Sociophysics – The last science

‘Truth’ is context-dependent
In context of my studies to date, and in particular my newfound understanding of the common good, I have experienced a surprising insight. I can best describe the occurrence as a spontaneous emergence, in my mind, of a conception of sociophysical phenomena. I was not yet aware of sociophysics and thought that I had coined the term to help define a path of study. Simply, I wanted a word to help me focus more closely upon the physical phenomena that emerge from social interactions. Searching for the literature of sociophysics, I was initially surprised to find a sparse population of recent mathematical probabilistic treatments and models, stemming from quantum physics early in the twentieth century, game theory in the mid-twentieth century, and analyses of computer modeling of adaptive networks early in our twenty-first century. My search soon led me near to the origin of the sociophysical concept – an absolute origin escapes me, though sociophysics seems closely tied to Aristotelian animism. Nevertheless, I now realize that sociophysics has presented itself in a variety of apparitions to many a kindred spirit. If it is a science, then it is the strangest, vaguest, and widest of them – indeed, it has been called “the science that comes after all the others” – and fascinatingly, the men who have studied it knowingly, were and for the greatest part still are outcast by orthodoxy. I certainly am no stranger to their ranks, perhaps that is part of the reason why I feel a sense of familiarity and belonging among the concepts exposed in the current exploration of ‘the last science’.

Previously, I have argued that abstract modeling (theorizing) simplifies reality, allowing only fractionated (quantized), and thus unreal understandings. Historically, fractionation (specialization; division of labor) has been the cost of good quality knowledge. In The Common Good: Part I, I have introduced Robert Rosen, a theoretical biologist who suggested that studies of biology would bring new knowledge to physics, and would change our understanding of science in a broad manner. The study and modeling of complex systems appears to drive in this direction; by my intuitive reckoning, increasingly complex modeling (interaction of theories) approaches ever closer to a good quality representation of reality, and thus a truer understanding of reality. It is for this reason that I have chosen to focus the current exploration upon the histories [NOTE A] of understanding and modeling of social interaction, which shall lead us to an integrated understanding of the current state of the art.

Two classes
Abstract: The abstract form of sociophysics is fundamentally dependent upon human knowledge, which has been composed of necessarily subjective experiences (observations) of an assumed objective reality. It is a science stemming from and attempting to formalize intuitive understandings of social phenomena, by use of mathematical tools developed and used in statistical physics.

Real: We must assume that in reality the physical phenomena that emerge from social interaction are independent of human knowledge; that they occur regardless of observation. Sociophysical phenomena are synergistic (non-additive effects resulting from individual acts) manifestations of the dynamic, physical interaction, consequence and feedback, occurring among networked actors. Examples of phenomena that emerge from social interaction include: ant and termite colonies, bacterial colonies, cities, brains, genetic networks, mycelial networks, glial networks, multicellular organisms, ecosystems, physical and abstracted knowledge, road systems, postal systems, the world wide web (internet).

A true false start: true within context of the me-generation; false within a deeper historical context
Galam (2004) tells us that during the late 1970’s statistical physics was gripped by the theory of phase transitions.(1) In 1982, despite the scandal of a university faculty’s retraction of researchers’ academic freedom due to political fears of institutional disrepute, S. Galam et al managed to publish a set of assumed “founding papers” on Sociophysics.(2) In reference to the first in the set, Galam himself comments that “in addition to modeling the process of strike in big companies using an Ising ferromagnetic model in an external reversing uniform field, the paper contains a call to the creation of Sociophysics. It is a manifesto about its goals, its limits and its danger. As such, it is the founding paper of Sociophysics although it is not the first contribution per se to it.” During the following decade, Galam published a series of papers on Sociophysics, to which he received no feedback. He tells of other physicists “turning exotic” during the mid-nineties, developing the closely related Econophysics, the purpose of which was to analyze financial data. Econophysics quickly gave rise to the so called “quants” of Wall Street – young physicists who were employed by investment bankers to develop algorithms allowing for the trading of complex derivatives, the abuse of which, by the pathological social milieu of the international finance trade, was responsible for the global economic crisis of 2008. Fully fifteen years after his initial publications and the assumed inception of the science of Sociophysics, Galam claimed some gratification in the recognition that a “few additional physicists at last started to join along it”. I deeply sympathize with his statement: “I was very happy to realize I was not crazy, or at least not the only one.” Nevertheless, Galam was and remains incorrect in regard to his position in the history of sociophysics; a history that began centuries before the me-generation.

Reading Galam’s personal testimony, I felt a crystallization of my intuition that the institutionalized position of a careering academic scientist makes for a very poor springboard from which to develop novel ideas and concepts, even if, as in Galam’s case, the ideology is not actually novel. Indeed, I myself have felt, and seen in colleagues, active restraint from pursuing interesting, albeit unorthodox ideas while bound by the rites of the ivory tower. Shameful though this situation is, it certainly is not a modern problem.

Halley, Quetelet and Comte
In his review of the sociophysics literature, Stauffer (2012) reports that the idea of applying knowledge of physical phenomena to studies of social behavior reaches at least two millennia into the past, naming a Sicilian, Empedokles, as the first to suggest that people behave like fluids: some people mix easily like water and wine, while others, like water and oil, do not mix.(3) Vague and philosophical, I hesitate to categorize this conception as sociophysics, though admittedly it does attempt at least metaphorically to fuse social and physical phenomena. Rather more accurate examples of sociophysics were Halley’s calculations of celestial mechanics and annuity rates, Quetelet’s Physique Sociale, and Comte’s Sociophysics. Let us now step through these chronologically.

halley-portrait-thumbnail
Edmund Halley

In 1682 Edmund Halley had computed an elliptical orbit for an object visible to the naked eye; a conglomerate of rock and ice, now known as Halley’s comet. He reasoned that it was the same comet as the one reported 75 years earlier, in 1607.(4) He communicated his opinion and calculations to Sir Isaac Newton, who disagreed on account of both the geometry of the object’s orbit and it’s reoccurrence. Nevertheless confident of his theory, Halley predicted that the object would reappear after his death, in 1759; he was proven correct by the comet’s timely visit. Since then, the orbital path followed by Halley’s comet has been confirmed as elliptical, passing beyond Neptune before returning to the vicinity of Earth and Sun with an average periodicity of 75 to 76 years, with variational extremes of 74 and 79 years due to the gravitational perturbations of giants Jupiter and Saturn.

Astronomy, massive bodies and gravitation are relevant to our exploration of sociophysics for three reasons to be expounded later. For the time being, it is important to point-out a fact about Halley that is much less recognized, though possibly more easily recognized as relevant to our current exploration.

In 1693 Halley constructed a mortality table from individual dates of birth and death; data collected by the German city of Breslau. Based upon this tabulation Halley went on to calculate annuity rates for three individuals. In his application of probability theory to social reality – now known as the actuarial profession – it seems Halley had been preceded, in 1671, by a Dutchman, Johannes de Wit. Though again, to his credit, Halley was the first man to correctly calculate annuity rates, based upon correct probabilistic principles.

quetelet_adolphe
Adolphe Quetelet

Adolphe Quetelet was a Belgian astronomer and teacher of mathematics, a student of meteorology, and of probability theory; the latter leading to his study of social statistics in 1830. Stigler (1986) tells us that astronomers had used the ‘law of error’ derived from probability theory to gain more accurate measurements of physical phenomena.(5) Quetelet argued that probabilistic theory could be applied to human beings, so rendering the average physical and intellectual features of a population, by sampling “the facts of life”. A graphical plot of sampled quantities renders a normal distribution and the Gaussian bell-shaped curve, hence the “average man” is determined at the normal position. In theory, individual characteristics may then be gauged against an average, “normal character”. Quetelet also suggested the identification of patterns common to both, normal and abnormal behaviors, thus Quetelet’s “social mechanics” assumed a mapping of human physical and moral characteristics, allowing him to formulate the argument that probability influences the course of human affairs, and thus that the human capacity for free-will – or at least the capacity to act upon free-will – is reduced, while social determinism is increased. Quetelet believed that statistical quantities of measured physical and mental characteristics were not just abstract ideas, but real properties representative of a particular people, a nation or ‘race’. In 1835, he published A Treatise on Man, and the Development of His Faculties, and so endowed to the culture of nineteenth century Europe a worldview of racial differences, of an “average man” for each subspecies of Homo sapiens, and hence scientific justification (logical soundness) for slavery and apartheid. Furthermore, Quetelet’s “average man” was presented as an ideal type, with deviations from the norm identified as errors.

Comte
Auguste Comte

Between 1830 and 1842 Auguste Comte formulated his Course of Positive Philosophy (CPP). From within our modern ‘global’ cultural milieu it is difficult to appreciate how widely accepted (‘globalized’) the ideology of positive philosophy was two hundred years ago, during the height of Eurocentric colonial culture, as positivism has received virtually no notice since the re-organizational events [NOTE B] imposed upon the politico-economic and cultural affairs of Europe after the Russian revolution and first world war.(6) The eclipse of positivistic ideology began with neo-positivism in philosophy of science, which lead to post-positivism. Strangely, it appears that the two later schools (neo- and post-positivism) have forgotten both, positive philosophy itself and the man who initiated and defended it, and even coined the term positivism [NOTE C]. However, Bourdeau (2014) tells that Comtean studies have seen “a strong revival” in the past decade, with agreement between modern philosophers of science and sociologists upon the ideologies propagated over 170 years ago. Points which were well established in positivism, but subsequently forgotten, have re-emerged in the modern philosophical milieu.

Re-emergent ‘truths’:
i) Scientific justification (logical soundness) is context-dependent.
ii) Science has a social dimension; science is necessarily a social activity with vertical (inter-generational) as well as horizontal (intra-generational) connections and thus also epistemic influences. Simply, science is a human activity, and humans are social animals.
iii) Positive philosophy is not philosophy of science, but philosophy of social interaction; Aristotelian political philosophy. Also, positivism does not separate philosophy of science from political philosophy.
iv) Cooperative wholeness; unity of acts and thoughts; unity of genes and memes; unity of dynamism and state.

“Being deeply aware of what man and animals have in common, Comte […] saw cooperation between men as continuous with phenomena of which biology gives us further examples.”
– Bourdeau (2014)

Comte made the purpose of CPP clear: “Now that the human mind has grasped celestial and terrestrial physics, – mechanical and chemical; organic physics, both vegetable and animal, – there remains one science, to fill up the series of sciences of observation, – Social physics. This is what men have now most need of: and this it is the principal aim of the current work to establish”.(7) He continued, saying that “it would be absurd to pretend to offer this new science at once in a complete state. [Nevertheless, Sociophysics will possess the same characteristic of positivity exposed in all other sciences.] This once done, the philosophical system of the moderns will in fact be complete, as there will be no phenomenon which does not naturally enter into some one of the five great categories. All our fundamental conceptions having become homogeneous, the Positive state will be fully established. It can never again change its character, though it will be forever in course of development by additions of new knowledge.”

In 1832 Comte was named tutor of analysis and mechanics at École Polytechnique. However, during the following decade he experienced two unsuccessful candidacies for professorship; he began to see ties severed between himself and the academic establishment after releasing a preface to CPP. In 1843 he published Elementary Treatise on Analytic Geometry, then in 1844 Discourse on the Positive Spirit, as a preface to Philosophical Treatise on Popular Astronomy (also 1844). By this time he was at odds with the academic establishment – essentially Comte had dropped-out of university. The reason for this does not seem to have been due to a lack of curiosity, neither to a lack of capacity, nor imagination, nor vision, nor even a simple lack of effort. Indeed, the situation resonates strongly with Einstein’s early academic situation, with Dirac’s late academic situation, and with Binet’s life-long academic situation. Galam’s experiences during the early 1980’s reverberate the same, unfortunate, if not pathological phenomenon of academic institution – interesting and curious, broad-reaching minds are generally met with hostile opposition from a fearful and mediocre orthodoxy.

