Complexity and Context-Dependency (version for Bath IOP Seminar)


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Extended version of my Complexity and Context-Dependency Talk given at the IOP seminar on "Complexity of Complexity" in Bath 19th Dec 2011.

This version talks more about looking for context in data.

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  • 3 parts to talk
  • Elsewhere I have argued about simplicity as a guide to truth
  • rest of the talk I
  • I am not claiming that such trade-offs are fixed, universal or simpleComes from modelling experienceTalk about validity, formality, complexity, generality
  • different modelling goals and kinds of validityschrodinger’s equation – we dont understand its analytic consequences but its still usefulnot john symonds “reasons” for not abandoning a fundermenatlist approach were a simple wish for no micarcles
  • e.g. thinking of a problem as an opportunity
  • although not reifiable, we may be able to recognise when we have got the context wrong
  • Example of someone who broke their leg for unlimited number of causesin the broken leg example we can exclude that gravity was too strongFormalisms such as Pearl are only applicable given a context
  • no reason to suppose that our brains happen to be evolved to directly understand a model adequate to much social phenomenaIt may be that we have to make do with lots of different context-specific simulations
  • Whilst fish live inhabit, we (as humans) inhabit society
  • Reader 1980, Man on Earth
  • Social Intelligence HypothesisWittgenstein, Vygotsky, TomaselloContexts are often described using their social features “I was talking to my mother”
  • leakage  noisenot the case where un-modelled aspects are effectively randomdiscuss random gas example
  • not possible to describe context entirely, but any hints are better than nonedifficult to represent many as contexts explicitly specified, but could be emergentit is standard practice to try and indicate context in social sciences, it should become the practice in SS
  • in fact we are generally only happy when a lot of different bridges are made between different models
  • We should abandon meta-physical commitments such as the theoretical completeness of science and emphasise its strong points – that it worksWhy is it reluctant to do so, because of the foundational status it gives science, but remember philsophy used to have this kind of status, and look what happened to it
  • Complexity and Context-Dependency (version for Bath IOP Seminar)

    1. 1. Complexity andContext-Dependency Bruce Edmonds Centre for Policy ModellingManchester Metropolitan University
    2. 2. Talk Outline1. Accepting Complexity2. Context-Dependency3. Social Context4. Looking for Context-Dependency5. Consequences for Science Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-2
    3. 3. Part 1: Complexity – everything that is not simpleComplexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-3
    4. 4. The Anti-Anthropocentric Assumption• That the universe is not arranged for our benefit (as academics)• e.g. that assumptions such as the following are likely to be wrong: – Our planet is the centre of the universe – Planetary orbits are circles – Risky events follow a normal distribution – Humans act as if they followed a simple utility optimisation algorithm• The one that I am particularly arguing against here is that our brains happen to have evolved so as to be able to understand models adequate to the phenomena we observe
    5. 5. Versions of this assumption• Whilst other animals have severe limitations and biases in their cognition, we don‟t• That our tools (writing, computers etc.) allow us to escape our limitations and biases to achieve general intelligence• That simplicity (that which is easier for us to analyse) is any guide to truth (other things being equal etc.)• If your model is not simple enough to analyse and understand, you are: (1) not clever enough, (2) lazy (have not worked hard enough), (3) premature (don‟t yet have the formal tools to crack it) or (4) mistaken• We would simply prefer it were so, e.g. that there are “no miracles”
    6. 6. Living with the AAA• Accepting that that much of the world around us is fundamentally beyond modeling that is both adequate and sufficiently simple and general for us to cope with• Acknowledging our (brain+tools) biases and limitations and so considering how we might extend our scientific understanding as much as possible• Phenomena that are simple enough for us to scientifically understand are the exception – the exception to be sought and struggled for• Simplicity is the exception – a science of non-simple systems makes no more sense than a science of non-red things
    7. 7. Possible modelling trade-offs• Some desiderata for simplicity models: validity, formality, simplicity and Analogy generality• these are difficult to Abstract generality obtain simultaneously Simulation What (for complex systems) Policy• there is some sort of formality Makers complicated trade-off Want between them (for each Data modelling exercise) validity
