What logical term or concept ought to be more widely known?

Various What scientific term or concept ought to be more widely known? Edge, 2017.

INTRODUCTION: SCIENTIA

Science—that is, reliable methods for obtaining knowledge—is an essential part of psychology and the social sciences, especially economics, geography, history, and political science. …

Science is nothing more nor less than the most reliable way of gaining knowledge about anything, whether it be the human spirit, the role of great figures in history, or the structure of DNA.

Contributions

As against others on:

(This is as far as I’ve got.)

Comment

I’ve grouped the contributions according to whether or not I think they give due weight to the notion of uncertainty as expressed in my blog. Interestingly Steven Pinker seems not to give due weight in his article, whereas he is credited by Nicholas G. Carr with some profound insights (in the first of the second batch). So maybe I am not reading them right.

My own suggestion would be Turing’s theory of ‘Morphogenesis’. The particular predictions seem to have been confirmed ‘scientifically’, but it is essentially a logical / mathematical theory. If, as the introduction suggests, science is “reliable methods for obtaining knowledge” then it seems to me that logic and mathematics are more reliable than empirical methods, and deserve some special recognition. Although, I must concede that it may be hard to tell logic from pseudo-logic, and that unless you can do so my distinction is potentially dangerous.

Morphogenesis

The second law of thermodynamics, and much common sense rationality,  assumes a situation in which the law of large numbers applies. But Turing adds to the second law’s notion of random dissipation a notion of relative structuring (as in gravity) to show that ‘critical instabilities’ are inevitable. These are inconsistent with the law of large numbers, so the assumptions of the second law of thermodynamics (and much else) cannot be true. The universe cannot be ‘closed’ in its sense.

Implications

If the assumptions of the second law seem to leave no room for free will and hence no reason to believe in our agency and hence no point in any of the contributions to Edge: they are what they are and we do what we do. But Pinker does not go so far: he simply notes that if things inevitably degrade we do not need to beat ourselves up, or look for scape-goats when things go wrong. But this can be true even if the second law does not apply. If we take Turing seriously then a seeming permanent status quo can contain the reasons for its own destruction, so that turning a blind eye and doing nothing can mean sleep-walking to disaster. Where Pinker concludes:

[An] underappreciation of the Second Law lures people into seeing every unsolved social problem as a sign that their country is being driven off a cliff. It’s in the very nature of the universe that life has problems. But it’s better to figure out how to solve them—to apply information and energy to expand our refuge of beneficial order—than to start a conflagration and hope for the best.

This would seem to follow more clearly from the theory of morphogenesis than the second law. Turing’s theory also goes some way to suggesting or even explaining the items in the second batch. So, I commend it.

Dave Marsay

 

 

Evolution of Pragmatism?

A common ‘pragmatic’ approach is to keep doing what you normally do until you hit a snag, and (only) then to reconsider. Whereas Lamarckian evolution would lead to the ‘survival of the fittest’, with everyone adapting to the current niche, tending to yield a homogenous population, Darwinian evolution has survival of the maximal variety of all those who can survive, with characteristics only dying out when they are not viable. This evolution of diversity makes for greater resilience, which is maybe why ‘pragmatic’ Darwinian evolution has evolved.

The products of evolution are generally also pragmatic, in that they have virtually pre-programmed behaviours which ‘unfold’ in the environment. Plants grow and procreate, while animals have a richer variety of behaviours, but still tend just to do what they do. But humans can ‘think for themselves’ and be ‘creative’, and so have the possibility of not being just pragmatic.

I was at a (very good) lecture by Alice Roberts last night on the evolution of technology. She noted that many creatures use tools, but humans seem to be unique in that at some critical population mass the manufacture and use of tools becomes sustained through teaching, copying and co-operation. It occurred to me that much of this could be pragmatic. After all, until recently development has been very slow, and so may well have been driven by specific practical problems rather than continual searching for improvements. Also, the more recent upswing of innovation seems to have been associated with an increased mixing of cultures and decreased intolerance for people who think for themselves.