Comte’s second great work – often referred to in the literature as Comte’s second career, was written between 1851 and 1854. It was regarded by Comte himself as his seminal work, and was titled First System of Positive Polity (FSPP). Its goal was a politico-economic reorganization of society, in accordance with scientific methods (techniques for investigating phenomena based upon gathering observable, empirical and measurable evidence, subject to inductive and deductive logical reasoning and argument), with the purpose of increasing the wellbeing of humankind – i.e. adaptation of political life based upon political episteme with the purpose of increasing the common good. This is precisely the Aristotelean argument (see The Common Good: Part I, under the heading Politikos). Though the sciences (epistemes) collectively played a central role in FSPP, positivism is not just science. Rather, with FSPP Comte placed the whole of positive philosophy under the ‘continuous dominance of the heart’, with the motto ‘Love as principle, order as basis, progress as end’. Bourdeau (2014) ensures us that this emphasis “was in fact well motivated and […] characteristic of the very dynamics of Comte’s thought”, though it seems as anathema to the current worldview as it did for Comte’s contemporaries, who “judged severely” – admirers of CPP turned against Comte, and publicly accused him of insanity.

Much like Nikola Tesla, Comte is reported to have composed, argued, and archived for periods of decades, periodically ‘observing the function of’ his systematic works, all in his mind. His death, in 1857, came too early for him to draft works that he had announced 35 years prior:
Treatise of Universal Education – intended for publishing in 1858;
System of Positive Industry, or Treatise on the Total Action of Humanity on the Planet – planned for 1861;
Treatise of First Philosophy – planned for 1867.

Polyhistornauts predicted
“Early academics did not create regular divisions of intellectual labour. Rather, each student cultivated an holistic understanding of the sciences. As knowledge accrued however, science bifurcated, and students devoted themselves to a single branch of the tree of human knowledge. As a result of these divisions of labor – the focused concentration of whole minds upon a single department – science has made prodigious advances in modernity, and the perfection of this division is one of the most important characteristics of Positive philosophy. However, while admitting the merits of specialization, we cannot be blind to the eminent disadvantages which emerge from the limitation of minds to particular study”.(7)

In surprising harmony with my own thoughts and words, Comte opined “it is inevitable that each [specialist] should be possessed with exclusive notions, and be therefore incapable of the general superiority of ancient students, who actually owed that general superiority to the inferiority of their knowledge. We must consider whether the evil [of specialization] can be avoided without losing the good of the modern arrangement; for the evil is becoming urgent. […] The divisions which we establish between the sciences are, though not arbitrary, essentially artificial. The subject of our researches is one: we divide it for our convenience, in order to deal the more easily with its difficulties. But it sometimes happens – and especially with the most important doctrines of each science – that we need what we cannot obtain under the present isolation of the sciences, – a combination of several special points of view; and for want of this, very important problems wait for their solution much longer than they otherwise need to”.(7)

Comte thus proposed “a new class of students, whose business it shall be to take the respective sciences as they are, determine the spirit of each, ascertain their relations and mutual connection, and reduce their respective principles to the smallest number of general principles.”

While reading this passage I was struck by the obvious similarity of its meaning to my own situation. I remain dumbfounded and humbled by the scale of foresight, so lucidly expressed by this great mind. For Comte had not simply suggested multi-disciplinary study, but a viewing though, and faithful acceptance of the general meanings rendered by the various scientific disciplines, together allowing for an intuitive, ‘heartfelt’ condensation of human knowledge.

Five fundamental sciences:
1) Mathematics
2) Astronomy
3) Physics
4) Chemistry
5) Biology

Sociology, then, is the sixth and final science. Each of these may be seen as a node in the network of human knowledge. Sociology, according to Comte, is the body of knowledge which will eventually allow for the networking of all human epistemes into a great unified field of human ideas.

Generalization: uneasy unification
Generalizing the laws of “active forces” (energy) and of statistical mechanics, Comte suggested that the same principle of interaction is true for celestial bodies and for molecules. Specifically, the center of gravity of either a planet or a molecule is focused upon a geometrical point, and though massive bodies may interact with each other dynamically, thus affecting each others relative position and velocity, the center of gravity of each is conserved as a point-state.

“Newton showed that the mutual action of the bodies of any system, whether of attraction, impulsion, or any notion other, – regard being had to the constant equality between action and reaction, – cannot in any way affect the state of the center of gravity; so that if there were no accelerating forces besides, and if the exterior forces of the system were deduced to instantaneous forces, the center of gravity would remain immovable, or would move uniformly in a right line. D’Lambert generalized this property, and exhibited it in such a form that every case in which the motion of the center of gravity has to be considered may be treated as that of a singular molecule. It is seldom that we form an idea of the entire theoretical generality of such great results as those of rational Mechanics. We think of them as relating to inorganic bodies, or as otherwise circumscribed, but we cannot too carefully remember that they apply to all phenomena whatever; and in virtue of this universality alone is the basis of all real science.”
– It should not escape the reader’s attention that in this passage Comte has effectively, albeit figuratively, plotted a graph of dynamically interacting point-states. The interactivity and cooperativity of massive bodies within a solar system or chemical reactants within a flask, both represent physically complex systems of dynamic social interaction – i.e. both are sociophysical systems. Implicit in this epistemological condensation is the fact that sociophysical systems are not necessarily alive, or biotic, or even organic.

After the completion of FSPP and his complete break with orthodox academia, Comte is said to have “overcome modern prejudices”, allowing him to “unhesitatingly rank art above science”.(6) Like Comte, I take the Aristotelian view that the arts are combinations of knowledge and skill; habitus and praxis; theory and method. Thus in a very real and practical sense the sciences are arts, from which it logically follows that Art ranks above Science. A rather more difficult pill to swallow, has been Comte’s Religion of Humanity, which he founded in 1849. Like Bourdeau (2014), I believe “this aspect of Comte’s thought deserves better than the discredit into which it has fallen”. My personal stance is due specifically to a previous uncomfortable encounter with an article on the topic of common goods, which was published by The Journal of Religious Ethics under the auspices of the United Nations(8). I had hesitated to include the paper and its contents in my previous work, due simply to fear – a fear reprimand by my peers, and a personal fear of straying from the “scientifically correct and peer reviewed path of learning”. As will become obvious, I have since realized that exclusion of study materials on the basis of fear alone is unreasonable, and that I should, and shall, attempt a rather more inclusive, better rounded education; critical thinking and good quality arguments remain of utmost importance.

“Reforms of society must be made in a determined order: one has to change ideas, then morals, and only then institutions.”
– Comte (cca. 1840)

The Religion of Humanity was defined with neither God(s) nor supernatural forces – as a “state of complete harmony peculiar to human life […] when all the parts of Life are ordered in their natural relations to each other […] a consensus, analogous to what health is for the body”. Personally, I understand this concept as the Tao, and more recently as deep ecology; inclusive of humanity but not exclusive to it. For Comte however, worship, doctrine and moral fortitude were oriented solely toward humanity, which he believed “must be loved, known, and served”.

Three components associated with the positivist religion:
i) Worship – acts; praxis; methods.
ii) Doctrine – knowledge; habitus; theories.
iii) Discipline (moral fortitude) – self-imposed boundaries, simultaneously conforming to, affirming, and defining the system of belief.

Two existential functions of the positivist religion:
i) Moral function – via which religion governs an individual.
ii) Political function – via which religion unites a population.

Ghetto magnetism
In this section we begin to explore the modern science of macro-scale physical phenomena, which result from micro-scale social interactions. The reader may find it useful to refer to the appended glossary of terms [NOTE D].

During the birthing period of quantum mechanical theory, “the concept of a microscopic magnetic model consisting of elementary [atomic] magnetic moments, which are only able to take two positions “up” and “down” was created by Wilhelm Lenz”.(9) Lenz proposed that spontaneous magnetization in a ferromagnetic solid may be explained by interactions between the potential energies of neighboring atoms. Between 1922 and 1924, Ernst Ising, a student of Lenz, studied the Lenz model of ferromagnetism, as a one-dimensional chain of magnetic moments; each atom’s field interacting with its closest neighbors. Ising’s name seems to have become attached to the Lenz model by accident, in a 1936 publication, titled On Ising’s Model of Ferromagnetism.

ising
Ernst Ising

Three energetic components of the Ising model:
i) Interaction between neighboring magnetic moments (atomic spins).
ii) Entropic forcing (temperature).
iii) Action of an externally applied magnetic field, affecting all individual spins.

Social interaction between neighboring atoms induces parallel alignment of their magnetic momenta, resulting in a more favorable energetic situation (lower entropy) when neighbors are self-similar; both +1, or both −1. Conversely, a less favorable situation results from opposing momenta (+1 next to −1).(10)

Ising_model_initial_1
Example of a the Ising model on a two dimensional (10 x 10) lattice. Each arrow represents a spin, which represents a magnetic moment that points either up (-1, black) or down (+1, red). The model is initially configured as a ‘random’ distribution of spin vectors.

Ising_model_initial_2
The same initial ‘random’ distribution of magnetic moments, showing ‘unfavorable’ alignments (circled in green).

Ising_model_initial_3
Clusters of spins begin to form (positive clusters circled in green, negative clusters circled in yellow) as a result of neighbor interaction, temperature, and the action of an externally applied magnetic field. As a result of entropy-reducing vector flipping, new ‘unfavorable’ spin alignments arise (circled in light blue), which will also tend to flip polarity.

In sociology, the term tipping point(11) refers to a rapid and dramatic change in group behavior – i.e. the adoption by the general population of a behavior that was rare prior to the change. The term originated in physics, where it refers to the addition of a small weight to a balanced object (a system previously in equilibrium), causing the object to topple or break, thus affecting a large scale change in the object’s stable state (a change of the system’s equilibrium); a change of stable state is also known as a phase transition.

The relation between cause and effect is usually abrupt in complex systems. A small change in the neighborhood of a subsystem can trigger a large-scale, or even global reaction. “The [network] topology itself may reorganize when it is not compatible with the state of the nodes”
– Juan Carlos González Avella (2010)

In relation to social phenomena, Morton Grodzins is credited with having first used the term tipping point during his 1957 study of racial integration in American neighborhoods.(11) Grodzins learned that the immigration of “black” households into a previously “white” neighborhood was generally tolerated by inhabitants as long as the ratio of black to white households remained low. If the ratio continued to rise, a critical point was reached, resulting in the en masse emigration of the remaining white households, due to their perception that “one too many” black households populated the neighborhood. Grodzins dubbed this critical point the tipping point; sociologist Mark Granovetter labeled the same phenomenon the threshold model of collective behavior.

