The tipping point concept of non-transitivity

(above - picture of a set of 3 non-transitive dice from Grand Illusions website.)

What I'm seeing is not that people can't "think big", because they can. The US President can go from tying his shoe to considering Armageddon in a heartbeat. We all are free to consider BIG problems or TINY problems and the "auto-zoom" feature of our brains makes whatever we're considering fill out mental screen.

So, it's easy to be misled by small examples into thinking they're BIG issues. We don't seem to come with "ground wires" that keep our feet on the same ground.

That's probably a lot of what goes on in my favorite Snoopy cartoon where he's lying on top of the doghouse and thinking:

Did you ever notice
that if you think about something at 2 AM
and then again at noon the next day
you get two different answers?

But this morning I'm focused on why it is that a loop is so surprisingly hard for people to grasp.

I think it's not the wider view or scale, because people can do that "zoom" so effortlessly they don't even see it happen.

I think its that

• The value of "constants" changes with scale, and
• the relative ordering is non-transitive.

People aren't overly baffled when what looks like a short-term great idea turns out, in the long term, to be a terrible idea. As Dennis the Menace said, standing in a corner for punishment, "How come dumb ideas look so great while you're doing them?"

But each time people run into this, it's like suggesting to a Labrador Retriever that it might be time to go for a walk. "Oh, my God! Yes! A Walk! What an astonishing idea!" (Thank you Dave Barry for that thought about Labs.) The idea is visible, and logical, and sensible, but somehow it fades away to nothing between uses. We keep forgetting it.

The most likely reason I can imagine for that is that there is a larger idea, a context idea, that this change-with-scale property violates or offends, and, as soon as our conscious mind lets go of it, the cleanup crew in our brain looks for where to put it back and, mystified by it, decides it must be trash, because it doesn't fit anywhere with something bigger we preserve.

That's the easy one.

The loop thingie is ten times harder for people to grasp, even once. Even when people see it, touch it, play with it, some part of their brain rejects the concept as "clearly false" and is preparing to disassemble and discard it as soon as possible to restore sanity and normalcy.

And the problem isn't with a loop. People grasp the concept "circle." People don't run screaming from a "hula hoop" toy. It's more subtle.

It's more like the sense when you put a twist in a loop of paper, ending up with a Mobius strip. This does not feel right. This is uncomfortable, and barely tolerable, regardless how many times you've played with them or tried to cut one apart lengthwise and failed.

But, no, it's even worse than that. It's an M. C. Esher type loop, with a twist in a dimension that we don't even recognize as a dimension when we TRY to focus on it with our full attention.

It's a property of the children's game "rock paper scissors" - where there are three rules:
rock smashes (beats) scissors
scissors cuts (beats) paper
paper covers (beats) rock

So, there is no "best" one. This turns out to be a much more widespread phenomenon that we would prefer. We see it but reject it. For most things with multiple dimensions, the term "best" is meaningless, but we're so attached to it, we want to make it true anyway. We can't get resolution if we admit that there is no "best mate" or "best house" or "best job" or "best employee" or "best candidate" or "best football team". If you compare them by pairs, each pair seems to have a "better", but if you make a map of "better" it has no top or "best", but instead goes in a loop, or more than one loop. It's uncomfortable and a little scary. Things we thought we could rely on turn out to be shaky. We try to forget it, and succeed. Over and over.





Here's the classic example - the "non-transitive dice" that Martin Gardner described decades ago, and that Ivars Peterson attributes to Bradley Efron, a statistician at Stanford University.


You can read about these, but you just have to buy a set, or build a set out of construction paper, and even then you can see it but you can't believe it. There is no best one of these 4 dice, or of the 3 at the top of this post. A beats B, B beats C, C beats D, and D goes around the end of the barn and comes back and beats A. It's a loop and it just seems wrong.

(So, warning, don't try to win money with these, because the loser will be convinced you must have cheated.)