In biological evolution mutations can lead to innovation, so evolution is not entirely pragmatic, but their impact is normally limited by the need to fit the current niche, so evolution typically appears to be pragmatic. The role of mutations is more to increase the diversity of behaviours within the niche, rather than innovation as such.

In social evolution there will probably always have been mavericks and misfits, but the social pressure has been towards conformity. I conjecture that such an environment has favoured a habit of pragmatism. These days, it seems to me, a better approach would be more open-minded, inclusive and exploratory, but possibly we do have a biologically-conditioned tendency to be overly pragmatic: to confuse conventions for facts and  heuristics for laws of nature, and not to challenge widely-held beliefs.

The financial crash of 2008 was blamed by some on mathematics. This seems ridiculous. But the post Cold War world was largely one of growth with the threat of nuclear devastation much diminished, so it might be expected that pragmatism would be favoured. Thus powerful tools (mathematical or otherwise) could be taken up and exploited pragmatically, without enough consideration of the potential dangers. It seems to me that this problem is much broader than economics, but I wonder what the cure is, apart from better education and more enlightened public debate?

Dave Marsay

 

 

Reasoning and natural selection

Cosmides, L. & Tooby, J. (1991). Reasoning and natural selection. Encyclopedia of Human Biology, vol. 6. San Diego: Academic Press

Summary

Argues that logical reasoning, by which it seems to mean classical induction and symbolic reasoning, are not favoured by evolution. Instead one has reasoning particular to the social context. It argues that in typical situations it is either not possible or not practical to consider ‘all hypotheses’, and that the generation of hypotheses to consider is problematic. It argues that this is typically done using implicit specific theories. Has a discussion of the ‘green and blue cabs’ example.

Comment

 In real situations one can assume induction and lacks the ‘facts’ to be able to perform symbolic reasoning. Logically, then, empirical reasoning would seem more suitable. Keynes, for example, considers the impact of not being able to consider ‘all hypotheses’.

While the case against classically rationality seems sound, the argument leaves the way open for an alternative rationality, e.g. based on Whitehead and Keynes.

See Also

Later work

Better than rational, uncertainty aversion.

Other

Reasoning, mathematics.

Dave Marsay

Out of Control

Kevin Kelly’s ‘Out of Control‘ (1994) sub-titled “The New Biology of Machines, Social Systems, and the Economic World” gives ‘the nine laws of god’which it commends for all future systems, including organisations and economies. They didn’t work out too well in 2008.

The claims

The book is introduced (above) by:

“Out of Control is a summary of what we know about self-sustaining systems, both living ones such as a tropical wetland, or an artificial one, such as a computer simulation of our planet. The last chapter of the book, “The Nine Laws of God,” is a distillation of the nine common principles that all life-like systems share. The major themes of the book are:

  • As we make our machines and institutions more complex, we have to make them more biological in order to manage them.
  • The most potent force in technology will be artificial evolution. We are already evolving software and drugs … .
  • Organic life is the ultimate technology, and all technology will improve towards biology.
  • The main thing computers are good for is creating little worlds so that we can try out the Great Questions. …
  • As we shape technology, it shapes us. We are connecting everything to everything, and so our entire culture is migrating to a “network culture” and a new network economics.

In order to harvest the power of organic machines, we have to instill in them guidelines and self-governance, and relinquish some of our total control.”

Holism

Much of the book is Holistic in nature, The above could be read as applying the ideas of Smuts’ Holism to newer technologies. (Chapter 19 does make explicit reference to JC Smuts in connection with internal selection, but doesn’t reference his work.)

Jan Smuts based his work on wide experience, including with improving arms production in the Great War, and went on to found ecology and help modernise the sciences, thus leading to the views that Kelly picks up on. Superficially, Kelly’s book is greatly concerned with technology that ante-dates Smuts, but his arguments claim to be quite general, so an apostle of Smuts would expect Kelly to be consist, but applying the ideas to the new realm. But where does Kelly depart from Smuts, and what new insights does he bring? Below we pick out Kelly’s key texts and compare them.

