The goal is
to analyze and facilitate how people, in the process of accessing
and using information on networks, create new knowledge
without premeditation. We argue that the symbiotic combination
of humans and smart networks will result in a previously unrealized
capability of collective problem identification and solution. This
capability is based on the pre-existing self-organizing dynamics
of social evolution. This symbiotic intelligence will greatly increase
the success of organizations in achieving their goals, better utilizing
their resources and preparing for the future. For the human society
as a whole, this new resource will improve our quality of life
and vitality as a species.
The Project Motivation
We envision an unprecedented capability in organizational
and societal problem solving will result from increased human activity
on smart distributed information systems, like the Internet. This
heightened capability will be an enhancement of the self-organizing
social dynamics that has guided human groups, organizations, and
societies for millennia. While sufficient connectivity is developing
on the Internet (and intranets) to activate this transformation,
the enabling methodologies and theories, along with the data needed
to test the new technologies, are lacking. A multi-disciplinary,
multi-institutional effort, The Symbiotic Intelligence Project, is
being developed at Los Alamos with other institutions to provide
the needed methodologies and theory for this new science. Los Alamos
is ideally positioned to coordinate and contribute to this effort
as the key laboratory in the study of complexity, artificial life,
distributed processing and precompetative technology transfer.
The
Role of Social Evolution
We have evolved social structures, and the supporting
dynamics, which enabled us to "solve" problems that threaten our
existence. With social evolution we can evolve and adjust to major
changes in our environment within our own lifetime. Social and biological
evolution use the same dynamic processes, and exhibit the same
characteristics, inherent to self-organizing systems:
- A "Solution" arises as a selection by the system
dynamics from a diversity of potential solutions.
- Complex global behavior is driven by loosely
connected relatively simple, local processors.
- The global characteristics of these systems
are: functionality greater than the individual subsystems, robustness,
persistent nonequilibrium, the capability to find solutions in
the presence of conflicting needs, and scalability without loss
of viability.
In social dynamics, the "local processor" is the
centralized problem solving of individuals, teams, organizations,
and governments. This centralized approach relies on the premiere
problem solving ability of humans to understand complex systems and
to be able to predict their outcome. As a species we have gone through
many fundamental reorganizations without centralized coordination:
from hunter-gatherer to agricultural to industrial societies.
The Role of Technology in Social Evolution
Technological advances have radically changed the
time and size (length) scales of the social dynamics, the prime example
being the increase in the maximum size of a social group. Through
advances in communication, transportation and information storage,
our largest social unit has grown from tribes, to cities, to city-states,
to nations, to regional coalitions, to almost a global society. Not
unexpectedly the next revolution will also occur because of a new
technology, the Internet.
Unique Capabilities of the Internet
The Internet has three significant capabilities
beyond prior human-technological systems:
- The Net integrates the breadth of knowledge
processes: information storage, communication of information, traditional
computing of huge amounts of data, and human processing of limited
but complex information. Until recently these were physically separated
processes that required human action to integrate.
- The Net captures the depth of knowledge
processes. The detailed signatures of the use of information are
captured by the Net. This information was previously obtainable
only at extreme cost.
- The Net transmits knowledge processes accurately.
Verbal human-to-human communication results in a rapid loss of
information a bit removed from its creator. By contrast, information
exchanged or related on the Net, whether in web pages or emails,
suffers minimal loss of information during transmission or linking,
in the same way that the contents of a book or journal are not
altered when exchanged.
With the stronger presence of these three capabilities
in human information processes, we propose that a knowledge "phase
change" will occur: these processes, which were previously lost or
diminished before, will contribute to an enhanced self-organizing
social dynamics.
A Simple Example of Collective Problem Solving
on the Net
Have you ever clicked the "Check out these related
titles!" on Amazon.com? These lists are not constantly updated by
staffs of librarians for 2 million books. Instead, these lists are
created by listing books purchased by people that have also ordered
the selected book. This is an ideal example of capturing information
that was previously lost ("depth of knowledge processes" above)
and of how patterns of individual behavior lead to useful collective
knowledge. Other demonstrations have been done or are in progress
that illustrate how humans on intelligent, distributed networks can
achieve useful collective solutions or knowledge, but with minimal
instruction and effort. (Show
me an another example.)
Simulations of Collective Problem Solving
Simulations are being done in T-Division at LANL
(for a detailed discussion see: Collective
Problem Solving: Functionality beyond the Individual ) that
support the fundamental assumption that more people can effectively
interact under "Internet-like" conditions than in traditional ways.
These also illustrate the desirable features of a large and diverse
self-organizing system. We ask the question: "What is the effect
of noise or information loss on a collective decision involving many
individuals?"
