Tuesday’s and Wednesday’s notes …

Yesterday, Tuesday, spent a fair bit of time travelling into London, so spent ~2 hours reading Revisting the Edge of Chaos: Evolving Cellular Automata to Perform Computations, by Melanie Mitchell et al (Complex Systems, 7, 89 – 130, 1993) and I made it to … wait for it …. page 8! However, I’m had a number of abortive attempts at reading this paper, so this is actually pretty good progress, seeing as it is making sense this time.

Continuing from where I left off on Monday. And I’m trying out numbered bullets to see if this will help later reference:

1. Simplifying complexity: a review of complexity theory, Steven M Manson, Geoforum 32 (2001), 405 – 414
1. “components of a system and their relationships are not an undifferentiated mass. Relationshop of differing strenghts between component parts define the internal structure of a system”
2. “components with expecially tight connection form sub-systmes, so even homogeneous components can support intenral diversity through realignment of relationships ot create non-identical sub-systems”
3. “any given component can belong to multiple sub-systems”
4. “A complex system owes its existence to relationships with its environment, defined as anything outside of the system, althought this division may not be sharp”
5. “Regardless of the actual boundary between a system and the environment, the former passes information, matter and energy through its internal structure. The actions and interactions of system components eventually create outflow from the system into the environnment”
6. “A complex system is not beholden to the environment — it actively shapes, reacts and anticipates. A system ‘remembers’ through the persistence of internal structure (JH Holland, 1992, Complex adaptive systems. Daedalus 121, (1), 17-30). Component and sub-systems withthe capacity to accommodate the influx of eneregy, matter and information from the environment will grow. Regulaly occurring external relationships encourage the growth of the same set of components and sub-systems. “
7. “A complex system can deal with truly novel sistuations because it has a wide array of internal components and sub-systems linked by complex relationships.  Some subset of these components may have some ability to accommodate a novel relationship. In the rare cases when no suitable components or sub-system exist, the system cannot respond to ew relationships iwth the environment, with potentially catastrophic results. “
8. “Teh capacities of a complex system are greate than the sum of its constituent parts”
9. “A system can have emergent qualities that are not analaytically tractable form the attributes of internal components (NA Baas and C Emmeche, 1997, On Emergence and Explanation, Santa Fe Institute, Santa Fe, NM)”
10. “Emergence is a function of synergism, whereby system-wide characteristics do not result from superposition (i.e. additivie effects of system components) but instead from interactions among components (JS Lansing and JN Kremer, 1993,  Emergent properties of Balinese water temple networks: coadaptation on a rugged fitness landscape. Am. Anthropol. 95 (1), 97 — 114)”
11. “a complex system constantly changes, largely through threee different types of transition. First, a key characteristi of a complex system if self-organisation, the property that allows it to change its internal structure in order to better interact with its environment. Self-organisation allows a system to learn through piecemeal changes in internal structure”
12. “… a system becomes dissipative when outside forces or internal pertubations drive it to a highly unorganised state before suddengly crosssing itno one with more organisations (WC Scheive and PM Allen (Eds.), 1982, Self-organisation and dissipative structures: applications in the physical and social sciences, University of Texas Press, Austin, TX)”
13. “… the term self-organised criticality refers to the ability of complex systems to balanace between randomness and stasis. Instead of occassionally weather a crisis, a system can reach a critical point where its internal strucutre lies on the brink of commapsing withut actually doing so (P Bak and K Chen, 1999 [I think -- changed from 1991 JE], Self-organised criticality, Sci. Am. January, 46 – 53)”
14. “Self-organised criticality is a form of self-organization where the rate of internal restructuring is almost too rapid for the system to accommodate byt necessary for its eventual survival (JA Scheinkman and M Woodofrd, 1994, Self-organised criticality and economic fluctuations, Am. Econ. Rev. 84 (2), 417 – 421) “
15. “Emergent social phenomena can disappear when one reduces the system into components or users too many statistical assumptions (WB Arthur 1994, Inductive reasoining and bounded rationality. Am. Econ. Rev., Papers Proc. 84 (2), 406 – 411)”
16. “… aggregate complexity illustrates how relationships are more imprtant than attributes in defining the nature of components”
17. “The notion of aggregate complexity creating emergence potentially address the micro-macro distinction in issues such as the relationship between agency and structure”
