Virginia Commonwealth University

VIRGINIA COMMONWEALTH UNIVERSITY Department of Information Systems School of Business

FALL 2007 Semester

INFO 658 E-COMMERCE

Exploiting Collective Intelligence

Research Paper presented by

Godefroy Foteu Summary

American Psychological Association [5] defines intelligence as the “ability to understand complex ideas, to adapt effectively to the environment, to learn from experience, to engage in various forms of reasoning, to overcome obstacles by taking thought.” Another, but not quite different, line of definition of intelligence comes from the Mainstream Science on Intelligence, in which Linda Gottfredson defines intelligence as, “a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.” These two definitions of intelligence lend themselves to the thought that intelligence is individualistic. What does it mean for a collectivity, community to be intelligent? Although an intellectual process is individual, the result of this process can be public. Collective intelligence can therefore be defined as the process of refining the results of individual intellectual process from members of a group in order to generate a collectively understandable and acceptable synergy that leads to group success. Collective as so defined has always been around. Why is it so important today than ever before? The main reason is the failure of businesses which, a priori, is defined as intelligent group. It is difficult to understand how a company, managed by top intelligent guys from MIT cannot capitalize on individual intellectual abilities to achieve business success. Are communities of bees, ants, and termites more intelligent or more knowledgeable in the use collective intelligence than communities of people? Didn’t some philosophical trend of thoughts defend that intelligence belongs only to human? May be God helps those miniature creatures achieve modelable strategies of success in their enterprises. One would think.

With the challenges of global economy, businesses are more than ever interpellated by the necessity to capitalize on the theory of collective intelligence for their success. Collective intelligence can be applied and used in all types of disciplines and businesses. Literature on this subject is abundant but old. This literature is in general from the pre- Internet or pre-globalization era. The purpose of this paper is to add to a little bit of seasoning to the existing literature by emphasizing gains businesses will incur by adopting the collective intelligence model. This paper uses several types of examples assimilable to limited case studies, such as Open Source Software, Collective Decision Making, Decentralized Managerial Mechanism, Wikipedia, Information Markets, etc., to support its thesis that collective intelligence suggests another way of thinking about things like organizational effectiveness, firm productivity, firm profitability, teamwork, and leadership, and cooperative engagement.

Table of Contents

1 Introduction...... 5 2 Overview of Collective Intelligence...... 6 2.1 Definition of CI...... 6 2.2 Types of CI...... 7 2.3 Five Great Ways to Harness Collective Intelligence [12]...... 8 3 The Swarm Theory: Collective Intelligence from Ant and Bee Colonies...... 10 3.1 Waiting Lines of Bees...... 10 3.2 Ant Trails...... 11 3.3 Collective intelligence: Cues in the Environment...... 11 3.4 Swarm Theory in Humans Interactions: Peer-To-Peer Network...... 12 4 Case Studies of CI:...... 13 4.1 Case for Open-Source software (OSS)...... 13 4.2 Case for Collective Decision-Making...... 14 4.3 Case for Decentralized Managerial Mechanism...... 15 4.4 Case for Information Markets...... 16 Conclusion...... 17 References:...... 19 1 Introduction

The 21st Century with its new communications technologies is increasingly changing the way people live and work together. The need to study the dynamics of business organizations, their strengths and weaknesses, with reference to the impact of new communications models, has never been so important. The underlying goal of studying the dynamics of business organizations is to fine a business model that optimizes production while maintaining an enjoyable working environment. One way to look into business organizations’ dynamics is to analyze the phenomenon of organization’s collective intelligence in insect colonies and find analogs in human’s organizations.

The purpose of this paper is to analyze via successful cases how business organizations can capitalize on their collective intelligence dynamic to achieve organizational effectiveness, admirable productivity, successful teamwork and leadership.

Collective Intelligence (CI) is all about sharing. Why is CI, defined as knowledge sharing and collective decision, so important today? Part of a solution to the question can be given by the new phenomenon called Internet. The fundamental essence of the Internet was about knowledge sharing: “It fostered intellectual collaboration in a way not previously possible [14].” It is therefore evident that the Internet would have a great influence on group dynamics, and ineluctably a group CI. But, instead of following the logic of analyzing the influence of the internet on CI, this paper works its way reversely towards showing how CI can be used in conjunction with the Internet to enhance group dynamics with regards to intelligence sharing.

