A little something to kick off the new week …

http://scholar.google.com/scholar?q=emergent+networks&hl=en&lr=&btnG=Search

Meeting with Will, 30-Nov-06 (TC) …

Notes

• with exceptions, ensure message tagging is verbose enough to make sense, and actually aid in the problme looking to solve

Thursday’s emergence …

Notes

• from http://www.gladwell.com/tippingpoint/index.html , Tipping Point – Net Version, How to Start a Revolution
• The tipping point is the moment when critical mass is reached
• amplification could be a exponential increase => rapid changes
• small changes can alter epidemics irrevocably
• “peer pressure is much more powerful than a concept of a boss”
• Jane Jacobs
• nothing from Wikipedia either on her (http://en.wikipedia.org/wiki/Jane_Jacobs) or her book (http://en.wikipedia.org/wiki/The_Death_and_Life_of_Great_American_Cities) seemingly about emergence
• “Whiteflight”
• from http://en.wikipedia.org/wiki/White_flight, mentioned in a few texts
• when white Americans moved from inner-city homes to surburban homes in 1950/60s, leaving black Americans in the centre of towns
• presumbably a tipping point/critical mass happened here
• Epidemiology note
• The Wikipedia article on epidemiology didn’t have anything of great importance
• Remaining things to look at
• Ants
• emergent networks

Emergence …

History

• Alan Turing’s morphogenesis paper appears to be key
• Evelyn Fox Keller and Lee Segal (1969) had an important paper in this area
• Is positive feedback important ???
• Buzzwords
• self-organisation studies
• bottom-up software
• supraorganism: describe colonies of social insects
• Key people
• Jane Jacobs (city neighbourhood formation)
• Marvin Minsky (distributed networks of human brains)
• Mitch Resnick — slime model
• also develop StarLogo: http://web.media.mit.edu/~mres/
• Clustering and coping
• from http://extremedemocracy.com/chapters/Chapter%20Six-Emergence.pdf
• book details:
• Extreme Democracy, p80 — p86 (mostly 82 and 83)
  by Jon Lebkowsky, Mitch Ratcliffe
  ISBN: 1-4116-3139-0
  Copyright: © 2005 Creative Commons Attribution-NonCommercial-ShareAlike 2.044
• clustering:
• indivdual parts of an emergent go about their business, and when a certain event happens — the tipping point — a cluster forms
• e.g. the slime mold emerging from single-cell organisms
• e.g. flocks of birds
• behind each formation there’s a shared group logic
• simple rules of signals
• by following these, indidiv. agents can organise themselves into higher-level shapes without a central leader
• systems for a large part rely on amplification of positive feedback
• think ants repeatedly laying down the same pheromone trail to strengthen it
• this approach very good at generating numbers — i.e. crowds — but poor at responsiveness (i.e. coping)
• copings:
• clusters lump together in a big mass; coping colonies solve problems
• respond quickly
• appears to demonstrate “group intelligence”
• 2 key elements different to those shown in clustering:
• communication between agenst
• need rel. complex semiotic code with which to communicate
• E. O. Wilson estimates that ants’ pheromone signlaing has ~24 ‘words’ — ‘food this way’, ‘danger’, etc
• meta-information about the state of the collective
• now bear in mind the system does not have a central command control that can communicate instructions to the entire collective
• in ants doing tasks, they appear to note how many times they bump into ants doing particular tasks. By keeping track of the ratio of ants doing what role, they can change their activity if the ratios get messed up
• instead of continual amplification of positive feedback, a self-checking system emerges
• From Malcom Gladwell’s tipping point page:
• http://www.gladwell.com/tippingpoint/index.html
• “Think, for a moment, about an epidemic of measles in a kindergarten class. One child brings in the virus. It spreads to every other child in the class in a matter of days. And then, within a week or so, it completely dies out and none of the children will ever get measles again. That’s typical behavior for epidemics: they can blow up and then die out really quickly, and even the smallest change — like one child with a virus — can get them started.”
• “One of the things I explore in the book is that ideas can be contagious in exactly the same way that a virus is.”
• “A meme is a idea that behaves like a virus–that moves through a population, taking hold in each person it infects.”
• Go look at this tomorrow: http://radio.weblogs.com/0107127/stories/2003/01/01/tippingPointNetVersion.html

Emergence: current issues …

• look at the links in this page: http://www.oreillynet.com/pub/a/network/2002/02/22/johnson.html
• author’s blog: http://www.stevenberlinjohnson.com/
• 1st chapter online: http://www.simonsays.com/content/book.cfm?tab=1&pid=410896&agid=2
• book referenced:
• Emergence: The Connected Lives of Ants, Brains, Cities and Software (Paperback) by Stephen Johnson
• http://www.amazon.co.uk/Emergence-Connected-Brains-Cities-Software/dp/0140287752/sr=8-1/qid=1164790019/ref=pd_ka_1/026-6861385-1198002?ie=UTF8&s=books
• talks about Malcom  Gladwell’s the tipping point:
• book link: http://www.gladwell.com/tippingpoint/index.html
• paraphrasing the main ideas: http://radio.weblogs.com/0107127/stories/2003/01/01/tippingPointNetVersion.html
• emergent networks appears to be a current theme
• might be worth a quick look at ants as well