18. “Another significant body of research lies in exploring emegence with cellular automata. These tessellations (e.g. grids) represents how the state of some phenomonon changes in time according to rules based on localised interactions of entities”
19. “Although research on how macro-scale phenomena arise form micro-interaction continues apace, less examinied is the effect of macro-structure on the micro-scale. “
20. “Some definitions of emergence go so far as to necessitate that lower level elements are unaware of their role in emergent phenomena (S Forrest (Ed), 1991, Emergent Computatin: Self-organization, Collective, and Cooperative Phenomena in Natural and Artifical Computing Networks, MIT Press, Cambridge, MA)”
2. And now the response! A response to simplifying complexity, Femke Reitsma, Geoforum 34(1): 13-16
1. “there are many different forms of research efforts in complexity over many different disciplines producing many different measures and definitions of complexity that all fall under the rather broad umbrella of Complexity Theory”
2. “… complexity theorists concur that the whole is greater than the sum of its parts”
3. “This aphorism seems to be the glue that binds the somewhat fragmentary nature of complexity research”
4. “instead of considering algorithmic, deterministic and aggregate complexity as separate types of Complexity Theory, they can be through of as different measures or defintions of the complexity of a system. Thus there are different theories of the definition of complexity rather than different types of Complexity Theory”
5. “The difficulty with the term complexity is that it suggers a semantic hangover from its well-accepted dictionary definition; ‘only a decade ago, “complex” simply meant made of many interrelated parts’ (C Koch and G Laurent, 1999, Complexity and the nervous system, Science 284 (5411), 96 – 98)”
6. How to define complicated: “A system is complicated if it can be given a complete and accurate description in terms of its individual constituents, no matter how many, such as a computer or the process of programming a VCR (P Cilliers, 1998, Complexity and Postmodernism: Understanding complex systmes. Routledge, London)”
7. “‘complication is a quantitative escalation of that which is theoretically reducible’ (GP Chapman, 1985, p370, The Epistemology of Complexity and Some Reflection s on the Symposium. The Science and Praxis of Complexity, Montpellier, The United Nations University)”
8. “A system is said to be complex when the whole cannot be fully understood by analyzing its components (P Cilliers, 1998, Complexity and Postmodernism: Understanding complex systmes. Routledge, London)”"
9. “Many techniques under the banner of Complexity Theory have little or nothing to do with complexity as such, where the word complexity is used to describe complicated or difficult systems, typically with many parts (B Edmonds, 1999, What is complexity? — The Philosophy of complexity per se with application to some examples in evolution. In: Heylighen, F., Aerts, D. (Eds), The Evolution of Complexity. Kluwer, Dordrecht.)”
10. “Chapman (GP Chapman, 1985, p370, The Epistemology of Complexity and Some
    Reflection s on the Symposium. The Science and Praxis of Complexity,
    Montpellier, The United Nations University) asserts that if the world can be explained in a reductionist manner ‘then “complexity” is not qualitatively different from “simplicity”, but mere quantitatively different’ “
11. “… niether algorithmic nor deterministic complexity, as Manson described them [see earlier article - JE] are complex in the sense described above. Manson discusses two forms of algorithmic complexity: the first is more commonly described as computational complexity and the second as algorithmic information theory. Neithger forumaulation of complexity is concerned with how the system may be characterised by its parts in a non-reductionist manner. Both translate complexity as complicated.”
12. “What are commonly referred to as deterministic measures of complexity, ar ethose that require the accounting of every bit in an object (Gell-Man and Crutchfield, 2001, Computation in Physical and Biological Systems: Measures of Complexity, Santa Fe Institute.)”
13. “Self-organised criticality, rather than the ‘ability of complex system to balance between randomness and stasis [taken from the Manson paper]‘, is that balance (commonly termed the edge of chaos) between chaos, which is not equivalent to randomness, and order. Furthermore, self-organized criticality is not a ‘type of transition’ but a type of system organisation “
14. “Chaos theory deals with simple, deterministic, non-linear, dynamic, closed systems. They are extremely sensitive to initial conditions resulting in a unpredicatbale chaotic response to any minute initial difference or perturbation.”
15. “Complexity theory focuses on complex, non-linear, open systems. Complex systems respond to perturbation by self-organizing into emergent forms that cannot be predicted from an understanding of its parts”