    8. 8. What is Essential to an Empirical Science?• Validity: agreement of models to what we observe (the evidence), not science otherwise• Formality: formal models (maths, simulation) are precise and replicable – essential to being able to build knowledge within a community of researchers• Simplicity: ability to analyse/understand our models, good to have but unattainable in general (AAA)• Generality: the extent of the applicability/scope of a single model, there needs to be some small generality to apply models in places other than where developed, but wide generality not necessary This talk argues for the following strategy: weakening the it another way, against the following strategy: … or, to put generality of our formal models to achieve weakening validity (e.g. the face of the preserve (the more validity in to analogy) to AAA illusion of) generality
    9. 9. Consequences of accepting lessgenerality…• A lack of models that cover all systems• Islands and layers of local consistency• Rather than a reduction between layers, a modelling relation• Rather than a “ladder” (total order) of sciences with the most fundamental at the base, a patchy network (partial order) of models• Rather than a neat system of “theories” and “models”, related clusters of models of different abstractions and generality Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-9
    10. 10. From this… Geography Social Sciences Ecology ReductionInference Psychology Zoology Biology Chemistry Physics
    11. 11. …to this! Weaker Modelling RelationsIslands of Local Consistency Clusters of Related Models Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-11
    12. 12. This raises some questions…• Does this make us all relativists?• Does this mean that scientific knowledge is just the same as other kinds of belief?• Does this mean that we should abandon formal models?• Does this mean that we cannot attain useful understanding of complex systems?• Does this mean that interdisciplinary science is hopeless? Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-12
    13. 13. … to which I answer “No”• The picture I paint already represents the reality of understanding non-simple systems• It just differs from some of the rhetoric of science, and hence the picture and beliefs many have about science• It does have some consequences for how we do science (to come in following slides)• Rather, accepting these realities will help us do better science by being aware of: – hidden assumptions – over-generalisations – reliance on single simple models Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-13
    14. 14. Part 2: Context-Dependency – and its cognitive rootsComplexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-14
    15. 15. Cognitive Context (CC)• Many aspects of human cognition are context- dependent, including: memory, visual perception, choice making, reasoning, emotion, and language• The brain somehow deals with situational context effectively, abstracting kinds of situations so relevant information can be easily and preferentially accessed• The relevant correlate of the situation will be called the cognitive context• It is not known how the brain does this, and probably does this in a rich and complex way that might prevent easy labeling of contexts
    16. 16. The Context “Heuristic” I• A strategy for dealing with the world by cognitively limited beings (humans)• CCs are associated with sets of relevant „background‟ assumptions, terms, norms, knowledge, etc. against which the explicit „foreground‟ learning, reasoning, events, etc. are conceptualised as occurring• To be useful kinds of situation needs to be reliably recognisable as a CC…• … and packages of foreground features need to be retrievable from this CC
    17. 17. The Context “Heuristic” II• Integrates the rich, “fuzzy”, and unconscious pattern recognition of CC with relatively “thin”, crisp and conscious learning and reasoning within a CC• Makes within CC reasoning, belief update feasible• Is dependent on the world being usefully separable into CCs – which is not necessarily the case• Also that learnt CC can be reliably recognised later• CC are learned flexibly – what counts as a meaningful CC but it is essential that relevant CC are reliably recognisable later• Considering the same situation w.r.t. different CC is often a useful cognitive tool
    18. 18. Talking about Context• What corresponds to CC in the brain may well not be: accessible to consiousness, introspection, clearly identifiable, or simple enough to defineThus it may not be meaningful to talk about “the” context of any particular event• Indeed people may consider the same situation using very different CCs• However sometimes, in retrospect, the CC can be roughly identified (especially if socially entrenched)• Even then it is frequently not explicitly described• For the sake of the discussion I talk about CC as if it were definite, given you understand that this is a simplification
    19. 19. About Context-Dependency• Context-dependency is not relativity since contexts can be reliably recognised (and/or corrected if wrongly recognised)• It is also not merely the “current audience” since contexts are developed, taught and reinforced over time (e.g. within academic fields)• But since it might be recognised in a “fuzzy” and unconscious manner the bounds of the context may not be reifiable in crisp terms• This is a heuristic – a strategy that may help push forward the boundaries of formal empirical science• There is some evidence that our cognition is context-dependent in many ways which means that to a considerable extent it may be unavoidable
