Revealing Invisible Landscapes Daniel Steinbock Stanford University

1. Revealing Invisible Landscapes Daniel Steinbock Stanford University

2. Outline • High-level introduction • Theory behind Particle Flow Networks • A simple example

3. Landscape Models • Intuitive metaphor for complex systems • Non-homogeneous state space

4. Physical Systems • Minimize on potential energy surface • e.g. ball rolling in a bowl, water flowing downhill

5. Genetic Systems • Populations of living organisms • Adapting on fitness landscapes • Height of landscape = fitness • Random variation and selection • Populations move uphill as more successful variants reproduce quicker

6. Social Systems • Diversity of social landscapes • Seeking positive outcomes • Plans, choices, ideas => mental landscapes • Searching for people on social fitness landscapes

7. Landscape Search • Generalize random search of ordered landscapes • Natural landscapes are structured such that random searches are successful

8. Particle Flow Networks • State space formalized as network • Particle = random walker • Particle swarm takes a statistical sample of possible random walk paths • Traces out characteristic landscape of the underlying state space

9. Typical Application • Social network structure is explicitly known • Social landscape is implicit • Emergent product of network dynamics

10. e.g. Trust Network • Trustworthiness is a function of how many people trust you and the trustworthiness of those people • Definition is recursive, emergent • To calculate it for one person we need to calculate it for the whole network

11. Summary • Landscape model of complex systems • Random walks work well on naturally ordered landscapes • Particle flow networks simulate random walk dynamics and reveal the landscape when only the network structure is known • Applicable to analysis and simulation of social networks and other complex systems

12. Thanks • Thank you ECCO • Thank you Francis & Marko • images & audio licensed under Creative Commons • Send questions and comments to daniel@sonic.net

13. Particle-Flow Networks for Individual and Collective Intelligence Marko Rodriguez, Francis Heylighen, Daniel Steinbock

14. Outline of the Presentation • What is a particle-flow network? • General paradigm for simulating intelligence. • How do particle-flow networks apply to individual-intelligence? • How do particle-flow networks apply to collective-intelligence systems?

15. Particle-Flow Networks Part 1

16. Particle-Flow Networks • Network: as defined by a set of nodes and directed-edges. • Particle-flow: discrete particles which travel through the network performing certain elementary functions.

17. Particle-Flow Networks • Edge-weight: refers to the probability that a particle at that node will take that outgoing edge at time step t + 1.

18. Particle-Flow Networks • Energy Content: the amount of energy currently in the particle • Decay-Scalar: the percentage of energy lost each time-step • Initial-Node: the node which created the particle • Current-Node: the current location of the particle • Path-Length: the amount of edges the particle has traversed

19. Particle-Flow Networks • Particle-storage: refers the amount of particles in a node at time step t. • Flow-amount: refers to the amount of energy that has flowed through a node over the period [0, 1, …, t ]

20. Particle-Flow Networks • Attractivity (a.k.a.-sink): refers to the probability that a node will hold a particle at time step t. An attractivity value of 100% means that none of the particles that reach the node ever leaves it.

21. Particle-Flow Networks • Programmically each particle is endowed with its own send() and recv() function. public void send(Node currentNode){ pathLength++; energyContent = energyContent * decayScalar currentNode.flow = currentNode.flow + energyContent; currentNode.storage = currentNode.storage++; } public void recv(Node currentNode){ if(RANDOM > currentNode.activity) { // view currentNode.outgoingEdges and make a hop currentNode.storage = currentNode.storage--; } }

22. General Paradigm for Intelligence Part 2

23. Cognition • Capability to infer from experienced to as yet not experienced phenomena. • Prediction, anticipation • Imagination, conception • Planning, problem-solving, decision-making • General form: input -> output • input = problem, condition, perception, present information... • output = solution, action, interpretation, expectation...