Between 1969 and 1972, economist Thomas Schelling published articles on the topic of racial dynamics, specifically segregation. Expanding upon the work of Grodzins, Schelling suggested the emergence of “a general theory of tipping”. It is said that Schelling used coins on a graph paper lattice to demonstrate his theory, placing ‘pennies’ (copper-alloy one cent pieces, representing African-American households) and ‘dimes’ (nickel-alloy ten cent pieces – representing Caucasian households) in a random distribution, while leaving some free places on the lattice. He then moved the pieces one by one, based upon whether or not an individual ‘household’ was in a “happy situation” – i.e. a Moore neighborhood, in which the nearest eight neighbors are self-similar.(12) At random, one self-dissimilar ‘household’ was moved to a Moore neighborhood, over time rendering a complete segregation of households, even with low valuation of individual neighbor preferences. In 1978 Schelling published a book titled Micromotives and Macrobehavior, in which he helped to explain variation in normative differences, tending over time to display a self-sustaining momentum of segregation. In 2005, aged 84, Schelling was awarded a share in the 2005 Nobel prize in economics, for analyses of game theory, leading to increased understandings of conflict and cooperation.(13)

Schelling
Thomas Schelling

“People get separated along many lines and in many ways. There is segregation by sex, age, income, language, religion, color, taste, accidents of historical location. Some segregation results from the practices of organizations; some is deliberately organized; and some results from the interplay of individual choices that discriminate. Some of it results from specialized communication systems, like different languages. And some segregation is a corollary of other modes of segregation: residence is correlated with job location and transport”.(14)
– Schelling (1971)

Under the heading Linear Distribution, in Schelling’s 1971 publication on the subject of social segregation, we find a direct analog to the original one-dimensional Lenz-Ising model. Schelling seems to have either appropriated the concept, citing neither Lenz nor Ising, or to have designed the model independently. His involvement in American foreign policy, national security, nuclear strategy, and arms control(13) certainly would have granted Schelling access to knowledge of theoretical works, including the so called Monte Carlo methods, undertaken at Los Alamos during and after the second world war.(15) However, for the purpose of our current exploration it is irrelevant how exactly Schelling arrived at his understanding, and indeed, as I have mentioned previously, sociophysics has emerged in a variety of apparitions, to studious individuals with widely differing perspectives.

Schelling_1D
“The line of stars and zeros […] corresponds to the odd and even digits in a column of random numbers. […] We interpret these stars and zeros to be people spread out in a line, each concerned about whether his neighbors are stars or zeros. […] Suppose, now, that everybody wants at least half his neighbors to be like himself, and that everyone defines ‘his neighborhood’ to include the four nearest neighbors on either side of him. […] I have put a dot over each individual whose neighborhood does not meet his demands. […] A dissatisfied member moves to the nearest point at which half his neighbors will be like himself at the time he arrives there. […] Two things happen as they move. Some who were content will become discontent, because like members move out of their neighborhoods or opposite members move in. And some who were discontent become content, as opposite neighbors move away or like neighbors move close. The rule will be that any originally discontented member who is content when his turn comes will not move after all, and anyone who becomes discontent in the process will have his turn after the 26 original discontents have had their innings.”
– Schelling (1971)

Under the heading Area Distribution, Schelling (1971) introduces a two dimensional (13 x 16) lattice, commenting that “patterning – departure from randomness – will prove to be characteristic of integration, as well as of segregation, if the integration results from choice and not chance.” Clearly, Shelling’s model of social segregation bares great similarity to Ising’s ferromagnetic model.

Stauffer (2012) reminds us that the formation of urban ghettos is a well known phenomenon, and suggests that New York’s Harlem is the most famous black district,(3) with a history stretching well over a hundred years. From 1658, Harlem was a Dutch settlement (or ghetto) named after the capitol of north Holland. African-Americans began to immigrate during the ‘great migration’, from about 1905, when former slaves from rural southern United States migrated to mid-western, north-eastern and western regions of the US. Harlem identified as a ‘black’ district in a Manhattan borough, during the early 1920s.

Indirectly, Stauffer poses an interesting question: Why is it that we spontaneously self-organize into groups of self-similar individuals? – or in the specific case of “ghetto formation” – Why is it that we like to live in communities of like-minded, ethnically and culturally similar individuals? The simplest and clearest answer to this question is surely that we are social animals, and that it is easier to socialize with self-similar individuals, than with strangers. However, stemming from this is the truly fascinating question: If it is true that we like to live in communities of self-similar individuals, then why do we not like to live in communities of self-similar individuals when forced to do so? As an example of the latter, Stauffer reminds us of the uprising, in 1943, of the Warsaw Ghetto, which did not self-assemble but was formed under command of Nazi Germany. Again, the simplest and clearest answer must be that we are social animals, though I cannot think of good reason in support of this example, other than revolutionary pressure due to innate principles of self-regulation and self-organization. Regardless, it would be nice to assume that precisely this kind of ambiguity, apparently intrinsic to sociology, has been at root of the epistemological rift between physics and sociology, as the result of a long-standing ideological tradition in physics of determinism. In reality, a deeper and rather more vexing explanation haunts us; it has become obvious that the ambiguity of social interaction is not restricted to messy life systems, but governs inorganic physical phenomena also.

Statistical physics, borne of quantum theory, has put a definitive end to physical determinism. The renormalization technique, ushered in during the mid-1970’s, seems to have been an attempt to conserve physical determinism, at least tentatively. However, renormalization is a theoretical hack – an attempt to abstractly force fundamentally complex, infinite, random, and thus fundamentally indeterminate phenomena to appear as if they were simple, precisely calculable, determinable facts. Physically, experimentally, reality is not clear. In fact, reality is fundamentally uncertain, and so remains non-understood; mysterious. Stauffer confirms the validity of Comte’s thoughts, suggesting that “cooperation of physicists with sociologists could have pushed research progress by many years”.

State of the Art
“The concept of Complex Systems has evolved from Chaos, Statistical Physics and other disciplines, and it has become a new paradigm for the search of mechanisms and an unified interpretation of the processes of emergence of structures, organization and functionality in a variety of natural and artificial phenomena in different contexts. The study of Complex Systems has become a problem of enormous common interest for scientists and professionals from various fields, including the Social Sciences, leading to an intense process of interdisciplinary and unusual collaborations that extend and overlap the frontiers of traditional Science. The use of concepts and techniques emerging from the study of Complex Systems and Statistical Physics has proven capable of contributing to the understanding of problems beyond the traditional boundaries of Physics.”
– Juan Carlos González Avella (2010)

In an interdisciplinary review of the literature defining adaptive co-evolutionary networks (AcENs), Gross & Blasius (2007) have listed five dynamical phenomena common to AcENs:
i) emergence of classes of nodes from an initially heterogeneous population
ii) spontaneous division of labor – in my opinion the same as (i)
iii) robust self-organization
iv) formation of complex topologies
v) complex system-level dynamics (complex mutual dynamics in state and topology)

We are to understand that the mechanisms giving rise to these emergent phenomena themselves emerge from the dynamical interplay between state and topology. Divisions of labor, for example, spontaneously emerge (self-organize) as a result of information feedback within an AcEN(16) This fact bolsters an argument that I have made previously, for a strong similarity between the epiphenomena of bacteria, gregarious insects and humans, in their respective cultures. Also supported by studies of AcENs, is my hitherto intuitive understanding that a diverse set of actors is fundamental to the production of common goods. In fact, it is now clear that cultural diversity is so fundamental to the dynamics of social phenomena, that divisions of labor necessarily and spontaneously emerge from an initially homogeneous population, due to random variations of nodal state (entropic forcing), degree and homophily.

Gross & Blasius (2007) have reported that self-organization is observed in Boolean and in biological networks, occurring within a narrow region of transition between an area of chaotic dynamics and a area of stationary dynamics. Metaphorically, one might say that between the vast and chaotic field of the unknown and the relatively large steady state of knowledge, lies a narrow field – a phase space of self-organizing possibility – i.e. intuition. Not at all surprisingly, life systems, like all complex adaptive systems, necessarily occupy this theoretically defined phase space. Further, Gross & Blasius talk of the “ubiquity of adaptive networks across disciplines”, specifying technical distribution networks such as power grids, postal networks and the internet; biological distribution networks such as the vascular systems of animals, plants and fungi; neural or genetic information networks; immune system networks; social networks such as opinion propagation/formation, the social media and market-based socio-economics; ecological networks (food webs), and of course biological evolution offers an historical depth of literature on the subject of AcENs. The authors mention that examples are also reported from chemistry and physics, but do not provide examples. Based upon our current exploration it seems fair to suggest at least the following: astronomical gravitational networks, molecular chemical reactant networks, geological networks (the interactive cycling of carbon, water, nitrogen, minerals, etc…), and of course quantum mechanical networks.

For me personally, the most difficult to fathom of these examples has been the astronomical gravitational network. However, I am now able to imagine the gravitational interaction of massive bodies at their various scales – planets, moons and comets within a solar system; solar systems within a galaxy; galaxies within local groups; local groups within clusters, etc – as nodes, with gravitation comprising the set of edges (connections) between massive bodies.

mass_distribution
Network geometry is obvious in models of Universal mass distribution.

Tabulated nomenclature of static and dynamic elements, for a selection of epistemes.

EPISTEME STATIC DYNAMIC
Metaphysics actor action
Graph theory node edge
Complex systems theory vertex link
Quantum theory particle wave
Electro dynamics theory field vector
Economic theory agent behavior
Astrophysics massive body gravitation
Chemistry reactant reaction
Molecular biology – central doctrine DNA transcription
Molecular biology – central doctrine mRNA translation
Biology organism survival
Evolutionary theory species adaptation

According to J. Avella (2010), the modeling of network dynamics has revealed a complex relationship between actor heterogeneity and the emergence of diverse cultural groups.(19) Network structure and cultural traits co-evolve, rendering qualitatively distinct network regions or phases. Put in more familiar terms: patterns of social interaction and processes of social influence change or differ in tandem, also network patterns and processes feedback upon each other. Thus social interactions exist as a dynamic flux in which distinct channels of interactivity form, sever, and re-form. From the collective interaction of agents, emerge temporary, sequential, non-equilibria – known as network states. The formation of network states is controlled by early-forming actors, whereas the later formation and continued rapid reformation of cultural domains, comprises the geometry – or ‘architecture’ – of a mature network; a network who’s dynamics have reached a dynamic steady state.

Furthermore, the ordered state of a finite system under the action of small perturbations is not a fixed, homogeneous configuration, but rather a dynamic and diversified, chaotic steady state. During the long term, such a system sequentially “visits” a series of monocultural configurations; one might imagine a systemic analogue to serial monogamy. Slow forming monocultures emerge under stable environmental conditions (low entropic forcing). Under less stable environmental conditions (high entropic forcing), monocultural domains undergo fragmentation and are replaced by a variety of rapidly forming and re-forming cultural domains, thus rendering a dynamic steady state. The relation between cause and effect is usually abrupt in complex systems. Indeed, “the [network] topology itself may reorganize when it is not compatible with the state of the nodes.”

Avella tells of a study by Y. Shibanai et al, published in 2001, analysing the effects of global mass media upon social networks. Shibanai et al assumed global mass media messages as an external field of influence – analogous to the external magnetic field in the Ising model – with which network actors (individual, and/or groups of nodes in a network) interact. The external field was interpreted “as a kind of global information feedback acting on the system”. Two mechanisms of interactive affect upon society by global media were identified:
i) The influential power of the global media message field is equal to that of real (local) neighbors.
ii) Neighbourly influence is filtered by feedback of global information, but effected only if and/or when an individual network node is aligned with a global media message.
Shibanai et al concluded that “global information feedback facilitates the maintenance of cultural diversity” – i.e. The propagation of messages promoting a state of global order and cultural unity, simultaneously enables and maintains a dynamic steady state of global disorder and multiculturalism.

Generally, considerations of equilibrium assume that the application of a field enhances order in a system. However, this is not always the case. To the contrary, Avella (2010) tells us that “an ordered state different from the one imposed by the external field is possible, when long-range interactions are considered” and fascinatingly, that “a spatially nonuniform field of interaction may actually produce less disorder in the [social] system than a uniform field.”