    20. 20. Why might the world we study beusefully split into such “contexts”• In some cases (e.g. in ecology or social science) contexts might be co-developed over time between the entities (e.g. a niche, or social context like a lecture)• In some others it may be the only practical way to proceed• In yet others our cognitive, unconscious tendency to deal with the world in terms of contexts might lead us to try and divide the world along less useful lines
    21. 21. Context and Causality• In almost all situations (and all social situations) there are an unlimited number of things that could be attributed as a cause• Related to “Causal Spread” (Wheeler); “Wild Disjunction” (Fodor); and “Embeddedness” (Granovetter)• Without a limitation as to the scope causation makes no sense• However given a context there are many factors that can be assumed to be insignificantly relevant and/or constant• Thus causality makes sense given a context, since it excludes most possibilities
    22. 22. Transcending Contexts• It is often desired that a model be generalised to a broader scope – From: M holds in context A & M’ holds in context B if A then M if B then M‟ – However A and B rarely precisely reifiable• Simplifying does not necessarily lead to greater generality (by leaving out what is essential for the case)• What one can leave out is a hypothesis only determinable by evidence and experiment
    23. 23. Context and Analogical Thinking• Humans are good at using analogies, relating an idea or example from one context to another• They build the mapping from the analogy to the a context “on the fly” largely unconsciously• The mappings are different each time an analogy is applied, thus not a reliable source of knowledge and each person might build a different mapping but can yield new insights and can guide research direction• Many published models do not have an explicit mapping to a domain, but are used as analogy• This is sometimes hidden, so when a simulation (or analytic model) models an idea which applies as an analogy to a domain and not directly, given a spurious impression of generality Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-23
    24. 24. Part 3: Social ContextComplexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-24
    25. 25. Social Intelligence Hypothesis (SIH)• Kummer, H., Daston, L., Gigerenzer, G. and Silk, J. (1997)• The crucial evolutionary advantages that human intelligence gives are due to the social abilities and structures it facilitates• This explains the prevalence of specific abilities such as: imitation, language, social norms, lying, alliances, gossip, politics etc.• Social intelligence is not a result of general intelligence applied to social organisation, but the essential core of human intelligence• in fact our “general” intelligence could be merely a side-effect of social intelligence
    26. 26. An Evolutionary Story Social intelligence implies that: • Groups of humans can develop their own, very different, (sub)cultures of technologies, norms etc. (Boyd and Richerson 1985) • These allow the group with their culture to inhabit a variety of ecological niches (e.g. the Kalahari, Polynesia) (Reader 1980) • Thus humans, as a species, are able to survive catastrophes that effect different niches in different ways (specialisation)Social Intelligence and Multi-Agent Systems, Bruce Edmonds, MALLOW 2010, Lyon, 31st August, slide 26
    27. 27. Social Context• Since humans are fundamentally social beings…• …social context is often most important• e.g. an interview, a party or a lecture• But social context may be co-determined, since: – Special rules, norms, habits, terms, dress will be developed for particular social contexts – The presence of special features, rules etc. make the social context recognisable distinct• Over time social contexts plus their features become entrenched and passed down• Social Context arises and is so recognisable as a result of cognitive and external features (e.g. building a lecture hall)
    28. 28. Implications of Context-Dependency in Social Science• Behaviour of observed actors might need to change sharply across different social contexts• The relevant behaviour, norms, kinds of interaction etc. might also need to change• Social contexts might need to be co-developed, changing and sometimes instituted (e.g. a lecture)• These may need to be different for different groups• Some kinds of social behaviour are necessarily context-dependent (compliance)• It is unlikely that a lot of key social knowledge, behaviour etc. will be generic and hence amenable to explicit programming
    29. 29. Part 4: Looking for ContextComplexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-29
    30. 30. Kaneko (1990)• Exhibited a system of parallel chaotic but weakly coupled processes• Each process seems chaotic and independent• But as system size increases, variance as a proportion of size does not disappear• Law of large numbers does not apply Globally coupled Variance(scaled by size) Model with random noise Size
    31. 31. An Illustration of Masked Context-Dependency Global models are simply uninformative when the phenomena is context- dependent Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-31