The nine Laws of God

The laws with my italics are:

Distribute being

When the sum of the parts can add up to more than the parts, then that extra being … is distributed among the parts. Whenever we find something from nothing, we find it arising from a field of many interacting smaller pieces. All the mysteries we find most interesting — life, intelligence, evolution — are found in the soil of large distributed systems.

The first phrase is clearly Holistic, and perhaps consistent with Smuts’ view that the ‘extra’ arises from the ‘field of interactions’. However in many current technologies the ‘pieces’ are very hard-edged, with limited ‘mutual interaction’. 

Control from the bottom up

When everything is connected to everything in a distributed network … overall governance must arise from the most humble interdependent acts done locally in parallel, and not from a central command. …

The phrases ‘bottom up’ and ‘humble interdependent acts’ seem inconsistent with Smuts’ own behaviour, for example in taking the ‘go’ decision for D-day. Generally, Kelly seems to ignore or deny the need for different operational levels, as in the military’s tactical and strategic.

Cultivate increasing returns

Each time you use an idea, a language, or a skill you strengthen it, reinforce it, and make it more likely to be used again. … Success breeds success. In the Gospels, this principle of social dynamics is known as “To those who have, more will be given.” Anything which alters its environment to increase production of itself is playing the game … And all large, sustaining systems play the game … in economics, biology, computer science, and human psychology. …

Smuts seems to have been the first to recognize that one could inherit a tendency to have more of something (such as height) than your parents, so that a succesful tendency (such as being tall) would be reinforced. The difference between Kelly and Smuts is that Kelly has a general rule whereas Smuts has it as a product of evolution for each attribute. Kelly’s version also needs to be balanced against not optimising (below).

Grow by chunking

The only way to make a complex system that works is to begin with a simple system that works. Attempts to instantly install highly complex organization — such as intelligence or a market economy — without growing it, inevitably lead to failure. … Time is needed to let each part test itself against all the others. Complexity is created, then, by assembling it incrementally from simple modules that can operate independently.

Kelly is uncomfortable with the term ‘complex’. In Smuts’ usage a military platoon attack is often ‘complex’, whereas a superior headquarters could be simple. Systems with humans in naturally tend to be complex (as Kelly describes) and are only made simple by prescriptive rules and procedures. In many settings such process-driven systems would (as Kelly describes them) be quite fragile, and unable to operate independently in a demanding environment (e.g., one with a thinking adversary). Thus I suppose that Kelly is advocating starting with small but adaptable systems and growing them. This is desirable, but often Smuts did not have that luxury, and had to re-engineer systems such as production or fighting systems, ‘on the fly’

Maximize the fringes

… A uniform entity must adapt to the world by occasional earth-shattering revolutions, one of which is sure to kill it. A diverse heterogeneous entity, on the other hand, can adapt to the world in a thousand daily mini revolutions, staying in a state of permanent, but never fatal, churning. Diversity favors remote borders, the outskirts, hidden corners, moments of chaos, and isolated clusters. In economic, ecological, evolutionary, and institutional models, a healthy fringe speeds adaptation, increases resilience, and is almost always the source of innovations.

A large uniform entity cannot adapt and maintain its uniformity, and so is unsustainable in the face of a changing situation or environment. If diversity is allowed then parts can adapt independently, and generally favourable adaptations spread. Moreover, the more diverse an entity is the more it can fill a variety of niches, and the more likely that it will survive some shot. Here Kelly, Smuts and Darwin essentially agree.

Honor your errors

A trick will only work for a while, until everyone else is doing it. To advance from the ordinary requires a new game, or a new territory. But the process of going outside the conventional method, game, or territory is indistinguishable from error. Even the most brilliant act of human genius, in the final analysis, is an act of trial and error. … Error, whether random or deliberate, must become an integral part of any process of creation. Evolution can be thought of as systematic error management.

