self-organised reality brian josephson department of physics university of cambridge
TRANSCRIPT
Self-organised Reality
Brian Josephson
Department of Physics
University of Cambridge
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Main theme
• Classical picture: fixed fundamental law
• This alternative: emergent laws
• Need to think in very different ways to the usual!
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Sources
• Maturana and Varela (autopoiesis)
• Steve Rosen (emergent order, phenomenology)
• Stu Kauffman (self-organisation and life)
• Ilexa Yardley (circular theory)
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Main themes
• Edge of chaos, order-disorder balance
• Life as intermediate level of order: chance, necessity and intelligence
• Cycles, attractors, ambiguity
• Mutual support, observation
• Circular thought, ‘post-explanatory reasoning’
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Edge of chaos
• Deterministic chaos: sensitive dependence on initial conditions, effective unpredictability
• ‘Edge of chaos’: boundary between order and chaos
• Rapid evolution
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Order-disorder interplay
• Order creates order out of disorder
(through ‘intelligent’ observation)
• Disorder introduces noise into order
• Systems form and disintegrate
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Life itself
• Between order and disorder
• Too much order, not alive
• Too much disorder, not alive
• Life shapes itself (autopoiesis)
• Confines itself to a limited range of possibilities
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Restricted range
• Range must be enough to be able to embody resources needed to be able to shape itself under all conditions
• Must act in a precise way, hence requirement for restriction
• Analogy: computer programs
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Cycles
• Cycles are a universal phenomenon associated with restricted range
• Too simple to be considered life, but perhaps essential component of life
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Subsystems and models
• Biosystems contain subsystems whose behaviour can be related to models
• Models show how parts work together
• With complex systems, all models are limited; there is always something outside a given model, possibly ‘intelligent’
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Chance, Necessity and Intelligence
• By chance, parts that can work together come together
• Working together is a necessity for continued existence of the collection
• The fact of continued existence can be deemed a manifestation of intelligence; thus intelligence naturally exists
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Observation and information
• Effective action is the effective use of information based on observation
• Triadic mechanism: one system coordinates the behaviour of two others (example: actions involved in walking)
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Significant interrelations
• These processes lead to systems becoming related to each other in a significant way
• Accumulation of parts leads to complex biosystems
• 1, 2, 3, many; emergent communities
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Dynamic systems perspective
• Model system as point in many-dimensional space
• Rigid system, minimal motion• Chaotic system, roaming widely• Intermediate case: roaming in a restricted region
(e.g. simple or extended cycle, attractor situation)• Forming and disintegrating
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Evolution/development
• Roaming limited by homeostatic shaping mechanisms
• Evolution/development from alternating conservative and active phases
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Conservative and active interplay
• End up with organisations most resistant to destabilisation: destabilisation in the interests of stability
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Joy of Approximation I
• System does not have to fit a specific model to be able to be functional (nature uses empirical approach)
• Advance via approximations
• Evolution finds good, combinable approximations
• Problematic for scientists!
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Joy of Approximation II
• A subsystem works ‘roughly like model X’
• Then something happens and it starts working roughly like modification Xso it goes on
• Life uses approximations but is not bound by them (open system)
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Observation
• Observation = key process: information processing with some meaningful outcome
• Develops to extract useful information
• Two systems can exchange information to assist in acting as one highly integrated system for specified tasks (assisted by 3rd member of a triad?)
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Achievement and potentiality
• Life continues till something works, what does not work gets discarded
• Chance observation of what works in a given context leads to progress
• Reality involves a field of potentiality, things that worked in some context in the past
• Emergence cannot be captured in a formula
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Potentiality and meaning
• The meaning of something is what may come of it, its potential
• Intelligent observer systems can take account of potentiality, thus connecting the real with the unreal
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History, known and ‘dark’
• The past affects present probabilities, in ways we cannot know in detail
• ‘Dark’ history; intelligent design?
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Objective reality
• [speculative] According to C S Peirce, ‘thirdness’, involving knowledge of the relationships between things, is the basis of objective reality
• Hence(?) a group of 3 observers can create something objective, which can be taken up by others
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Objective Space
• By extension, something with the characteristics of a space of a particular kind can be created by a group of observers acting in coordination with each other
• cf. flocking of birds
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What is this all about, anyway?
• Ideas have been extracted from both biology, and physics (order-disorder component) to get something that may be more general
• By making unclarity an integral part of science we may be getting closer to the truth
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Application
• We have abducted principles from biology in order to apply them elsewhere (fundamental physics)
• Characteristic quantum properties such as wholeness, indeterminism, the role of the observer in deciding between alternatives, come out naturally within this approach
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Application to psi
• Intelligent observation systems can take account of meaning and potentiality
• They discover meaningful possibilities, and develop relationships with them so as to help realise them (cf. Schoenberg on musical creativity)
• Space is a constructed phenomenon and action is possible outside it, so not limited by space (observers can be outside space-time)
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Application in conventional physics
• Something to do for string theorists out of a job (?)
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