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TOPDRIM: Update WP2

March 2013. Rick Quax, Peter M.A. Sloot. TOPDRIM: Update WP2. Outline. Our research so far ( bird’s eye view) Information dissipation (ID) in networks ID in immune response to HIV ID in financial market Addressing WP2 tasks Ideas for collaboration.

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TOPDRIM: Update WP2

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  1. March 2013 Rick Quax, Peter M.A. Sloot TOPDRIM: Update WP2

  2. Outline • Our research so far (bird’seye view) • Information dissipation (ID) in networks • ID in immune response to HIV • ID in financial market • Addressing WP2 tasks • Ideasforcollaboration

  3. Our view of a complex system • node dynamics + complex network = complex system + = Each node has a statewhich it changes over time The system behavior is complexcompared to an individual node Nodes interact with each otheri.e., their states influence each other

  4. Our view of a complex system • node dynamics + complex network = complex system problem + = Each node has a statewhich it changes over time The system behavior is complexcompared to an individual node Nodes interact with each otheri.e., their states influence each other

  5. Information processing in complex systems • Let’s say the state of A influences the state of B… state state interaction Node A Node B

  6. Information processing in complex systems • We wouldliketo ‘see’ influencespreading state state interaction Node A Node B

  7. Information processing in complex systems state • Different influences spread through the networksimultaneously Node C state state interaction Node A Node B state Howto makemakethisquantitative? Node D

  8. Solution: information theory? How much informationis stored in A? Entropy: state state How much informationin A is also in B? state Node A Node A Mutual information Node B (pitfall: MI = causality + correlation)

  9. Information dissipation How long is the informationabout a node’s stateretained in the network? Information dissipation time measures of influence of a single nodeto the behavior of the entirenetwork! How farcan the informationabout a node’s state reachbeforeit is lost? Information dissipationlength

  10. Our research #1

  11. Information dissipation time • Node dynamics: (local) Gibbs measure • I.e., edgesrepresentaninteractionpotentialtowhich a node can quasi-equilibrate • Network structure • Large • Randomizedbeyonddegreedistribution • growslessthanlinear in

  12. Results: analyticalandnumerical Information dissipation time D(s)of a node s Number of interactionsof a node proof: D(s) willeventuallybea decreasingfunction of ks

  13. Our research #2

  14. Cell types in immune responseandtheirinteractions Susceptibility of HIV immuneresponse toperturbation Agent-basedsimulations IDT Susceptibility of immune system

  15. Our research #3

  16. Leadingindicatorin financialmarkets We are nowworking onan agent-based model ofbanksthatcreate a dynamicnetwork of IRS contracts, tostudycriticaltransitions

  17. How this fit the Tasks in WP2

  18. Task 2.1 • “(…) In particular, UvA will derive an analytical expression for the information dissipation.” • We have definedandanalyzedboth information dissipationtime as well as information dissipationlength • IDT in review process at J. R. Soc. Interface • IDL in review process at ScientificReports

  19. Cell types in immune responseandtheirinteractions Task 2.2 • “UvA will study the decay rate of information as function of noise to identify it as a universal measure of how susceptible the system is to noise (…) for a variety of network topologies” • We didnotyet start this exact task • Possiblecollaboration: comparethismeasurewith the ‘barcode’ of the network • We are exploringanimplementation in the ComputationalExploratory (Sophocles) • However, we are studyinga more specificproblem: • “How susceptible is the HIV immune response to perturbations (such as therapy) over time?” • Application: at which moment in time should HIV-treatment bestarted? • ‘Complex’ network in the sense that thenode dynamics are complex, not the networktopology Susceptibility of immune system

  20. Task 2.3 • “UvA will develop a critical dissipation threshold which any system must exceed before it can transition as a whole.” • We do not (yet) have ananalyticalexpressionfor a threshold • We havestudied the use of ‘information dissipationlength’ todetect a criticaltransition(Lehman Brothers) in the financial derivatives market (real data) • In revisionprocess at ScientificReports

  21. Task 2.4 • Refineandintegrate • …

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