And that’s it for today. Realised my library books are *quite* overdue, so will be doing something else tomorrow probably, and then returning to this at a later point — I guess next week, with Coding Friday coming up?

It be Monday: what do we know? …

With a swimming mind, a brief overview of what I’ve come across over the past few weeks and days:

• From Strogatz’s Sync:
• p56 – 57: Yoshiki Kuaramoto’s model in self-organisation, about 2 coupled oscillators.
• Imagine them as two runners on a circular track, trying to keep at the same pace to talk to one another. If one is going too fast, it slows down to allow the other to catch up; the slower runner speeds up by the same amount. Such corrective action tends to synchronise the oscillators.
• No communication takes place between the runners, other than visually watching one another.
• Now, imagine a larger group of runners (oscillators). Each runner looks at each other, calculates a velocity correction relastive to each other, and averages them all to obtain the actual correction to be made. In this scenario, the lead runner slows down, the slowest runner speeds up, and those in the centre get mixed messages about whether to speed up or slow down.
• (He also detailed information about order parameters — left out for now, but could be returned to
• Original paper: “Self-entrainment of a population of coupled nonlinear oscillators”, in International Symposium on Mathematical problems in Theoretical Physics, edited by H Araki (Springer-Verlag: Lecture Notes in Physcis, vol 3, 1975, p420 – 422)
• Better version in Y. Kuramoto, Chemical Osciallations, Waves and Turbulence (Berlin: Spring-verlag, 1984)
• p118: some notes about:
• GPS
• about how emergent systems work not well understood
• p159: where “self-rganised criticality” comes from
• “Self-organised criticality: an explanation of 1/f “, Per Bak, Chao Tang and Kurt Wiesenfeld, Physciual Review Letters 59 (1987), p381 – 384
• p255: power laws:
• “… when a physicist sees a power law, his eyes light up. For power laws hint that a system may be organizing itself. Ther arise at phase transisation, when a a system is posed at the brink, teetering between order and chaos”
• this section is about small-world theory
• p264: Mark Granovetter and about thresholds of mobs turning into riots:
• He “… assumed that each person’s descision whether to riot or not is dependent on what everyone else is doing. Instigators will behin rioting even if no one else it. Other poiple need to see a critical numbher of other causing mayhem before they’ll join in. That critical number — the person’s threashold — is assumed to be distributed across the population according to some probability distribution”.
• p286 Epilogue:
• “Complexity theory taught us that many simple units interacting according to simple rule could generate unexpected order. But where complexity theory has largely failed is in explaining where the order comes from, in a deep mathematical sense, and in tying the theory to real phenomena in a convinving way. For these reasons, it has had little impact on thethinking of most mathematcians and scientists. “
• Simplifying complexity: a review of complexity theory, Steven M Manson, Geoforum 32 (2001), 405 – 414
• some of my quick notes:
• not just one theory
• covers lots of areas, therefore research hard due to cross over
• breaking it down into separate areas leads to reductionism, which isn’t what complexity theory is looking to solve
• 3 types of complexity division:
• algorithmic
• deterministics
• aggregate
• “Advocates of complexity theory see it as a means of simplifying seemingly complex systems
• “‘Algoithmic complexity’, in the form of mathemactical complexity theory and information theory, contends that the complexity of a system lies in the difficulty faced in describing systems charactertistics”
• “‘Deterministic theory’ deals with chaos theyr and catastrophe theory, which posis that the interaction of two or three key variable can create largely stable systems prone to sydden discontinuities”
• “‘Aggregate complexity’ converns how individual elements work in concnert to create systems with complex behavior”
• “these approaches (JE: see above) are complemenatry”
• “Most importantly, all three kinds of complexity are concerned with how the nature of a system may be caharacterized with reference to its constituent parts in a non-reductionist manner”
• “Coimplexity theory, however, may be traced back to conceptual antecedents such as the ‘philosophy of the organism’ (Whitehead A. N., 1925, Science and the Modern World. Macmillan, New York), neural networks (McCulloch W. S. and Pitts W., A loogical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys. 5, 115 – 137 1943 ), cybernetics (Wiener N.,  1961, Cybernetics: Or, Control and Communication in the Aniaml and the Machine. MIT Press, Cambridge, MA), and cellular automata (von Neumann J., Theory of Self-Reproducing Automata, University of Illinois Press, Champaign-Urbana 1966)”
