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Yin And Yang
In Bill Livingston’s current incarnation of the D4P, the author distinguishes between two mutually exclusive types of orgs. For convenience of understanding, Bill arbitrarily labels them as Yin (short for “Yinstitution“) and Yang (short for “Yang Gang“):
The critical number of “four” in Livingston’s thesis is called “the Starkermann bright line“. It’s based on decades of modeling and simulation of Starkermann’s control-theory-based approach to social unit behavior. According to the results, a group with greater than 4 members, when in a “mismatch” situation where Business As Usual (BAU) doesn’t apply to a novel problem that threatens the viability of the institution, is not so “bright” – despite what the patriarchs in the head shed espouse. Yinstitutions, in order to retain their identities, must, as dictated by natural laws (control theory, the 2nd law of thermodynamics, etc), be structured hierarchically and obey an ideology of “infallibility” over “intelligence” as their ideological MoA (Mechanism of Action).
According to Mr. Livingston, there is no such thing as a “mismatch” situation for a group of <= 4 capable members because they are unencumbered by a hierarchical class system. Yang Gangs don’t care about “impeccable identities” and thus, they expend no energy promoting or defending themselves as “infallible“. A Yang Gang’s structure is flat and its MoA is “intelligence rules, infallibility be damned“.
The accrual of intelligence, defined by Ross Ashby as simply “appropriate selection“, requires knowledge-building through modeling and rapid run-break-learn-fix simulation (RBLF). Yinstitutions don’t do RBLF because it requires humility, and the “L” part of the process is forbidden. After all, if one is infallible, there is no need to learn.
Shall And Shall Not
For a controlled system to remain viable and stable, Ashby’s law of requisite variety requires that the system controller(s) exhibit a wider variety of behavior than the system controllee(s). This can be accomplished by either the controller increasing its variety of responses to controllee disturbances, or by decreasing the variety of controllee disturbances relative to its own variety of control responses, or both.
In order to comply with Ashby’s law (in conjunction with several other natural laws – 2nd law of thermo, control theory, Turing’s infallible/intelligence thesis, etc), Bill Livingston asserts that membership in any institution requires the internalization, either consciously or (more likely) unconsciously, of the following set of “shall” and “shall not” rules:
As you can see, suppressing variety in the controllee population is the preferred method of a controller aiming to satisfy Ashby’s law. The alternative, increasing its own variety of response relative to controllee variety of disturbance, requires learning and development. By definition, infallible controllers don’t need to learn and develop. They stopped learning when they achieved the status of “infallible” – either by force or by illusion.
So, what do you think? Did Mr. Livingston hit the bullseye? Miss by a mile?
D4P Has Been Hatched
Friend and long time mentor Bill Livingston has finished his latest book, “Design For Prevention” (D4P). I mildly helped Bill in his endeavor by providing feedback over the last year or so in the form of idiotic commentary, and mostly, typo exposure.
Bill, being a staunch promoter of SCRBF feedback and its natural power of convergence to excellence, continuously asked for feedback and contributory ideas throughout the book writing process. Being a blabbermouth and having great respect for the man because of the profound influence he’s had on my worldview for 20+ years, I truly wanted to contribute some ideas of substance. However, I struggled mightily to try and conjure up some worthy input because even though I understood the essence of this original work and it resonated deeply with me, I couldn’t quite form (and still can’t) a decent and coherent picture of the whole work in my mind.
D4P is a socio-technical process for designing a solution to a big hairy problem (in the face of powerful institutional resistance) that dissolves the problem without causing massive downstream stakeholder damage. Paradoxically, the book is a loosely connected, but also dense, artistic tapestry of seemingly unrelated topics and concepts such as:
- Alan Turing’s thesis of infallibility vs. intelligence
- Leveraging nature’s physical laws, with a special emphasis on suspending the 2nd law of thermodynamics and entropy growth
- W. Ross Ashby‘s cybernetic law of requisite variety
- Thorstein Veblen’s theory of the leisure class
- Nash Equilibriums in game playing
- Rudolf Starkermann‘s mathematical analysis of social group system behavior
Bill does a masterful and unprecedented job at connecting the dots. The book will set you back, uh, $250 beaners on Amazon.com, but wait….. there’s a reason for that astronomical price. He doesn’t really care if he sells it. He wants to give it away to people who are seriously interested in “Designing For Prevention”. Posers need not apply. If you’re intrigued and interested in trying to coerce Bill into sending you a copy, you can introduce yourself and make your case at vitalith “at” att “dot” net.
Update 12/29/12:
The D4P book is available for free download at designforprevention.com. The second edition is on its way shortly.
Powerful Tools
Cybernetician W. Ross Ashby‘s law of requisite variety states that “only variety can effectively control variety“. Another way of stating the law is that in order to control an innately complex problem with N degrees of freedom, a matched solution with at least N degrees of freedom is required. However, since solutions to hairy socio-technical problems introduce their own new problems into the environment, over-designing a problem controller with too many extra degrees of freedom may be worse than under-designing the controller.
In an analogy with Ashby’s law, it takes powerful tools to solve powerful problems. Using a hammer where only a sledgehammer will get the job done produces wasted effort and leaves the problem unsolved. However, learning how to use and wield powerful new tools takes quite a bit of time and effort for non-genius people like me. And most people aren’t willing to invest prolonged time and effort to learn new things. Relative to adolescents, adults have an especially hard time learning powerful new tools because it requires sustained immersion and repetitive practice to become competent in their usage. That’s why they typically don’t stick with learning a new language or learning how to play an instrument.
In my case, it took quite a bit of effort and time before I successfully jumped the hurdle between the C and C++ programming languages. Ditto for the transition from ad-hoc modeling to UML modeling. These new additions to my toolbox have allowed me to tackle larger and more challenging software problems. How about you? Have you increased your ability to solve increasingly complex problems by learning how to wield commensurately new and necessarily complex tools and techniques? Are you still pointing a squirt gun in situations that cry out for a magnum?
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