Concise name for a proposed new systems theory and eventual science of the development and evolution of organization on all scales and substrates in the Universe. Coined in 2010 by EDU scholar Georgi Georgiev.
The name is composed of the following elements:
- devo = development
- evo = evolution
- logos = words, thought, study, principles, truth, science (Greek)
Devology is a shorter and more effective version of Universal Evolutionary Development Studies (UEDS) a phrase used since 2000 by EDU scholar John Smart. With UEDS, development is the noun, the proposed overarching framework and process, and evolution the adjective, the subordinate process modifying development. Likewise, devology also places universal development first, as a framework for understanding universal evolutionary process. This perspective is underrepresented in modern evolutionary science and systems theory. Note that devology/UEDS is not developmentalism, another useful but limited term that focuses primarily on developmental processes, minimizing or ignoring evolutionary ones, in the same way that evolutionism is a useful but limited approach to evolutionary change that ignores any concomitant process of universal development.
See the EDU Project page for a sample of the broad variety of work that has already been done on devology. An increasing number of interdisciplinary scholars are currently active in seeking a unifying devologist perspective that explains its hierarchical complexification from the origin of the Universe to present society and beyond. See for example the work of Eric Chaisson (2001) and Stuart Kauffmann (1993), among many others.
Quantity to Quality Transition
One devological process that has been observed in many systems is the apparently deterministic transition from quantity to quality. This was proposed first by GWF Hegel (1812), was famously noted by P.W. Anderson (1972) in discussion of emergence, and has been used most recently by Robert L. Carneiro (2000) to explain progressive development. Within any complex adaptive system, larger systems create greater evolutionary diversity, specialization, cooperation, and competition, leading to higher developmental forms of organization, more resilience and process memory, and significantly greater efficiencies.
A large part of Carneiro's work in anthropology was measuring the number of people in autonomous Amazon villages, and counting the number of organizational treats, such as number of institutions. The dependence of thus measured quality on the quantity of people is linear, deterministic and with a feedback mechanism. As quantity is necessary in order to increase the quality, quality is also necessary to accommodate the increase of quantity. A village which grew in number of people but failed to create new institutions could not be kept as a system and eventually went into a conflict and fissed into two villages of smaller size. The lesson is that if we fail to increase the quality of organization in our society as our population grows, conflicts are inevitable which have an underlying cause to reduce the population. On the other hand if we successfully organized our society, it can accommodate ever increasing number of people without a limit in the Universe. So the progress in quantity and quality happens in iterative way through small steps.
Anthropologist Joe Henrich (2004a,2004b) has argued that just a few cognitively complex individuals have historically been insufficient to initiate major human cultural complexity transitions. Instead, a large population of imitative and adaptive minds has been required. The reverse has also been true: sudden loss of population has generally led to loss of cultural complexity. As archeologist Rhys Jones (1977) first proposed and others have since supported, a major loss in Tasmanian population, following the closure of the Bass Strait land bridge connecting Tasmania to Australia 8,000 years ago, produced a slow but steady loss of tool kit complexity over subsequent millennia. Evolutionary anthropologists Peter J. Richerson and Robert Boyd (2005) note that Tasmania transitioned from a culture rich in boats and other complex tools equivalent to the rest of Australia, to a culture that, when discovered by European explorers in the 1800's, had the simplest tool kit known for any living people. Though it still retained a population of four thousand people, the culture had lost of hundreds of tools, and no longer fished for food. Cultural memory and specialization were not sufficient to maintain tool kit and behavioral diversity.
Similarly, were we to keep our current level of technological automation the same (e.g., assume no new breakthroughs), and either suddenly or gradually reduce our world population to one million, total system quality would be drastically reduced worldwide, as we would have lost a massive amount of specialized and imitative human knowledge capital. Most forms of modern life would also disappear in this thought experiment, including electricity production, highways, car production and everything else.
A conclusion one might draw from this is that in order for social quality, as measured by the organization and efficiency of the human society, to continue to increase in the future we will need progressively larger quantities and specializations of people. This however would be premature, as today there is another complex adaptive system, technology, which accelerates in its capacity and autonomy every year, and which has itself already reached human equivalence or human replacement in a narrow range of cognitive and physical tasks.