• “Complexity theory also owes much to general system theory given shared foci of anti-reductionism and hoslistic appreciation of system interconnectedness (von Bertalanffy, 1968, General Systems Theory: Foundation, Development, Applications. Allen Lane, London)”
• “… complexity often concerns non-linear relationships between constantly changing entities. ”
• “… this stocks-and-flows perspective emphasizes quantities of flow and not necessarily theiry quality. Complexioty research employs techniques such as artifcial intelligence to examine qualitative characterisitcs such as the sumbolic content of communication”
• “… eomplexity research concerns how complex behaviour evolves or emerges from relatively simple local interactions between system compenents over time”
• “Complexity research contends that systems have emergent or synergistic characteristics that cannot be understood without reference to sub-component relationships”
• “… important to note that complexity research is also concerned with how systems change and evolve over time due to interaction of their consitutent parts”
• Algorithmic complexity
• “One measure of algorithmic complexity calculated the effort required to solve a methamtical problem”
• “This body of work (information theory – JE) identifies complexity as the simplest computational algorithm that can reproduce system behavior”
• Deterministic complexity
• “Deterministic complexity has four key characteristcs:
• 1/ the use of deterministic mathematics and mathematical attractors
• 2/ the notion of feedback
• 3/ sensitivity to initial condidtions and bifurcation
• 4/ the idea of deterministic chaos and strange attactors”
• “an ‘attractor’, a value towards which a system varaible tends to settle over time”
• “negative feedback occurs when changes in one variable forces itself or other key variables to settle on a stable value”
• “positive feedback is self-reinforcing and results in one or more variable moving rapidly towards a point of no return, as when population dies out or grows indefinitely
• “the overla state of chaotic or catastrophic systems are sensitive to small, increametnal changes in key varaiables”"
• “under certain conditions … the system is sensitive to initial conditions. This term refers to siutaions where small changes in the intial system configuration may lead to large non-linear effects”
• “The potential for system vairables to jump suddenly from one attractor to another is term bifurcation (Feigenbaum M.J. 1980, Universal behaviour in nonlinear systems. Los Alamos Sci 1, 4 – 27)”
• “Catastrophic attrqactors are two-dimensional curves or three-dimensional surfaces defined by the interaction of two or three system variables. Along most of a catastrophic attractor, any change in one variable typically reults in a change of similar magnitude in other variables. Catastrophic attracters, however, have occasional discontinuities where a small change in one variable results in a lrage ‘catastrophic’ chage in another”
• Aggregate complexity
• “Complexity research increasingly consders systems of linked components, or aggregate complexity”
• “Algorithmic and deterministic complexity rely of simple mathematical equations and a number of assumptions of how complex system work. Aggregate complexity instead attempts to access the holism and synergy resulting from the interaction of system components”
• “The heart of aggregate complexity lies in relationships between components. In an economy … components are consumers, firms, and the state … they exchange and redistribute information, matter and energy. In ecology, key entities are flora and fauna with relationships largely degined through matter and energy exchanges. “
• “A complex system is degined more by relationshops than by it’s constituent parts. A single person in an economy can consume and produce goods and knowledge, boycott firms and contribute to the underground economy. A tree in an ecosystem is important to biogeochemcial processes. Inderstanding and tracing the relationship of a single entiry is difficult, while training them in an entire system verges on the impossible. Given the number and variety of these relationships, they extend beyond simple feedback into higher order, non-linear processes not amenable to modeling with traditional techniques (Costana R., Wainger, L., Folke, C. et al, 1993, Modeling complex ecological economic systems: towards an evolutionary, dynamic understanding of people and nature. Bioscience 43 (8), 545 – 555)”
• “There is omniscience or constantly updated body of information”
• “Apart from Gaia theory, it is hard to argue that any one ecological component or sub-system ‘knows’ what other components are doing”

… and I’m stopping there for today. Where does the time go?

