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

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.

4. PRINCIPLES

5. RELATIONALISM
MOVING PERSPECTIVE

6. MODULARITY
MODULARITY
=
ROBUSTNESS & ADAPTIVITY

7. WAVES
OSCILLATORY DYNAMICS
=
METASTABILITY

8. transient dynamics
affordances + perception

11. 1. NETWORKS

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 flow

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 flow of in~formation in network
• activation and propagation patterns
• identification 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).

25. Exercise: Deconstruction

27. https://infranodus.com/chinesemedicine/organs?hide_always=1
Exercise: Fluid Elasticity

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?

30. 2. CONTAGION

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

32. Different susceptibility of every node

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

36. What is 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.

38. What is chaos?

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, influenced 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 finally

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)

52. Exercise
connectivity basis
breathing + movement + state

53. Exercise
contamination
breathing + movement + state

55. 3. INFILTRATIONTACTICS

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 influence 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 one’s 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 influenced)

68. 1 2
43

70. #improvisation #polysingularity #perception
tension
relaxed
far away
close contact
fast
slow

71. Exercise
discussion

72. Kello, C.T.,Anderson, G. G., Holden, J. G., &Van Orden, G. C. (2008).The Pervasiveness of 1/f Scaling in Speech Reflects the Metastable Basis of Cognition. Cognitive Science, 32(7), 1217–31
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), 381–384.
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), 428–435.
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), 440–442.
Kitano, H. (2004). Biological robustness. Nature Reviews. Genetics, 5(11), 826–37
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. 1860–1865). Nice: IEEE.
Reynolds,A. M., Bartumeus, F., Kölzsch,A., & van de Koppel, J. (2016). Signatures of chaos in animal search patterns. Scientific 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), 1955–1961
D’Mello, S., Dale, R., & Graesser,A. (2011). Disequilibrium in the mind, disharmony in the body. Cognition & Emotion, 00(00), 1–13. 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), 357–372
Kuperman, M., & Abramson, G. (2001). Small World Effect in an Epidemiological Model. Physical Review Letters, 86(13), 2909–2912. 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