“While trends toward globalization provide more means of contact between more people, these same venues for interaction also demonstrate the strong tendency of people to self-organize into culturally defined groups, which can ultimately help to preserve overall diversity.”
– J. Avella (2010)

Respectfully, I urge the reader to allow themselves a few moments of meditation upon this rather subversive finding.

A dynamic steady state exists in a network until a process of social influence such as an external environmental perturbation or an internal social perturbation, exceeds some threshold (tipping point), as a result of which the current network steady state is eroded and reformation of ongoing network dynamics occurs, rendering a new dynamic steady state. Put another way: above some threshold, a given perturbation causes an abrupt change in social interactions, leading to a new (though ultimately temporary) dynamic steady state. Co-evolution implies that the processes of social influence change as the result of multilateral feedback mechanisms between social interactions, environmental forcing, and/or the eccentric actions of some individual or group.

Three distinct phases of complex (adaptive, co-evolutionary) networks:
Phase I) A large component of the network remains connected and co-evolutionary dynamics lead to a dominant monocultural state.
Phase II) Fragmentation of the monocultural state begins, as various cultural groups form in the dynamic network. However, these smaller groups remain stable in the presence of ongoing stochastic shocks; peripheral actors are either absorbed into a social group or are forced out. “Social niches are not produced through competition or selection pressure but through the mechanisms of homophily and influence in a co-evolutionary process.[…] Thus, even in the absence of selection pressures, a population can self-organize into stable social niches that define its diverse cultural possibilities.”
Phase III) Fragmentation of cultural domains leads to high levels of heterogeneity. Avella (2010) teaches that the very high levels of heterogeneity observed in network models are “empirically unrealistic in most cases; however, they warn of a danger that comes with increasing options for social and cultural differentiation, particularly when the population is small or there is modest cultural complexity. Unlike cultural drift, which causes cultural groups to disappear through growing cultural consensus, a sudden flood of cultural options can also cause cultural groups to disappear; but instead of being due to too few options limiting diversity, it is due to excessive cultural options creating the emergence of highly idiosyncratic individuals who cannot form group identifications or long-term social ties.”

Confirming what we have learned from Ising and Schelling, Avella tells that “[actors] have a preference for interacting with others who share similar traits and practices”, and this fact “naturally diversifies the population into emergent social clusters.” However, we have also learned that a highly idiosyncratic actor, who is either unrecognized or even disconnected from a local area network, may still play an influential role upon the greater network (society). Thus, highly idiosyncratic individuals, devoid of group identifications and/or long-term social ties, rather than posing a danger, may be potentially highly relevant to social processes, if only in the sense that collective idiosyncrasy exists as a reservoir of unused or even unknown options and opportunities – a pool of potential, perhaps similar to that of genomic mutants; a diverse set of resources from which may emerge novel solutions to challenges and previously un-encountered situations.

Indeed, precisely this scenario appears to have been the case at the emergence of life on Earth (see: LUCA and the progenotes, in Part II: Empirical observations and meta-analyses, of The Common Good), during which the progenote population represented a collective, albeit semi-disconnected, network of highly idiosyncratic individuals with no strong group identification or long-term social ties. Also learned in Empirical observations and meta-analyses, a local area network catastrophe is catastrophic only for a highly adapted (specialized) monoculture, and may be problematic for small ‘satellite’ cultural groups that are to a lesser extent adapted to the current network topology. However, highly idiosyncratic, even disenfranchised actors in the current dynamic, network steady state, may experience homophilic pressure and thus social connectivity in the dynamic steady state which emerges from a phase transition of the network topology.

Avella (2010) has confirmed that cultural heterogeneity (multicultural dynamics, and even outright anarchy) is a deep aspect of reality. Anarchy and chaos appear to be near the source, or indeed to be the source of physical and social order. That is to say a variety of ordered states spontaneously emerge from anarchical, chaotic systems. “Social diversity can be maintained even in highly connected environments” – i.e. Even under intense pressure to conform, diversification and hence diversity, emerge and persist.

Vinkovic & Kirman (2006), remind us that the purpose of the Schelling model is “to study the collective behavior of a large number of particles”,(16) and that the model illustrates the emergence of aggregate phenomena that are not predictable from the behaviors of individual actors. In economics theory individual agents make decisions based upon a “utility function” (personal preference), an idea that can be interpreted in physical terms, as: particle interactions are driven by changes of internal energy. A direct analogy is made between the interactions of life systems (humans, insects, fungi, plants, bacteria, etc…) and physical systems (gases, liquids, solids, colloids, solutions, etc…) by treating particles as agents. “In the Schelling model utility depends on the number of like and unlike neighbors. In the particle analogue the internal energy depends on the local concentration […] of like or unlike particles. This analogue is a typical model description of microphysical interactions in dynamical physical systems […]. Interactions between particles are governed by potential energies, which result in inter-particle forces driving particles’ dynamics.”

It is understood then, that from the collective behaviour of individual agents, emerge clusters of self-similar agents. Fascinatingly, Vinkovic & Kirman report finding that aggregates of empty space play a “role” in the dynamics of agent clustering; stressing the importance of the number of empty spaces in the initial, random, configuration of an experimental lattice. Specifically, “an increase in the volume of empty space results in more irregular cluster shapes and slower evolution because empty space behaves like a boundary layer”. Clearly, in their analytical study, the authors assume that aggregates of empty space express a “behavior”, thus implying that “empty space” has some capacity to act; specifically, stabilizing nearby clusters by preventing them from direct contact with each other. Simply, we are to acknowledge the collective agency of aggregations of agentless locations on the lattice; the collective action of actorless, “free” space.

Empty_space_actors
Plots of an agent based (Schelling) model.
The two dimensional experimental lattice is composed of (100 x 100) = 10000 cells. Each cell is either empty (white) or is occupied by one agent (red or blue). Numbers of empty cells in initial random configurations are shown.
Increased cluster size correlates with decreased value of x. Increased sizes of empty space clusters are shown (circled in green) for both initial configurations.
– graph adapted from Vinkovic & Kirman (2006)

I have managed to find only a tiny scattering of scientific works attributing some significance to empty space. One example is from the statistical analysis of graphical data plots; Forina et al (2003), have introduced an empty space index, the purpose of which is to quantify the fraction of information space on a given graph, that does not hold any “experimental objects”.(17) However, the authors are careful to point out that the empty space index cannot be confused with a clustering index. Another, perhaps more commonly known example stems from astronomy; voids.

Like Serge Galam, Stephen Wolfram is also a self-proclaimed hobbyist exploring sociophysics. In his philosophical treatment of space-time(18) Wolfram (2015) suggests that “maybe in some sense everything in the universe is just made of space.” Wolfram speaks of what I choose to call aether (see: A Spot of Bother and Aether), saying:
“As it happens, nearly 100 years [before Special Relativity, people] still thought that space was filled with a fluid-like ether. (Ironically enough, in modern times we’re back to thinking of space as filled with a background Higgs field, vacuum fluctuations in quantum fields, and so on.)”

Conclusion
It must be stressed that the epistemic condensation of sociology and physics may be ascribed to any of the periodic elements; to the sub-atomic scale as well as the astronomic scale; to mathematical and theoretical, albeit complex, models of reality; and of course to life systems.

We have viewed through empirically observable phenomena, at some aspect of reality that is more fundamental than those which we have observed.

Critically, this cannot be science, as the absolute boundary of the scientific method, and thus science itself, is empiricism (sensual observation and manipulation). Any thing that we think we see and do beyond or through what we actually observe and affect, is not science. We are left with only one logical possibility: that our newfound knowledge of reality is metaphysical. Ultimately we must categorize it as Art.

Notes
A) There are at least three separate histories of sociophysics; one stemming from philosophy, one from quantum physics, and one from sociology.
B) In the vocabulary of complex systems modeling and co-evolutionary adaptive networks theory one may rightly define such reorganizational events as a change of topological dynamics.
C) As well as positivism, Comte coined the words sociology and altruism.(6)
D) Glossary of terms relevant to network models:
Node: The node is the principal unit of a network. A network consists of a number of nodes connected by links. Depending on context, nodes are sometimes also called vertices, agents, actors, or attractors.
Link: A link is a connection between two nodes in a network. Depending on context, links are also called edges, connections, actions or interactions.
Degree: The degree of a node is the number of nodes to which it is connected; i.e. degree = links/node. The mean degree of the network is the mean of the individual degrees of all nodes in the network.
Neighbors: Two nodes are said to be neighbors if they are connected by a link.
Dynamics: Depending on context, dynamics refers to a temporal change of either the state or the topology of a network.
Evolution: Depending on context, evolution refers to a temporal change of either the state or the topology of a network.
Frozen node: A node is said to be frozen if its state does not change in the long-term behavior of the network. In certain systems the state of frozen nodes can change nevertheless on an even longer topological time scale.
Topology: Refers to a specific pattern of connections between the nodes in a network.
State: Depending on context, state refers to either the state of a networked node or the state of the network as a whole – including the nodes and the topology.
Small-world: Refers to a network state in which distant, indirectly connected, nodes are linked via a short average path length.
Scale-free: Refers to a network state in which the distribution of node degrees follows a power law.
Homophily: Refers to spontaneous attraction between self-similar nodes; literally self love.

Bibliography
1) S. Galam, “Sociophysics: a personal testimony”, (2004), Laboratoire des Milieux Désordonnés et Hétérogènes, arXiv, http://arxiv.org/abs/physics/0403122
2) S. Galam, Y. Gefen and Y. Shapir, “Sociophysics: A mean behavior model for the process of strike”, (1982), Journal of Mathematical Sociology, 9, p. 1-13.
3) D. Stauffer, “A Biased Review of Sociophysics”, (2012), Institute for Theoretical Physics, Cologne University, arXiv, http://arxiv.org/abs/1207.6178
4) G. Heywood, “EDMOND HALLEY: ASTRONOMER AND ACTUARY”, (1985), ???
5) S. Stigler, “Adolphe Quetelet (1796-1874)”, (1986) Encyclopedia of Statistical Sciences, John Wiley & Sons, http://mnstats.morris.umn.edu/introstat/history/w98/Quetelet.html
6) M. Bourdeau, “Auguste Comte”, (2014), Stanford Encyclopedia of Philosophy, http://plato.stanford.edu/archives/win2015/entries/comte/
7) H. Martineau, “The Positive Philosophy of Auguste Comte”, (1896), Batoche Books (2000), http://socserv2.socsci.mcmaster.ca/econ/ugcm/3ll3/comte/Philosophy1.pdf
8) J. O’Connor, “MAKING A CASE FOR THE COMMON GOOD IN A GLOBAL ECONOMY: The United Nations Human Development Reports [1990-2001]”, (2002), The Journal of Religious Ethics, Vol. 30, No. 1, p. 155-173, http://www.jstor.org/stable/40017930
9) S. Kobe, “Ernst Ising 1900-1998”, (2000), Technische Universität Dresden, Institut für Theoretische Physik, http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-97332000000400003
10) J. Selinger, “Ising Model for Ferromagnetism”, Chapter 2 of Introduction to the Theory of Soft Matter: From Ideal Gasses to Liquid Crystals, (2016), http://www.springer.com/978-3-319-21053-7
11) “Tipping point”, https://en.wikipedia.org/wiki/Tipping_point_%28sociology%29
12) D. Vinkovic and A. Kirman, “A physical analogue of the Schelling model”, (2006), Proceedings of the National Academy of Science, http://www.pnas.org/content/103/51/19261.full
13) “Thomas Schelling”, https://en.wikipedia.org/wiki/Thomas_Schelling
14) T. Schelling, “DYNAMIC MODELS OF SEGREGATION”, (1971), Journal of Mathematical Sociology, Vol. 1, p. 143-186, http://www.tandfonline.com/doi/abs/10.1080/0022250X.1971.9989794
15) “Monte Carlo method”, https://en.wikipedia.org/wiki/Monte_Carlo_method
16) T. Gross & B. Blasius, “Adaptive coevolutionary networks: a review”, (2007), Journal of The Royal Society, http://rsif.royalsocietypublishing.org/content/5/20/259.short
17) M. Forina, S. Lanteri, C. Casolino, “Cluster analysis: Significance, empty space, clustering tendency, non-uniformity. II – Empty space index”, (2003), https://www.researchgate.net/publication/10619726_Cluster_analysis_Significance_empty_space_clustering_tendency_non-uniformity_II_-_Empty_space_index
18) S. Wolfram, “What Is Spacetime, Really?”, (2015), http://blog.stephenwolfram.com/2015/12/what-is-spacetime-really/
19) J. Avella, “Coevolution and local versus global interactions in collective dynamics of opinion formation, cultural dissemination and social learning”, (2010), Institute of Interdisciplinary Physics and Complex Systems, http://digital.csic.es/handle/10261/46275

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The Common Good: a semi-rational emergent property of complex collective interaction between diverse actors – Part II

The common good invariably requires diversification, manifest as random fluctuations within the biological phase space from which emerge divisions of labour, and thus necessarily, inequalities among individuals comprising a social collective. Entropic forcing drives increases of the common good, via increased diversity, to an apparent limit.