In order to achieve its goal, this paper is organized into three sections: the first section is an overview of the CI. The second section presents the collective intelligence organizational model based on the theory of “Swarm Intelligence. [7]” The last section, which is divided into several subsections present some cases of successful applications of CI Our hope is that this paper will, not only add to the multitude of already existing literature on the subject, but also, will provide reader with convincing cases that support the need of collective intelligence model in today’s business organizations. We did not intend to provide the reader with an exhaustive expert work on the subject of CI, but rather, to achieve a learning goal as student in the Master of Science in Information Systems Program. This paper presents some limitations that are worth mentioning here: our limited knowledge of the subject and the short time we were allotted for producing the paper. However, we hope we have provided the reader with some initial inspiring materials, and wish we will be given the opportunity to carry a more complete study of the subject.

2 Overview of Collective Intelligence

2.1 Definition of CI Human being is by nature a social entity. This aspect of human life, which is characterized by collaborative activities, can explain the assertion that collective intelligence has existed for at least as long as humans have. But this old age phenomenon is now occurring in dramatically new forms that can be labeled Collective Intelligence Revolution (CIR). It is obvious that with new communications technologies, people and Businesses have discovered the power of knowledge sharing: collaborative intelligence. Wikipedia offers a great an example of collective intelligence in the sense that the Wikipedia community has developed an organizational model that allows thousands of people from all over the world to collectively create an intellectual product without centralized control, almost based on people voluntarism. Wikipedia motivates thousands of volunteers around the world to create the world's largest encyclopedia. Another example of collective intelligence can be seen in political parties, which mobilize large numbers of people to form policy, select candidates and to finance and run election campaigns. The last example is “ Innocentive ,” which lets companies easily tap the talents of the global scientific community for innovative solutions to tough R&D problems. This examples show that the Internet has redesigned the knowledge sharing and collaboration structure by spanning it off the boundaries of academic environments and other small groups and making it a global phenomenon. It has therefore become more important than ever to understand collective intelligence at a deep level to be part of its revolution and take advantage of the new possibilities. Based on the characteristics described above, the working definition that can be provided about collective intelligence identifies groups of individuals who are collectively doing things that seem intelligent. Collective intelligence does not exist only in interactions among humans. Beehives and ant colonies are examples of groups of insects doing things like finding food sources that seem intelligent. A single human brain can also be defined as a collection of individual neurons that collectively act intelligently.

2.2 Types of CI Since the advent of globalization triggered by the Internet, very interesting cases of collective intelligence have emerged that can be grouped into three types:

 Cognition-based CI

 Cooperation-based CI

 Coordination-based CI Cognition-based collective intelligence is concerned with harvesting individual intelligence to increase a group IQ in predicting outcomes that could not be easily predictable at an individual level. An example of this type of CI is the Information Markets that will be presented below as case study. Cooperation-based CI is defined as working together, sharing tasks and accomplishing projects as team. Cooperation-based CI, not more than other types, is practically challenged by human action as selfishly motivated. CI examples such as Open-Source Software and OpenSocial Networks, which will be analyzed below as case studies, belong to cooperation-based CI. Coordination-based CI is aligned with the Decision-making processes, and Decentralized managerial mechanism, which will be studied in the next sections. These cases are just the beginning. With new information technologies, it is now possible to harness the intelligence of huge numbers of people, connected in very different ways and on a much larger scale than has ever been possible before. The key question is: how can we organize this revolution is such a way that it does not turn out to become “The Tragedy of the Commons?” The necessity to raise this question was pressing even though it will not be answered in this paper due to the limitations mentioned earlier. However, this paper answers, or attempts to answer, not only an awareness question: Are people and businesses aware of the collective intelligence revolution and its benefits?, but also the challenge of determining the type of technological system that could enable humans to behave analogically to the “Swarm Intelligence [7].” Before delving into providing some response to the questions raised above, it is worth indicating a suggested process for harnessing CI:

