Collaborative Intelligence

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A-PR Hypothesis

complementarity needed for
collaborative intelligence

Subjectivity – our diverse
POVs and interpretations

Objectivity – the facts of
the world we interpret



von Ahn on Human

AI Conferences
Animating Time Data
Climate Collab
Darwin papers
Geo-tagger's World Atlas
Innovation Networks
Kirschner Lab
London Open Street Map
Microbes–Mind Forum
MIT Center for
Collective Intelligence

Planet Innovation
Recommender Systems
Vinge on Singularity
Wall Street Journal

Tm Berners-Lee Weaving the Web
Berners-Lee –
Weaving the Web

Paul Ehrlich Humanity on a Tightrope
Ehrlich - Humanity
on a Tightrope

Kevin Kelly What Technology Wants
Kelly – What
Technology Wants

Bert Holldobler and EO Wilson Superorganism

Robert Ulanowicz - A Third Window: Natural Life Beyond Newton and Darwin
Robert Ulanowicz
A Third Window

Robert Axelrod Evolution of Cooperation
Axelrod – Evolution
of Cooperation

Robert Axelrod Complexity of Cooperation
Axelrod – Complexity
of Cooperation

Paul Ehrlich Humanity on a Tightrope Kevin Kelly What Technology Wants
Hansen, Schneiderman, Smith - Analyzing Social Networks with NodeXL

Hansen, Schneiderman,
Smith – Analyzing Social
Networks - Node XL

Jerry Fodor What Darwin Got Wrong
J Fodor & M P
What Darwin Got Wrong

Eva Jablonka and Marion Lamb Evolution in Four Dimensions
E Jablonka & M Lamb
Evolution in 4D

Marc Kirschner and John Gerhart - The Plausibility of Life
M Kirschner & J Gerhart –
Plausibility of Life

J Scott Turner The Tinkerer's Accomplice
J Scott Turner
Tinkerer's Accomplice

J Scott Turner The Extended Organism
J Scott Turner
The Extended Organism

J Scott Turner The Tinkerer's Accomplice J Scott Turner The Extended Organism

Mary Jane West-Eberhard Developmental Plasticity in Evolution
MJ West-Eberhard –
Plasticity & Evolution

J Scott Turner The Tinkerer's Accomplice J Scott Turner The Extended Organism Jerry Fodor & Massimo Piattelli-Palmarini - What Darwin Got Wrong Jerry Fodor & Massimo Piattelli-Palmarini - What Darwin Got Wrong Marc Kirschner and John Gerhart - The Plausibility of Life Eva Jablonka and Marion Lamb Evolution in Four Dimensions Mary Jane West Eberhard Developmental Plasticity and Evolution Derek Hansen, Ben Schneiderman, and Marc Smith - AnalyzIng Social Networks with Node XL Derek Hansen, Ben Schneiderman, and Marc Smith - AnalyzIng Social Networks with Node XL

ALife simulations &
collaborative intelligence

To manage a problem-solving environment to achieve collaborative intelligence there's need to measure problem and project complexity. ALife simulations might offer a way to tackle this challenge.

Physicist Christoph Adami notes that the artificial life (ALife) community has adopted different approaches to define and measure ecological complexity. Physical sequence complexity describes the complexity of species inhabiting a single niche. But, while most of life on Earth participates in complicated ecosystems, it is not clear how to define, or measure, the complexity of such assemblies. A measure of ecosystem complexity would be valuable, since ALife experiments (possibly in vivo experiments) could correlate ecosystem complexity with response to perturbations. ALife simulations could be used to form hypotheses about the impact of ecosystem complexity in the real world. Adami proposed that we need to learn how to guide evolution by shaping the evolutionary landscape. Work on evolutionary landscapes over the last ten years has attempted to characterise landscapes, both locally and globally. We know that we get what we select for. But generally we have no idea what to select for.

Remediation addresses complex adaptive systems where a single change can trigger a cascade of impacts. ALife, because of its capacity to play out scenarios, may outperform mathematical simulations, revealing principles of evolutionary landscapes, e.g. mean distance between peaks, size of neutral networks, and optimal evolutionary paths. Chemist Natalio Krasnogor proposed defining virtual life within a “viosphere” to identify signatures (meta-tags) that could be used to trace evolutionary activity. Such an experiment could help to define appropriate ecosystem remediation strategies, avoiding mistakes with unforeseen consequences.


