Transcript tori - Sorin Solomon

Material for the Organizing of the
Complexity NoE
CONTENTS:
-(Edited) One-liners extracted from
recent internet papers repositories
(as objective data on current community interests)
-Basics of Complexity
(main concepts and mechanisms)
-Detailed Examples of 2 Specific Directions:
-Distributed Control Systems of Interacting Agents
-Web-Internet Intelligence
Edited selection of
“Random” key phrases from
last 3 months
articles repositories
SPATIALLY DISTRIBUTED AGENTS
Extending game theory to spatially distributed stochastic players.
- Spatial Segregation,
- equilibrium selection in spatial games
Looking for extended structures rather than mere correlations.
Localization of information
and
identification of information flow patterns
in
information processing /
storing /
learning
distributed systems.
NETWORKS
Representing systems of agents in terms of networks
(links= interactions)
Analyzing
chemical / ecological / genetic / proteonics / quasispecies
systems in terms of such networks.
Finding new dynamical basis for network topology / organization.
geometrical shape  functional properties
topological characteristics  dynamical relevance.
most connected  most important? Other rules?
Efficient immunization of populations and computers .
STATISTICAL “MECHANICS”
Departures from naive white/ normal noise, gaussian errors
Applying statistical mechanics,
rather than just statistics in “data mining”.
Applying Entropy and other Stat Mech concepts to financial systems
(e.g. efficient market  detailed balance).
Adopting phase-transitions terminology and methodology in
information systems:
processing , storage, evolution, efficiency, robustness.
Game Theory methods  Statistical mechanics methods
(prisoner dilemma, optimization of learning, etc).
Efficient / Collective
Learning / adapting
via
genetic algorithms / co-evolutionary dynamics.
MASSIVE DATA UNDERSTANDING
What is Meaning ?
Richer approach than the purely cognitive one
(statistical emergence).
Emergence of meaning from simple mechanical individual elements.
Introducing rudimentary psychological elements in
agents based models.
Statistical mechanics of intelligent agents; semantic networks?
New kind of Information Theory:
local, noisy comparisons => robust filter
Identifying mates, communities,
via
collective / emergent data mining.
Percolation-like behavior = crucial property of
discrete spatially distributed systems;
dramatic transitions un-seen by
mean field
(where everybody speaks (a little) to everybody
and people buy/ get sick (a little)).
WEB and INTERNET
Properties of Large-Scale Peer-to-Peer Systems.
Internet Traffic as an
spatially extended
statistical mechanics system of
interacting agents.
Packet dynamics on various
networks geometries and
communication protocols.
Design,
prediction and
control of
networks and protocols.
Avoiding crashes.
TRAFFIC FLOW
Microsimulations of Car flow with
realistic drivers.
human decision factors
control measures, traffic lights
jams dynamics
Traffic state identification from incomplete information
Human Crowds as excitable media
COMPLEXITY NoE BASICS
COMPLEXITY NoE
-The Complexity community
-interdisciplinary character
BUT
-common problematics and methodology.
-potential for synthesizing a large portion of reality into
a well defined and integrated discipline.
-Supporting Complexity is scientifically and socially justified.
-The support has to be awarded to Complexity as such:
- there is no hope that funds allocated to the classical fields will
end up being used for the advancement of Complexity.
Initiation and Scope of Complexity
When "More Is Different" (1972 Phil Anderson)
- life emerges from chemistry,
- chemistry from physics,
- conscience from life,
- social conscience/ organization from individual conscience etc.
• microscopic interactions in many phenomena may be different
• yet be explained as realizations of a common dynamical mechanism
(e.g. in physics: Spontaneous Symmetry Breaking. )
Complexity in Artificial Artifacts
emergence of complexity may take place in human created artifacts ,
e.g.
• collections of simple instructions turn into a complex distributed
software environment,
• collections of hardware elements turn into a world wide network,
• collections of switches / traffic lights turn into communication /
traffic systems etc,
the laws of emergence are independent of whether they are
implemented on elementary objects consisting of
•silicon,
•vacuum tubes, or
• neurons.
