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.

1. identifying emergence
in complex systems
@thejunglejane

2. if you put 50 ants on a table

3. if you put 500,000 ants on a table

4. adding more ants

5. relatively simple

6. foraging for food
building nests
raising livestock
waging war
burying their dead

7. innate immune system
adaptive immune system

8. take the lower levels for granted

9. principle of computational irreducibility

10. the collective is irreducible to the individual

11. the whole must be greater than the sum of its parts

12. emergence

13. disorganized v. organized complexity

14. Per Bak
Chao Tang
Kurt Wiesenfeld

15. self-organized criticality

16. simple
distributed
scalable

17. spend water to get water

18. collective regulation

19. ants are doing TCP

20. the independent discovery
of TCP/IP, by humans

22. David Winter
keepturningleft.co.uk

23. consensus

24. scale-free correlation

25. high signal-to-noise ratio

26. effective perceptive range

27. seven nearest neighbors

28. robustness

29. many evolutionary cycles
in many different environments

30. natural selection for collective behavior

31. we have many biological analogs
of computational problems

32. ants and congestion control

33. starlings and consensus

34. slime mold and network-routing

35. swarms and distributed search

36. neuronal spiking and probabilistic inference

37. fly brains and max independent sets

38. problem of representation

39. top-down feedback

40. simple and abstract

41. thank you

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