Biologist Marc Kirschner said, “Weak linkage underpins our theory. Some scientists think that how evolution proceeds is explained by the nature of the transcriptional apparatus and gene regulatory networks. True. Many changes occur in regulating gene expression. But these scientists neglect the fact that reordering, making different combinations of components, is not the really hard problem. The hard problem is to make components so that, when you recombine them, they’ll function together.”

What are the functional characteristics and behavioral properties of interoperable systems? What characterizes biological systems such that new combinations of genes can function in compatible ways to produce useful outcomes? If what is required is simply to generate highly successful new combinations from which the environment can select, evolution is merely a combinatorial problem. This begs the question (and Darwin’s dilemma): How are those highly successful new combinations produced? And how could understanding how interoperability in living systems inform the design of the next generation of computing systems to support collaborative intelligence?

This section addresses the need for tools to support collaborative intelligence that mimic attributes of living systems, e.g.

• Self-organizing systems, from robotics to distributed collaboration, from sustainable remediation (as a complex adaptive system) to computational learning systems;

• Evolvable rules where evolvability can be tested for survivability in its present ecosystem, rather than measured against a pre-set future goal, beyond the traditional paradigm generally used, even with genetic algorithms, of ‘reducing the difference between the present state and a goal state';

• Facilitated Variation as a computational problem-solving stategy where the computational genotype is modified to generate an expressed phenotype by means of developmental mechanisms that interact and co-evolve as the computational genotype matures in its ecosystem;

• Homeostasis and its implications for predicting the behavior of complex systems, e.g. in organizational innovation;

• Ecosystem diversity and its correlation to stability, adaptability, and evolvability, enabling more rapid response to perturbations;

• Risk and Tradeoff Optimization, examining the vulnerability of specialization and the efficiency/ inefficiency of generalization;

• Scalability parameters for computational innovation;

• Granularity and filtering in pattern recognition;

• Timescales, synchronous vs asynchronous updating, hybrid mixes (some aspects specialized, others generalized) e.g. “the lion versus the virus” — When is it best to be biggest (most complex and specialized), versus simple and generalized (or simple and specialized, or complex and generalized)? How can design be optimized by specializing in some domains, while remaining general and adaptable in others?

• How scaling impacts structure and dynamics of social organizations, such as cities and institutions or corporations, economies of scale, growth, innovation and ecosystem sustainability

Assumptions.
1. Lifelike systems can inform new approaches to collaborative computing, addressing the practical imperative for more sophisticated design methods across disciplines, responsive to Earth's biosphere as a synergetic system.
2. Principles of design are manifest in emergent complexity.
3. Interventions in complex ecosystems can impact life's futures, and be informed by research on life-like systems.

FUTURE DIRECTIONS. Ecosystem forecasting and sustainable remediation is an ideal testbed to integrate Decision Support Systems that can later be replicated elsewhere. Because understanding each sustainable remediation problem requires visualizing its complex system of variables as a whole, with all of its physical components dynamically linked and naturally constrained, an integrated decision support framework needs capacity for

• Linking models to simulate alternative impacts given indicator changes. Once calibrated, the model, together with other linked models addressing many indicators in a gven problem, can be used for Ecosystem Forecasting and for projecting “what if?” scenarios for given sets of variables and growth scenarios. Future capacity for scenario-building will allow planners to try out alternative hypotheses and associated decision sets to observe how scenarios play out in a simulated environment and explore outcomes, intentional or not. 
  
  
• Animation of alternative urbanization scenarios. Data can be used to construct an animation that shows the extent and pace of urbanization in the region. Computer models of urbanization calibrated with historic data will be useful for Ecosystem Forecasting, projecting changes for given sets of variables and growth scenarios. The Data City Network initiative is committed to fostering greater awareness of the impacts of urbanization.
  
  
• Multi-scale views: in and out zooming. Integrated planning requires capacity to see systems at all relevant scales. The challenge is to recognize ranges-of-scale relevant for each planning/ management question through integration-modeling methods and advanced GIS spatial analysis and visualization techniques.
  
  
• Knowledge domain visualization. Beyond the organization of geo-spatial data and its links, knowledge domain visualizations give users a way to identify research areas, experts, institutions, grants, publications, journals, etc. in their area of interest. In addition, they can assist to identify interconnections, the import and export of research between fields, the dynamics (speed of growth, diversification) of scientific fields, scientific and social networks, and the impact of strategic and applied research funding programs etc.
  
  
• Process maps: status of collaborative problem-solving. Visualization of problem information is seldom adequately complemented by visualization of the problem-solving process. Information visualization goes beyond viewing static data to enable users to collaborate and to control and update the environment. It offers 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.
  

References

Marc Kirschner presentation at the California Academy of Sciences. March 14, 2009.

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