Well, as always, you must be wondering what this all has to do with health care or the problems of the world. So, back a few days ago I posted an analysis I did of why so many airlines are running late these days. Included in that was this loop diagram, that I made up, that you can click on to zoom up to readable size.



This one is a circle of "blame", where the blame is "non-transitive." Each set of people, in their local world, can blame the next group down the chain for the problem, and is clearly "right" -- which would be OK except that the list of blamee's goes in a full circle back to the "blamers."

Again, if you view this one box and it's neighbors at a time, it seems fine and makes sense. But if you put them all together in a circle, something seems to have gone terribly wrong.
Like this Esher print I love (from Wikipedia)


Or this one of stairs from Wikipedia.

There is wrongness there. But the wrongness is subtle.

That happens a lot more than we hold in our heads to be true.

So, where this comes down to Earth is the following conclusion. If people are going to learn about system dynamics and feedback loops, we need to get them past the point where very simple loops like the ones shown above, are perfectly sensible and acceptable, instead of where they are now, which suffers the mental version of tissue-rejection.

The problems will not come to us. We must go to them.

There is no way to make a circle into a line, regardless how "linear" a little part of the edge is if we simply elect to ignore the parts that go out of sight on each side of a narrow field of view.

Three facts seem to be true:

• Closed circles of causality make us queasy.
• Closed circles of "blame" make us and our legal system very uncomfortable.
• Closed circles of "blame" that show that what's happening to us is our own fault coming back to haunt us with a lag time and amplification are just intolerable thoughts and are rejected out of hand instantly. That's crazy talk.
  

We need to learn to be able to see BOTH lines and circles of causality without becoming queasy and needing a drink.

Suggestions welcome as to how to do that.

Wade

-------
from Ivar Peterson's MathTrek

Gardner, Martin. 1987. Nontransitive paradoxes. In Time Travel and Other Mathematical Bewilderments. New York: W.H. Freeman.

______. 1983. Nontransitive dice and other probability paradoxes. In Wheels, Life, and Other Mathematical Amusements. New York: W.H. Freeman.

One possible source of nontransitive dice is toy and novelty collector Tim Rowett. He offers a set of "Magic Dice" along with rules for several games at http://www.grand-illusions.com/magicdice.htm. You can find out more about Rowett's collection at http://www.grand-illusions.com/tim/tim.htm.

Scale and Scope-Creep

Our mental model of the nature of the world dominates our thinking on what kind of problems we should be starting with.

The classic view, emphasized repeatedly by academics to new students, is to keep the focus narrow, to work on the smallest problem possible and do it well. Large problems are "bad", and very large problems are "world hunger" or impossible:



But, if we look at problems like antenna radation with near and far fields that are both easy, and just a middle field that is difficult, we have to ask if the actual curve doesn't look more like this one, with "easy" parts at each end and the "hard" part in the middle. At the low end, one thing dominates and other terms can be ignored. At the high end, a different thing dominates, and everything else can be ignored. It's only in the middle that nothing can be ignored and the problem becomes too hard to do.

An example was the water in the faucet. At a molecular level, we can model the motion of several, possibly 100 molecules. More gets harder and harder. But if we keep going up to the level of ten to the tenth molecules, we get solvable problems, just with different terms. We now have terms like "water pressure" and "volume" and "flow rate" that mean nothing at the molecular level. At the water level, looking back to the molecular level, it now looks hard, what with all the probability distribution functions and quantum mechanical effects and waves instead of particles, etc.





But, as Marsden Bloise pointed out in an article I read long ago that simply transfixed me, the reality of LIFE is that it has a "curiously laminated" quality, with levels that make sense to us (cells, organs, systems, people, teams, companies, planets" separated by stuff between the easy levels, like filling in an OREO brand cookie, that is squishy and hard to analyze.

That model, then, is more like this picture:



The conclusion is that LIFE has levels at which there are meaningful concepts, separated by spaces in which we can't find meaningful concepts. Each of the levels we know of has academics studying it, and they are treated as if they were entirely different universes, not different parts of the very same single LIFE object.

