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EightOS Workshop: Networks and Nature

Dmitry Paranyushkin
Dmitry Paranyushkin

際際滷show from the EightOS July 2019 workshop in Berlin. Learn more and RSVP for the future programmes: http://8os.io

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OS ACTIVATION SESSION, BERLIN, 6-7 JULY 2019 NETWORKS AND NATURE
OS is a body~mind operating system.
Programming our behavior using the physical movement as a programming language.
PRINCIPLES
RELATIONALISM MOVING PERSPECTIVE
MODULARITY MODULARITY = ROBUSTNESS & ADAPTIVITY
WAVES OSCILLATORY DYNAMICS = METASTABILITY
transient dynamics affordances + perception
EightOS Workshop: Networks and Nature
EightOS Workshop: Networks and Nature
1. NETWORKS
A network can be a representation of a process in time (e.g. a record of interactions) as well as a physical infrastructure for informational 鍖ow
In any case, the structure of the network determines the dynamic qualities of the process that it represents.
The nodes are the entities, the edges are the relations between them a social network at a party
collaborator networks The nodes are the people, the edges are the collaborations between them
moon white round rotates earth sun cluster 1: the moon is white and round cluster 2: the moon rotates around the earth cluster 3: the earth rotates around the sun structural gap = novel ideas networks of ideas, text networks
The most resilient system is the human brain perception - activation of different groups of neurons across the different frequency spectrums Rabinovich, M. I., & Muezzinoglu, M. K. (2010).Bhowmik, D., & Shanahan, M. (2013).
Young vs Old brains Meunier et al (2008)
Network of Body Organs
Exercise: Feeling the Body Network nodes and relations
inter~action? Network theory can help us understand the dynamics of any system, based on: the network structure the 鍖ow of in~formation in network activation and propagation patterns identi鍖cation of the critical points
Key Network Metrics: Betweenness centrality: which nodes are most likely to appear on the path between any 2 randomly chosen nodes. = global connectors
Key Network Metrics: Communities: which nodes are more closely related together and on which basis?
Key Network Metrics: Network structure Source: Connected Brains Watts, D. J., & Strogatz, S. H. (1998).
Exercise: Deconstruction
EightOS Workshop: Networks and Nature
https://infranodus.com/chinesemedicine/organs?hide_always=1 Exercise: Fluid Elasticity
your body as a network which part interacts with which other part which part interacts with the environment what is the feeling / intention that it produces what are the key nodes? how do they affect the rest of the structure what is resistance and what is adaptation in this context?
EightOS Workshop: Networks and Nature
2. CONTAGION
networks represent a process in time certain network structures lead to oscillations oscillations = non equilibrium stability Creating conditions in the physical / social body for a certain kind of dynamics
Different susceptibility of every node
Different kinds of structures
source: Nodus Labs / Complex Networks by Sole &Valverde 2004
source: Christos Makropoulos small-world scale-free random high entropymedium entropy medium-low entropy
What is entropy?
Entropy is not a measure of disorder. The second law of thermodynamics says that entropy increases over time.A system strives towards an equilibrium, that is, equal distribution of energy across all of its elements. We associate it with disorder, but only because from our subjective perspective of life total equilibrium is a stasis, death.
What is chaos?
Chaos is not disorder or randomness. In chaos theory chaotic system is the one that is sensitive to initial conditions, unpredictable in the short-term, but and may exhibit orderly behavior over time. 1/f (pink / fractal) noise is the symptom of a chaotic system. distribution of connections in small-world (and scale free) networks follow the same power law as the 1/f noise.
Within a certain timespan / context: the higher is the value of the oscillation (e.g. an amplitude of movement or scale degree of change), the less frequent it is. The smaller the change, the more often it happens. Kello, C.T.,Anderson, G. G., Holden, J. G., &Van Orden, G. C. (2008)
random network: most nodes have an equal number of connections scale-free network: most nodes (Y) have a few connections, only some many
If life represents a certain order, time represents entropy, then chaos may be the product of this dialectic.