Tues … Cybernetics and emergence …

• maybe this journal: AIP Conference Proceedings — June 20, 1996 — Volume 376, pp. 133-157
  Introduction to chaos and the changing nature of science and medicine
• Complexity and Philosophy, http://arxiv.org/ftp/cs/papers/0604/0604072.pdf

Monday book reading …

My requested library books have turned up — yahay!! — and currently going through them to see what they contain. First up, Nature’s Flyers, by David E. Alexander:

• chapter 1 on how flying happens is quite good, so may take notes to improve my drafting report
• other than that, none of the chapters looked at so far have much relevant detail…

Friday coding: Threads …

In the first Coding Friday, I’m looking at what threads are — i.e. the literature/theory behind them. The plan is to find out what they are, then have a go at implementing them: first in Python (today), then in Jython (which may involve looking at things in Java) (next Friday, 01-Dec-06).

Hopefully the new Coding category on the blog has been correctly set up also, so should all fit nicely into that category …

Threads:

• Google stats:
• java threads: 25M results
• python threads: 1.5M results
• jython threads: 0.5M results

• http://en.wikipedia.org/wiki/Thread_%28computer_science%29
• short for a thread of execution
• threads are a way for a computer program to split itself into 2 or more simultaneously or pseudo-simulataneously running tasks
• different from a process
• [Note: I think a 'process' in this context is equivalent to running an application -- think the process tab in Task Manager]
• multiple threads can be exectured on many computer systems:
• this multithreading occurs by time sclincing, where a processor switches between different threads
• … so the processor is really doing one thing at a time, but happens so fast it gives the illusion of simultaniety to the end user
• allows mutliple threads to exist within the context of a single process, sharing process’ resources but able to execute independently
• issues with threading can lead to problems with the coding: e.g. race conditions, deadlocks, etc
• processes cf. threads
• processes:
• typically independent
• carry considerable state
• have separate address spaces
• interact only through system-provided inter-process communication mechanisms
• threads
• share the state information of a single process
• share memory and other resources directly
• OSes typically implement threads either through preemptive multithreading or cooperative multithreading:
• preemptive generally preferred — the OS can decdied when context switching should take place
• disadv. is that context switching could happen at an inappropraite time, causing priority inversion/other bad effects

Threading in Python:

• ‘Threading makes heavy use of Operating System capabilities and is NOT as portable (no matter what language you’re programming in) than most code.’
• http://www.wellho.net/solutions/python-python-threads-a-first-example.html
• … but how will Jython fair … ?
• the above has a reasonably simple ping example also
• Tried 2nd example on this page >> http://www.devshed.com/c/a/Python/Basic-Threading-in-Python/1/ << which works … but does the for loop at the bottom create, in effect, 20 threads?

import threading
theVar = 1
class MyThread ( threading.Thread ):

def run ( self ):

global theVar
print ‘This is thread ‘ + str ( theVar ) + ‘ speaking.’ print ‘Hello and good bye.’
theVar = theVar + 1

for x in xrange ( 20 ):

MyThread().start()

• (have got a feeling WordPress will stuff the above code eg. up — oh well …)
• (NB the python re module is for regular expressions (shocker!))
• I’d suggest reading through this PDF in more detail later on: http://heather.cs.ucdavis.edu/~matloff/Python/PyThreads.pdf

NB last post …

Last post was from:

T. De Wolf, and T. Holvoet, Emergence Versus Self-Organisation: Different Concepts but Promising When Combined, Engineering Self Organising Systems: Methodologies and Applications (Brueckner, S. and Di Marzo Serugendo, G. and Karageorgos, A. and Nagpal, R., eds.), Lecture Notes in Computer Science, 2005, Volume 3464, May 2005, pages 1 – 15