2.3 Five Great Ways to Harness Collective Intelligence [12] 1) Be the hub of a hard to recreate data source - This is a classic Web 2.0 concept and success here often devolves to being the first entry with an above average implementation. Examples include Wikipedia, eBay, and others which are almost entirely the sum of the content their users contribute. And far from being a market short on remaining space, it's its lack of imagination that's often the limiting factor for new players. So don't wait until it's perfect, get your collective intelligence technique out there that creates a user base virtually on its own from the innate usefulness of its data. Just be careful and avoid crowded niches, like peer production news. 2) Seek Collective Intelligence Out - This is the Google approach. There is an endless supply of existing information waiting out there on the Web to be analyzed, derived, and leveraged. In other words, you can be smart and use what already exists instead of waiting for it to be contributed. For example, Google uses hyperlink analysis to determine the relevance of a given page and builds its own database of content which it then shares through its search engine. Not only does this approach completely avoid a dependency on the ongoing kindness of strangers it also lets you build a very big content base from the outset. 3) Trigger Large-Scale Network Effects - This is what Katrinalist and CivicSpace did and many others have done. This is arguably harder to do than either of the methods above but it can be great in the right circumstance. With one billion connected users on the Web, the potential network effects are theoretically almost limitless. Smaller examples can be found in things like the Million Dollar Pixel Page. That's not to say that network effects don't cut both ways and are probably not very repeatable, but when they happen, they can happen big. 4) Provide a folksonomy - Self-organization by your users can be a potent force to allow the content on your site or social software to be used in a way that more befits your community. It's the law of unintended uses again, something Web 2.0 design patterns strongly encourage. Allow users to tag the data they contribute or find and then make those tags available to others so they can discover and access things in dynamically evolving categorization schemes. Use real-time feedback to display tag clouds of the most popular tags and data; you'll be amazed at how much better your software works. It worked for Flickr and del.icio.us and it'll probably work for you too. 5) Create a Reverse Intelligence Filter - Like Ellyssa points out, the blogosphere is the greatest example of this and sites like Memorandum have been using this to great effect. The idea is that hyperlinks, trackbacks, and other information references can be counted and used as a reference to determine what it's important. Combined with temporal filters and other techniques and you can create situation awareness engines easily. It sounds similar to #2 but it's different in that it can be used with or without external data sources and is aimed not at finding but at eliding the irrelevant altogether as an active filter.

Of course, these are not the only ways collective intelligence can be exploited. The Swarm theory and the case studies below will present additional strategies to harness CI.

3 The Swarm Theory: Collective Intelligence from Ant and Bee Colonies

Colonies of social Insects such as bees, and ants display a type of collective behavior that can be defined as Swarm Intelligence [7]. This definition implies a well-organized collaborative behavior generated from primitive interactions among members of the group to solve problems beyond capability of individual members. Swarm Intelligence in social insect colonies is characterized by: (i) Self-organization: Decentralized and unsupervised coordination of activities (ii) Adaptability: Response to dynamically varying environment and (iii) Robustness: Accomplishing groups’ objective even if some members of the group are unsuccessful. How does the Swam intelligence work? The process uses what can be called the waiting line awareness strategy.

3.1 Waiting Lines of Bees In bee colonies, it describes interactions between members of the colony and the environment that leads to dynamic distribution of foragers to efficiently collect nectar from array of flower patches (food sources) that are capricious in terms of profitability to the colony. In brief, foraging bees visiting flower patches, not only return to the hive with nectar, but also with a profitability rating of respective patches. At the hive, forager bees interact with receiver bees to offload collected nectar which also provides feedback on the current status of nectar flow into the hive. This feedback mechanism sets a response threshold for an enlisting signal. An amalgamation of response threshold and profitability rating (function of nectar quality, nectar bounty and distance from the hive) establishes the length of the enlisting signal known as waggle dance. The waggle dance is performed on the dance floor where inactive foragers can observe and follow. Effectively, each active forager bee provides a feedback on her local flower patch whilst observing bees have access to the set of attractive food sources being capitalized by the colony. However, individual foragers do not acquire the full set of global knowledge but rather randomly select a dance to observe from which they can learn the location of the flower patch and leave the hive to forage. The resulting self-organized proportionate allocation pattern, derived from multiple and proportionate feedback on goodness of food sources, validated by experimental study on real honey bee colonies.