PROPAGATION OF INFLUENCE Scale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008)
EPIDEMIC MODELS S: Susceptible, I: Infected, R: Removed/Recovered (Ball 1997; Newman 2002; Newman et al 2006;Watts 2002) S I R S I S S I R S
LOCAL CONTAGION Information Cascades: herd-like behavior, in鍖uenced by the others, when conversion threshold is exceeded (Watts 2002; Hui et al 2010; Young 2002) most friends adopted a trend, so the blue node does the same 鍖nally
GLOBAL CONTAGION Message =Virus Watch the video on vimeo.com/36958670
CONTAGION = INFLUENCE sharing a state, transferring into another state
THE NETWORK STRUCTURE DEFINES How many different states a system can hold How those states will change from one to another (propagation) How will the system evolve over time
example from immunology networks with low entropy, high order do not propagate networks with high entropy, random connections - propagate very quickly but very short-lived networks with scale-free (1/f) fractal (self-repetitive) structure, can both propagate and maintain
2. RANDOM SHORTCUTS Scale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008) * not too many!
Therefore: the structure of our infrastructure will determine how oscillatory our system will be. The more independent oscillations are available, the more distinct states this system will have (metastability)
Exercise connectivity basis breathing + movement + state
Exercise contamination breathing + movement + state
EightOS Workshop: Networks and Nature
3. INFILTRATIONTACTICS
1. GIANT COMPONENT Most nodes must belong to the same component for the global epidemics to occur (Watts 2002; Newman et al 2006) no connections between the nodes = cascades not possible many connections between the nodes = cascades can occur
2. RANDOM SHORTCUTS Scale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008) * not too many!
moon white round rotates earth sun cluster 1: the moon is white and round cluster 2: the moon rotates around the earth cluster 3: the earth rotates around the sun structural gap = novel ideas 3. IDENTIFY STRUCTURAL GAPS
EightOS Workshop: Networks and Nature
4. FOCUS ON HUBS OR BECOME ONE They will help spread the in鍖uence further
EightOS Workshop: Networks and Nature
5. START WITH A GROUP Rapid spread of disease within tightly connected communities can lead to an epidemic outbreak even if the links are loose
WHY? Because once the contagion is spread within the group, it will spread across super-network to the other groups (Ball 1997).
STRATEGIES OF RESISTANCE Leave the number of susceptibles the same in each group, thus preventing the virus from spreading within and throughout. Better than random nodes, but still not perfect - immunize random groups Optimal - leave the same number of susceptibles in each group
POLYSINGULARITY Belonging to several distinct communities at once; Introducing a degree of randomness in ones interactions; Integrating periphery, expelling the hubs; Maintaining an overview of the existing centers, while belonging to one of them at every moment of time;
SUSCEPTIBILITY Stay in one community or state; Explore it for a while; Focus on the local interactions; Hold on to the connections you have; Belong to what you are a part of; Immerse; No outside;
Exercise network formations game entity and contagion (follow + get in鍖uenced)
1 2 43
EightOS Workshop: Networks and Nature
#improvisation #polysingularity #perception tension relaxed far away close contact fast slow
Exercise discussion