Here the problem of competition is addressed. Here Kelly supposes that the only viable strategy in the face of complexity is blind trial and error, ‘the no strategy strategy’. But the main thing is to be able to identify actual errors. Smuts might also add that one might learn from near-misses and other potential errors.

Pursue no optima; have multiple goals

 …  a large system can only survive by “satisficing” (making “good enough”) a multitude of functions. For instance, an adaptive system must trade off between exploiting a known path of success (optimizing a current strategy), or diverting resources to exploring new paths (thereby wasting energy trying less efficient methods). …  forget elegance; if it works, it’s beautiful.

Here Kelly confuses ‘a known path of success’ with ‘a current strategy’, which may explain why he is dismissive of strategy. Smuts would say that getting an adequate balance between the exploitation of manifest success and the exploration of alternatives would be a key feature of any strategy. Sometimes it pays not to go after near-term returns, perhaps even accepting a loss.

Seek persistent disequilibrium

Neither constancy nor relentless change will support a creation. A good creation … is persistent disequilibrium — a continuous state of surfing forever on the edge between never stopping but never falling. Homing in on that liquid threshold is the still mysterious holy grail of creation and the quest of all amateur gods.

This is a key insight. The implication is that even the nine laws do not guarantee success. Kelly does not say how the disequilibrium is generated. In many systems it is only generated as part of an eco-system, so that reducing the challenge to a system can lead to its virtual death. A key part of growth (above) is o grow the ability to maintain a healthy disequilibrium despite increasing novel challenges.

Change changes itself

… When extremely large systems are built up out of complicated systems, then each system begins to influence and ultimately change the organizations of other systems. That is, if the rules of the game are composed from the bottom up, then it is likely that interacting forces at the bottom level will alter the rules of the game as it progresses.  Over time, the rules for change get changed themselves. …

It seems that the changes the rules are blindly adaptive. This may be because, unlike Smuts, Kelly does not believe in strategy, or in the power of theory to enlighten.

Kelly’s discussion

These nine principles underpin the awesome workings of prairies, flamingoes, cedar forests, eyeballs, natural selection in geological time, and the unfolding of a baby elephant from a tiny seed of elephant sperm and egg.

These same principles of bio-logic are now being implanted in computer chips, electronic communication networks, robot modules, pharmaceutical searches, software design, and corporate management, in order that these artificial systems may overcome their own complexity.

When the Technos is enlivened by Bios we get artifacts that can adapt, learn, and evolve. …

The intensely biological nature of the coming culture derives from five influences:

    • Despite the increasing technization of our world, organic life — both wild and domesticated — will continue to be the prime infrastructure of human experience on the global scale.
    • Machines will become more biological in character.
    • Technological networks will make human culture even more ecological and evolutionary.
    • Engineered biology and biotechnology will eclipse the importance of mechanical technology.
    • Biological ways will be revered as ideal ways.

 …

As complex as things are today, everything will be more complex tomorrow. The scientists and projects reported here have been concerned with harnessing the laws of design so that order can emerge from chaos, so that organized complexity can be kept from unraveling into unorganized complications, and so that something can be made from nothing.

My discussion

Considering local action only, Kelly’s arguments often come down to the supposed impossibility of effective strategy in the face of complexity, leading to the recommendation of the universal ‘no strategy strategy’: continually adapt to the actual situation, identifying and setting appropriate goals and sub-goals. Superficially, this seems quite restrictive, but we are free as to how we interpret events, learn, set goals and monitor progress and react. There seems to be nothing to prevent us from following a more substantial strategy but describing it in Kelly’s terms.

 The ‘bottom up’ principle seems to be based on the difficulty of central control. But Kelly envisages the use of markets, which can be seen as a ‘no control control’. That is, we are heavily influenced by markets but they have no intention. An alternative would be to allow a range of mechanisms, ideally also without intention; whatever is supported by an appropriate majority (2/3?).