So we have Politics and Sociology and Psychology an Biology and Cell Biology, which hardly ever talk to each other, but, in reality, are just different aspects of the same LIFE entity that humans are in the middle of. As we begin to understand that these levels interact, perhaps even causally as seen from above (but not from below), we begin to figure out that there is really only one large complex thing here, not many small distinct separate things. We have to unfragment what we know about LIFE and reassemble the pieces.

ANYWAY, my point is that sometimes LARGER is actually EASIER, as the study of "water" is much easier than the study of "quantum mechanics of a dense population of H20 molecules."

So, the same thing is true, in my mind, about organizational functioning. There is no point in saying we are going to "solve" the individual and small-team problems first, and then, if there is time left over, move on to the much harder department and company scale problems we face.

First, we'd be missing all the easy cherry-picking solutions at the higher levels.
Second, since everything is connected to everything else, like some huge "mobile" hanging structure, it's actually not possible to "solve" any one level without at least partly solving the level above it. We've found that out in Public Health and Psychology -- there's little point in trying to change one person or one tribe, because, when we walk away, the surrounding systems push back on them and the person or tribe reverts to their old behavior.

The best solution, then, would actually address every level simultaneously, and ask the question of "What would be a win-win-win solution here?"

In Public Health, that would mean asking what would simultaneously address personal, corporate business, city, state, and population level needs, and national identity needs? Instead, too much effort is put into trying to sub-optimize the problem, and solve "environmental health" at the expense of jobs, that then backfires because the unemployed workers then have worse health than before.

Larger is smaller. Bigger is Easier. It is neither ill-advised nor a waste of time nor unimaginably complex to address large issues ahead of small ones. Like water versus molecules, sometimes the larger problems are much easier to solve.

And, like my post on the fragmentation of social groups, if we don't address the larger problem, the gaps and holes that neglect produces manages to defeat, neutralize, and devastate all the work we did "solving" local problems. All our work is wasted if we train people to perfection and no one thinks the problem that arises is theirs to fix.

Similarly, the Toyota Production Model and "lean" thinking can help us greatly in addressing local efficiencies, and even set up some global concepts in terms of "pull" and the impact of stopping a whole line because anyone, at any level, is having a hard time doing their own job because of difficulties someone else exported to them. Those are great concepts.

But those concepts still start at the bottom and work upwards. They leave the separate and mostly independent base uncovered of starting at the top and working downwards, and finally meeting in the middle.

It seems that both ends should be worked on simultaneously, for best results.




Ref: Near field, far field.
MC Escher - Ascending and Descending.

Systems Thinking and the emergence of new Life

T.S. Eliot, in the Four Quartets , said

We shall not cease from exploration
And the end of our exploring
Will be to arrive where we started
And know the place for the first time.

I'd like to suggest, as others have, that the process of exploration is more a spiral or helix than a circle, and we, individually and collectively, may go through multiple cycles of coming back to where we were, except that now it's "different" than the last time we were "here."

There is a motion, very much akin to an actual mechanical screw, in which we apply a rotational force or motion, but the result is a much harder to achieve motion in a wholly different direction.

As I sometimes do, I was rereading Practical Challenges of Systems Thinking and Modeling in Public Health this morning, seeing if the article had changed in meaning since I last read it [ Trochim WM, Calebra DA, Milstein B, Gallagher RS, Leischow SJ, American Journal of Public Health, March 2006, V96, No. 3]. Most of those people are at Cornell, in fact, in my old stomping ground, MVR Hall. Bobby Milstein is at the CDC, and Scott Leischow at NCI.

As one of my favorite philosophers, Charles Schulz's dog Snoopy, noted one day:

Did you ever notice
that if you think about a problem at 2 AM
and then again at noon the next day
you get two different answers?

Anyway, I got a different answer I wanted to share.