Explorations are made of philosophical (Part I) and empirical (Part II) studies in politics, biology, and economics.

Cooperation via collective divisions of labour is a necessary prerequisite to biological metabolism and reproduction. A collective comprising diverse actors is thus assumed fundamental to the planetary biome. The preponderance of benefit (here designated ‘the common good’) that emerges for actors (individuals and groups), is mediated by Woesean collective cooperation, defined as “a diverse community of cells(note A) surviving and evolving as a biological unit.”(1)
– see Part I for (note A) and reference (1).

“Diversity is an asset with which to confront uncertainty.”
– Groschl, 2013

Part II: Empirical observations and meta-analyses

Diversified-specialized: a modern economical perspective
The concept of diversified specialization is introduced and discussed in some detail by Farhauer & Kröl (2012), in an empirical study of German kreisfreie städte (cities with county status).(28) The study speaks of Marshall-Arrow-Romer (MAR) externalities, and of Jacobs externalities; both are forms of knowledge spillover. The former generating advantages due to specialization in the local environment, the latter generating advantages due to diversification in the local environment.

A diversified sector structure fosters cross-sectoral (‘Jacobs’) spillovers and lessens the impact of sector-specific demand shocks upon the regional economy. However, cities specializing in several sectors profit from both, MAR and Jacobs knowledge spillovers. Diversified-specialised cities combine the benefits of higher productivity due to specialization, with the advantages of a diversified structure, such as cross-fertilization among differing sectors, thus exhibiting higher growth rates than either specialized or diversified cities.

Specialization is risky. When a highly specialized local economy is exposed to a negative demand shock, local unemployment tends to increase dramatically, resulting in a local economic recession, or possibly even leading in an economic, and eventually cultural collapse of the entire region. In an extreme case the industry sector begins to wholly collapse, causing a widespread cascading shockwave.(29)

Sector-specific demand shocks are better absorbed by a diversified economy. It is reasonable to assume that a diversified economic environment, or indeed the diversified skill-set of an individual, generally allows for greater stability; or biologically speaking, greater fitness via increased adaptive capacity. The viability of a culture surely is in the common interest of all individuals comprising it, whether they are directly or indirectly integrated into the local culture (economy and/or ecology). Thus economic and cultural stability (viability) may reasonably be viewed as a common good.

Farhauer & Kröl report that diversified cities are generally larger, more crowded and chaotic, rendering a business environment that is less efficient and more costly than that found in a specialized city. Interestingly then, diversification requires more space than specialization, not simply geographically but also potentially; a larger realm of possibility (a larger phase space) defines diversified actors.

“Smaller cities tend to be specialised and, as a result, more productive which indicates a negative influence of city size on productivity. However, in large cities inputs can be utilised more efficiently – i.e. put to the best possible use – by means of which productivity is higher.”
– Farhauer & Kröl, 2012

Hitting squarely the predictions rendered by the hypothesis upon which the current thesis rests(note F), the diversified-specialized theory appears to be inconclusive and ambiguous, yet it is obvious that if population number (city size) does not make a clear difference in productivity, then a diversified approach is better, if only because it renders a more stable and viable situation for all stakeholders. And indeed Farhauer & Kröl do report that numerous empirical studies correlating regional sector structure (either diversified or specialized) with economic growth, have found greater employment rates in diversified regions. Critically though, the study promotes the concept of ‘diversified-specialization’ as more productive, more innovative and more stable than either diversified or specialist structures are on their own. Thus a “region specializing in a certain combination of related sectors is likely to experience higher growth rates than a region specializing in an unrelated portfolio or in one sector only.”

An indeterminate confusion in the literature relevant to the empirical study of local economies has been reported; some studies concluding that a city is specialized, while others say the same city is diversified. Farhauer & Kröl tell that “many cities exhibit multiple specialisations, but – apart from specialization in a few sectors – they show a diversified structure at the same time.” One could easily assume that Farhauer & Kröl are fence-sitting on their suggestion of diversified-specialized cities. Rather, I would suggest they have taken a pragmatic perspective, indicative of diversity and diversification as fundamental to local economies; that is to say, specializations cannot exist in the absence of diversity, and that specializations emerge from a milieu of diverse actors. Arguably, the same may be said of local ecologies.

Furthering the economy/ecology analogy, the authors tell that “companies benefit from proximity to upstream and downstream firms […]” – a statement that is strikingly reminiscent of biological commensal symbiosis between upstream and downstream metabolisms, and of the current best guess regarding the origin of life on Earth; the constitution of the last universal common ancestor. Most fascinating of all, due to its similarity with the inefficient process of photosynthetic primary production, is the statement “cities with lower productivity levels are characterised by higher growth rates.”

LUCA and the progenotes
The idea that any group of modern organisms inherited their genes from a single common ancestor is naive. Much more likely is that the last universal common ancestor (LUCA) was a complex and diverse, sophisticated global community.(30) Early life forms were particularly promiscuous, sharing their genes in a process called horizontal gene transfer (HGT); moving genetic materials, signals, metabolic components, and other resources between cells without necessarily reproducing the entire cell.

“Most researchers now believe we should think of LUCA as a pool of genes shared among a host of primitive organisms [though] some biologists believe that horizontal gene transfer makes LUCA unknowable.”
– Whitfield, 2004

Whitfield (2004), proposes that individual cellular components of the LUCA collective may have independently learned how to solve similar problems, such as membrane construction, or the extraction of energy from certain organic molecules, and that HGT allowed for promiscuous sharing of genes coding such solutions with other cells in the commune.

The cellular functions of modern organisms rely on complex enzymatic machinery. Generally enzymatic components are encoded by several noncontiguous genes, which may be located in different regions of the genome. In contrast, the earliest genes would each have encoded an enzymatic product able to function as a stand-alone functional module – “like cassettes that can be loaded, removed and replaced. Antibiotic-resistance genes are like that today.”

The darwinian threshold, estimated to have occurred 3.5 billion years ago, represents the point in biological history when inheritance and mutation of genes replaced HGT as the dominant mode of evolution; individual cells became more complex and their functions became less interchangeable.

Carl Woese (1998), proposed that the LUCA was not a discrete entity, but a diverse community of cells surviving and evolving as a collective.(31) “This communal ancestor has a physical history but not a genealogical one. The [LUCA] cannot have been a particular organism, a single organismal lineage. It was communal, a loosely knit, diverse conglomeration of primitive cells that evolved as a unit, and it eventually developed to a stage where it broke into several distinct communities, which in their turn become the three primary lines of descent. – The universal ancestor is not an entity, not a thing. It is a process […]. Progenotes(note G) were very unlike modern cells. Their component parts had different ancestries, and the complexion of their componentry changed drastically over time. All possessed the machinery for gene expression and genome replication and at least some rudimentary capacity for cell division. But even these common functions had no genealogical continuity, for they too were subject to the confusion of lateral gene transfer. Progenotes are cell lines without pedigrees, without long-term genetic histories. With no organismal history, no individuality or “self-recognition,” progenotes are not “organisms” in any conventional sense.”

Individually, progenotes differed metabolically, their small genomes necessitating individual metabolic simplicity. Collectively however, the diverse and noncontiguous genome of the progenote population was totipotent, and HGT greatly facilitated the spread of innovations through the population, endowing the progenote community with an enormous evolutionary potential.

“not individual cell lines but the community of progenotes as a whole […] survives and evolves”
– Woese, 1989

Glansdorff et al (2008), teach that “the origin of viruses and their possible role in evolution have opened new perspectives on the emergence and genetic legacy of LUCA”.(32) Order and its corollary, organization, have increased during the evolution of biological systems. Complexity remains a rather poorly defined concept, except in the abstract sense of non-computability; irrationality.

Molecular genetic studies have allowed researchers to infer a sophisticated genomic and metabolic capacity for the LUCA. Generally, the view is one of a diversified and promiscuous community, collectively housing a wide spanning genetic redundancy. “It is indeed very likely that most cells in an ancestral community having engendered the diversity of metabolic functions found in the three Domains possessed more than a single copy of every essential gene as well as numerous paralogous genes. This redundancy could have been selected for as an important survival factor for cells with a still primitive, not fail-safe division mechanism.” As we shall see later, functional redundancy, and an apparent ceiling thereof, is documented as an aspect of the relationship between diversity and productivity.
LUCA_diagram_Glansdorff
Schematic representation of hypothetical emergence and legacy of the LUCA(33)

Promiscuous and multiphenotypic, dynamic and unstable, LUCA existing as a continual process of unregulated (or poorly regulated) incorporation and/or rejection of innovations via lateral exchanges of genomic and/or catalytic components, presumably via a merging process similar to phagocytosis, between cells devoid of rigid envelopes, living as a community in a broad range of temperatures and chemical environments. The community concept allows for the explanation of major transitional events in evolution, via genetic exchanges within an ancestral and promiscuous community, generating a large variety of forms from which new classes of entities may independently emerge at a new level of complexity. “The emergence of the first Domain must have been the outcome of a crisis rather than a progressive development.”

“Above a certain level of diversification and catalytic interconnections, the [prebiotic] system would undergo ‘catalytic closure’, thereby becoming capable of self-replication.” Catalytic closure refers to a situation in which all catalysts (enzymes) required for metabolisis are synthesized within a cellular system. However, catalytic closure does not necessitate all the catalysts to be enclosed within an individual cell membrane, as evidenced by the many and varied examples of obligate symbiosis, including for example our own human state of obligate syntrophy, facilitated by the microbiome of our digestive tract.

The picture painted here, is one of LUCA and the progenotes, as metabolically and morphologically overlapping heterogeneous communities, continually shuffling around genetic material, which may have been composed of RNA, or DNA, or even a combination of the two. A great but not completely localized conglomeration of biologically diverse actors, collectively producing a common good. Taking a broad view, it may not be terribly unrealistic to assume that the modern planetary biome, driven by a vast variety of symbioses, still exists in this more-or-less promiscuous and evolvable state of nature.