Friday: yesterday’s musings and today’s coding …

Very quickly: yesterday looked at the response to Simplifying Complexity by Mason, A Response to Simplifying Complexity by F Reitsma (Geoforum, 13 – 16, 2003). Again, more thoughts on a fairly fluid topic — difficult to put my finger on it at the moment. Very frustrating.
Today some coding action: managed to get Java to print 10 of the same string to a text file. But can I get a list of 0 – 10 to print, through a while loop? Can I ‘eck. Grrr. Will play more over the w/e I think.

Wednesday: Dealing with Complexity …

Started to go through Dealing with Complexity: An Introduction to the Theory and Application of Systems Science by Robert L. Flood and Ewart R. Carson, which I picked up from the library. A number of useful quotes, but still feel that I am going about this the wrong way … need to start asking more targetted questions I feel: what is the purpose of complexity theory, why did the field originate, etc. Very frustrating at the moment to try and answer the questions I’m after !!!

Tuesday: Cellular automata …

Today went through about 1/2 of Revisiting the edge of chaos: Evolving cellular automata to perform computations by M Mitchell, PT Hraber, JP Crutchfield – Complex Systems, 1993 – cecs.pdx.edu to see if I could come across anything … nothing jumped out at me, I’m afraid.

I’m finding the process of working to the strucutre Will and I discussed last Tuesday strangley challenging. It’s more of a framework to writing, whereas I suppose I’ve have complete creative freedom to this point. The framework, instead, asks lots of little questions, which unfortunately seem to take a blessedly long time to answer! ;-) However, I’m thinking that once I get past the first report in this style, it will hopefully be easier: I’ll start to look at things with a more “report generation” focus/mindest (I hope!).

 Still thinking about where the time goes … and still recognise that I need to be more “aggressive” in defneding my time. I need to start employing some system of skimming journals I think, to work out whether or not a particular reference will be of use, ahead of reading the full article.

Interestingly though, the epilogue of Sync by Strogratz has a useful quip about complexity theory though …

Monday: shouting at browser time …

Trying to write a complexity report … going pretty badly at the moment, in all fairness. I’m struglling to adapt what exists in the literature into what I want to address and answer — very frustrating!!

Currently trying to answer what is the motivation behind the field, but not coming up with clearcut answers. In my mind, the field of complexity theory is to do with trying to explain “things” holistically … but I’m not convinced of my own thinking at this point: questions like “well, why do you think that?” shed no new light. Also, the balance between complexity and emergence seems to include a fair degree of overlap.

However, on one hand came across this site: The Complexity & Artificial Life Research Concept
for Self-Organizing Systems which looks interesting, but it appears to be a private company …? The FAQ seems useful for a quick def’n of terminology.

NB Just found this => http://journal-ci.csse.monash.edu.au/ci/vol08/green05/green05.pdf — looks reasonable.

And time: where does it go!?!

Tueday: complexity redaing/writing ..

