Workshop on Causal/Influence Networks July 2009

1. Workshop on Causal/Influence Networks July 2009 C.A. Hooker PhD (physics), PhD (phil.) FAHA

2. Autonomy = I/MR synchrony

3. Autonomy is important • demarcation of living systems • Organisation, global constraint (not order) is fundamental • grounding for agency • frames the evolution of intelligence and intentionality,

4. Comparative system order • PropertySystem Kind • GASCRYSTALCELL • Internal bondsNoneRigid, passiveAdaptive, active • Directive ordering*Vweak/s Vstrong/s Mod/Vcomplex • ConstraintsNone LocalGlobal • OrganisationNoneNoneVery high • * Directive ordering is spatio-temporally selective energy flow

5. Autonomous Agents [AAs] AA interrelations are grounded in autonomy, → SDAL: • Self-directed (= feedback-evaluated behavioural adaptation) • Anticipative (= feedforward on evaluation) • Learning (= feedback-evaluated adaptation of self-directedness) AAs are finite, → uncertainty, heuristics, satisficing

6. SDAL Example: detective • Synergy between profile development and investigation method → simultaneously moves itself towards its goal and improves its capacity to move towards its goal. • Solves open problems: ill defined = problem, method, solution criteria [all deep design problems] • Captures science research cycles • E.g. ape language research • Adaptive method, e.g. error treatment • Captures integrated modelling & management method

7. SDAL and scientific niche creation • Key to scientific progress is its capacity for synergistic new multiplexed niche creation. • Cf. Lasers as distance/time measuring, imaging, energy-transferring devices, and impact on sci. instruments, methods & models + economic technologies with $ feedback to sci. • Sci. SDAL: sci. uses its new niches, created from specific problem solutions, to improve its learning capacity. • e.g. observation → context dependence, many weak bonds, idiosyncrasy (curbs current network enthusiasm) Contrast military constraints?

8. Evolution of endogenous regulation • Darwinian model: ‘Transparent phenotype’ Open VSR → regulated VSR • Autonomous Systems Model: Organised Phenotypes

9. The major organisational evolutionary transitions LIFE’S CONSTRAINTS [SUFFER!] • FINITUDE + FALLIBILITY • DISSPATIVENESS + DELICACY LIFE’S SOLUTION [ORGANISE!] • AUTONOMY • ANTICIPATIVENESS • APTNESS • ADAPTIVENESS LIFE’S BELL’S & WHISTLES [ENJOY!] • SOCIALITY • SELF-DIRECTEDNESS • AGENCY • INTELLIGENCE • CULTURE

10. Enabling constraints for adaptiveness • Communal Social • adaptiveness Insects • dominates • Multicellular • Body Cultures • Chimpanzee • Bonobo • Human • Social • Birds • Slime Moulds • Individual • adaptiveness A-social • Dominate Organisms • 0 1 2 • Ratio of usable individual parametric plasticity between isolate and communal states.

11. Culture: technology Technologies are amplifiers Technology as culture = technology as a-cultural: • Objects, methods/tools, possibilities, language common across diverse agents • Each group and agent exploits idiosyncratic possibilities context-dependently Example – computers in markets • Import (tech) ≈ possibilities, agent range, access Tech as kth order culture = tech as < kth order a-cultural • music, fashion as cultural technologies • language as head-altering tool = technology

12. Culture as dynamical • Technologies as dynamical entrainments in a rugged entrainment landscape • Institutions as self-organised emergents: Hayek to Lansing to Shi Modelling • genetic Darwinism: bioevolution :: memes: cultural dynamics [Dawkins: Jablonka/Lamb :: Blackmore: ?] • Rubber sheets & oscillators: shaped/shaping • Agency, idiosyncrasy & coherence limits (e.g. control functions, Woese on gene sharing) Military culture: Centralised →? Technologies?

13. Some proto-cultural dynamical distinctions I SHMO: simple harmonic oscillator. DCC: dynamically collective constraint. Model 1: a set of independent SHMOs. • System state = aggregate of individual states. • No DCCs. All collective phenomena are patterns determined only by initial (or boundary) conditions. • Social example: the distribution of objects in refuse.

14. Some proto-cultural dynamical distinctions II • Model 2: model 1 + small, local pair-wise interactions between SHMOs. • System state = perturbation of model 1 state by addition of local pair-wise corrections. • Weak local DCCs responsible for collective wave-like perturbation propagation. • For increased interaction strength &/or less local interaction, stronger &/or more global DCCs emerge generating further collective phenomena, e.g. entrainment, chaotic behaviour. • Social example: pair-wise reflex interaction behaviour.

15. Some proto-cultural dynamical distinctions III • Model 3: model 2 + interactions modified by SHMO integrative memory. • System state = joint product of SHMO states and interaction states. Memory is some function of past interactions and constrains current interaction form and strength. • Emergence of global DCCs constraining SHMO behaviour in relation to collective properties. • Social example: pre-recording socially referenced behaviours.

16. Some proto-cultural dynamical distinctions IV • Model 4: model 3 + integrative memory referenced to a shared global field. • System state = joint product of SHMO states, interaction states, and field state. Field interacts locally with all SHMOs (realised, e.g., by a rubber sheet to which they are attached or an electromagnetic field which their movements collectively generate). • Emergence of strong global DCCs constraining SHMO behaviour in relation to collective properties based on inherent field dynamics. • Social example: socially recorded referenced behaviours.