For economics, Kelly’s laws are suggestive of Hayek, whereas Smuts’ approach was shared with his colleague, Keynes. 

Conclusion

What is remarkable about Kelly’s laws is the impotence of the individuals in the face of ‘the system’. It would seem better to allow for ‘central’ (or intermediate) mechanisms to be ‘bottom up’ in the sense that they are supported by an informed ‘bottom’.

See Also

David Marsay

Quantum Evolution

NewScientist No. 2794 8 Jan. 2011, p 28.

This notes the effect of epigenetics on the variability/uncertainty of inheritence, notes the benefits of this for populations in surviving sudden changes in the environment, and speculates (as did Smuts, below) that this could have come about due to natural selection. That is, under natural selection organisms avoid over-adaptation as long as they are subject to harsh enough and frequent enough shocks.

See Also

Smuts’ Holism and Evolution, Peter Allen

David Marsay

Holism and Evolution

Holism and evolution 1927. Smuts’ notoriously inaccessible theory of evolution, building on and show-casing Keynes’ notion of uncertainty. Smuts made significant revisions and additions in later editions to reflect some of the details of the then current understanding. Not all of these now appear to be an improvement. Although Smuts and Whitehead worked independently, they recognized that their theories were equivalent. The book is of most interest for its general approach, rather than its detail. Smuts went on to become the centennial president of the British Association for the Advancement of Science, drawing on these ideas to characterise ‘modern science’.

Holism is a term introduced by Smuts, in contrast to individualism and wholism. In the context of evolution it emphasises co-evolution between parts and wholes, with neither being dominant. The best explanation I have found is:

“Back in the days of those Ancient Greeks, Aristotle (384-322BCE) gave us:

The whole is greater than the sum of its parts; (the composition law)
The part is more than a fraction of the whole. (the decomposition law)

Composition Laws” (From Derek Hitchins’ Systems World.)

Smuts also develops LLoyd Morgan’s concept of emergence,  For example, the evolutionary ‘fitness function’ may emerge from a co-adaptation rather than be fixed.

The book covers evolution from physics to personality. Smuts intended a sequel covering, for example, social and political evolution, but was distracted by the second world war, for example.

Smuts noted that according to the popular view of evolution, one would expect organisms to become more and more adapted to their environmental niches, whereas they were more ‘adapted to adapt’, particularly mankind. There seemed to be inheritance of variability in offspring as whole as the more familiar inheritance of manifest characteristics, which suggested more sudden changes in the environment than had been assumed. This led Smuts to support research into the Wegner hypothesis (concerning continental drift) and the geographic origins of  life-forms. 

See also

Ian Stewart, Peter Allen

David Marsay

Synthetic Modelling of Uncertain Temporal Systems

Overview

SMUTS is a computer-based ‘exploratorium’, to aid the synthetic modelling of uncertain temporal systems. I had previously worked on sense-making systems based on the ideas of Good, Turing and Keynes, and was asked to get involved in a study on the potential impact of any Y2K bugs, starting November 1999. Not having a suitable agreed model, we needed a generic modelling system, able to at least emulate the main features of all the part models. I had been involved in conflict resolution, where avoiding cultural biases and being able to meld different models was often key, and JC Smuts’ Holism and Evolution seemed a sound if hand-wavy approach. SMUTS is essentially a mathematical interpretation of Smuts. I was later able to validate it when I found from the Smuts’ Papers that Whitehead, Smuts and Keynes regarded their work as highly complementary. SMUTS is actually closer to Whitehead than Smuts.

Systems

An actual system is a part of the actual world that is largely self-contained, with inputs and outputs but with no significant external feedback-loops.  It is a judgement about what is significant. Any external feedback loop will typically have some effect, but we may not regard it as significant if we can be sure that any effects will build up too slowly. It is a matter of analysis on larger systems to determine what might be considered smaller systems. Thus plankton are probably not a part of the weather system but may be a pat of the climate.

