Problem maps can evolve into navigable user interfaces — open frameworks (partial
patterns) to tap the pattern recognition and completion capabilities of users. They serve as
vehicles to order incoming information in process, and to enable participants to see where their
work fits on a map of the overall problem-solving process.

Problem mapping is a tool to support decision-making and knowledge synthesis. Both the probability of an earthquake, and perception of its probability, figure in the design of innovative strategies to address this risk. When a catastrophic earthquake strikes, people, resources, and strategies must rapidly respond and collaboratively align, self-organising to address new needs. What are the design parameters for a framework that can be rapidly deployed in situations like this?

Problem mapping tracks the status of collaborative problem-solving. Visualization of problem information needs to be complemented by visualization of the problem-solving process. Information visualization goes beyond viewing static data to enable users to collaborate to manage and update their problem-solving environment, offering simple ways to navigate through information so the user can see where he and other users are on a map of their collaborative problem-solving process.

The diagram below illustrates one example, developed by Zann Gill to support a Smart Cities initiative. The objective in developing the tool was to provide systematic support for gathering information and identifying priorities in the planning process. A suite of problem-mapping and project visualisation “empty construct” plug-ins to kickstart new projects, and to enable project champions to share knowledge and experiences in process, is needed to build effective systems for communication across disciplines.

The Periodic Table exemplifies one instance of an empty construct to stimulate discovery. Dmitri Mendeleev picked up an abandoned earlier attempt to complete the invention of the Periodic Table of Elements in 1869, representing “periodic law” before electronic theory existed to explain this periodicity. At first Mendeleev’s Periodic Table was treated with distrust as a “mystic scheme.” Some dozen years later new elements were discovered with properties that matched what the open slots in the Table of Elements predicted. This simple two-dimensional diagram enabled pattern recognition and predicted new elements decades before their discovery. The Table laid the foundation for scientific and technological breakthroughs in advance of complete information, guiding the comparative study of elements for over forty years until Henry Moseley discovered the principle of atomic numbers in 1913. Although knowledge of atomic numbers enabled an improved system, the concept of atomic numbers depended upon the Periodic Table for its discovery.

The confusion of elements before Mendeleev’s Table of Elements existed illustrates how seemingly disordered information contains implicit structure, the starting point for Mendeleev’s conceptual breakthrough. The Table in turn provided the next level of implicit structure — an intellectual framework through which implicit pattern could be recognised and made explicit. The Periodic Table is a classic model, showing how “empty constructs” facilitate knowledge-building.
 
As in evolution, the ecosystem plays a significant role in determining what survives and thrives. Mendeleev’s Periodic Table may be viewed as providing “the situation architecture” to enable collaboration on discovery of chemical elements. Design of a web-based ecosystem to inspire pattern completion (as did Mendeleev’s Periodic Table) requires developing a framework that provides the game rules to enable pattern recognition.

A website is now dedicated to archiving all representations of the Periodic Table. In the site by Murray Robertson and John Emsley, developed with support from the Royal Society of Chemistry of the United Kingdom, the viewer can interact with Visual Elements of the Periodic Table.

Practical Applications. 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 and rapid response.

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).

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.

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.

References.
J.W. von Spronsen. 1969. The Periodic Table of Elements: A History of the first hundred Years. Amsterdam: Elsevier.

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Image Credit. Andrew Wunsche
Left. Random Boolean Networks

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