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CAUSATION, ORGANISATION & EMERGENCE Fabio Boschetti and David Batten CSIRO, Australia
CAUSATION, ORGANISATION & EMERGENCE
Fabio Boschetti and David Batten
CSIRO, Australia
Warnings
Summary of lucubrations over many yearsWork in progressClear conclusions need developing Speakers background:
Numerical optimisation Modelling (physical, ecological, social) Relation between computation and Complex System Science Can we do CSS on a computer at all? What CSS?
What are the minimum ingredients I need to generate both causation and emergence?
Ultimate testYou really understand an algorithm when you've programmed it (Chaitin, 1997)
What are the minimum ingredients I need to generate both causation and emergence?
Understanding Prediction
Outline not all behaviours are causal it is useful for us to discriminate between entailment and causation it is useful to identify causation with intervention there is a strong relation between causation and emergence
not all emergent processes are causal all causal processes are emergent
it is very hard to make sense of this picture only in terms of behaviours it is easier in terms of interaction or relations or organisation
some relations act by constraining elements behaviour -> symmetry breaking (maybe these can be modelled)some relation act by generating novelty (these require external intervention = open system)
not all behaviours are causal it is useful for us to discriminate between entailment and causation it is useful to identify causation with intervention Entailment: logical necessity or physical inevitabilityP Q or PQ or If P then QIntervention: an action external to the system that produces an effect by altering the course of a process
Intervention: an action external to the system that produces an effect by altering the course of a processCausation as intervention: by imposing a chosen perturbation on event a and observing the consequence on event b we may be able to unravel the underlying causal relation between a and b (Pearl)Useful causation requires control. Clearly it is valuable to know that malaria results from mosquitoes.
while it is true that mosquitoes follow the laws of physics, we do not usually say that malaria is caused by the laws of physics (the universal cause).
That is because we can hope to control mosquitoes, but not the laws of physics Pattee, 1997
Intervention: an action external to the system that produces an effect by altering the course of a processCausation as intervention: by imposing a chosen perturbation on event a and observing the consequence on event b we may be able to unravel the underlying causal relation between a and b (Pearl)Causation as control: we can hope to control mosquitoes, but not the laws of physics Pattee, 1997Causation as agency: an event A is a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence of B. (Menzies and Price, 1993)Neither intervention nor agency imply human intervention; they represent a relation
Neither intervention nor agency imply human intervention; they represent a relation
Neither intervention nor agency imply human intervention; they represent a relation
Intervention: an action external to the system that produces an effect by altering the course of a processCausation as intervention: by imposing a chosen perturbation on event a and observing the consequence on event b we may be able to unravel the underlying causal relation between a and b (Pearl)Causation as control: we can hope to control mosquitoes, but not the laws of physics Pattee, 1997Causation as agency: an event A is a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence of B. (Menzies and Price, 1993)Causation as asymmetry: asymmetry in correlation, asymmetry in agency/control,Principle of Independence (Hausman, 1998)
Causation as asymmetry: asymmetry in correlation, asymmetry in agency/control,Principle of Independence (Hausman, 1998)Multiple effects of common causes need to be correlated; multiple causes of common effect do not
We can intervene in the cause to alter the effect; we can not intervene on the effect to alter the cause
Independence principle: every effect must have at least two independent causes
Intervention: an action external to the system that produces an effect by altering the course of a processCausation as intervention: by imposing a chosen perturbation on event a and observing the consequence on event b we may be able to unravel the underlying causal relation between a and b (Pearl)Causation as control: we can hope to control bacteria and mosquitoes, but not the laws of physics Pattee, 1997Causation as agency: an event A is a cause of a distinct event B just in case bringing about the occurrence of A would be an effective means by which a free agent could bring about the occurrence of B. (Menzies and Price, 1993)Causation as asymmetry: asymmetry in correlation, asymmetry in agency/control,Principle of Independence (Hausman, 1998)
Emergence
there is a strong relation between causation and emergence
not all emergent processes are causal all causal processes are emergent
Pattern Formation Prediction
Intrinsic emergence Information processing for trade agents
Emergence of causal power we can intervene on the stock market and affect the economyCausal emergence: the arising of a system property on which intervention can be exerted without manipulating the system components (Boschetti and Gray, 2007).