Still reverberating from my post on the Ten Lessons from Physics yesterday, I realized there is another candidate for important lessons in reducing complexity to a manageable level from physics that may be relevant to "Systems Thinking."

I already described yesterday how immensely complex interactions at one level, such as gas molecules, can get reduced to a few very simple but aggregate-scale concepts on the next level, such as "pressure, volume, and temperature", that are meaningless words on the individual molecule's level. In between those scales there was a forbidden zone, computationally intractable from either end.

Near the first easy end, only a few things only occasionally interacting, we have some room to actually compute the motions of several planets or billiard balls - much to the dismay of students of introductory physics.

What I can bring home from my foray into physics, however, is the news that there is also an easy zone at the other end - the end of immense numbers and immense interaction.

There is a continuum actually, and a sort of "continuous paradigm shift" as you go from the first easy end (few things, little interaction) to the other easy end (huge number of things and interactions) and that is in "what goes away."

At the lower easy end, the interactions go away, and we think in terms of "objects", mostly, that have properties, such as position and velocity.

At the upper easy end, it is the objects that go away, and we think in terms of persistent interactions that have self-energy and properties. This "regime" is the one that most of the visible universe inhabits, except for some relatively cold rocks (planets). It is the world of plasma, of high-energy physics, of stars, of the huge interactions at the centers of galaxies.

This is akin to the near-field, mid-field, and far-field of a radiating antenna - the math is relatively easy at either very near the antenna, or very far from the antenna - it's only at intermediate distances away that things become totally strange and slippery. I discussed that issue before.

In any case, we have here a two-dimensional entity, not a one-dimensional one, so it is now capable of being non-transitive and forming what Hofsteader called "a strange loop", akin to M.C. Escher's waterfalls or staircases. As the number of actors and intensity of interactions increases, one dimension, objects, diminishes in importance, the other dimension, interactions, increase in importance, until we reach the limit point, mathematically, and physically, where the initial objects simply drop out of the equation entirely and we are left only with the persistent interactions - which, voila, close the loop, the snake bites its tail, and these persistent interactions become the "objects" for the next level of existence and we start our next turn
around the greater helix of Life.

It's a smooth transition, easily modeled, not a "leap". It only looks like a leap if we go across the screw threads, not along them.

So, the meaning of "an entity" is really only locally defined, in an asymptotic way.. well below its level, the concept or entity is too large to be perceived, as "temperature" is to a molecule or "admission to Harvard" is to one of our body's cells. Then, as we think about sliding up to larger and larger scales, this concept has very strong meaning and dominates the local equations in the local time frame at the local scale, and then, as we keep on going, it goes away again forever, as does our problem starting our car on January 12th in terms of international relations.

OK, nice metaphor, but how is it useful? Do we have a name for these "persistent interactions"?

Aha! In physics, one type of persistent interaction is a "soliton". I'll skip the details, but these are waves that don't die out as they travel, but just keep on going. Fascinating.

What I do want to suggest for these persistent interactions is, at one scale, the idea of self-aware regulatory feedback loops. This concept is applicable at any scale, size, or time-frame and seems to be invariant and a core building block of life. Cells do it. The endocrine system does it. People do it. Corporations do it. Nations do it. Ecosystems do it. Policy makes and lawyers actively work on "regulations" that are, one realizes, actually supposed to be part of an on-going dance and regulatory feedback loop. Hospitals and doctors and the Centers for Medicare and Medicaid do this dance. The fact that there is a dance is the constant, and it's a very specific kind of dance that Control System Engineers know very well, and have tools to deal with.

