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© Zann Gill 2008 – 2010 For more on applications, see |
A practical imperative is one outcome of humanity’s success as a technological species — to be wiser decision-makers, moving beyond competing for advantage toward collaborating to create the next stage of civilization and sustainability on Planet Earth. Is Nature’s “world game” a game that we civilized humans can collaboratively play? Two seminal thinkers produced complementary breakthrough ideas concurrently. Buckminster Fuller propounded his ideas about synergy, design science, and World Game, while James Lovelock collected the data to formulate the Gaia Hypothesis. The Gaia Hypothesis and World Game fit together like hand and glove. Lovelock expressed the complexity of global environmental sustainability as a synergetic system, carefully tuned and self-regulating. Fuller proposed World Game as a way to address the system that human civilization has disrupted, to restore the balance of Earth as a synergetic system. Al Gore would later adopt that metaphor — Earth in the Balance. Nine core operating principles of design: Described in more detail: 1. Inventing from uncertainty implies that many alternatives are equally possible at the outset. ZIG and ZIR start from maximum uncertainty — the neutrality of randomly distributed pixels and many possible images of zebras. Where traditional methods start by defining the problem, putting boundaries around it, design harnesses uncertainty. 2. Setting criteria for decision-making that can be revised based on new information allows recognition of unexpected patterns, triggers for innovation. By sacrificing possibility, specification converges toward a novel outcome, whereas fixed definitions and rigid boundaries limit the scope of the problem at the outset and can block innovation. 3. Convergence starts from uncertainty, spiraling from blurriness toward increasing clarity and focus. Complementary dynamics of divergence and convergence generate raw material, avoiding bottlenecks, such as consensus-seeking. A bottleneck is a required, but unlikely, contingency that might break the chain of events required to originate novelty. To create hypotheses about the origin of life, scientists identify alternative pathways, scenarios, and events that would have been plausible, or highly probable, and choose more probable development paths, converging on an outcome that could not be predicted in advance. This is a strategy, not only to reconstruct the past, but also to design the future. 4. Using tolerance spectra as opportunity windows limits what variations can be accepted into an evolving entity, allowing variations that fall within spectra to be accepted for interpretation, assimilation, and possible adaptation. Greater tolerance opens wider windows of possibility. 5. Interpreting variation, deviation or error in the context of the evolving system aims to recognize usable variation, deviation or error to be adapted for use in context. Interpretation after-the-fact transforms variation from random to non-random. Adaptation then converges toward innovative outcomes. Whereas bottlenecks constrain the path (sequence of steps in a problem-solving process), tolerance windows constrain its frame (whole pattern emerging). 6. Assessing interim results allows integration to complement elimination. Guided by criteria to decide which interim results to retain and interpret, ZIG and ZIR draw their zebra. In contrast to Karl Popper’s theory that scientific knowledge advances by testing and eliminating least fit theories, evolutionary emergence allows definitions and boundaries to start blurry, co-evolve and emerge to increasing specificity and focus as interim results are interpreted. 7. Recognizing emergent patterns in partial data and augmenting incomplete patterns innovates from uncertainty with flexibility to recognize fuzzy patterns emerging. In contrast, by allowing only the final result to be tested, Popper fails to recognize partial, not yet testable, patterns emerging, which should survive to be brought into focus. 8. Linking collaborative, autonomous components into emergent networks, as ZIG and ZIR create a zebra image by continually adapting an earlier imperfect image into a better one develops new synergies. Collaborative autonomy enables agents jointly to recognize and develop shared synergies. Whether molecules collaborate to invent life, or organelles collaborate to make a functioning cell, or arguments collaborate to construct a hypothesis, or humans collaborate to solve a problem, in each case components bring their uniqueness and autonomy to the collaborative process. Instead collaborative autonomy in human problem-solving avoids the lowest-common-denominator of committees, where individual uniqueness is lost and diversity is weeded out to achieve consensus. As in an ecological niche, the interacting players do not have the same needs so collaborative autonomy does not require agents to seek consensus. 9. Converging toward innovation by seeking coherence. By preserving “genetic diversity,” rich raw material is retained, both in Nature and in human problem-solving. Diverse needs are positioned in a larger framework, enabling the process in which they collaborate to converge toward increasing synergy and coherence.
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