The term system may also be used for a model of a system, but here we mean an actual system.

Temporal

We are interested in how systems change in time, or ‘evolve’. These systems include all types of evolution, adaptation, learning and desperation, and hence are much broader than the usual ‘mathematical models’.

Uncertain

Keynes’ notion of uncertainty is essentially Knightian uncertainty, but with more mathematical underpinning. It thus extends more familiar notions of probability as ‘just a number’. As Smuts emphasises, systems of interest can display a much richer variety of behaviours than typical probabilistic systems. Keynes has detailed the consequences for economics at length.

Modelling

Pragmatically, one develops a single model which one exploits until it fails. But for complex systems no single model can ever be adequate in the long run, and as Keynes and Smuts emphasised, it could be much better recognize that any conventional model would be uncertain. A key part of the previous sense-making work was the multi-modelling concept of maintaining the broadest range of credible models, with some more precise and others more robust, and then hedging across them, following Keynes et al.

Synthetic

In conflict resolution it may be enough to simply show the different models of the different sides. But equally one may need to synthesize them, to understand the relationships between them and scope for ‘rationalization’. In sense making this is essential to the efficient and effective use of data, otherwise one can have a ‘combinatorial explosion’.

Test cases

To set SMUTS going, it was developed to emulate some familiar test cases.

  • Simple emergence. (From random to a monopoly.)
  • Symbiosis. (Emergence of two mutually supporting behaviours.)
  • Indeterminacy. (Emergence of co-existing behaviours where the proportions are indeterminate.)
  • Turing patterns. (Groups of mutually supporting dynamic behaviours.)
  • Forest fires. (The gold standard in epidemiology, thoroughly researched.)

In addition we had an example to show how the relationships between extremists and moderates were key to urban conflicts.

The aim in all of these was not to be as accurate as the standard methods or to provide predictions, but to demonstrate SMUTS’ usefulness in identifying the key factors and behaviours. 

Viewpoints

A key requirement was to be able to accommodate any relevant measure or sense-making aid, so that users could literally see what effects were consistent from run to run, what weren’t, and how this varied across cases. The initial phase had a range of standard measures, plus Shannon entropy, as a measure of diversity.

Core dynamics

Everything emerged from an interactional model. One specified the extent to which one behaviour would support or inhibit nearby behaviours of various types. By default behaviours were then randomized across an agora and the relationships applied. Behaviours might then change in an attempt to be more supported. The fullest range of variations on this was supported, including a range of update rules, strategies and learning. Wherever possible these were implemented as a continuous range rather than separate cases, and all combinations were allowed.

Illustration

SMUTS enables one to explore complex dynamic systems

SMUTS has a range of facilities for creating, emulating and visualising systems.

By default there are four quadrants. The bottom right illustrates the inter-relationships (e.g., fire inhibits nearby trees, trees support nearby trees). The top right shows the behaviours spread over the agora (in this case ground, trees and fire). The bottom left shows  a time-history of one measure against another, in this case entropy versus value of trees. The top-left allows one to keep an eye on multiple displays, forming an over-arching view. In this example, as in many others, attempting to get maximum value (e.g. by building fire breaks or putting out all fires) leads to a very fragile system which may last a long time but which will completely burn out when it does go. If one allows fires to run their course, one typically gets an equilibrium in which there are frequent small fires which keep the undergrowth down so that there are never any large fires.

Findings

It was generally possible to emulate text-book models to show realistic short-run behaviours of systems. Long term, simpler systems tended to show behaviours like other emulations, and unlike real systems. Introducing some degree of evolution, adaptation or learning all tended to produce markedly more realistic behaviours: the details didn’t matter. Having behaviours that took account of uncertainty and hedged also had a similar effect.

Outcomes

SMUTS had a recognized positive influence, for example on the first fuel crisis, but the main impact has been in validating the ideas of Smuts et al.

Dave Marsay