Discreteness and Autocataliticity as Complexity Origins
•discrete character of the individuals is
crucial for emergence
•continuum approach => uniform static world,
microscopic granularity => macroscopic collective objects
- adaptive properties
- survival and development.
•mechanism : auto-catalyticity.
time variations of a quantity ~ (stochastic factor) x
current value.
Power laws and their origin
•1897 Pareto :wealth: NOT the usual fixed scale distributions
(Gaussian, Exponential)
BUT "power law" distribution.
•Similar effects : meteorite sizes, earthquakes, word frequencies,
human populations connections / relations
and lately internet links.
• power laws = conceptual bridge between
-microscopic elementary interactions and
-macroscopic emergent properties.
GENERIC MECHANISMS
•autocatalytic character of the elementary interactions
•stochastic systems of logistic Lotka-Volterra type.
Davis [1941] No. 6 of the Cowles Commission for Research in
Economics, 1941.
No one however, has yet exhibited a stable social order,
ancient or modern, which has not followed the Pareto pattern
at least approximately. (p. 395)
Snyder [1939]:
Pareto’s curve is destined to take its place as
one of the great generalizations of human knowledge
Montroll [one of the great of this century stat mech]
(in “Social dynamics and quantifying of social forces”)
“almost all the social phenomena, except in their relatively
brief abnormal times obey the logistic growth''.
Universal Dynamics of Concept Networks
Dynamical networks  "lingua franca" among complexity workers.
nodes = system parts / properties the
links = relationships.
changes in the networks (nodes, links) = evolution of system
Sequences of changes of the network =>
novel network = novel object
a handful of universal sequences = most novelty emergence
(ideas / products / proteonics / society)
global network features  system collective properties
•
(quasi-)disconnected network components
 (almost-)independent emergent objects
•
scaling properties of the network
 power laws,
•
long-lived (meta-stable) network topological features
(super-)critical slowing down dynamics
=> knowledge of the relevant emerging features of the network
 devise methods to
- expedite by orders of magnitude desired processes
or
- delay / stop un-wanted ones.
Multiscale decomposition as Expression of Understanding
The time to
-separate two stones connected by a weak thread
is much shorter than the time that it takes for
-each of the stones to decay to dust.
Together  Follow dynamics of O(106yrs) with time steps of O(sec).
Solution
representation of each sub-process at the appropriate scale.
Identifying and labeling the relevant collective objects at each scale
=>
expression of the understanding of the emergent dynamics
organizes automatically vast amounts of correlated information:
- internet
- fNMR
The Algebraic multigrid
representation of a given network at coarser scales
basic steps:
• freezing together a pair of strongly connected nodes into a single
representative node.
• repeating this operation iteratively,
•ends up with nodes which stand for large compounds of strongly
connected microscopic objects.
•The algorithmic advantage is that the rigid motions of the collective
objects are represented on the coarse network by the motion of just
one object.
•One can separate in this way the various time scales.
2 EXAMPLES OF DIRECTIONS
(distributed control and internet / web)
Described in more detail
When IT gets a mind of IT-self
SURPRISE: a bunch of man-made artifacts
develop a mind of themselves
- immense variability in the nature of the elementary components
yet
- their complex collective features are analogous.
PROBLEM
-The knowledge of the field in charge with the components
is not adequate to deal with the overall system.
OPPORTUNITY: complexity + IT 
Design the simple interactions between the elementary components
such as to
Evolve collectively towards a desired global behavior.
CHALLANGE: design interaction protocols and feedback
mechanisms that insure the self-organization of the work in an
optimal way.
Horizontal Interaction Protocols and Self-Organized Societies
old world  distinct organizations:
• big ones -- strict hierarchical chain of command
• small ones -- everybody in close "horizontal" personal contact.
New world:
-third sector (public non-profit organizations),
-fast developing specialized activities,
-ad-hoc merging and splitting of organizations,
• need for “horizontal” (non-hierarchical) communication
in large organizations !
•nobody knows how to make and keep under
control a non-hierarchical large organization.
Hope:
local protocols may lead to the emergence of some
global "self-organizing" order.
 identification of modern " Hammurapi codes of laws"
which to regulate (and defend) the new "distributed" society.