Collective divisions of labour: biological multi-dimensionalism
Clonal populations of wild type Bacillus subtilis can diversify to express at least five (documented) distinct cell types, each associated with a specialized function.
1) Motile cells express flagella, which propel cells in low viscosity environments.
flagellum
Schematic diagram of flagellar structure.

2) Surfactin-producing cells secrete an amphiphilic surfactant compound that acts to reduce the surface tension of water, as well as functioning as a communication signal, and as an antimicrobial agent (anti-bacterial, anti-viral, anti-fungal, anti-mycoplasmal, and hemolytic). The various services rendered by Surfactin are embedded within the communal micro-habitat, thus bettering the living conditions for all cells comprising the local cellular collective, for this reason Surfactin is considered to be a public good.
Surfactin
Structural formula of a surfactant.

3) Matrix-producing cells secrete extracellular polymeric substances (EPS), the structural protein TasA, and a variety of antimicrobial compounds. EPS acts in a similar manner to the extracellular matrix in higher animals; a biotic medium surrounding and binding cells, facilitating temporary storage and transfer of information and resources between cells, and generally functioning to buffer the cellular collective from environmental stressors. As a component of the EPS, TasA assembles into amyloid-like fibers that attach to cell walls and play a critical role in the formation of various colony morphologies, and in some modes of colonial expansion. The EPS, including the various functional compounds and morphologies embedded within it, is considered to be a public good.
biofilm
Scanning electron micrograph of biofilm produced by collective secretion of EPS by B. subtilis.

4) Protease-producing cells secrete enzymes that facilitate nutrient acquisition. Secreted proteases are considered public goods.
protease_action_diagram
Schematic diagram of protease function

5) Sporulating cells produce stress-resistant bodies (spores) that can survive extended periods of adverse environmental condition.
endospore
Electron micrograph showing an endospore held within a cell body.

Here then is a tentative list of possible states – the phase space of evolutionarily stable strategies of B. subtilis. Importantly, relative proportions of the various specializations observed in any individual colony develop as a result of the environmental condition(s) experienced by the cell collective, and are geared to propagate and increase the common good. Specifically, Gestel et al (2015), have shown that migration of B. subtilis over a solid surface is dependent upon cellular differentiation of cells in a clonal colony, into two distinct phenotypes; surfactin-producing cells and matrix-producing cells. Collectives of these cell types form highly organized structures that the authors have named ‘van Gogh bundles’; tightly aligned, elastic filamentous loops; chains of cells that push themselves away from the colony edge. The geometries of van Gogh bundles are mediated via mechanical cellular interactions, with small-scale local changes (cell elongation, division, orientation, and polar interactions) at the level of individual cells determining the collective properties of expanding filamentous loops, emergent at the colony level.(33)

B_subtilis_migration
Two distinct cellular phenotypes arising from differentiation of a clonal population of wild type B. subtilis. Surfactin-producing cells (red), matrix-producing cells (green).(34)

Though migration surely is a good strategy for cells living in a limiting environment, we cannot rightly assume that individual bacterial cells are aware of colony-level (organismal) behaviors. In the specific example studied by Gestel et al cells live on a solid surface making individual ‘selfish’ action (flagellar motility) impossible. Apparently the only manner in which individual cells can migrate away from such an environment is via diversified and cooperative, collective action. Though environmental stimuli are important determinants of the differing growth phases of cell collectives, cell differentiation is also inherently stochastic. Gestel et al tell that “under constant environmental conditions, cells can spontaneously differentiate [metabolically switching] into matrix-producing cell chains that are preserved for a number of generations due to a regulatory feedback loop.”

B. subtilis is not the only ‘unicellular’ or ‘single-celled’ species to exhibit a multicellular lifestyle. “Filamentous structures also occur during the colony growth of Paenibacillus vortex and B. mycoides.” Also B. cereus has been shown to switch to a multicellular lifestyle when grown on filter-sterilized soil-extracted soluble organic matter (SESOM) or artificial soil microcosm (ASM) – physical models of environmental conditions that cells encounter in soils. In all four microbial species, multicellularity allows for and facilitates migration via emergent common goods. Interestingly, the domesticated strain B. subtilis 168, which is documented as defective in surfactin production, cannot make the switch to a multicellular lifestyle when grown on SESOM or ASM.

There is an interesting observation to be made here in regard to ESS theory. The mathematical, logical descendent of game theory, is depicted in the literature essentially as a binary system, comprising cooperative and altruistic ‘dove’ actors, versus selfish and aggressive ‘hawk’ actors. In contrast, B. subtilis is presumed to be a quinary system of evolutionary stable strategies, comprising five expressible types of actor, as well as the higher-level collective actor(s) that emerge from synergy between groups of cellular actors – “the formation of van Gogh bundles depends critically on the synergistic interaction of surfactin-producing and matrix-producing cells.”

“Some problems can be solved only when individuals act together. This applies to bacteria in the same way that it applies to humans.”
– Gestel et al, 2015
cooperative_ants
Stigmergic ants cooperate to move a large food article to the nest. Individuals lifting the load cannot ‘see’ where the nest is; a ‘driver’ (bottom of image) nudges the ‘lifters’ in the direction of the nest.

The diversity-productivity relationship
Difficulties in finding or creating metrics of the common good are widespread. Bouter (2010), has professed that “knowledge is a common good”, pointing out that “finding good indicators of scientific quality is no easy task”. Recognizing that “research is becoming less and less the exclusive province of the universities”, Bouter calls for “co-operation in a variety of changing contexts”. In specific regard to evaluation of the societal relevance of scientific research, he has suggested there is “plenty of room for discussion about the validity of the indicators, the optimum level of detail and weighing up the relative importance of its various aspects. […] However, it is clearly too early to adopt a strong quantitative approach.”(34) In fact, there is no standard metric of the common good.

Standardized quantification of diversification and specialization processes, and of diversified or specialized states, has also proven largely intractable, with various researchers using, or creating, differing working definitions and tools. Nevertheless, studies of diversity have been endowed with a probabilistic metric called the diversity index. This theoretical object has been interpreted in a variety of ways; relatives of the diversity index have been used by ecologists in studies of the relationship between plant diversity and ecosystem function, generally showing that “productivity increases with diversity”(35). From these studies has emerged a statistical model of “a fundamentally important ecological pattern”(36) called the diversity-productivity relationship (DPR).

Zhang et al (2012), tell that the DPR “has received considerable attention during the past two decades”, and that numerous grassland experiments have demonstrated positive DPRs; that is, production of biomass increases with increased biodiversity.(37) A positive DPR coexists with increases of resource use, nutrient retention and cycling, niche differentiation and inter-species facilitation. Generally, the greater the diversity of organisms in an ecosystem, the better each organism (or group) is able to survive and reproduce, due to increases of nutrient abundance, resource availability, habitat partitioning and mutualistic symbioses. Critically, the DPR body of knowledge includes insignificant, and negative, as well as positive effects of biodiversity on productivity. These should be expected however, as results of physical (environmental) limitation, and differences of assumption and quantification in individual studies.

DPR studies tend not to show direct links between ecological mechanisms and positive DPRs. This failure, or inability, results partially from the form of scientific inquiry; a necessarily narrow field of view, focused upon one, or a very few, specific aspect(s) of the object or process being studied. In a meta-analysis of global forest productivity, Zhang et al, have commented that the majority of “DPR studies have chosen species richness as the measure of species diversity to define and interpret DPRs. However, richness alone cannot fully represent species diversity in relation to ecosystem functioning because it ignores the influence of species evenness (relative abundance) on [interspecies] interactions. The lack of understanding of species evenness in DPRs is presumably limited by traditional experimental and statistical methods.”

Zhang et al, chose three dimensions of productivity in their DPR meta-analysis.
1) Biomass: Kg of cellulose, though in reality a great deal more and varied biological material is present.
2) Volume: m3 of forest canopy,
3) Basal area: m2 of forest floor.

The former two (biomass and volume) vary with biological activity, the latter is invariant; all three represent limited common goods. It is important to realize that none of these dimensions, neither individually nor collectively, account for actual forest ecosystem productivity, because a great deal of biological activity crucial to aboveground production of biomass and volume occurs below the forest floor, in the shallow layer of topsoils ignored by the global meta-analysis. Similarly, other obvious environmental factors, such as solar radiation and meteorological water, have been excluded, presumably along with a vast array of less obvious or unknown factors. Even so, Zhang et al have concluded, in agreement with the majority of DPR studies, that positive DPRs are a global phenomenon in forest ecosystems, commenting that “polycultures are generally more productive than mono-cultures”, and that evenness of the canopy volume, as well as contrasting traits between various organisms, are central components of positive diversity-productivity relationships. Furthermore, they report the existence of a diversity plateau at the high end of the species richness range, resulting from functional redundancies among species cohabiting an ecosystem. Thus, ecosystemic synergy is driven toward a diversity-productivity ‘ceiling’, imposed by functional redundancy, which we may well define as homeostasis of the common good.

This last point exposes what I believe to be a fundamental sociophysical phenomenon of critical importance to the understanding of common goods and of sustainable development; natural limits are imposed upon all complex systems. Interestingly, if shade is viewed as a phenomenon emerging from the metabolic activities of plant growth, and that shade produced by these conditions drives speciation, then we may rightly consider shade to be a limited common good.

Trogisch (2012), has focused upon processes occurring below the forest floor, specifically the states of nitrogen and leaf litter decomposition in soil samples from a subtropical forest. He has suggested that primary productivity and nutrient cycling be considered common goods, and has confirmed a consensus regarding the reduced vulnerability of diversified ecosystems to environmental stress. Furthermore, he has proposed functional redundancy among diverse species as a systemic stabilizer, allowing ecosystem functions and services to remain unchanged, or less affected, after environmental perturbation.(38)

“Forests account for 80% of the world’s plant biomass and are therefore a main driver and component of the Earth’s biogeochemical cycles. Their versatile services such as climate regulation and protection of soil resources, denotes them as one of the most important terrestrial ecosystems for human wellbeing.” Indeed one may justly argue that forest ecosystems are common goods that propagate wellbeing for a vast, uncounted, number of species.

A most remarkable passage in Trogisch’s thesis teaches that “decomposition dynamics in mixed leaf litter often show non-additive effects so that [nitrogen] is released at a faster rate than predicted from decomposition rates of corresponding single-species leaf litter. Such litter diversity effects during decomposition can lead to a feedback reaction positively influencing plant productivity”. Thus, species diversity affects irrational, non-computable, synergistic processes, that act to increase and stabilize the common good.

Jacobs knowledge spillover: relating the DPR with the common good in an economic context
Jane Jacobs questioned why some cities grow and others decay. Her theory of agricultural origin, published in 1969, proposed that agricultural knowledge and practical technologies emerged from a diversified human collective. Jacobs concluded that “high and sustained levels of innovative behavior and entrepreneurship inevitably result in the increased diversification and complexity of the local economic base over time and that a diversified urban economy provides the best setting for entrepreneurial and innovative behavior”. Thus, increases in the number and diversity of divisions of labor endow an economy with an increased capacity for production of goods and services.(40)

Reviewing Jacobs, Desrochers & Hospers (2007) list four characteristics of economic systems(39) that are also common to biological systems:
1) Development is dependent upon the self-organization of numerous and various complex relationships, from which differentiations emerge, giving rise to an organ from which further differentiations emerge.

2) Expansion (growth) is dependent upon the capture and use of energy. The greater the diversity of means for capturing, using, recapturing, and reusing energy before its discharge from the system, the more resilient the system is.

3) Self-maintenance (constitutive self-regulation) is an intrinsic systemic process, incorporating positive and negative feedback, along with aspects of development and growth.