How can ALife simulations promote collaborative intelligence
of regional populations and guide them, both to prepare for, and to respond to, a catastrophe if it occurs? ALife simulations can help us to become "pattern recognizers" before catastrophes occur. Systems for disaster response can be established in advance based on guidance from simulated experiments.


One example of the type of large-scale collaborative process that requires collaborative intelligence: the US Geological Survey (USGS) predicted in 2003 a 62% chance of a Bay Area earthquake of 6.7 or greater on the Richter scale within 30 years (USGS Fact Sheet, 2003). Los Angeles was predicted to experience a similar large earthquake with even worse consequences. Such an earthquake could be large enough to generate system-wide effects with escalating and cascading side-effects: water supply mains broken; transportation routes destroyed or blocked; landslides; food and water shortages, with consequences for everybody in these regions.

The USGS Land Use Portfolio Model helps communities assess risk in a holistic way. (Bernknopf et. al. 2006). Tools under development encourage individuals, organisations, and whole communities to assess and prepare for risks. But realtors don't want to hear, or relay, a forecast that might adversely affect property values. Politicians don't want to know, if the forecast requires expending budget to initiate protective measures. The challenge for USGS is how to design a game-like environment to attract participation and speculation about the consequences, a game to increase awareness and preparedness.

Possible scenarios to enlist proactive participation are tried through gaming techniques. Information gathered from distributed agent or sensor systems is raw material for analytics and knowledge synthesis. Geo-mapping could prepare rapid responder teams in advance for some aspects of the next big California earthquake. But major data collection and interpretation must necessarily occur post-event. What that post-event will look like is as unpredictable in advance as was Hurricane Katrina.

A proactive experiment by USGS to motivate citizen participation in scenario-building employed geo-mapping to support spatial decision-making and knowledge synthesis through time. Each player, from her own unique perspective asks, What information do I need to assess my risks associated with this property purchase? Perception of risks is as relevant to behavior as probabilities of losses. Since individuals differ in their perception of risks, and their assessment of the costs of preparedness, this experiment becomes a way to survey public perception (Bernknopf et. al. 2003).





Practical Applications.


The distant (or not so distant) future vision of a system to support collaborative intelligence will be inspired by life-like systems with autonomy and capacity for pattern recognition (the A-PR Hypothesis), enabling them to navigate. Life-like capacity for pattern recognition and choice may exhibit what Babbage alluded to as a violation, an unpredictability to which the system must be able to adapt, and intelligence to respond to random unpredictable elements, accepting and adapting usable random variations. Babbage, the computer visionary, may have seen beyond the possibilities of today’s algorithmic computing to an era of intelligent computing with the unpredictabilities of life. If so, he anticipated that collaborative intelligence will require the capacity to accept and respond to unpredictability.


Innovation (or Knowledge) Networks link participants, while maintaining their uniqueness and collaborative autonomy such that knowledge can evolve as networks grow, with potential for emergent, unpredictable patterns and innovative outcomes.

Problem mapping
a priori, in contrast to information visualisation after-the-fact, generates visual frameworks, or “empty constructs” to structure the process of knowledge-gathering. Problem maps can evolve into navigable user interfaces. These open frameworks (partial patterns) tap the pattern recognition capabilities of users, serving as vehicles to order incoming information in process, and for use by participants during the problem-solving process. A classic example of a problem map is Dmitri Mendeleev’s Periodic Table of Elements, which prompted chemists to look for elements that appeared logically likely to exist, based upon the pattern of the Table.

Tim Berners-Lee, James Hendler and Ora Lassila. 2001. The Semantic Web: a new form of Web content that is meaningful to computers will unleash a revolution of new possibilities. Scientific American. May 17.


©2011 Zann Gill Please attribute, linking to this site.
: webmaster at collaborative-intelligence. org

Image Credit. Andrew Wunsche
Left. Random Boolean Networks

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Tim Berners-Lee Weaving the Web