- The insert in the figure shows the demonstration
maze. The main figure shows the effect on the collective solutions
as more individuals contribute to the collective solution, for
two different sets of random numbers. The number of steps of
the collective is normalized by the average number of steps of
the individuals contributing to collective solution.
The idealized problem is a maze which represents
a bounded system where individuals are faced with a sequence of decision
points that lead to a final goal. In the above figure, a demonstration
maze is shown with two of the possible 14 minimum paths of 9 steps
highlighted. A simulation consists of two phases. In the Learning
Phase the individuals explore the maze, with no global sense
of the problem, until the goal is found. In the Application Phase they
apply, but do not modify, what they have learned to "solve" the problem.
Because the rules in the two phases involve random choices, a population
of individuals result which represent a diversity of performance
and preferred paths. For now, these individuals are taken to be independent,
because we are interested in isolating the effect of collective decision
making from the processes of shared learning. To find a collective
solution, the individual contributions are then combined in various
ways and then the same Application Phase is applied. This collective
solution is then compared to the average performance of the individuals
in the collective.
In the sample maze, the average number of steps
of an individual to "solve" the maze is 34.3 in the Learning Phase
and 12.8 in the Application Phase for 100 individuals. If a collective
solution is calculated by just a simple average of the individual's
contribution, the performance is better than the average individual
when more than five individuals are included. Also, one of the minimum
paths is found for collectives with more than 20 individuals. This
optimal solution (nine) is found even though the rules have no global
sense of the problem and do not select for a minimum path. This minimum
solution is, therefore, an emergent
property of the system. The system also exhibits classical chaotic
behavior in that a small change in the collective path preference
can result in an entirely different, but still near optimal, path.
The collective solution is remarkably robust. Degradation
of the individual's contribution, however implemented, generally
had no effect on just postponed the collective convergence to the
minimal path. For example, by appropriate modification of the individual's
contribution to the collective, we find that the less
dominant opinions of the individual have no effect on the performance
of the overall collective decision (but does make the collective
solution more noisy). Also the collective solution is insensitive
to moderate amounts of random noise.
A few effects were found to degrade the collective
solution. The worst degradation (figure above) was found to be the
random selection of a "leader" during the solution, with a different
individual selected at each node. It was also observed that the collective
solution is degraded if only the "better" performing individuals
contribute (those with shorter path lengths in the Application Phase)
to the collective solution. This is a surprising result illustrating
that even a diversity of performance is important to a collective
solution. Using this idealized problem, it is can be shown that a
diversity of learning experiences leads to a better performance in
the collective decisions and that the collective is significantly
more robust than the average individual in adapting to unexpected changes
in the final goal or the structure of the maze.
These simulations also give important information
on how
to best capture the diversity of the population in the creation
of a self-organizing system. It is found that the diversity of
experience in the problem space is essential to the collective solution,
but not the relative importance of this experience. In contrast,
it was also found that the individual's diversity for preference
at a node is not important, but it is essential that the relative
importance of the individual's preferences be captured.
These simulations demonstrate that under the ideal
conditions of the Net (no loss and breath and depth of diversity
of an individual's preferences), large numbers of individuals can
contribute to a collective decision and the collective performs much
better than the average individual. One could imagine duplicating
the same "simulations" on the Net, but with real individuals solving
the maze, instead of the Learning Rules. Because the individual performance
would likely be greatly improved, one would expect the collective
solutions to be able to solve more complex mazes with fewer numbers
of individuals. With the addition of the concept of a hierarchical
structure of systems, the simple idea presented in these simulations
can be extended to more and more complex systems. Therefore, while
the current simulations are admittedly simplified, they may capture
the fundamental
nature of systems for collective decision making.
The Future
The implications of the ideas under investigation
are far reaching to the future success of organizations, society,
and economics. With the increased use of the Net in our economy and
society, many of the problems that have challenged traditional forms
of management and organizations may now find solutions. The importance
of this alternative will become more significant as the complexity
of our world increases and our traditional ways of solving problems
fail. The main consequence will be for management and governments
to facilitate the use of collective problem solving when problems
arise in systems that are not understandabl
Learn More about the Project
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Additional Links for Symbiotic Intelligence
Project
Good summary: http://www.computerworld.com/managementtopics/management/story/0,10801,64912,00.html
http://www.fathom.com/media/trailDemo.html
http://itri.loyola.edu/ConvergingTechnologies/
http://www.edu-cyberpg.com/Technology/trends.html
Relevant workshop: http://www.santafe.edu/~dynlearn/colcog/
For the symintel site:http://www.man.ac.uk/sociologyonline/course/soccyber/self.htm
Long list of referrals: http://www.mikro.org/Events/OS/wos2/Levy-pp/liensIC.html
