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identifying emergence
in complex systems
@thejunglejane
if you put 50 ants on a table
if you put 500,000 ants on a table
adding more ants
relatively simple
foraging for food
building nests
raising livestock
waging war
burying their dead
innate immune system
adaptive immune system
take the lower levels for granted
principle of computational irreducibility
the collective is irreducible to the individual
the whole must be greater than the sum of its parts
emergence
disorganized v. organized complexity
Per Bak
Chao Tang
Kurt Wiesenfeld
self-organized criticality
simple
distributed
scalable
spend water to get water
collective regulation
ants are doing TCP
the independent discovery
of TCP/IP, by humans
David Winter
keepturningleft.co.uk
consensus
scale-free correlation
high signal-to-noise ratio
effective perceptive range
seven nearest neighbors
robustness
many evolutionary cycles
in many different environments
natural selection for collective behavior
we have many biological analogs
of computational problems
ants and congestion control
starlings and consensus
slime mold and network-routing
swarms and distributed search
neuronal spiking and probabilistic inference
fly brains and max independent sets
problem of representation
top-down feedback
simple and abstract
thank you
This document is being distributed for informational and educational purposes only and is not an offer to sell or the soli...
Identifying Emergent Behaviors in Complex Systems - Jane Adams
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Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 1 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 2 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 3 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 4 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 5 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 6 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 7 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 8 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 9 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 10 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 11 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 12 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 13 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 14 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 15 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 16 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 17 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 18 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 19 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 20 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 21 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 22 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 23 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 24 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 25 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 26 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 27 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 28 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 29 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 30 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 31 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 32 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 33 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 34 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 35 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 36 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 37 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 38 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 39 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 40 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 41 Identifying Emergent Behaviors in Complex Systems - Jane Adams Slide 42
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Identifying Emergent Behaviors in Complex Systems - Jane Adams

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Forager ants in the Arizona desert have a problem: after leaving the nest, they don’t return until they’ve found food. On the hottest and driest days, this means many ants will die before finding food, let alone before bringing it back to the nest. Honeybees also have a problem: even small deviations from 35ºC in the brood nest can lead to brood death, malformed wings, susceptibility to pesticides, and suboptimal divisions of labor within the hive. All ants in the colony coordinate to minimize the number of forager ants lost while maximizing the amount of food foraged, and all bees in the hive coordinate to keep the brood nest temperature constant in changing environmental temperatures.

The solutions realized by each system are necessarily decentralized and abstract: no single ant or bee coordinates the others, and the solutions must withstand the loss of individual ants and bees and extend to new ants and bees. They focus on simple yet essential features and capabilities of each ant and bee, and use them to great effect. In this sense, they are incredibly elegant.

In this talk, we’ll examine a handful of natural and computer systems to illustrate how to cast system-wide problems into solutions at the individual component level, yielding incredibly simple algorithms for incredibly complex collective behaviors.

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Identifying Emergent Behaviors in Complex Systems - Jane Adams

  1. 1. identifying emergence in complex systems @thejunglejane
  2. 2. if you put 50 ants on a table
  3. 3. if you put 500,000 ants on a table
  4. 4. adding more ants
  5. 5. relatively simple
  6. 6. foraging for food building nests raising livestock waging war burying their dead
  7. 7. innate immune system adaptive immune system
  8. 8. take the lower levels for granted
  9. 9. principle of computational irreducibility
  10. 10. the collective is irreducible to the individual
  11. 11. the whole must be greater than the sum of its parts
  12. 12. emergence
  13. 13. disorganized v. organized complexity
  14. 14. Per Bak Chao Tang Kurt Wiesenfeld
  15. 15. self-organized criticality
  16. 16. simple distributed scalable
  17. 17. spend water to get water
  18. 18. collective regulation
  19. 19. ants are doing TCP
  20. 20. the independent discovery of TCP/IP, by humans
  21. 21. David Winter keepturningleft.co.uk
  22. 22. consensus
  23. 23. scale-free correlation
  24. 24. high signal-to-noise ratio
  25. 25. effective perceptive range
  26. 26. seven nearest neighbors
  27. 27. robustness
  28. 28. many evolutionary cycles in many different environments
  29. 29. natural selection for collective behavior
  30. 30. we have many biological analogs of computational problems
  31. 31. ants and congestion control
  32. 32. starlings and consensus
  33. 33. slime mold and network-routing
  34. 34. swarms and distributed search
  35. 35. neuronal spiking and probabilistic inference
  36. 36. fly brains and max independent sets
  37. 37. problem of representation
  38. 38. top-down feedback
  39. 39. simple and abstract
  40. 40. thank you
  41. 41. This document is being distributed for informational and educational purposes only and is not an offer to sell or the solicitation of an offer to buy any securities or other instruments. The information contained herein is not intended to provide, and should not be relied upon for investment advice. The views expressed herein are not necessarily the views of Two Sigma Investments, LP or any of its affiliates (collectively, “Two Sigma”). Such views reflect significant assumptions and subjective of the author(s) of the document and are subject to change without notice. The document may employ data derived from third-party sources. No representation is made as to the accuracy of such information and the use of such information in no way implies an endorsement of the source of such information or its validity. The copyrights and/or trademarks in some of the images, logos or other material used herein may be owned by entities other than Two Sigma. If so, such copyrights and/or trademarks are most likely owned by the entity that created the material and are used purely for identification and comment as fair use under international copyright and/or trademark laws. Use of such image, copyright or trademark does not imply any association with such organization (or endorsement of such organization) by Two Sigma, nor vice versa.

Forager ants in the Arizona desert have a problem: after leaving the nest, they don’t return until they’ve found food. On the hottest and driest days, this means many ants will die before finding food, let alone before bringing it back to the nest. Honeybees also have a problem: even small deviations from 35ºC in the brood nest can lead to brood death, malformed wings, susceptibility to pesticides, and suboptimal divisions of labor within the hive. All ants in the colony coordinate to minimize the number of forager ants lost while maximizing the amount of food foraged, and all bees in the hive coordinate to keep the brood nest temperature constant in changing environmental temperatures. The solutions realized by each system are necessarily decentralized and abstract: no single ant or bee coordinates the others, and the solutions must withstand the loss of individual ants and bees and extend to new ants and bees. They focus on simple yet essential features and capabilities of each ant and bee, and use them to great effect. In this sense, they are incredibly elegant. In this talk, we’ll examine a handful of natural and computer systems to illustrate how to cast system-wide problems into solutions at the individual component level, yielding incredibly simple algorithms for incredibly complex collective behaviors.

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