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


Gephi Graph Viz
Microbes–Mind Forum
Planet Innovation
SMR Foundation

von Ahn on Human

AI Conferences
Animating Time Data
Climate Collab
Darwin papers
Do Some Good
EO Wilson Foundation
Geo-tagger's World Atlas
Howe on Crowdsourcing
Innovation Networks
Kelly - Hivemind
Kirschner Lab
London Open Street Map
Los Alamos – Symbiotic
Mechanical Turk
MIT Center for
Collective Intelligence

Recommender Systems
SETI @ Home
Turner Fieldwork
Vinge on Singularity
Wall Street Journal

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

Kevin Kelly What Technology Wants
Kelly – What
Technology Wants

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

James Surowiecki Wisdom of Crowds
Wisdom of Crowds

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

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 Random Boolean Networks Andrew Wunsche

Image Credit above
Andrew Wuensche, DDL
Random Boolean Networks

Next generation social networks as innovation networks

bringing collaborative intelligence into social networks

As next generation social networks become innovation networks
to support collaborative problem-solving, and the Internet grows to petabytes of data,
effective search will become increasingly difficult. Social networks can evolve into a
means not only to produce ever-more information but also to process, annotate,
organize and share information for collaborative problem-solving.

Living systems operate as innovation networks
, harnessing principles of collaborative intelligence: quorum-sensing in bacteria; ants as more than collective intelligence systems of pre-programmed robots, the theory of facilitated variation operating in our cells via weak linkage.

Social Networks and the information explosion now serve each other. To search an expanding universe of information, we need to cluster information, to rely on the recommendations of friends and colleagues whose judgment we trust. We need new taxonomies that can self-organize and evolve.

Rosvall Bergstrom

In Mapping Change in Large Networks Martin Rosvall and Carl T. Bergstrom examine a set of scientific fields that show the major shifts in the last decade. Significance clusterings for citation networks in 2001, 2003, 2005, and 2007 columns in the diagram are horizontally connected to preceding and succeeding significance clusterings by stream fields. Each block in a column represents a field and the height of the block reflects citation flow through the field. Fields are ordered from bottom to top by their size. A darker color indicates the significant subset of each cluster, e.g. all journals clustered in the field of neuroscience in year 2007 are colored to highlight the fusion and formation of neuroscience.

Rosvall and Bergstrom argue that "change is a fundamental ingredient of interaction patterns in biology, technology, the economy, and science itself: interactions within and between organisms change; transportation patterns by air, land, and sea all change; global financial flow changes; and the frontiers of scientific research change.

"Networks and clustering methods have become important tools to comprehend instances of these large-scale structures, but without methods to distinguish between real trends and noisy data, these approaches are not useful for studying how networks change. Only if we can assign significance to the partitioning of single networks can we distinguish meaningful structural changes from random fluctuations. We show bootstrap resampling accompanied by significance clustering provides a solution to this problem."

In January 2011, LinkedIn announced a new application called InMaps. Users can visualize their LinkedIn Network(s) to better leverage their networks and to identify potential to strengthen and extend them. D.J. Patil, Chief Scientist of LinkedIn commented on how InMaps augments their Career Tree.

Microsoft's NodeXL extension makes it easy to extract, analyze and visualize social media networks. NodeXL can connect directly to social networking website, such as Twitter, to import its network data for analysis in Excel. As an example, the diagram below shows people who tweeted the word Wikileaks on December 10, 2010. Using NodeXL, you can import network data of people who have recently tweeted any term that interests you.

Social Network of Wkileaks Tweets December 10, 2010
from Derek Hansen, Ben Schneiderman & Marc Smith
Analyzing Social Networks with Node XL: Insights from a Connected World


The Pareto principle
that 80% of the effects/outputs come from 20% of the causes/inputs provides a rationale for visualization to know which inputs are producing 80% of the outputs.

Email Network

Social network analysis software is used to recognize patterns in the interactions and relationships between individuals or organizations. Typically individuals or organizations are represented as nodes and interactions or relationships as edges.

The most popular recent use has been to model and analyze behavior in social spaces like Facebook and Twitter, but other groups, such as ChromaScope, are using these tools to analyze patterns of communication in emails (image left).

Because failures of communication are often found in dysfunctional teams, analyzing and understanding patterns of communication can reveal where breakdowns may occur and target remedial action.


Although in some cases it's not immediately obvious how these multi-threaded images could be useful, Helen Campbell gives a good example: "At the beginning of a legal case, today’s legal teams may be presented with terabytes of emails and documents collected from individuals of interest (aka custodians) but may have little or no idea about what’s in those emails and documents or how to identify items of interest (aka responsive documents). This is such an issue that whole suites of software have been developed to assist with what is known as Early Case Assessment (e.g. Clearwell), attempting to solve the problem by analyzing the document set by topic, key phrases or terms so that the legal team can begin to develop a search strategy."

Goldman Sachs failed mortgages

Practical Applications.
Network visualizations are particularly effective at showing cascades. InFlow social network analysis software was used to show failed/foreclosed mortgages from Cleveland, Ohio that ended up in investment vehicles sold by Goldman Sachs. The failed investments were made up of mortgages from all over the country. The map below shows only those failed mortgages originating from Northeast Ohio.

The outer ring on the map [black nodes] are actual properties in Cleveland and NE Ohio with failed mortgages.

The next ring, of blue nodes, are the various Trusts that mortgage-lending institutions created to securitize the mortgages and sell to Wall Street.

The inner ring of green nodes are major banks that created and/or administered the Trusts and finally the focal point of all inflows is the magenta colored node — Goldman Sachs.


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.

From micro level cellular mechanisms to macro level population genetics, evolution manifests a coordinated, multi-level Dyadic Model for innovation that next generation social networks could apply in decision support systems for eco-sustainability. Exponential acceleration of technology innovation extends the acceleration of biological evolution, posing challenges and opportunities. Competing hypotheses about the origin and evolution of life show nature’s adaptive mechanisms, evolvability, and the algorithmic implications of evo-devo debates, and also suggest a new collaborative computing paradigm, which can increase the effectiveness of human teams as “evolving, multi-agent ecosystems,” harnessing evolutionary principles to increase our collaborative intelligence.

Our current, conventional problem-solving model, based primarily upon “analysis of the facts,” is inappropriate for many of the problems we face today, which require a systematic (design) approach to synthesis and ways to acknowledge the role that human perception of the facts, and capacity for pattern recognition, plays in problem-solving.

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.


Situation architecture
addresses the key factor of contextualization. Meaning is interpreted in context and may differ, not only with different interpreters, but in different contexts.


is the optimisation of tradeoffs to maximise environmental stewardship within the context of project planning priorities and, as defined by the Brundtland Commission, “the needs of the present without compromising the ability of future generations to meet their own needs.”


is whole system behavior that cannot be predicted from the behavior of its parts.
Synergetics is the dynamics through which synergies are produced; both synergy and synergetics were key principles in the work of Buckminster Fuller.


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

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Goldman Sachs failed mortgages