24. Knowledge • Knowledge = collection of “if…then” rules • A -> B, B -> C, B -> D, D -> E, E -> A, E -> F, … • Connections can be: deductive, abductive, semantic, causal, probabilistic, associative... • Examples • banana -> fruit • drop stone -> stone falls • winter -> snow • dog -> cat

25. Knowledge Network • Rules determine a weighted network • Nodes A, B, C... = concepts, categories, distinctions • Links A -> B = expectancy of B, given A • Weights = degree of expectation or conditional probability • Learned through experience

26. Cognitive processing • Making inferences with complex inputs • Input = Different nodes are activated to different degrees • Activation propagates along links • Activating new nodes • activations combine and interact • Output = nodes in which most activation settles

27. Individual Intelligence Part 3

28. Individual Intelligence • Intelligence = problem-solving ability • Intelligence (quantitative) = efficiency with which network finds good solutions • Spreading activation is a very demanding process • Activation propagates along links • activations diffuse, combine and interact • Energy dissipates • -> not enough may remain to activate best solution

29. Individual Intelligence • IQ test: measure of fluid intelligence • Example questions: • Which one is most like the first word? • love : death, hate*, beginning, family • Which word of the second list best fits in the first list? • touch, taste, smell, see : cry, swim, climb, hear* • Which of the following is least like the others? • dog, car*, bird, fish

30. WordScore: an IQ simulator • Uses particle flow network to solve test • Network = based on Word Association data • Given words = Initial nodes • Potential solution words = Sinks • Answer = sink that collects most/least particles • Gets about 75% correct = 3 x better than chance • About average IQ for 12 year-old? • Improves/worsens depending on parameter settings • Decay rate, number of particles, link strengths...

31. Demonstration WordScore

32. Collective Intelligence • Part 4

33. Collective Intelligence • Collective Intelligence = distributing problem-solving over many individuals • selecting right person to tackle each (sub)problem • Network representation • Nodes= individuals • Links = trust or knowledge relationships • Flow = propagating questions to the right individuals

34. Homophilic Networks • In word-networks edges connect similar words (similarity by association). • In social-networks edges connect similar people. • Friendship networks: amicability • Trust networks: opinions/perspectives • Co-authorship networks: expertise

35. The Collective Mental Map • A collective of individuals creates a footprint of activity which can be used as a map of the community. • Particle-swarms allow you to search that map to interact with individuals. • Provide them user specific information • Problems or Solutions • Provide them decision-making influence • Problem-Solving Influence

36. Opinion-Based Representative Decision-Making System • Given a particular opinion poll, if all individuals of the society participate in the decision-making process, the result is X. • Given any subset of the group, is the decision derived by this subset still X? SOLUTION: A method to holographically represent the collective’s decision-making behavior within any subset of the collective.

37. Full Participation = Active Voter (A = 100%) Decision = (0.8 + 0.5 + 0.8 + 0.9) / 4 = 0.75 Goal is to achieve this value as voter participation wanes.

38. Waning Participation Decision = (0.5 + 0.9) / 2 = 0.7 Error = 0.05 = | 0.7 – 0.75 |

39. Simulation on a population of 1,000

40. Trust-Networks edge(i,j) = 1 - │opinion(i) – opinion(j) │

41. Trust-Networks Two members of the community are voicing their opinion on a particular topic.

42. Trust-Networks 100 100 100 100

43. Trust-Networks 125 100 175

44. Trust-Networks 150 Decision = (250 * 0.9) + (150 * 0.5) / 400 = 0.75 250 Error = 0.00 = | 0.75 – 0.75 |

45. Simulation on a population of 1,000 K=0 K~3

46. Benefits of K~3 Network • Since K~3 networks are the most optimal, individuals in the group need only know 3 other individuals to create a good model of the whole. Practical in terms of human what we see in already existing social-networks. • If K=N-1 was the most optimal network then this would be less promising since everyone would need to relate to everyone in the group. Fully connected social-networks do not appear in nature.

47. Demonstration Dynamically Distributed Democracy

48. The Problem Domain • What about representative networks that are not ‘opinion’-based, but more ‘expert’-based? • How should the network account for the context of the problem? • Should everyone have the same initial distribution of particles for decision-making?

49. Subset Mapping • Whole to Subset Mapping: Modeling the whole of the network within a subset of the whole.

50. Subset Mapping • Subset to Subset Mapping: identifying the most representative nodes relative to a particular input subset. The domain is the initial subset.