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.

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).

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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.

References
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.

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Image Credit. Andrew Wunsche
Left. Random Boolean Networks

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