Following yesterday’s meeting with Will, tryng to work out how to write up the complexity report in an artice format. As such, need to do some additional reading, I think, to help answer additional questions. Saw these articles which should help:

• Manson, S. M. (2001). “Simplifying complexity: a review of complexity theory.” Geoforum 32 (3): 405-414.PDF (http://hegis.umn.edu/pubs/Manson_2001_Geoforum_Simplifying_complexity_RT.pdf)
• critque here: Reitsma, Femke (2003) “A Response to Simplifying Complexity” Geoforum 34(1): 13-16 http://www.era.lib.ed.ac.uk/bitstream/1842/1057/1/reitsma03Manson.pdf
• this seems quite good: need to sit and parse in more detail
• also look at this page: http://home.wxs.nl/~gkorthof/kortho32.htm a review of Kauffman’s At Home in the Universe
• See here http://www.santafe.edu/~mgm/ for the Gell-Mann “What is complexity?” paper

Notes from meeting Will (13-Feb-2007, F2F Reading) …

Research

• continuing to delve into complexity theory
• looking at Gaia hypothesis recommended: mathematics of system also
• would adjusting the parameters of a Gaia Daisyworld model mirror what would happen with a flock of birds?

Writing (Emergence report feedback)

• think of a standard journal article structure:
• Introduction
• … to include:
• Scope
• Motivation
• Aims/Objectives of the field
• Benefits (maybe same as Aims/Obj. at times)
• Background
• what field is trying to do
• asking questions and answering them okay
• ~20% paragraphs written this way
• can also use titles like “Principles of …”, “Background of …”
• Discussion
• including counter arguments
• summary of what has been found, including what is not relevant
• Conclusion/Summary
• always supply one
• References
• Bibliography
• useful to have at early stage, as can use as record to self to say what you looked at, what wasn’t very useful, etc
• misc.
• try not to use “etc”
• add in own insight:
• write in 3rd person
• write in a style that it is easy to infer from context that it is own thoughts
• highlight new work: “in this novel method” …
• try not to use conjunctions
• watch apostrophes
• use a spell checker more than once …!
• with accepted acronyms, just put it into brackets
• e.g. ” …with the Traveling Salesman Problem (TSP)”
• try not to overuse bullet points
• highlight the point you are trying to make
• consider supplying additional information to show benefits of bullets
• it is possible to write such a report “backwards”:
• Conclusion, Introduction, Discussion, Background
• looking for a common story to run through the entire report, to bind it together — not “siloed” chapters lumped together
• remember: looking to point out the benefit/worth of research

Coding

• look at the Connect 4 website for details on getting Java to write a log file
• Abstraction Algorithm Research site
• sip189.reading.ac.uk
• may need to use a client-server model
• Java server’s are able to write to disk

Monday: Gaia …

Been a bit slack recently, so a quick heads up as to my activities:

Research

• been attempting to write up my complexity notes … but without much joy! Seems difficult to really lock things down … I am left with a number of bullet points, really. Seems difficult to explain on what I have found.
• today been looking at the Gaia theory, through Richard Mitchell’s teaching page
• the issue I’m having is firstly understanding the Gaia theory, and secondly relating it back/seeing the connection to flocking
• there could/should be connections here to a certain degree, but Gaia theory appears to have negative feedback loops involved — i.e. it can self regulate/control. I suppose the question I need to ask myself, and to find out, is whether flocks display negative feedback on any level — do they self regulate …?

Coding

• in short, sick of reading about coding … I want to do!! However, not sure where to really begin:
• start to make sense of “big” flocking model, as seen in the Killer Games in Java book
• rework an earlier model I’ve created — one of the flocking models, or the following model — in Java
• find a simple model of flocking in Java on the Internet, and play with/manipulate
• try to get the flocking code working in something like Robocode (i.e. stick robots into an arena, and give each of them flocking code instead of fighting code)
  
• something else …
• I recognise the fact I need to do something, and am falling into the analysis/paralysis trap, so need to pick one and run with it. Might do the lucky pick thing — still all options into a hat, pick one out and see what gut feel says about the selection (good/bad (pick again)) — just to get moving.

Monday: Writing up Complexity report ….

As it says in the title, starting to write up complexity report today.

NB looking at the Sante Fe website (http://www.santafe.edu/), are either of the following available through it?:

• Melanie Mitchell, Peter T. Hraber, and James P. Crutchfield. Revisiting the edge of chaos: Evolving cellular automata to perform computations. Complex Systems, 7:89–130, 1993.
• Melanie Mitchell, James P. Crutchfield and Peter T. Hraber. Dynamics, Computation, and the “Edge of Chaos”: A Re-Examination