followed Adam Smith’s philosophies and it seems likely that Darwin’s theory of evolution leans heavily on Smith’s ‘Invisible Hand’, an early description of how a complex, self-sustaining, self-replicating and self-improving, system (society) emerges from memetic competition.
It is a short step from these concepts to the idea that today’s organisations are not the result of organograms, process-flows and Gantt charts, but emerged from the chaotic interactions of memes; and were then captured post facto by business administrators keen to convert their observations into reductionist tools.
Algorithmic
In 2009, Facebook launched algorithms that evaluated the impact of messages on user- engagement by monitoring the effect on their ‘likes’, ‘forwards’, ‘comments’ and other. It also analysed users’ networks and identified those ‘friends’ likely to respond to similar messages. It would also test user-responses to messages they would not normally receive. It was, as is now well known, able to profile users with a high level of accuracy in terms of character type, socio-economic class, family status, hobbies, preferences and other. The aim was to maximise engagement (aka, clicks). The algorithms were able independently to reset their own parameters according to feedback received on engagement. Click-engagement stimulated revenues, initially through corporate advertising and later through political messaging, enriched Facebook fabulously.
What emerged was a symbiosis of algorithms and human operators. The operators grew rich and may have felt in control. In practice there is a question about whether any control existed. Sarah Wynn-William’s book, ‘Careless People’ describes how wealth generated by the algorithms led to the expansion of Facebook’s influence and a change in its culture. In her personal telling, the benevolent, well-intentioned culture of the start-up became corrupted by wealth and power, to the point where Facebook lobbied to avoid child-safeguards, dismantled its stewardship, and may have supported brutal autocracies like Myanmar where it saw opportunities for monopolistic growth.
What was the role of the algorithms? They are not sentient; but could they be intelligent? It does not matter. What we see is emergence of a complex system. What we have learned is that emergence is more powerful than intelligence. In this chosen example the algorithms exploited the intelligence of Facebook staff. We should worry less about the possibility of superintelligence in the near future and more about emergence from those sixteen year old algorithms.
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