Cellular AutomataHumanPattern FormationCausal power not all emergent processes are causal all causal processes are emergent
ModelRules Behaviour ofEconomically Rational AgentExperimental EconomicsIntervention
ModelEvent c is not caused by b since after b happens c follows as a logic necessityWhat changes c changes also b (correlated)What causes c?What do I need to do to actuate a chance in c?If I can not interact with the run, I have to change input or codeControl lies only in the input and codeLogic entailment (Rosen)Two alternatives:If I can interact with the run I need to preconceive all possible interventions, since they need to be written in the code
Impossible
ModelCausation as intervention: by imposing a chosen perturbation on event b and observing the consequence on event c we may be able to unravel the underlying causal relation between b and c (Pearl)Causation as agency: an event b is a cause of a distinct event c just in case bringing about the occurrence of b would be an effective means by which a free agent could bring about the occurrence of c. (Menzies and Price, 1993)Causation as asymmetry: asymmetry in correlation, asymmetry in agency/control, Principle of Independence (Hausman, 1998)
Logic entailmentEffective control / causation
Useful causation requires control. Clearly it is valuable to know that malaria results from mosquitoes. ..
while it is true that mosquitoes follow the laws of physics, we do not usually say that malaria is caused by the laws of physics (the universal cause).
That is because we can hope to control mosquitoes, but not the laws of physics Patte, 1997
Useful causation requires control. Clearly it is valuable to know that malaria results from mosquitoes. ..
while it is true that mosquitoes follow the laws of physics, we do not usually say that malaria is caused by the laws of physics (the universal cause).
That is because we can hope to control mosquitoes, but not the laws of physics Patte, 1997
Distributed sensorsFeatures detection algorithmNew discoveriesNew scientific laws
AI RulesIntelligence
00011011000110110001101100011011..01101100011011000110110001101100..Statistical Complexity = C1Statistical Complexity = C2
Machine 1Words = {00, 01, 10, 11}Transition = {0001, 0110, 10 11, 11 00}..00011011000110110001101100011011..0110110001101100011011000110
Change in statistical complexity..non stationarity..
111011..00..Statistical Complexity = C1Statistical Complexity = C2C1C2
00011011000110110001101100011011..22233233222332332223323322233233..
Machine 1Words = {00, 01, 10, 11}Transition = {0001, 0110, 10 11, 11 00}0001101100011011000110110001101122233233222332332223323322233233halthalt0022
010010010100011011000110110001122233233222332332223323322233233halt13..
What happened
13 is not a possible word for either Machine 1 or Machine 2 It is not a wff (well-formed-formula) for either systemsIt is genuinely novel
3301
010010010100011011000110110001122233233222332332223323322233233halt13..3301
Ingredients
Some behaviour Some basic interactionSome ability to handle novel input
Machine 1Words = {00, 01, 10, 11}Transition = {access memory 3 steps back and copy two consecutive symbols}0100100101000110110001101100011332223323322233233222332332223323310halt13..
Types of behaviours
Entailments RelationsGeneration of higher level unitCausation
Outline not all behaviours are causal it is useful for us to discriminate between entailment and causation it is useful to identify causation with intervention there is a strong relation between causation and emergence
not all emergent processes are causal all causal processes are emergent
it is very hard to make sense of this picture only in terms of behaviours it is easier in terms of interaction or relations or organisation
some relations act by constraining elements behaviour -> symmetry breaking (maybe these can be modelled) some relation act by generating novelty (these require external intervention = open system)
What are the minimum ingredients I need to generate both causation and emergence?
SummaryEntities need to do something; have properties or behaviours
Entities need to interact; in order to have anything new happening
Interactions may happen as entailments; which creates a new closed system/unit
Some interaction may be causal; these are characterised by a special kind of relation; they require certain asymmetries to occur
At a different scale/scope, the relation allowing intervention may not be detected and the system may appear as an entailment
The behaviour should not be fully determined in order to generate real novelty
The behaviour should not be determined only in terms of structures in the system; there should be some space to process structures not seen before
Normally, in our models, we do not account for interaction and we fully specify behaviours and properties
SummaryLimitations of formal systemsClosed systems, No novelty, Uncomputability, Chaos Closed | Complex Systems | OpenFar from equilibrium, energy & information flows, Novelty Importance of organisation to generate new behavioursSelf-organisation, Prigogine, Laughing..Causation as a relation between entities/processesAgency theory, Menzies and Price, Pattee.. Causal asymmetriesHausman (1998)Statistically novel causal behaviours Statistically novelnon-causal behaviours Ability to handle novel situationsGenuinely novelcausal behaviours Genuinely novelnon-causal behaviours InteractionInternal to the systemExternal to the system
Things to checkMathematical / formal tools to describe changes in context and structure (group theory and beyond)Shadelength = Poleheight * F [Sunangle ]F [Sunangle ] = Poleheight / ShadelengthShadelength Poleheight * F [Sunangle ]F [Sunangle ] Poleheight / ShadelengthGroup = {A, Property, Property, , .. } Closed to interaction Forward problemInverse problem
Things to checkUltimate testYou really understand an algorithm when you've programmed it (Chaitin, 1997)Ultimate questionMathematical / formal tools to describe changes in context and structure (group theory and beyond) Relation between hardware and software computer science and biologyMore on causal asymmetries and HausmanIntuitive perception of causality from shape and symmetries in terms of history of an entityIn general many of the things I do not know are surely well known in other fields..