Kello, C.T.,Anderson, G. G., Holden, J. G., &Van Orden, G. C. (2008).The Pervasiveness of 1/f Scaling in Speech Re鍖ects the Metastable Basis of Cognition. Cognitive Science, 32(7), 121731 References Yamada, N. (1995). Posture as a Dynamic Stable State of a Body. Research and Clinical Center for Child Development. Bak, P.,Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality:An explanation of the 1/ f noise. Physical Review Letters, 59(4), 381384. Katerndahl, D., Ferrer, R., Best, R., & Wang, C.-P. (2007). Dynamic patterns in mood among newly diagnosed patients with major depressive episode or panic disorder and normal controls. Bystritsky, a, Nierenberg, a a, Feusner, J. D., & Rabinovich, M. (2012). Computational non-linear dynamical psychiatry:A new methodological paradigm for diagnosis and course of illness. Journal of Psychiatric Research, 46(4), 428435. Rabinovich, M. I., Huerta, R.,Varona, P., & Afraimovich,V. S. (2008).Transient cognitive dynamics, metastability, and decision making. PLoS Computational Biology, 4(5), e1000072 Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440442. Kitano, H. (2004). Biological robustness. Nature Reviews. Genetics, 5(11), 82637 Matthey, L., Righetti, L., & Ijspeert,A. J. (2008). Experimental study of limit cycle and chaotic controllers for the locomotion of centipede robots. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 18601865). Nice: IEEE. Reynolds,A. M., Bartumeus, F., K旦lzsch,A., & van de Koppel, J. (2016). Signatures of chaos in animal search patterns. Scienti鍖c Reports, 6, 23492. Reynolds,A. M., Smith,A. D., Menzel, R., Greggers, U., Reynolds, D. R., & Riley, J. R. (2007). DISPLACED HONEY BEES PERFORM OPTIMAL SCALE-FREE SEARCH FLIGHTS. Ecology, 88(8), 19551961 DMello, S., Dale, R., & Graesser,A. (2011). Disequilibrium in the mind, disharmony in the body. Cognition & Emotion, 00(00), 113. http://doi.org/10.1080/02699931.2011.575767 Bhowmik, D., & Shanahan, M. (2013). Metastability and inter-band frequency modulation in networks of oscillating spiking neuron populations. PloS One, 8(4), e62234 Bhowmik, D., & Shanahan, MRabinovich, M. I., & Muezzinoglu, M. K. (2010). Nonlinear dynamics of the brain: emotion and cognition. PhysicsUspekhi, 53(4), 357372 Kuperman, M., & Abramson, G. (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, 86(13), 29092912. http://doi.org/10.1103/PhysRevLett.86.2909 Bateson, G (1975). Steps to an Ecology of Mind. Routlege www.noduslabs.com | www.8os.io | www.polysingularity.com Meunier et al (2008).Age-related changes in modular organization of human brain functional networks

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EightOS Workshop: Networks and Nature

  • 1. OS ACTIVATION SESSION, BERLIN, 6-7 JULY 2019 NETWORKS AND NATURE
  • 2. OS is a body~mind operating system.
  • 3. Programming our behavior using the physical movement as a programming language.
  • 12. A network can be a representation of a process in time (e.g. a record of interactions) as well as a physical infrastructure for informational 鍖ow
  • 13. In any case, the structure of the network determines the dynamic qualities of the process that it represents.
  • 14. The nodes are the entities, the edges are the relations between them a social network at a party
  • 15. collaborator networks The nodes are the people, the edges are the collaborations between them
  • 16. moon white round rotates earth sun cluster 1: the moon is white and round cluster 2: the moon rotates around the earth cluster 3: the earth rotates around the sun structural gap = novel ideas networks of ideas, text networks
  • 17. The most resilient system is the human brain perception - activation of different groups of neurons across the different frequency spectrums Rabinovich, M. I., & Muezzinoglu, M. K. (2010).Bhowmik, D., & Shanahan, M. (2013).
  • 18. Young vs Old brains Meunier et al (2008)
  • 19. Network of Body Organs
  • 20. Exercise: Feeling the Body Network nodes and relations
  • 21. inter~action? Network theory can help us understand the dynamics of any system, based on: the network structure the 鍖ow of in~formation in network activation and propagation patterns identi鍖cation of the critical points
  • 22. Key Network Metrics: Betweenness centrality: which nodes are most likely to appear on the path between any 2 randomly chosen nodes. = global connectors
  • 23. Key Network Metrics: Communities: which nodes are more closely related together and on which basis?
