Revealing Invisible Landscapes

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• Revealing Invisible Landscapes
• Daniel Steinbock
• Stanford University

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Outline

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

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Landscape Models

• Intuitive metaphor for complex systems
• Non-homogeneous state space

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Physical Systems

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

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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

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Social Systems

• Diversity of social landscapes
• Seeking positive outcomes
• Plans, choices, ideas gt mental landscapes
• Searching for people on social fitness landscapes

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Landscape Search

• Generalize random search of ordered landscapes
• Natural landscapes are structured such that
  random searches are successful

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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

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Typical Application

• Social network structure is explicitly known
• Social landscape is implicit
• Emergent product of network dynamics

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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

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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

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Thanks

• Thank you ECCO
• Thank you Francis Marko
• images audio licensed under Creative Commons
• Send questions and comments to daniel_at_sonic.net

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Particle-Flow Networks for Individual and
Collective Intelligence

• Marko Rodriguez, Francis Heylighen, Daniel
  Steinbock

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Outline of the Presentation

1. What is a particle-flow network?
2. General paradigm for simulating intelligence.
3. How do particle-flow networks apply to
   individual-intelligence?
4. How do particle-flow networks apply to
   collective-intelligence systems?

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Particle-Flow Networks

• Part 1

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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.

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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.

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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

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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

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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.

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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 gt currentNode.activity)
• // view currentNode.outgoingEdges and make a
  hop
• currentNode.storage currentNode.storage--

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General Paradigm for Intelligence

• Part 2

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Cognition

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

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Knowledge

• Knowledge collection of ifthen rules
• A -gt B, B -gt C, B -gt D, D -gt E, E -gt A, E -gt F,
• Connections can be deductive, abductive,
  semantic, causal, probabilistic, associative...
• Examples
• banana -gt fruit
• drop stone -gt stone falls
• winter -gt snow
• dog -gt cat

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Knowledge Network

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

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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

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Individual Intelligence

• Part 3

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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
• -gt not enough may remain to activate best solution

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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

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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...

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Demonstration
WordScore
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Collective Intelligence

• Part 4

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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

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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

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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

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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 collectives decision-making behavior within
  any subset of the collective.

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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.
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Waning Participation
Decision (0.5 0.9) / 2 0.7
Error 0.05 0.7 0.75
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Simulation on a population of 1,000
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Trust-Networks
edge(i,j) 1 - opinion(i) opinion(j)
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Trust-Networks
Two members of the community are voicing their
opinion on a particular topic.
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Trust-Networks
100
100
100
100
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Trust-Networks
125
100
175
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Trust-Networks
150
Decision (250 0.9) (150 0.5) / 400 0.75
250
Error 0.00 0.75 0.75
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Simulation on a population of 1,000
K3
K0
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Benefits of K3 Network

• Since K3 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 KN-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.

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Demonstration
Dynamically Distributed Democracy
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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?

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Subset Mapping

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

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Subset Mapping

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

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Collective Peer-Review Process

• Problem Should manuscript X be published for the
  community?
• Problem-Routing Which members of the community
  should review manuscript X?
• Problem-Solving Influence Of those individuals
  what is the relative influence each member should
  have? DEMONSTRATION
• Solution The communities decision on manuscript
  X.
• Solution-Routing Which members of the community
  would be interested in the published manuscript X.

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Peer-Review Decision-Making

• Given an unpublished manuscript, determine the
  amount of influence each member of the
  community should have regarding accepting or
  rejecting the manuscript.
• Who should have more decision-making influence?
  A Nobel Laureate in Chemistry or a less-renowned
  computer-scientist?
• SOLUTION Depends on the manuscript domain

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Co-Authorship Networks

• When two scientist co-author a paper an edge
  between them is created within the scientific
  communities co-authorship network.

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Co-Authorship Networks

• Bollen Digital-Libraries Impact Rating Methods
• Hussel Co-Authorship Network Visualization
• Nelson Impact Rating Methods
• Luce Digital-Library Architectures and Measures
• Van de Sompel OAI-PMH Co-Authorship Networks
• Vemulapeli Digital-Library Architectures
• Marks Co-Authorship Networks
• Liu OAI-PMH E-Print Architectures

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Demonstration
References 1 Smith, J. 2 Guy, L. 3 Man, P.
Peerper
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Results
Referee Name Influence Recent Interests Related to Paper
Sompel, HV 0.09844 OAI-PMH and Co-Authorship Networks
Bollen, J. 0.08594 Digital-Libraries and Network-Based Impact Metrics
Carr, L. 0.08516 Digital-Libraries and Open Archive Services
Hall, W. 0.08066 Knowledge Management and Digital-Libraries
Rocha, L.M. 0.07892 Document Recommendation Systems
Lagoze, C. 0.05328 Digital-Library Architectures and Services
Harnad, S. 0.04883 Open Citation Linking and Digital-Library Architectures
Hitchcock, S. 0.04177 Electronic Journals and Citation Linking
Blake, M. 0.04156 OAI Repositories and Citation Linking
Jiao, Z. 0.03386 E-Print Services
Bergmark, D. 0.03262 Digital-Libraries and OAI-PMH
Miles-Board, T. 0.02049 Digital-Libraries
Davis, H.C. 0.01211 Digital-Libraries and Adaptive Linking
Roure, D.D. 0.01125 Dissemination of Scientific Information Services
French, J.C. 0.01081 Digital-Library Distributed Searching and Interfaces
Brody, T. 0.00986 OAI-PMH and Open Citation Linking
References 1 Smith, J. 2 Guy, L. 3 Man, P.
FA
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Conclusion

• Good life