ReferencesHausman, D., 1998. Causal asymmetries. Cambridge University Press., Cambridge.Menzies, P. and Price, H., 1993. Causation as a secondary quality. The British Journal for the Philosophy of Science 44:187-203.Pattee, H., 1997. Causation, Control, and the Evolution of Complexity. In: P.B. Andersen, C. Emmeche, N.O. Finnemann and P.V. Christiansen (Editor), Downward Causation. University of rhus Press, rhus, pp. 322-348.Laughlin, R., 2005. A Different Universe: Remaking Physics from the Bottom Down Basic Books, New York.Leeuwen, J and Wiedermann, J, The emergent computational potential of evolving artificial living systems. Source, AI Communications archive. Volume 15 , Issue 4 Milner, R., 1993. Elements of interaction: Turing award lecture. ACM, pp. 78-89.Wegner, P., 1997. Why interaction is more powerful than algorithms. ACM, pp. 80-91.Wiedermann, J. and Leeuwen, J., 2002. The emergent computational potential of evolving artificial living systems. IOS Press, pp. 205-215.
ReferencesBoschetti, Causality, emergence, computation and unreasonable expectations, Synthese, in print.Prokopenko, Boschetti & Ryan, 2009, An Information-Theoretic Primer On Complexity, Self-Organisation And Emergence, Complexity, DOI: 10.1002/cplx.20249.Batten, Salthe & Boschetti, 2008, Visions of Evolution: Self-organization proposes what natural selection disposes, Biological Theory, Vol. 3, No. 1, Pages 17-29Boschetti, McDonald & Gray, 2008, Complexity of a modelling exercise: a discussion of the role of computer simulation in Complex System Science, Complexity, 13, 6, pp 21-28Boschetti & Gray. 2007, A Turing test for Emergence, in M. Prokopenko (ed.), Advances in Applied Self-organizing Systems, Springer-Verlag, London, UK, 2007 , pp 349-364Boschetti & Gray, 2007, Emergence and Computability, Emergence: Complexity and Organization, Volume 9 Issues 1-2, 120-130
For more information
http://www.per.marine.csiro.au/staff/Fabio.Boschetti/
Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Rather than describing what a Complex System is, we show what a Complex System does, by telling the story.
many large, but not too many, components interact in a non trivial fashion; this roughly set the boundary of the system the system is open, that is, receives energy/information/matter from the environment) the interactions lead to symmetry breaking, which in turns, lead to coordinated behaviour to do so, no central director/template is used, so we say that the system self-organisesThe coordination results in patterns detectable by an external observer; that is, structures can provide new properties to the systems itself and consequently new behaviours emerge from the system coordination and emergence may arise from response to environment, which we call adaptation when adaptation occurs across generations at a population level we say that the system evolved now, at new scale, the system can be identified as a novel unit coordinated emergent properties give rise to larger scale effects. These can be observed as coherent entities at lower resolution than is needed to observe the components. The system can be identified as a novel unit of its own and can interact with other systems/processes, that is, it becomes a building block for new iterations and the cycle can repeat
Our purpose is thus to describe what we mean by Complexity, emergence, self-organisation and adaptation, in such a way that the above story is coherent and meaningful.Self-Organisation is the spontaneous increase in complexity of a system.
Since no system is isolated and no purely spontaneous dynamics is possible, we can then more realistically write:
in a self-organising system, the amount of information flowing from the outside is strictly less than the change in statistical complexity within the system
IOutside < C(t+t)- C(t)
If, rather than talking of information, we would like to talk about energy, we may say that
Energy(stimulus)