While the accelerating change in technology for the last two centuries has been largely paralleled and fueled by the acceleration of human population, this will likely no longer be the case going forward. First, as Ben Wattenberg (2005) and others have noted, global human population is now widely proposed to peak mid-21st century at 9-11 billion individuals, due to a demographic transition that occurs in all technologically developed cultures. Second, scholars such as Ray Kurzweil (2005) and others propose the emergence of human-equivalent or human-surpassing technological minds around the same time as we reach global human population saturation and possible decline.
Again, for social quality to continue to advance in such a world, the quantity to quality transition constraint would argue that the number and diversity of technological minds must at that point greatly increase. As John M. Smart (2002) and other scholars have argued, rapid growth in the quantity and variety of technological minds may have dramatically less spatial, temporal, energetic, and material resource requirements than the growth of human population has required.
Classes of evolutionary developmental systems
In order to study the process of increased order in the Universe we need to divide it into subsystems, keeping in mind that the general laws are universal through all of them. What is different is that at each new level there are new sets of emergent laws that do not exist in the previous level. We can roughly divide the subsystems in the Universe according to the disciplines that study them. After the Big Bang there was only atomic systems, which starts with hydrogen and complexifies all the way to the heavy atomic elements. Physics studies the laws of complexification of atoms, and must include not only evolutionary but also developmental theory. Chemistry studies the laws of aggregation, interaction, and complexification of atoms and molecules, and must also include both evolutionary and developmental approaches. Molecules organizing to form life are in the domain of biology, and must also include evolutionary and developmental (probabilistic models of life origin and complexification) models. In the devological perspective, sociology, economics, and political science must also have evolutionary and developmental components. These evolutionary and developmental trends seem likely to also include technology, and to continue in the future, even though we today have only speculative models and early data for the science that will study it.
Measures of levels of organization
One possible measure is the precision with which the elements in a system are positioned. There is a progressing trend in this starting with primitive human tools, precise on the scale of a centimeter; simple machines, precise at the level of millimeter; machines at the level of automobiles, precise at the level of a micron and computer chips, precise at the level of nanometer. Each next level of development was achieved by increasing the precision of organization, which allows increasing density in time, space and matter.
- Anderson, P.W. (1972), More is Different: Broken Symmetry and the Nature of the Hierarchical Structure of Science, Science 177 (4047):393–396.
- Carneiro, Robert L. (2000) The Transition from Quantity to Quality: A Neglected Causal Mechanism in Accounting for Social Evolution, PNAS 97:12926-12931.
- Chaisson, Eric (2001) Cosmic Evolution: The Rise of Complexity in Nature, Harvard U. Press.
- Hegel, G.W.F. (1812) The Science of Logic, George Allen & Unwin.
- Henrich, Joe (2004a) Demography and Cultural Evolution: Why adaptive cultural processes produced maladaptive losses in Tasmania. American Antiquity 69(2):197-21.
- Henrich, Joe (2004b) Cultural Group Selection, Coevolutionary Processes and Large-scale Cooperation (with Commentaries and Reply), Journal of Economic Behavior and Organization 53:3-35 and 127-143.
- Jones, Rhys (1977) The Tasmanian paradox. In: R.V.S. Wright (ed.), Stone Tools as Cultural Markers: Change, Evolution and Complexity, pp.189-204. Australian Institute of Aboriginal Studies.
- Kauffman, Stuart (1993) The Origins of Order: Self-Organization and Selection in Evolution, Oxford U. Press.
- Kurzweil, Ray. (2005) The Singularity is Near: When Humans Transcend Biology, Penguin.
- Richerson, Peter J. and Boyd, Robert. (2005) Not by Genes Alone: How Culture Transformed Human Evolution, U. Chicago Press.
- Smart, John. (2002) Understanding STEM, STEM+IC and STEM Compression in Universal Change. Retrieved from AccelerationWatch.com.
- Wattenberg, Ben. (2005) Fewer: How the New Demography of Depopulation Will Shape Our Future, Ivan R. Dee.