Francis Heylighen
Principia Cybernetica Project
Collective Intelligence
Intelligence = problem-solving capacity
increases
with the number of problems that can be solved
with quality of solutions
Collective intelligence
synergy
group can find more/better solutions than the sum of solutions found
by individuals
Examples
working groups, organizations
football teams
complex adaptive systems
Obstacles to collective intelligence
cognitive limits of individuals
complexity of coordination
noisy communication
power games
establish "pecking order"
ignore contributions of lower ranking individuals
Problem-Solving
Problem = difference between present state and goal
Problem representation:
search space: set of all possible states
action: changes one state into another
preference function: to select best action for state
depends on goal and domain knowledge
"mental map": structured as network
Problem-solving
find (shortest) path from present state to goal
can be improved by:
more actions/more states
better selection
How can we extend this framework to collective problem-solving?
Coordination without Communication
Stigmergy
external sign that incites individuals to work on it
e.g. heaps of mud incite other termites to add to the heap
Collective action requires a shared "object"
e.g. ball in football game
object is focus for action
External memory
state of the object registers state of the problem
actions by different individuals change the state
results of actions are accumulated in a "memory trace"
Examples
notes, documents
shared "blackboard" or "workspace"
pheromone trails left by ants
Shared Mental Maps
Stigmergic object + Mental map => Shared Mental Map (SMM)
shared access
all agents can locally "read" the SMM
(all) agents can locally "write" to the SMM
therefore, SMM must be external to the agents
represents collective domain knowledge
functions as external memory
has more knowledge than any individual
supports collective problem-solving
keeps track of what has already been achieved
shows possible alternatives (actions)
helps decision-making by suggesting best trajectory
Dynamics of Collective Problem-solving
agents can contribute to the SMM in different ways-
from simplest to more complex:
Averaging
individual preferences are linearly added
robust and reliable because of law of great numbers
no direct interaction
e.g. democracy, N. Johnson simulation
Discussion
individuals make suggestions and give arguments
allows improving on democratic decisions
e.g. meetings, GDSS, CSCW
Self-organization
non-linear interaction with pos. and neg. feedback
weak signals can be amplified
noise can be eliminated
e.g. ant trails
Division of labor
different individuals contribute to different subproblems
specialization allows greater expertise
Workflow
subproblems are automatically directed to most competent individuals
"active" SMM
Market mechanisms
price functions as stigmergic signal to attract "suppliers"
competition selects for most competent contributors
disadvantages: positive feedback can get out of hand, loss of
diversity through monopoly formation
From DIS to Global Brain
DIS provide perfect support for a worldwide SMM
perfect, unlimited memory
accessible by everyone
easy to change
Traditional DIS lack the needed intelligence
no active guidance of problem-solving
little support for dynamics of cooperation
New techniques can add all necessary functions
support active, collaborative problem-solving
make DIS self-organizing
from external memory to external "brain"
The "Global Brain" metaphor can help us design a better DIS
implementing most of the methods discussed
Stages in Global Brain Development
Metasystem Transitions
emergence of higher level of complexity/control
One-to-one communication
simple reflex <-> telephone, telegraph
One-to-many communication
mass media, TV, radio
Many-to-many communication
complex reflex <-> DIS
Associative Learning
synaptic learning <-> creation of links
Combining non-linked information
thought <-> DIS agents
Creating new knowledge
creative thought <-> knowledge discovery
The Learning Web
Until now, the Web only "learns" through its users
poor linking patterns
too complex for efficient coordination
Associative Web learning
Hebb rule: concepts encountered together are linked more strongly
Adaptive hypertext learning rules:
if A->B->C, then:
frequency: strengthen A->B
transitivity: strengthen A->C
symmetry: strengthen B->A
These local rules lead to global self-organization (cf. Bollen)
Web learning creates links and preferences
link strength proportional to usage
averaging of individual preference with pos. feedback
possibility for "suggestions" (e.g. annotations)
Collaborative Filtering
Associations implicit in patterns of co-occurrence
Examples
Co-occurrence data are available in many places
indexes of links to pages on same subject
pages that are linked in different places
individual bookmarks, buying patterns, web caches, etc.
Data can be used to create links and preferences
probability of co-occurrence P(A|B) determines association strength
Search Agents
The Web can actively support problem-solving
user formulates problem ("query")
program or "agent" searches through web for solutions
Search engines
user enters keywords
engine finds documents with these terms
requires global index
documents must contain exact keywords
not very selective
The Thinking Web
Spreading activation
application of local search
key documents are "activated"
activation follows links to connected documents
Agents can find experts
e.g. from expressed interests
use "outside consultants"
allows "workflow"
Agents as "thoughts" in the global brain
spread by following associations
locally combine distributed information
Solving Complex Problems
Ill-Structured Problems
only vague associations
e.g. diarrhoea, constipation, cramps, colon, gas, bloating...
instead of "How to cure Irritable Bowel Syndrome?"
spreading activation finds all related documents
including documents on Irritable Bowel Syndrome
may need several iterations
Logical Queries
XML standard allows the use of ontologies on the web
different types of links and nodes: ISA, HAS_PART, ...
can turn web into semantic network (cf. Joslyn)
allows "intelligent" inferences
e.g. "Which birds cannot fly?"
activation restricted to the right type of links
Knowledge Discovery
"Serendipitous" agents
cf. presentation of A. Riegler
roam the web to find unexpected regularities
"data mining" with unlimited database
Discover general rules
apply different machine learning techniques
e.g. most instances of property A have also property B
-> A implies B
Discover new concepts
clustering of similar ideas to produce new categories
produce new documents by integrating existing ideas
e.g. indexes
Conclusion
Collective intelligence requires Shared Mental Map
can be represented as network of nodes, links and preferences
individuals should be able to read and write the SMM
let collective knowledge emerge
SMM should actively guide problem-solving
Techniques to make the Web into a better SMM:
learning links from usage
inferring links from co-occurrence
using agents to search the network
search engines
spreading activation
semantic inferences
locating experts
using agents to discover new knowledge
Thus, the Web can support a true collective intelligence: a "Global Brain"