Thursday — history time …

From

• emergence and self-organisation show diff. characteristics of a system’s behaviour
• they can occur in isolation and co-exist
• Emergence:
• History
• not new — see J. Goldtein, Emergence as a contruct: History and issues. Emergence vol. 1 (1999)
• concepts like:
• “whole beofre its parts” — i.e. to consider an explanation in terms of the global behaviour more important than how system works at local level
• “Gestalt” — i.e. a config. or pattern of elements so unified that it cannot be described as the sum of it’s parts (German word normally translated as “whole” or “form”)
• … been about since the ancient Greeks
• NB emergence not pre-given: a dynamical construct arising over time
• Used >100 yrs ago by English philosphere G. H. Lewes 1875
• he distringuished btw “resultant” and “emergent” chemical cmpds resulting from a chem. reaction
• Lewes’ term borrowed in 1920s in emergent evolutionsim or proto-emergentism movement
• debated lots; used against reductionism, saying everything in a system can be reduced to the sum of it’s parts
• no great answers how the lower-level inputs are transformed to the higher-level outputs during emergence
• Complexity theory (neo-emergence) attempts to address lack of understanding around emergence.
• Four central schools of rsearch, ea. influence the way emergence in complex systems studied:
• Complex Adaptive systems theory
• (from Santa Fe Institute)
• explicitly uses term “emergence” to refer to macro-level patterns from interacting agents
• Holland, J.: Emergence: from Chaos to Order. Addison-Wesley (1998)
• Kauffman, S.: At Home in the Universe: the Search for the Laws of Self-
  Organization and Complexity. Oxford University Press (1995)
• Langton, C.: Studying artificial life with cellular automata. In Farmer, D., Lapedes,
  A., Packard, N., Wendroff, B., eds.: Evolution, Games, and Learning: Models for
  Adaptation in Machines and Nature, Proceedings of the Fifth Annual Conference
  of the Center for Nonlinear Studies. (1986)
• Nonlinear dyanmical systems theoy and Chaos theory
• puts forwards the central concept of attractores — specific behaviour to which the system evolves
• e.g. strange attractor
• David Neuman classifies as an authentically emergent phenomenon
• Newman, D.: Emergence and strange attractors. Philisophy of Science 36 (1996)
• The synergetics school
• was one of a number of scholl sthat initiated the study of emergence in physcial systems
• describe the idea of an order paramter that influences which macro-level coherent phenomena a system exhibits
• Haken, H.: The Science of Structure: Synergetics. Van Nostrand Reinhold, NY
  (1981)
• Far-from-equilibrium thermodynamics
• introduced by Ilya Prigogine
• refers to emergent phenomena as dissipative structures arising at far-from-equilibria cinditions
• Nicolis, G.: Physics of far-from-equilibrium systems and self-organization. In
  Davies, P., ed.: The New Physics. Cambridge University Press (1989)
• 2 important characteristics of emergence:
• global behaviour arising from the interation of local parts
• that global behaviour cannot be traced back to the indivi. parts
• Working definition of emergence
• suggested as:
• “system exhibits emergence when there are coherent emergents at the macro-level that dynamically arise form the interactions between the parts at the micro-level. Such emergents are novel with respect to the individual parts of the system”
• in def. above:
• “emergent”: general term to denote the result of the process of emergence
• properties, behvaiour, structure, patterns, etc
• “level”: refers to points of view:
• “macro-level” — the system as a whole
• “micro-level” — the perspective of the individual entities that make up the system
• evidence for this definition:
• micro-macro effect
• interactions at the micro-level lead to patterns/etc ocurring at the macro-level
• these properties are called “emergents”
• gloabl behav. of the system results from the interactions between the individ. entireis of the system
• radical novelty
• global behaviour novel cf. the individ. behav. at the micro-level
• individs. at micro-level have no explicit representation of the macro-level beahv.
• macro-level emergents are not reducible to the micro-level parts of the system
• NB radical novelty occurs because collective behav. is not readily understood from the behaviour of the parts
• collective behav. implicitly contained in the behav. of the parts if they are studies in the context in which they are found
• you can’t study emergent props. by taking a system apart and looking at the parts (reductionism) — however, you can study it by looking at each part in context of the whole system
• coherence
• refers to a logical and consistent correlation of parts
• emergents appear as “integrated wholes”, that maintain a sense of indentify over time — i.e. maintain a persistent pattern
• coherence spances and correaltes the separate lower level components into higher level unity
• correlations between components are needed to reach a coherent whole
• this coherence called “organisational closure”
• interacting parts
• parts need to interact
• without interactions, macro-level behav. never arise
• these emergents arise because of the interactions
• dynamical
• in emergent systems, emergents arise as system evolves over time
• think game of life
• such emergent is a new kind of behav. that becomes possible at certain points in time
• think tipping points
• “as a dynamical construct we can relate the appearance of emergents to the appearance of new attractors in dynamical systems, e.g. bifurcations”
• decentralised control
• using only the local mech. to influence global behav.
• no central control
• no single part of the system directs macro-level behav.
• actions of the parts are controllable; the actions of the whole are not directly controllable
• cf. business organisations ….?
• this characteristic = consequnce of the radical novelty required for emergence
• centralised control only poss. if that central part of the system has a repsentation of the global behav. e.g. a plan
• two-way link
• in emergent systems, bidirectional link btw. macro-level and micro-level:
• from micro –> macro: parts give rise to an emergent structure
• from macro –> micro: emergent structure influences it’s parts
• higher level props. have causal effects on the lower level, ie. downward causation
• e.g. path formation with ants: the emergent path influences the movement of the micro-level ants because they follow the pheromones
• robustness and flexibility
• need for decentralised control and that no single entity can have a representation of the global emergent => no single entity can be a single point of failure
• emergents relatively insensitive to perturbations or errors
• increasing damage => decreased performance, but with “gradual/graceful” degradataion
• quality of output will decrease gradually without sudden loss of fucntion
• failure/replacement of an entity will not lead to complete failur or emegent
• e.g. in a traffic jam: cars can replace one another, but the jam still remains