4) Evasion of systemic collapse incorporates self-maintenance, bifurcation, positive and negative feedback, and emergency adaptations, together helping to ensure systemic longevity. However, entropic effects are certain to impact upon any system, as a gradual increase of disorder (disorganization) in internal (systemic) and external (environmental) structures.

The similarities between ecology and economy in regard to the relationship between diversity and productivity are striking. Critically however, the economic literature ignores, or fails to identify, the presence of natural limits to productivity imposed by a diversity plateau; a functional redundancy among local actors. Building upon Desrochers & Hospers (2007), I propose that the emphasis of economics in modern culture has switched from natural diversity and complexity to artificial specialty and simplicity; from a natural stable-state driven by dynamism, to an unnatural unstable-state propagated by statism; from divergent inefficient creativity, to convergent efficient monotony.

As seems to be the case with all researches attempting to relate diversity and productivity, Desrochers & Leppala have admitted that quantification of frequency and relative importance of Jacobs spillovers (diversity index of knowledge sharing) could not be measured satisfactorily, commenting that “simply because something is immeasurable does not mean that it is necessarily unobservable, unintelligible or unimportant.”(40)

The synergistic function of complex systems identified here as the Jacobs spillover and the DPR is reminiscent of the messy workspace phenomenon – in which the current project(s), may ‘shake hands’ with past works and even future hopefuls, allowing for greater capacities of creative problem solving, insight, adaptation and innovation. Vohs et al (2013), have reported that “disorderly environments […] can produce highly desirable outcomes, […] encourage novelty-seeking and unconventional routes, [thus stimulating] creativity, which has widespread importance for culture, business, and the arts.”(41) Strangely, and rather irrationally, Vohs et al have omitted the sciences in their list of beneficiaries, thus apparently denying scientific pursuits the privilege of “disorderly environments”.

In 1945, the economist and Nobel laureate Friedrich Hayek suggested that “any approach, such as that of mathematical economics with its simultaneous equations, which in effect starts from the assumption that people’s knowledge corresponds with the objective facts of the situation, systematically leaves out what is our main task to explain.” He believed that “objective or scientific knowledge is not the sum of all knowledge”, that there are other unorganized kinds of knowledge. Critical of economic theory, Hayek proposed that, in reality, no one has perfect information, only the capacity and skill to find information.(42) Thus the reality of economics is not, as commonly held by economists, a pure logic of choice, but rather “knowledge relevant to actions and plans”.(40)

“Unfortunately for mathematical economists, this kind of knowledge [relevant to actions and plans] cannot enter into statistics: it is mostly subjective”.(40)
– Friedrich Hayek, 1945

“There is something deadening to the human mind in uniformity; progress comes through variation.”(40)
– Malcom Keir, 1919

Desrochers & Leppala (2011) describe an essential aspect of creativity (divergent thinking) as “the capacity to look beyond the normal application context of artifacts and ideas”. Creative, inventive and innovative progress, leading to increases in diversity, knowledge and productivity, is facilitated by opportunities for specialists to explore areas in which they are not experts, and to work on several different projects simultaneously, by means of a variety of familiar and unfamiliar methods. This pair of practical concepts is the path to polymathy. Unsurprising then, that polymaths are viewed by history as individuals who have produced the greatest common good – in the sense that they have given, most often at no cost, greatly useful intellectual gifts to humankind.

Common uncertainty: the diversity index
In a meta-analysis of global economic development, aimed at drawing generic conclusions for all countries with available data, Kaulich (2012), echoes the concerns of Farhauer & Kröl (2012), Bouter (2010), Zhang et al (2012), and Desrochers & Leppala (2011), reporting that “different and sometimes conflicting definitions and measurements of diversification/specialization have been used, together with different datasets”.

The economies of all countries are based upon agriculture, with the successful export of agricultural goods allowing for diversification away from primary production, via the manufacture of initially simple products, leading to increasingly sophisticated activities. Diversification, claims Kaulich, is intrinsic to, and is the driving force of economic development.

Kaulich has also found a positive relationship, specifically between the diversity of products exported by an economy and its per capita level of income.(46) At “quite a high level of income per capita” (~ $22,000 / year) economic diversification of the average country slows down, lead by the manufacturing sector toward a plateau. Thus, as a country transitions from a developing to a developed economy, it simultaneously encounters a diversity ‘ceiling’, which limits its economic growth. This pattern is very similar to the ecological DPR, in which productivity is driven toward a diversity ‘plateau’ imposed by functional redundancies among species cohabiting an ecosystem. Is it fair, then, to speak of an economic diversity-income relationship, and of economic homeostasis?

“A country’s economic growth may be defined as a long-term rise in capacity to supply increasingly diverse economic goods to its population.”(43)
– Kuznets, 1971

“Whatever it is that serves as the driving force of economic development, it cannot be the forces of comparative advantage as conventionally understood. The trick seems to be to acquire mastery over a broader range of activities, instead of concentrating on what one does best.”(44)
– Rodrik (2004)

“The common notion to specialize in “what one does best” as a means to achieve economic prosperity and hence poverty reduction seems to be fundamentally wrong.”(45)
– Kaulich, 2012

Kaulich cites an earlier report, UNIDO (2009), suggesting that re-specialization may occur at the high-income end of economic development. This affords a diplomatic position within the diversity vs. specialization debate, which Kaulich makes masterful use of, posing that economic theories arguing exclusively for or against economic specialization appear contradictory, but may both be correct, albeit identifiable at differing points in the economic development of a country. However, his own analysis of global trade data does not conclusively show a U-curve, suggestive of a decrease in economic diversification at the high-income end in combination with continued increase of income. Instead, Kaulich has confidently reported an L-curve.
Diversification_curve
Sketch graph showing economic diversification increasing with product sophistication and income per capita, leading to a diversity-income plateau.
– adapted from UNIDO (2012)

In stating that “successful policies for economic diversification cannot consist of a top-down process with a static set of rules for the private sector”, the UNIDO working paper clearly advocates a policy admissive of complexity; reliant upon self-regulation, and based upon bottom-up self-organization of diverse actors.

Discussion:
The use of various diversity indices in empirical studies of ecologies and of economies, has produced a pattern among observations. A generally positive relationship is identified between quantitative measures of diversity and productivity, leading to a plateau at the high diversity end of abundance and evenness.

One must ask: is the observed limit a physical, entropic, phenomenon, or an artifact of the diversity index? Irrationally, I prefer the former, and suggest that various independent empirical studies have collectively identified an apparent homeostatic epiphenomenon of sociophysical dynamism; steady-state animism on a macro scale, perhaps even a planetary scale. A common-good-state-of-nature.

It should be appreciated that the terms ‘synergy’, ‘epiphenomenon’ and ‘sociophysics’ sit rather uncomfortably within the envelope of science, because their meanings act as signposts toward an understanding of metaphysics. Perhaps Rosen intuited correctly that relational studies of living systems may produce new knowledge of physics and result in profound changes for science?

At the very least, scientific understandings of economics and politics appear to be fundamentally incorrect, requiring revisions permitting the inclusion of non-computable phenomena, emerging from interactions between diverse actors to produce common goods.

Notes:
F) Hypothesis:
i) Universally, the collective efficiency of a diverse set of actors is greater than that of a specialized set of actors.
η(ΣAd > ΣAs) → U

ii) Locally, the collective efficiency of a specialized set of actors is greater than that of a diverse set of actors.
η(ΣAs > ΣAd) → L

Where U is universal (i.e. global) effect, L is local effect, η is efficiency, Σ is sum (collective), Ad is diverse actor, As is specialized actor.

Hypothetical predictions:
A diverse set of actors is a necessary prerequisite for the emergence of specialized actors.
A diverse set of actors is a necessary prerequisite for the emergence of common goods.

G) Progenotes are defined as organic elements comprising the communal ancestor, identified in the lineages now assumed as the phylogenetic ‘tree of life’.

Bibliography:
28) O. Farhauer & A. Kröl, “Diversified Specialisation – Going One Step Beyond Regional Economics” Specialisation-Diversification Concept”, (2012), JAHRBUCH FÜR REGIONALWISSENSCHAFT, Vol.32, Number 1, p.63-84, http://www.uni-passau.de/fileadmin/dokumente/fakultaet/wiwi/VWL/Agglo-Text_120110_Homepage.pdf

29) “The collapse of manufacturing”, (February, 2009), The Economist, http://www.economist.com/node/13144864

30) J. Whitfield, “Origins of life: Born in a watery commune”, (2004), Nature Vol. 427, p. 674-676, abstract: http://www.nature.com/nature/journal/v427/n6976/full/427674a.html

31) C. Woese, “The Universal Ancestor”, (1998), Proceedings of the National Academy of Sciences of the USA, 95(12): 6854–6859, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC22660/

32) N. Glansdorff, Y. Xu & B. Labendan, “The Last Universal Common Ancestor: emergence, constitution and genetic legacy of an elusive forerunner”, (2008), Biology Direct, http://www.biologydirect.com/content/3/1/29

33) J. Gestel, H. Vlamakis, R. Kolter, “From Cell Differentiation to Cell Collectives: Bacillus subtilis Uses Division of Labor to Migrate”, (2015), PLOS Biology, http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002141

34) L. Bouter, “Knowledge as a common good: the societal relevance of scientific research”, (2010), Higher Education Management and Policy, Vol. 22/1, http://www.keepeek.com/Digital-Asset-Management/oecd/education/knowledge-as-a-common-good_hemp-v22-art8-en#page1

35) J. van Ruijven and F. Berendse, “Diversity-productivity relationships: Initial effects, long-term patterns, and underlying mechanisms”, (2004), Vol. 102.3, PNAS, abstract http://www.pnas.org/content/102/3/695.abstract

36) H. Hillebrand and B. Cardinale, “A critique for meta-analyses and the productivity-diversity relationship”, (2010), Ecology, Vol. 91.9, p. 2545-2549, http://snre.umich.edu/cardinale/wp-content/uploads/2013/02/Hillebrand_Cardinale_Ecology_2010.pdf

37) Y. Zhang, H. Chen, P.Reich, “Forest productivity increases with evenness, species richness and trait variation: a global meta-analysis”, (2012), Journal of Ecology, Vol.100, p.742–749, http://forestecology.cfans.umn.edu/prod/groups/cfans/@pub/@cfans/@forestecology/documents/article/forestproductivityincreases.pdf

38) S. Trogisch, “The functional significance of tree diversity for soil N-pools, leaf litter decomposition and N-uptake complementarity in subtropical forests in China”, (2012), ETH ZURICH, http://e-collection.library.ethz.ch/eserv/eth:6313/eth-6313-02.pdf

39) P. Desrochers & S. Leppala, “Opening up the ‘Jacobs Spillovers’ black box: local diversity, creativity and the processes underlying new combinations”, (2011), Journal of Economic Geography, Vol 11, p. 843–863, abstract only http://joeg.oxfordjournals.org/content/11/5/843

40) P. Desrochers and G-J. Hospers, “Cities and the Economic Development of Nations: An Essay on Jane Jacobs’ Contribution to Economic Theory”, (2007), Canadian Journal of Regional Science, Vol. 3(1), p. 115-130, http://geog.utm.utoronto.ca/desrochers/CJRS_Jacobs.pdf

41) K. Vohs et al, “Physical Order Produces Healthy Choices, Generosity, and Conventionality, Whereas Disorder Produces Creativity”, (2013), Psychological Science Vol 24(9), p. 1860–1867, abstract http://pss.sagepub.com/content/early/2013/08/01/0956797613480186.abstract

42) B. Godin, “The Knowledge Economy: Fritz Machlup’s Construction of a Synthetic Concept”, (2008), http://www.csiic.ca/pdf/godin_37.pdf

43) S. Kuznets, “Modern Economic Growth: Findings and Reflections. Prize Lecture”, (1971), Lecture to the memory of Alfred Nobel, http://www.nobelprize.org/nobel_prizes/economic-sciences/laureates/1971/kuznets-lecture.html

44) D. Rodrik, “Industrial Policy for the Twenty-First Century”, (2004), Harvard University, https://www.sss.ias.edu/files/pdfs/Rodrik/Research/industrial-policy-twenty-first-century.pdf

45) F. Kaulich, “Diversification vs. specialization as alternative strategies for economic development: Can we settle a debate by looking at the empirical evidence?”, (2012), Development Policy, Statistics and Research Branch, UNIDO, http://www.unido.org//fileadmin/user_media/Publications/Research_and_statistics/Branch_publications/Research_and_Policy/Files/Working_Papers/2012/WP032012_Ebook.pdf

iconoclast

This is the third of three posts, exploring the connections between cultural morality, sustainable development, and iconoclasm.