I've gone on at great length in my recent MPH Capstone presentation at Johns Hopkins about these loops, and the fact that a closed, regulatory feedback loop is a qualitatively different animal than a "network" of interactions or a "web" of interactions or some Systems Dynamics Causal Loop Diagram. The loop has several properties that are very distinct from what is sloppily called "feedback":

• The loop persists over time.
• The loop is self-aware and can tell "itself" from "other" and do repairs, that is it has some kind of rudimentary immune system and damage repair system.
• To accomplish stability in a changing world, and overcome the problems Godel pointed out, the loop probably has formed alliances with other loops, either horizontally (peer loops) or vertically (management loops) where "you tell me if I'm losing it without realizing it and I'll do the same for you."
• The goal of the regulatory process has to include stability and survival, yes, but on top of that there is some goal, or direction, or "intent", and that goal may, in fact, be set by other loops (management) or peers (norms) or the surrounding context (tissue), or distant context (the pituitary gland).

Such loops will consequently differentially survive and persist, and end up dominating the landscape. They will tend to survive and persist best, if they are compatible with the larger scale activity that is going on, of course - if they are "aligned" with larger and larger contexts - but in Life this is a bidirectional game and the larger context goals may still be in flux and controllable by the lower level actors - until we get "phase lock" in that vertical dimension, the image finds a home and latches into place, and the whole entity is now stable on multiple levels at once. A solution has been found. Life, at a whole new level, has emerged.

Such loops are also exquisitely powerful in a computational sense. It's a long story, but closed feedback loops are IIR and open feedback loops are FIR, and IIR is infinitely more powerful than FIR. I'll get back to that tomorrow. In any case, no Control System Engineer would even imaging using an "open-loop" system if a "closed-loop regulatory feedback control system" were available. You get much better performance, stability, cost, robustness, response-time, tunability, etc. with closed-loop systems.

So, where's that get us? It's time to head off to my day job. Let's lash this down.

Of all the possible, N-factorial loops on causal loop diagrams, or in physical reality of the whole hierarchy of self-organizing Life, which includes people and corporations and nations, the winners will be the relatively persistent closed feedback loops. These will form, in many real senses, meta-life, or actors in their own rights with their own agendas and local worlds and local reference frames in which they are important and most of the rest of life is reduced to "an environment" in which they swim.

This "meta-life" will emerge at ever higher and higher organizational levels.

If you want to tweak, or tune, or improve the behavior and outcomes of any of these local entities (cells, the pancreas, the endocrine system, a person, a team, a corporation, the tobacco industy, the auto industry, a whole culture or nation or broken nation or proto-nation), then this model suggests focusing on finding a way to nudge it towards this stable and viable condition. "Nudge" means find existing proto-regulatory feedback loops that are almost closed, and close them. This pushes the whole "holon" (Ken Wilber's word) towards a stable attractor, a hold-fast, a sort of resonant notch or Lagrange point, where it can exist consistently with Life above and Life below it efficiently and effectively.

This is consistent with the focus on "microsystems" of the Institute of Medicine's Crossing the Quality Chasm, but goes beyond that and suggests a type of completion or stabilization of an actual meta-life process is the key variable to a sustainable intervention. It suggests what to look for, what to measure, and how to tweak it, and a fundamental principle of physics that supports that type of intervention as being principle-based.

It's also consistent with Senge's Presence and Stephen Covey's "8th habit" and focuses on the use of local feedback processes to bring about a change in a person or small team or an entire corporation that needs "restructuring" to return to an innovative, agile, productive state.

And it's consistent with my previous posts that Public Health, by its own logic, has to embrace improving the performance of corporations as well as populations of people, because both types of meta-life interact strongly, and, as many people in southeast Michigan are aware, if the corporate health is not well, the employee lives will not be well either. These do not need to be in conflict, and should be totally consistent objectives from both sides.

Neat. We should try simulating this and see how it behaves. We need to know how to measure such things, how to parametrize them, how to reverse engineer the underlying simple model from observable features, and how to nudge the various mathematical poles in the control-world towards the correct quadrant and tell (with p<0.01) whether our intervention is working or not - or, more precisely, we need to navigate and steer the intervention, in a minute-by-minute short-scale piloting fashion, around the rocks and to completion and need this compass to recover our direction after each swerve to avoid some obstacle or seize some favorable wind.