The emergence of traffic jams from single cars
the network of streets is highly documented
and
the cars motion can be measured with perfect precision.
Yet
the formation of jams is not a trivial sum of these.
SIMILAR
motion of masses of humans in structured places,
especially under pressure
(in stadiums as match ends, or in theaters during alarms).
Importance: many human lives.
Traffic Lights on the Spot
Centralization = major paradigm for control theory
deterministic,
and
can be developed in a straightforward manner.
portable communication abilities and processing power
 paradigm shift :
traffic information, instructions, regulations and signals
transmitted (and enforced) directly to your car :
Your car will slow and stop at "red lights"
(unless you explicitly choose to override)
Reciprocally, the traffic regulator program will take into account
your travel plans, constraints, and the condition of your car
in issuing its orders to the other cars.
Unmanned Aerial Traffic
3D fit for self-organized (flock) motion:
- less intricate obstacles and easier collision avoidance then 2D
- the danger for hurting humans is less direct in unmanned vehicles
-humans do not have a head start in 3D navigation skills compared
to computers (as opposed to hundreds of thousands of years in 2D).
Releasing central control will allow:
-adaptive creation, joining and splitting of large flocks
-travel at arbitrary distance from home.
-Reduce reaction time
-enlarge significantly the set of possible tasks
-possibility to share flock members location and visual information
=> ad-hoc super-organism
dramatic improvement of collective
-navigation,
-intelligence
-ground objects identification
From Integrated Robot Flocks to Dividuals
While
1 human >
1 robot
one may have
100 humans < 100 robots.
- simpler objects sharing information
more efficient than
-a collection of more intelligent object with limited communication
-robots do not first transform the information in words.
 can share and integrate directly visual, and
other non-linearly structured information.
-the amount, speed and precision of the data they can share are
virtually unlimited.
-Human communication channels not sufficient for fast, precise and
efficient integration of their knowledge / information / intelligence.
-robots with their capability to determine and share their exact
relative position and transmit in detail the raw data "they see“.
Dividuals
Flocks of communicating robots NOT as an integrated flock,
BUT as spatially divided individuals
(call them dividuals)
Unlike biological creatures,
the artificial ones
do not have to be spatially connected:
-can have a lot of eyes and ears spread over the entire hunting field.
-do not need to carry-over the reproduction organs
when the teeth (mounted on legs) go to kill the pray
(the stomach too can be brought-in only later,
in case there is a killing).
Hunting becomes more like farming…
The Internet Challange
Making the Net Work
Billions of dollars are lost every year in damage due to
-bottlenecks,
-congestions,
-Denial Of Service
caused by
-malicious attacks,
- negligence, mistake or simply by
- mis-design.
applications and businesses do not move to the Internet due to it
Network design resides today in the realm of computer engineering
the algorithms themselves are limited to their “bag of tricks”.
FUTURE: algorithms, which treat Internet as a statistical ensemble:
•flexible,
•vibrant,
•trustworthy Internet.
Encounters of the Web kind
Science Fiction paradigm:
planet-wide distributed computer system  super-brain
Yet, we do believe that a large enough collection of interacting
elements can produce more than their sum:
web could develop emergent properties much
beyond the cognitive capabilities of its components.
individual computer < the individual human
But:
"parapsychological" properties of the computers:
-any image perceived by one of them at one location of the planet can
be immediately shared as such by all.
-they can share their internal state with a precision and candor that
even married couples of humans can only envy.
Recognizing and “Contacting” Emergent Web Intelligence
psychological obstacle:
People’s insensitivity to even slightly different forms of
"intelligence".
( In fact various ethnic / racial groups have repeatedly denied one
another such capabilities in the past.)
Instead of trying to force upon the computers the human version of
intelligence (as tried unsuccessfully for 30 years by AI), one should
be more receptive to the kind of intelligence the collections of
computer artifacts are "trying" to emerge.
An useful attitude is to approach the contact with web in the same
way we would approach a contact with a extraterrestrial potentially
intelligent being.
A complementary attitude is to study the collective activity of the
web from a cognitive point of view, even to the level of drawing
inspiration from known psychological processes and structures.