Clasm_Chludov_detail_9th_century
Iconoclast from Byzantine Greek εἰκονοκλάστης (literally “image breaker”). Iconoclasm is the deliberate destruction within a culture of the culture’s own religious icons and other symbols or monuments, usually for religious or political motives.1

”A major reorientation is needed in many policies and institutional arrangements at the international as well as national level. The time has come to break away […] to break out of past patterns. Attempts to maintain social and ecological stability through old approaches to development and environmental protection will increase instability. Security must be sought through change.”
– the Brundtland report (introduced earlier in this series, in “Moral Deference of Sustainability“).

The same sentiment was expressed beautifully, albeit more concisely, by Albert Einstein:
We can’t solve problems by using the same kind of thinking we used when we created them.

The Periplanetans
A couple of decades ago, upon returning to Vancouver from travels abroad, my brother had invited me to stay with a small group who had squatted the upper floor of a Chinese supermarket, in Chinatown, during winter. There was no heating. The rooms, which may have been meant for use as storage areas or offices, were already inhabited by squatters; I had set up my tent to one side of the ‘common area’ (display space) over a well used polyurethane foam mattress core and folded cardboard boxes. This arrangement acted well to insulate me from the cold and from other curious scavengers.

The squat had two microwave ovens, standing side by side in the ‘kitchen’. The machines were being made use of by two squatter species; us (Homo sapiens) and them (Periplaneta americana). Preiplanetans were regularly observed perambulating the upper reaches of both microwave oven cavities. Interestingly, they were never observed on the the lower walls or floor/platter. Due partly to the gloomy ambient condition of the ‘kitchen’, and the fact that no observations were performed while the oven was not in use by a human, insects were observed only while the oven was operating, and thus the cavity was lit by it’s own internal light source. It was fascinating to discover that the Preiplanetan colony had made use of warm spots, on the rear exterior and within the ventilation channels of each machine, as ‘preschool’ redundancies. That is to say the colony was making use of physically protected and heated areas of the machines, as incubators for the ootheca and nymph stages of the Preiplanetan life cycle.

microwave_edit_1 field_distribution
Schematic diagram of microwave oven, showing upper corner areas (highlighted in green) joining the walls and ceiling, in which Periplanetans were observed. Computer model of a 2D slice, of a 3D electric field distribution inside a microwave oven cavity2. Note the relatively low field density (low power potential) in corner regions.

“Scientists are discovering that […] cockroaches are actually highly social creatures; they recognise members of their own families, with different generations of the same families living together. Cockroaches do not like to be left alone, and suffer ill health when they are. And they form closely bonded, egalitarian societies, based on social structures and rules. Communities of cockroaches are even capable of making collective decisions for the greater good. By studying certain species of cockroach, we may even be able to learn some insights into how more advanced animal societies evolved, including our own.”3

Two cockroach species (Blattella germanica and Periplaneta americana) that have adapted to human habitats, have become model species for sociobiological studies revealing complex systems of social organization via various forms of communication and group dynamics. Though closely related to termites, roaches are not eusocial, they are described as gregarious and egalitarian. “[Gregarious] species present yet another form of sociality where individuals of all developmental stages and from various genetic lineages co-exist in open and more or less fluid (yet integrated) aggregates […] characterised by a common shelter, overlapping generations, non-closure of groups, equal reproductive potential of group members, an absence of task specialisation, high levels of social dependence, central place foraging, social information transfer, kin recognition, [sophisticated communication and emergent forms of cooperation], and a meta-population structure.”4
meetingmetapopulation
A metapopulation is a group of subpopulations.5
– note that this image was sourced from a computer science document describing computer models of knowledge ecosystems as part of adaptive management. The image served as an analogy: “local meetings […] produce information for higher level processes”. Also of interest, is that the source document was authored by a person whose principal area of research is defined as:
“the dynamics and impact of pests and diseases, particularly the effects of climate and weather.”
The research approach is described as:
“multi-disciplinary, multi-agency knowledge-based systems and decision support systems, [including] the use of Artificial Intelligence […], customized document generation, and internet-based system deployment.”

It is surprisingly easy to overlook the significance of what we have encountered here; a convergence of insect population dynamics, human population dynamics, and information population dynamics. However, as we shall see in the following section, population dynamics need not be computable (rational) in order to be effective realities.

Personally, I would add that this is the kind of thing I felt in April of last year, when in the Preamble to “Refraction of the State of Nature“, I wrote:
“As I continue to explore, now reaching for novel connections as well as topics of exploration, I am beginning to catch fleeting glimpses of a unity. Not a Grand Unified Theory, nor the Theory of everything sought by theoretical physicists, but a more modest unity of the handful of ideas explored in these pages, to date.”

Synergy versus Stigmergy
Synergy is widely misunderstood as a synonym for ‘mystical activity’. Literally, the term refers to cooperativity, and is derived from Ancient Greek σύν (sún “together”) and ἔργον (érgon “work”)6. However, synergy implies an outcome of cooperation that is in principle unpredictable from the action of the cooperating agents. Thus it seems fair to assume that the term represents ‘irrational (non-computable) emergent phenomena’, also known as strong emergence7, and ‘a whole greater than the sum of its parts’.

Stigmergy may be interpreted as a rationalized form of synergy, and may be described as a self-organizing and self-regulating process (i.e. an operational mode) mediated by indirect cooperation between multiple agents8, which collectively give rise to qualitative meta-phenomena not attainable by the individual agents. We had touched upon this concept in an earlier post, titled Governance, under the heading “How does nature govern her systems?”.

Inefficient use of energy feeds the world
“Sunlight plays a much larger role in our sustenance than we may expect: all the food we eat and all the fossil fuel we use is a product of photosynthesis, which is the process that converts energy in sunlight to chemical forms of energy that can be used by biological systems. Photosynthesis is carried out by many different organisms, ranging from plants to bacteria. The best known form of photosynthesis is the one carried out by higher plants and algae, as well as by cyanobacteria and their relatives, which are responsible for a major part of photosynthesis […].”9
algae-cells
Algal cells

Fascinatingly, even though photosynthesis produces all the food we eat and all the fossil fuel we use, it is a remarkably inefficient process:
– “[The] theoretical maximum efficiency of solar energy conversion is approximately 11%. In practice, however, the magnitude of photosynthetic efficiency observed in the field, is further decreased by factors such as poor absorption of sunlight due to its reflection, respiration requirements of photosynthesis and the need for optimal solar radiation levels. The net result being an overall photosynthetic efficiency of between 3 and 6% of total solar radiation.”10

– “[The maximum conversion efficiency of solar energy to biomass ranges from 4.6% to 6%, at 30 degrees C and today’s 380 ppm atmospheric CO2.]”11

– “Due to losses at all steps in biochemistry, one has been able to get only about 1 to 2% energy efficiency in most crop plants. Sugarcane is an exception as it can have almost 8% efficiency. However, many plants in Nature often have only 0.1 % energy efficiency.”12

In contrast to ancient photosynthetic cells, state of the art photovoltaic cells approach 45% energy efficiency, but they do not self-organize, adapt, or reproduce. Certainly there seems little hope of them feeding the world.
PVeff
Timeline of solar cell energy conversion efficiencies13

Importantly, the reader is not to assume that the meaning here is entirely antagonistic toward photovoltaic technology. Simply, a trend of increasing energetic efficiency, like increasing gross domestic product, is extremely unlikely to solve any of our existential (environmental) problems. Rather, an entirely different set of thoughts – an entirely different worldview – is necessary. It is precisely at this point in our train of thought that iconoclasm becomes of critical importance.

A networked collective of inefficient nodes, even if cooperation between them is indirect and discontinuous, can produce vastly more efficient outcomes than is predictable from efficient operations at the level of individual agents.
It does not always compute, may be irrational and thus immeasurable. It flows naturally among and between us; among and between all living things. It flows from Sun to Earth, through water and rock, emerging spontaneously as life … as the Higgs field … as the Aether.

It is not, and can never be ‘good science’. The Church of Reason will have great difficulty defining it, though our great House of Arte has always imagined it clearly, and continues to tap it regularly.

In our modern culture the image of irrational faith over rational knowledge (see image below) is surely the most difficult icon to break. If you do manage this feat of iconoclasm, then you will see clearly that irrationality is the denominator, and rationality the numerator.
Q
Only a fraction of everything imaginable is knowable.

Bibliography and Notes
1) http://en.wikipedia.org/wiki/Iconoclasm

2) T. Santos etal, “3D Electromagnetic Field Simulation in Microwave Ovens: a Tool
to Control Thermal Runaway”, (2010), COSMOL Conference (excerpt), http://www.comsol.com/paper/download/63024/santos_paper.pdf

3) M. Walker, “Why cockroaches need their friends”, (2012), BBC Nature, http://www.bbc.co.uk/nature/17839642

4) M. Lihoreau et al, “The social biology of domiciliary cockroaches: colony structure, kin recognition and collective decisions”, (2012), International Union for the Study of Social Insects, http://link.springer.com/article/10.1007%2Fs00040-012-0234-x (abstract only)

5) A. Thomson, “Knowledge Ecosystems”, (2012), Adaptive Knowledge Management, http://adaptivekm.com/ke_more.html

6) http://en.wiktionary.org/wiki/synergy

7) http://en.wikipedia.org/wiki/Emergence#Strong_and_weak_emergence

8) http://en.wiktionary.org/wiki/stigmergy

9) W. Vermaas, “An Introduction to Photosynthesis and Its Applications”, (2007), Center for Bioenergy & Photosynthesis, Arizona State University, http://photoscience.la.asu.edu/photosyn/education/photointro.html

10) K. Miyamoto et al, “Renewable biological systems for alternative sustainable energy production”, (1997), Food and Agriculture Organization of the United Nations, http://www.fao.org/docrep/w7241e/w7241e05.htm

11) Zhu et al, “What is the maximum efficiency with which photosynthesis can convert solar energy into biomass?”, (2008), Department of Plant Biology, University of Illinois, http://www.ncbi.nlm.nih.gov/pubmed/18374559

12) Govindjee & Govindjee, “What is Photosynthesis?”, (ca. 2000), School of Life Sciences, University of Illinois, http://www.life.illinois.edu/govindjee/whatisit.htm

13) G. Wilson & K. Emery, “Best Research-Cell Efficiencies”, (2014), National Renewable Energy Laboratory (NREL), Golden, CO, http://en.wikipedia.org/wiki/Solar_cell#mediaviewer/File:PVeff%28rev140511%29.jpg