    32. 32. Cleveland Heart Disease Data Set – theprocessed sub-set usedIn processed sub-set:• 281 entries• 14 numeric or numerically coded attributes• Attribute 14 is the outcome (0, 1, 2, 3, 4)• Some attributes: age, sex, resting blood pressure (trestpbs), cholesterol (chol), fasting blood sugar (fbs), maximum heart rate (thalach), number of major vessels (0- 3) colored by flourosopy (ca)• From the Machine Learning Repository
    33. 33. General Correlations (1% Sig) Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-33
    34. 34. Fitting a Global Model (R=56%)Num = -0.01*age + 0.17*sex + 0.20*cp + 0.00*trestbps + 0.10*restecg + -0.01*thalach + 0.23*exang + 0.18*oldpeak + 0.16*slope + 0.43*ca + 0.14*thal + -0.60 (+/- 0.83) Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-34
    35. 35. Looking for Clusters in HD Data Set(Start of Process) Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-35
    36. 36. After Solutions Locally Evolve Speciation of Solutions In some areas no solution dominates Some Solutions Spread over area of applicability Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-36
    37. 37. Final Set of Clustered Solutions • Final solution set after some time. • Still complex but some structure is revealed • Note presence of “fbs” despite not being globally correlated and that “chol” helped define the context space Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-37
    38. 38. Part 5: Consequences for ScienceComplexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-38
    39. 39. Consequences of Context-Dependency I: not ignoring context• Much modelling happens with a single context in mind, in which it can be case it can be ignored but only if – everyone is using the same idea of this context – there is no significant “leakage” of causation from outside the background, that is the scope is wide enough to include all significant influencing factors• Unfortunately the indication of the intended scope is often only implicit
    40. 40. Implications for Modelling ComplexSystems• It is very useful to describe, as far as possible, the intended scope of a model• Applying a model developed within one context elsewhere (including a more general scope) is difficult• No easy way to transcend context• Difficult to reify contexts to get generality Ignoring context may have the result that ourmodels are either (a) subtly and critically misleading or (b) merely analogies in computational form
    41. 41. Consequences of Context-Dependency II: clusters of models• Instead of having one model for one phenomena we may end up with a cluster of loosely-related models that each represent different aspects of it• Given that we want to understand our models and only a few models are analytically tractable this happens anyway• Separate but related models may avoid over-generalised models that do not directly relate to anything observed (or another model) and rely on imprecise interpretation
    42. 42. Example: an Ideal Gas Ideal Gas Laws Macro Data Models AnalyticalDerivations using simplifying assumptions Simulation Models Atomic Model of Micro data and an Ideal Gas understanding
    43. 43. Consequences of Context-Dependency III: layers of models• Given we need both rigour (understanding our models) and relevance (clear mapping to what is observed) in our models...• We might have complicated, descriptive simulations that relate in a more direct way to data models of what we observe: a Data-Integration Model (DIM)• But then need to model the DIM with simpler simulations to understand it and check its programmed correctly• As well as (maybe, hopefully) be able to generalise from it (and other similar DIMs) to a model which generalises certain aspects but to a wider scope
    44. 44. The Approach in the SCID Project Complexity and Context-Dependency, Bruce Edmonds, IOP Seminar on “The Complexity of Complexity” , Bath, Dec 2011. slide-44
    45. 45. Consequences of Context-Dependency IV: noise• Whilst some noise comes from an identified source within a system (e.g. heat noise)• Other noise comes from without (e.g. the babble of a crowd around a conversation)• This kind of noise is recognisable as an extra- contextual “leak”, due to the imperfection of the context heuristic (or poorly chosen context)• Such noise is not necessarily random…• …indeed it may well be more productive to look for the context-dependency rather than “dumping” the lack of fit as random “noise”• (Possibilistic as opposed to probabilistic models)• e.g. Peter Allen‟s “Invaders” in Evolution
    46. 46. Conclusions• In the face of complexity, context-dependency is unavoidable• Accepting and thinking about context-dependency need not lead to sloppy, relativistic or bad science• In fact it is desirable, since simple systems are the special case and other trade-offs are worse…• …such as gaining generality at the loss of validity• It may lead to pushing the boundaries of science forward a bit more and avoiding some pitfalls• It could motivate a move to a more complex and structural understanding of what is observed• We may end up with a “patch work” of locally coherent model clusters of a lot of different kinds – science may have to be more like zoology• Science being imperfect or incomplete does not invalidate its project
    47. 47. The End Bruce Edmonds http://bruce.edmonds.nameCentre for Policy Modelling The SCID Project