Manuel Baltieri

@manuelbaltieri

Postdoc . Embodied and enactive cognition, AI, ALife, Active Inference. Information (uncertainty) and control theory (dynamics) for cognitive science.

Tokyo
Vrijeme pridruživanja: svibanj 2009.

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  1. 29. sij

    (A very patient) concludes his talk with a few key points after a long series of questions

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  2. 29. sij

    Measuring emergence as the sum of downward causation and causal decoupling with definitions given by..

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  3. 29. sij

    Downward causation vs causal coupling. The whole (through V) causing a particular element of X in the future vs V predicting V in the future

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  4. 29. sij

    (This definition relies on what Pedro talk about earlier regarding synergistic channels)

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  5. 29. sij

    ’ definition of causal emergence with a more practical way to define V based on the synergy of all the components of X

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  6. 29. sij

    Emergence as the ability to predict the future of the components of a process X, only when all the components of X are available to define a supervenient variable V which contains unique information

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  7. 29. sij

    ..2b) emergence is just due to the fact that the underlying is too complicated to explain otherwise (it doesn’t exist but it’s helpful as a concept)

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  8. 29. sij

    ..2a) strong emergence (emergence does exist)

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  9. 29. sij

    Approaches to emergence: 1) no emergence..

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  10. 29. sij

    First, what is emergence? “The sum is more than the parts”

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  11. 29. sij

    Last for , , on causal emergence

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  12. 29. sij

    To give meaning to Phi, Pedro proposes to ground synergy in Shannon information as a channel capacity give a definition of a “synergistic channel”

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  13. 29. sij

    Looking at Phi alone doesn’t explain much about a system (these three examples have the same Phi according to IIT 2), but using Phi ID one can show different contributions that help in tracking the role of different variables and their role for Phi

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  14. 29. sij

    Phi ID applications: we can find for example what makes some phi measures go negative and “rescue” them (if we so choose) by decomposing the measure and finding what makes it go negative

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  15. 29. sij

    How do we measure IIT? There are few proposals but they don’t seem to agree too much.. (see )

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  16. 29. sij

    .. a new definition was introduced (Phi ID) to find synergy and redundancy in IIT (an example)

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  17. 29. sij

    IIT (2.0) doesn’t cope too well with synergy and redundancy (they can’t be easily defined using PID) so..

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  18. 29. sij

    Part 2: what is partial information ti in decomposition? (With a formal definition of redundancy and synergy)

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  19. 29. sij

    ..showing a complementary view on the O-information measure previously defined

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  20. 29. sij

    The origins of IIT, where synergy makes a first appearance (in IIT at least)

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