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(A very patient)
@_fernando_rosas concludes his talk with a few key points after a long series of questionspic.twitter.com/6AYkmNETJL
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Measuring emergence as the sum of downward causation and causal decoupling with definitions given by..pic.twitter.com/AKT5G4lEN6
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Downward causation vs causal coupling. The whole (through V) causing a particular element of X in the future vs V predicting V in the futurepic.twitter.com/1goUqJFH6Y
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(This definition relies on what Pedro talk about earlier regarding synergistic channels)pic.twitter.com/28t1YZaYrx
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@_fernando_rosas’ definition of causal emergence with a more practical way to define V based on the synergy of all the components of Xpic.twitter.com/P4qlKHPvkK
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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 informationpic.twitter.com/mF0UO2DZZg
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..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)pic.twitter.com/q79G6TzFkS
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Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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First, what is emergence? “The sum is more than the parts”pic.twitter.com/OgrfTmsiW1
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To give meaning to Phi, Pedro proposes to ground synergy in Shannon information as a channel capacity give a definition of a “synergistic channel”pic.twitter.com/IJknQEupd6
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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 Phipic.twitter.com/o26eAAB2VU
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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 negativepic.twitter.com/uJmoER4oje
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How do we measure IIT? There are few proposals but they don’t seem to agree too much.. (see https://www.mdpi.com/1099-4300/21/1/17 …)pic.twitter.com/WenGArEHrE
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.. a new definition was introduced (Phi ID) to find synergy and redundancy in IIT (an example)pic.twitter.com/pekLCimyAZ
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IIT (2.0) doesn’t cope too well with synergy and redundancy (they can’t be easily defined using PID) so..pic.twitter.com/kLo4TXndW6
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Part 2: what is partial information ti in decomposition? (With a formal definition of redundancy and synergy)pic.twitter.com/hkQYAI3eeN
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..showing a complementary view on the O-information measure previously definedpic.twitter.com/mPlNHJtiM8
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The origins of IIT, where synergy makes a first appearance (in IIT at least)pic.twitter.com/4apxDJ1x5X
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