3.2 Ant Trails Each ant in the colony acts in a rather simple way, but together they end up doing something intelligent, like discovering the shortest path between their nest and a food source. Ants leave a trail of pheromone when they explore their environment. When ants which leave the nest simultaneously find a food source each ant follows its own trail back to the nest. The ant which returns to the nest first has taken the shorter path. Thus the scent of the shortest trail leading from the nest to the food-source gets stronger. When other ants leave the nest they follow the trail marked most intensively with pheromone, because it indicates the shortest path to a food source. These ants will emphasize the scent of the pheromone trail again. Thus an ant can reason, that a trail with a very strong scent will be an efficient way to a food source. 3.3 Collective intelligence: Cues in the Environment

The two examples of social behavior described above emphasize a collective intelligence strategy in that, an individual can derive information from cues which are given by a collaboratively coordinated behavior of the groups. The bees and ants are practicing some kind of context awareness, which makes them collectively intelligent. These cues in the environment are very simple but efficient.

3.4 Swarm Theory in Humans Interactions: Peer-To-Peer Network The idea presented under this title is an attempt to suggest a type of intelligent systems that would provide human being with a mean to develop organizational behavior analogue to the Swarm Intelligence in Bee and Ant colonies. Thinking of Swarm theory as an organizational model for human society suggests that the dynamics of such an organization would be characterized by collective cognitive, cooperative, coordinative interactions defined by decentralized and unsupervised collaborative activities. Like with any new model, idea, or theory, there are proponents and dissidents. Both groups have interesting facts to sustain their position. Dissidents of a human information system model as corollary of the Swarm intelligence based their arguments on the selfish nature of human action. It is worth drawing some similarity here with the opponents of the creation of the Internet. “The idea of network as a service was a new thing, and it was difficult to convince everybody a) that it was a good idea and b) that it was legal [14].” The creation of the Internet was the result of organization dynamics characterized by the ignorance of convention and the adoption of cooperative spirit. For proponents of the idea of the applicability of Swarm Intelligence in humans interactions, all that is needed is “the model of a network where no one is in charge. [14]” Preliminary analysis of network topology shows that the type of network that would best fit this description is a Peer-To-Peer (P2P) network as opposed to traditional network which has in most cases been centralized. The P2P network encompasses information distribution process in which information packets actively travel through the net to those nodes for which they are relevant. “The underlying mechanism is rooted in the principles of Swarm Intelligence and relies on the dissemination of artificial pheromones, where each pheromone represents one particular relevance criterion that applies to a given information packet. Information packets leave trails of these pheromones at each node they visit and move along gradients of pheromone concentration in order to reach regions in which they are highly relevant. Nodes in turn exploit pheromone concentrations to optimize the network structure [15].”

4 Case Studies of CI:

4.1 Case for Open-Source software (OSS) Open-source software, also called free software is computer software for which the source code has been made available in the public domain under a license that meets the Open Source Definition and standards. OSS originates, many people believe, around this individualistic assertion: “I'll start building something and release it to the community. I'll get feedback from a lot of users, some of whom will fix bugs, write documentation, and build extensions. All of that feedback will create a better product.”

With regards to this assertion, it can be inferred that OSS, like CI, is also all about collaborating and sharing wisdom, knowledge and intelligence on specific project for the purpose its improvement or enhancement. Sufficient literature exists that narrates about the benefits of OSS as unbounded collaborative software development. And it will not be interesting to continue that narrative trend in this paper. However, the interesting thing in the OSS is its evolvement to Open Platform and consequently OpenSocial, OpenCyber.

The fight for a Competitive OpenSocial Business Model: Google, MySpace, and FaceBook OpenSocial is a set of common application programming interfaces (APIs) for web-based social network applications. OpenSocial provides people with Internet platform for social interactions. This is fascinating thing to watch about OpenSocial is that it can be described a cyber market competitive “chess” game in which, Google is currently maneuvering to corner FaceBook, which eclipsed MySpace. FaceBook success is due to the fact that in addition to offering individual social networking, it also lets company that wants to join in party do so by building third party applications.

4.2 Case for Collective Decision-Making One of the main barriers to collaboration in a decision making process may be the difficulty in achieving agreement when diverse viewpoints exist. This can make effective decision-making more difficult. Even if collaborative members do manage to agree, they are very likely to be agreeing from a different perspective. This is often called business cultural boundary. A business culture in which rank or job title is important makes it hard for a lower rank person who may be more qualified than their superior for a job to collaborate. In general, the process is no different from military culture in which the lower rank person is told what to do. This type of organizational culture does not lend itself to collective intelligence. Another aspect of organizational behavior that does not fit with collective intelligence is the organization individuals’ self interest. Some researchers believe that group sizes have an impact on collaborative participation in decision-making. Further, group size reduces the share of benefit but increases the cost of participation

How can we create a decision-making system based on collective intelligence? This type of system would be based on the belief that better decision can be made by harnessing computer technology to enable collective intelligence, that is, to enable the synergistic and cumulative channeling of the vast human and technical resources now available over the internet to address a systemic problem. The approach would be to develop a new class of web-mediated discussion and decision making forum which uses innovative combinations of internet-mediated interactions, collectively generated idea repositories, computer simulation, and explicit representation of argumentation to help large, diverse, and geographically-dispersed groups systematically explore, evaluate, and come to decisions concerning systemic challenges.