Key findings from public health

Healthy "people" aren't localized rocks, but are normally well-interconnected bidirectionally into the social fabric around them.

Social connectivity is the most robust predictor of internal, "physiological", "biomedical" outcomes, such as morbidity, mortality, survival rate of surgery, resistance to infection, level of depression, outcome of diabetes, obesity, "mental" health, you name it.

Prevention is a thousand times more cost effective than repair. ( A lesson from software engineering and many other fields as well.)

The caring human loving touch of another individual is very important to human health and healing. Infants who aren't touched do poorly or simply die.

All interesting social phenomena (such as relationships, jobs, teams, family, stress, love, sex, the economy, depression) involve intimately bidirectional feedback loops.

But, classical statistical measures and attitudes, based on prediction of yields of crops, assume critically that causality is defined in one direction only, and that all phenomena of interest can be "isolated" from context and one part of it varied by the experimenter while other parts of it are "held constant." None of that applies to "complex adaptive systems", including social systems, which are inextricably interconnected, context-dependent, interdependent, and riddled with bidirectional feedback loops. Since the tools and expertise breakdown when applied to these areas, rather than admit that the tools and expertise are inadequate, the problem space is instead defined as "non-scientific" or "soft-science" and demeaned as unimportant or "non-scientific."

Possibly due to such schizophenia, the US "healthcare" system behaves as if none of the above solid empirical facts were known. There is no focus on social connectivity, less than 2% of the budget is spent on prevention, and machines and processes have replaced people at the bedside. People are treated like machines, and diseases are treated as if they were independent of each other and the rest of peoples lives. "People" are reduced to "patients". "Caregivers" are too busy to stay and chat for a while with "patients" and are increasing renamed "providers" which is ironic, since mostly they consume resources, particularly money, while being forced by "the system" to be too busy to stick around and observe the actual outcomes of their "treatments" on the people they serve. It's a lose-lose scenario, disliked by the patients, disliked by the caregivers, and apparently continues to exist because it's loved by the insurance companies. The whole thing needs to be rethought based on the above new facts of life.

Perhaps, not surprisingly then, the outcomes of the US Healthcare system are terrible, compared to peer countries. Infant mortality is something like 19th in the world. Costs are huge but a recent study showed that the BEST quartile of US citizens (the rich) have health outcomes worse than the WORST quartile of British citizens in the UK. (ref ?). Depression, obesity, diabetes are widespread and rampant epidemics in the US.

But, efforts to build healthcare interventions that are designed around social connectivity and whole persons are demeaned and ridiculed as being "non-scientific", or avoided because the feedback loops make computing "p-values" problematic for academic researchers, for whom such mathematical bases for certainty are held with a sort of blind obsession despite the fact that the assumptions of the theory (General Linear Model) don't fit the problem they're trying to address.

The result is that the most effective interventions are known, and involve teams of people assisting individual humans to modify or control their behavior and life style, but the advocates of these interventions are academically shunned and have to present their work in embarrassment in back rooms. The Office of Behavioral and Social Science Research (OBSSR) within NIH is treated like an awkward in-law.

Probably the single best book that summarizes interventions in health care that actually work is Health Program Planning : An Educational and Ecological Approach by Lawrence W. Green and Marshall W. Kreuter, now in it's fourth edition. (c) 2005 McGraw Hill, initial version written in 1961. It was around that year that non-communicable diseases began to replace communicable diseases as the leading causes of death, disability, and impaired quality of life, but the older, biomedical model had a very tightly held death-grip on the "health care industry."

On page 3 of that book the authors note:

Ecological approaches have proven difficult to evaluate because the units of analysis do not lend themselves to rand assignment, experimental control, and manipulating characteristic of preferred scientific approaches to establishing causation. Although the linear isolatable cause-effect model of scientific problem solving remains the point of departure for the training of health professionals, practitioners find ... they cannot ignore the contextual reality that health status is unquestionably influenced by an immensely complex ecological system. ...