  • 24. Key Network Metrics: Network structure Source: Connected Brains Watts, D. J., & Strogatz, S. H. (1998).
  • 28. your body as a network which part interacts with which other part which part interacts with the environment what is the feeling / intention that it produces what are the key nodes? how do they affect the rest of the structure what is resistance and what is adaptation in this context?
  • 31. networks represent a process in time certain network structures lead to oscillations oscillations = non equilibrium stability Creating conditions in the physical / social body for a certain kind of dynamics
  • 33. Different kinds of structures
  • 34. source: Nodus Labs / Complex Networks by Sole &Valverde 2004
  • 35. source: Christos Makropoulos small-world scale-free random high entropymedium entropy medium-low entropy
  • 37. Entropy is not a measure of disorder. The second law of thermodynamics says that entropy increases over time.A system strives towards an equilibrium, that is, equal distribution of energy across all of its elements. We associate it with disorder, but only because from our subjective perspective of life total equilibrium is a stasis, death.
  • 39. Chaos is not disorder or randomness. In chaos theory chaotic system is the one that is sensitive to initial conditions, unpredictable in the short-term, but and may exhibit orderly behavior over time. 1/f (pink / fractal) noise is the symptom of a chaotic system. distribution of connections in small-world (and scale free) networks follow the same power law as the 1/f noise.
  • 40. Within a certain timespan / context: the higher is the value of the oscillation (e.g. an amplitude of movement or scale degree of change), the less frequent it is. The smaller the change, the more often it happens. Kello, C.T.,Anderson, G. G., Holden, J. G., &Van Orden, G. C. (2008)
  • 41. random network: most nodes have an equal number of connections scale-free network: most nodes (Y) have a few connections, only some many
  • 42. If life represents a certain order, time represents entropy, then chaos may be the product of this dialectic.
  • 43. PROPAGATION OF INFLUENCE Scale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008)
  • 44. EPIDEMIC MODELS S: Susceptible, I: Infected, R: Removed/Recovered (Ball 1997; Newman 2002; Newman et al 2006;Watts 2002) S I R S I S S I R S
  • 45. LOCAL CONTAGION Information Cascades: herd-like behavior, in鍖uenced by the others, when conversion threshold is exceeded (Watts 2002; Hui et al 2010; Young 2002) most friends adopted a trend, so the blue node does the same 鍖nally
  • 46. GLOBAL CONTAGION Message =Virus Watch the video on vimeo.com/36958670
  • 47. CONTAGION = INFLUENCE sharing a state, transferring into another state
  • 48. THE NETWORK STRUCTURE DEFINES How many different states a system can hold How those states will change from one to another (propagation) How will the system evolve over time
  • 49. example from immunology networks with low entropy, high order do not propagate networks with high entropy, random connections - propagate very quickly but very short-lived networks with scale-free (1/f) fractal (self-repetitive) structure, can both propagate and maintain
  • 50. 2. RANDOM SHORTCUTS Scale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008) * not too many!
  • 51. Therefore: the structure of our infrastructure will determine how oscillatory our system will be. The more independent oscillations are available, the more distinct states this system will have (metastability)
  • 56. 1. GIANT COMPONENT Most nodes must belong to the same component for the global epidemics to occur (Watts 2002; Newman et al 2006) no connections between the nodes = cascades not possible many connections between the nodes = cascades can occur
  • 57. 2. RANDOM SHORTCUTS Scale-free networks with shortcuts are better in propagating, dense networks are better for cascades. (Kuperman 2001; Yan et al 2008) * not too many!
  • 58. moon white round rotates earth sun cluster 1: the moon is white and round cluster 2: the moon rotates around the earth cluster 3: the earth rotates around the sun structural gap = novel ideas 3. IDENTIFY STRUCTURAL GAPS
  • 60. 4. FOCUS ON HUBS OR BECOME ONE They will help spread the in鍖uence further
  • 62. 5. START WITH A GROUP Rapid spread of disease within tightly connected communities can lead to an epidemic outbreak even if the links are loose
  • 63. WHY? Because once the contagion is spread within the group, it will spread across super-network to the other groups (Ball 1997).