This type of system would be representative of all elements of a group without class restriction. It will represent the democratic aspect of the collective intelligence strategy. I sometimes tend to think that this would be a utopia if it is to be applied at a country level to management an election process, i.e. presidential elections.

4.3 Case for Decentralized Managerial Mechanism We mention in an earlier section that collective intelligence was characterized by a decentralized and self-organized collaborative intelligence. Designing a system that will take individual-level rules of behavior and interaction, and integrate them such as to produce a desired collective pattern in a group of human or non-human agents is the goal of a decentralized managerial mechanism. This type of system would be difficult because group’s aggregate-level behavior may not be easy to predict or infer from the individuals’ rules. However, some researches have demonstrated that “interesting” patterns of collective behavior can be discovered when one does not know in advance what the system is capable of doing: generic situation in swarm design [7] Honey bees show the impressing ability to choose collectively (swarm intelligence) between nectar sources of different quality by selecting the energetic optimal one. This interesting ability can be used to simulate a multi-agent system of a cohort of foraging bees. By exploring such a system with regards to the economic results of foraging decisions in a fluctuating environment, the dynamics and efficiency of decentralized managerial system can be investigated in terms of potential costs and benefits.

In the Swarm Intelligence [7], the whole decentralized decision process is based on competition among dancing bees, which guide new (naive) bees to their foraging targets. These guided bees also can perform dances after their successful returns, thereby providing a positive feedback loop. Bees dance longer and more often for better sources, leading to race conditions among these feedback loops. Finally nearly the whole group of foraging bees converges to the optimal nectar source. In addition to the selection of nectar sources, bees regulate their nectar intake rate accordingly to the current nectar processing workforce (= decentralized workload balancing). Homecoming foragers consider the queuing delays they have experienced while waiting for available food-storing bees. If this delay is long, foraging bees will gradually cease their recruitment dances. If the delay is very long, they will even stop their own foraging flights, thus lowering the collective foraging activity.

Decentralized target selection and workload balancing mechanism are very good examples for swarm intelligence, because the decisional component is not located inside the single individual bee. In contrast to that, it is located in the overall system, arising as an emergent phenomenon of self organization.

4.4 Case for Information Markets A lot of business organizations, such as Hewlett Packard, Eli Lilly, Google etc., are increasingly beginning to use things called prediction markets where people buy and sell predictions about future events (like sales of their products) in ways that leads to more accurate predictions in many cases than traditional market research or polling or other techniques. Prediction markets, also called decision markets, idea futures, event derivatives, or virtual markets are collaborative market spaces where interactively place their speculative market predictions. How does a speculative information market fit with the notion of collective intelligence? Information markets, like collective intelligence is all about sharing information. The increasing diffusion of online collaborative projects confirms the idea that it is more effective to identify the solution of a problem by turning to collaborative knowledge rather than by trusting to the wisdom of the few. According to James Surowiecki, author of The Wisdom of the Crowds, the aggregation of knowledge is particularly useful when the three following aspects are present:  Cognition - problems which have a definitive answer, which we can try to predict, considering the information available and that which is missing (for example: who will win the next elections?);  Coordination - where the solution has an impact on a group and the search for the solution is more effective if it emerges thanks to the fact that everyone acts in their own personal interest (markets work this way);  Cooperation - when the optimal solution has an impact on a group and depends on the trust which is established among its members so that each perceives the existence of a collective interest which is greater than the individual interest (for example, non-profit initiatives).