To address those systems in our planning, we must first be able to see them ...
By definition, ecological sub-systems do not operate in isolation from one another ... [but] interact with one another to influence health. [We need] a kind of ecological map or "web" or "systems model" enabling us to visualize the network of relationships that need to be taken into account as we plan our intervention strategy tailored to the unique circumstances of the target population and the place where they live and work.

The primary tools up to this task are described by John Sterman in his tome Business Dynamics, 999 pages in length. The simpler techniques of mapping on a white-board is known as Causal-Loop Diagramming or CLD. These qualitative webs can be assigned some semi-quantitative values, such as directionality and general magnitude (large, small, strong, weak) and then simulated using tools such as Vensim (tm).

That, however, is a lot of work. "Systems thinking" didn't show up in the MPH curriculum until 2006, and is absent, by that name, in most courses, even at leading universities. Only MIT and Worcester Polytechnic Institute seem to have embraced these tools, although the Ross School of Business at the University of Michigan is starting to build a systems thinking program after the auto industry started demanding it.

Note that the pressure for innovation here is from business, and the academics are lagging behind, sometimes kicking and screaming, in stage 2 of Schopenhauer's three stages:

All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident.
Arthur Schopenhauer

So, this pretty much summarizes the state of affairs today. Johns Hopkins Bloomberg School of Public Health has started a new department of Health Behavior along the lines of the new theory, but most health and public health people are famously non-quantitative, and so they are attempting to think through such problems mentally, unassisted by available tools used in other industries for over 50 years now in systems dynamics.

And, the biomedical establishment has a strong lock on most thinking and peer-review journals, and alternates denial and violent opposition to the "new paradigm" which it perceives as a throwback to mystical soft thinking instead of a more general version of the scientific method that can embrace feedback loops and complex adaptive systems without distortion of the tools or violation of the assumptions behind the models and statistics.

Even at Hopkins in the department of Epidemiology, the ratio of new thinkers to old-paradigm thinkers is essentially 3 to 70, and this new paradigm is ridiculed, rejected, opposed, despised, by most old-school thinkers who wish the answer to health had stayed down the microscope, under control, where they had strong muscles and good intuition - instead of showing up increasingly outside the window of the lab, in the social fabric of society, in all the places the scientists grew up despising and where their tools and muscles and intuition all fail.

So, where does that leave us humans?

Apparently, we can't expect either academics or health care workers to take the lead in fixing this terrible mess, and business is going to have to get down to business and do something about it.

(This is not without precedent - the center of innovation in the USA has increasingly moved out of universities and into businesses, despite the very strong marketing campaign with the opposite message. Witness the pulling-teeth it's taken to get systems thinking into the Ross Business School curriculum.)

Business today is much more cybernetic on a real-time basis than academia, and utilizes "good enough" models which, with cybernetic feedback control, get the job done and produce the desired outcomes - - while driving academics crazy because the underlying models are "so bad."
The National Institutes of Health is still heavily dominated as well by biomedically oriented researchers of the old school, who resist the new paradigm.

So, with a few exceptions, industry money may be the only way to advance health care in serious ways, and address the findings at the top of this post sometime this century when we're still alive to care about it.

We have, as in so many of M.C. Escher's paintings, (see this link:
http://en.wikipedia.org/wiki/Image:Escher_Waterfall.jpg
created a world that is locally-sensible and globally nonsense, but few people working locally are motivated to address the global wrongness, and no Masters or PhD student or young researcher would be encouraged to tackle a "large" problem, and so it sits there, unaddressed by academia and a thorn in the side of everyone: patients, doctors, nurses, payers, industry.
Like Escher's paintings, one is hard pressed to see or point to exactly "where" the wrongness is, and yet, standing back, it's clearly wrong.

That's where things are today.


[ M.C. Escher website: http://www.mcescher.com/ ]