  • 64. STRATEGIES OF RESISTANCE Leave the number of susceptibles the same in each group, thus preventing the virus from spreading within and throughout. Better than random nodes, but still not perfect - immunize random groups Optimal - leave the same number of susceptibles in each group
  • 65. POLYSINGULARITY Belonging to several distinct communities at once; Introducing a degree of randomness in ones interactions; Integrating periphery, expelling the hubs; Maintaining an overview of the existing centers, while belonging to one of them at every moment of time;
  • 66. SUSCEPTIBILITY Stay in one community or state; Explore it for a while; Focus on the local interactions; Hold on to the connections you have; Belong to what you are a part of; Immerse; No outside;
  • 67. Exercise network formations game entity and contagion (follow + get in鍖uenced)
  • 72. Kello, C.T.,Anderson, G. G., Holden, J. G., &Van Orden, G. C. (2008).The Pervasiveness of 1/f Scaling in Speech Re鍖ects the Metastable Basis of Cognition. Cognitive Science, 32(7), 121731 References Yamada, N. (1995). Posture as a Dynamic Stable State of a Body. Research and Clinical Center for Child Development. Bak, P.,Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality:An explanation of the 1/ f noise. Physical Review Letters, 59(4), 381384. Katerndahl, D., Ferrer, R., Best, R., & Wang, C.-P. (2007). Dynamic patterns in mood among newly diagnosed patients with major depressive episode or panic disorder and normal controls. Bystritsky, a, Nierenberg, a a, Feusner, J. D., & Rabinovich, M. (2012). Computational non-linear dynamical psychiatry:A new methodological paradigm for diagnosis and course of illness. Journal of Psychiatric Research, 46(4), 428435. Rabinovich, M. I., Huerta, R.,Varona, P., & Afraimovich,V. S. (2008).Transient cognitive dynamics, metastability, and decision making. PLoS Computational Biology, 4(5), e1000072 Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of small-world networks. Nature, 393(6684), 440442. Kitano, H. (2004). Biological robustness. Nature Reviews. Genetics, 5(11), 82637 Matthey, L., Righetti, L., & Ijspeert,A. J. (2008). Experimental study of limit cycle and chaotic controllers for the locomotion of centipede robots. In 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 18601865). Nice: IEEE. Reynolds,A. M., Bartumeus, F., K旦lzsch,A., & van de Koppel, J. (2016). Signatures of chaos in animal search patterns. Scienti鍖c Reports, 6, 23492. Reynolds,A. M., Smith,A. D., Menzel, R., Greggers, U., Reynolds, D. R., & Riley, J. R. (2007). DISPLACED HONEY BEES PERFORM OPTIMAL SCALE-FREE SEARCH FLIGHTS. Ecology, 88(8), 19551961 DMello, S., Dale, R., & Graesser,A. (2011). Disequilibrium in the mind, disharmony in the body. Cognition & Emotion, 00(00), 113. http://doi.org/10.1080/02699931.2011.575767 Bhowmik, D., & Shanahan, M. (2013). Metastability and inter-band frequency modulation in networks of oscillating spiking neuron populations. PloS One, 8(4), e62234 Bhowmik, D., & Shanahan, MRabinovich, M. I., & Muezzinoglu, M. K. (2010). Nonlinear dynamics of the brain: emotion and cognition. PhysicsUspekhi, 53(4), 357372 Kuperman, M., & Abramson, G. (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, 86(13), 29092912. http://doi.org/10.1103/PhysRevLett.86.2909 Bateson, G (1975). Steps to an Ecology of Mind. Routlege www.noduslabs.com | www.8os.io | www.polysingularity.com Meunier et al (2008).Age-related changes in modular organization of human brain functional networks