Surowiecki maintains that these problems can be more effectively resolved by seeking the involvement of several people who have partial knowledge of the problem. The combination of all the opinions expressed, in fact, produces a solution which is generally better than that generated by interrogating a small number of experts. For this to happen, however, four conditions are necessary:  intellectual diversity, since it is necessary for there to be differing opinions and for everyone to contribute their own “small” piece of knowledge to resolving the problem;  independence, so that those involved must be able to think without being conditioned and not be directly influenced by what the other members of the group think;  decentralization, because this encourages specialization and diversity; aggregation, since it is necessary that the individual opinions expressed help to form a single collective opinion

Collective intelligence, as generated through information markets could be used as a tremendous resource for individuals, organizations and society. There is information to be discovered simply through the proper aggregation of individual’s ideas and opinions. However, developing trust in collective intelligence is of prime concern for the success of the phenomenon as a predictive tool. Conclusion In this paper, we have presented a collective intelligence model by using the Swarm Intelligence theory. We have also some successful cases of use of collective intelligence. The collective intelligence phenomenon is increasingly revolutionizing our business environment through the use of the Internet. However, many people are still not aware of its benefits and others are still skeptical. Researchers at the Center for Collective Intelligence [3] assert that there is a lot of hype and prejudice going around about collective intelligence. On the one hand, there are people who think that collective intelligence is magic; they think that just doing things “collectively” will make everything great. On the other hand, there are people who are prejudiced against the very notion of collectiveness and decentralization. Very recently for instance, there have been a number of people who’ve looked at the success of Wikipedia and pointed out ways in which is not perfect. And then, based on that, they have argued that nothing without central control can ever be successful. I think both of these extremes beliefs are equally wrong. Collective intelligence is not always good. However, a very important thing is help build a solid scientific foundation. References:

[1] Collective intelligence, From Wikipedia, the free encyclopedia, http://en.wikipedia.org/wiki/Collective_intelligence

[2] Blog of Collective Intelligence, http://www.community-intelligence.com/blogs/public/

[3] Research Projects, Center for collective Intelligence, Massachusetts Institute of Technology, http://cci.mit.edu/research/projects.html

[3] Craig Hamilton, Come Together: Can we discover a depth of wisdom far beyond what is available to individuals alone? Enlightenment Magazine, http://www.wie.org/j25/collective.asp

[4] Carl Andersona and Nigel R. Franks, Teams in animal societies, Behavioral Ecology Vol. 12 No. 5: 534-540, International Society for Behavioral Ecology, 2001. http://beheco.oxfordjournals.org/cgi/content/full/12/5/534

[5] APA Press Release, APA Task Force Examines the Knowns and Unknowns of Intelligence, American Psychological Association, 1995.

[6] Linda Gottfredson, Mainstream Science on Intelligence, Wall Street Journal, December 13, 1994.

[7] Bonabeau, E. & Meyer, C. 2001. Swarm intelligence. A whole new way to think about business, Harvard Business Review 5: 107–114.

[8] Carl Anderson and Tucker Balch, Proceedings of the 2nd International Workshop on the Mathematics and Algorithms of Social Insects, Georgia Institute of Technology, 2003

[9] Muhammad A., M. Egerstedt M. 2003b. Decentralized Coordination with Local Interactions: Some New Directions. Workshop on Cooperative Control, Block Island, RI. (Submitted)

[10] Anderson, C. and Bartholdi, J. J. 2000. Centralized versus decentralized control in manufacturing: lessons from social insects, In McCarthy, I. P. and Rakotobe- Joel, T., editors, Complexity and Complex Systems in Industry, Proceedings University of Warwick, 19th-20th September 2000, pages 92–108.

[11] John W. Sutherland, Extending the Reach of Collective Decision Support Systems: Provisions for Judgment-Driven Exercises, Virginia Commonwealth University, Richmond, VA. [12] Dion Hinchcliffe’s Web 2.0 Blog, Five Great Ways to Harness Collective Intelligence, Jan. 2006. The 6 essential things you need to know about Google's OpenSocial, No. 2007. Social Computing Magazine, http://web2.socialcomputingmagazine.com/

[13] James Surowiecki, The Wisdom of the Crowds,

[14] John Markoff, The team that put the Net in orbit, The New York Times, SLIPSTREAM, December 07, 2007

[15] Arne Handts, Design of Collective Intelligence, http://blog.handtwerk.de/2007/08/29/design-of-collective-intelligence/

[16] 3rd EuroNGI IA.8.2 - New Trends in Network Architectures and Services: 1st International Workshop on Self-Organizing Systems (IWSOS 2006); http://www.iwsos.net.fmi.uni-passau.de/home.html