In a mechanistic world, cause is always direct. An agent acts and we see the result. In a world that is complex this is not the case. causes are many, interacting and often the removal of one factor can be compensated for by a self organization of the system so that it is hardly missed. A good example of this is the "war on drugs" (Meaning illegal drugs rather than the ones sold as medicine). The idea that a dealer is a real cause of the problem (a direct cause) is easily overshadowed by arresting that person only to find that their function in the system is taken over; possibly by someone more effective and more able to avoid arrest. In an human organism we often attack a problem with a drug (legal) using it as a "magic bullet" only to find that the system has other ways to accomplish the result that was stopped by that drug. I once did the computer modelling in a cancer chemotherapy study headed by the director of the Massey Cancer Institute at the Medical College of Virginia where we were faced with this amazing ability of cancer cells to adapt to what we were trying to do and had to design a more complex approach to deal with a complex causal system. This is the realm in which George Lakoff's approach to politics dwells as well as Rosen's causal models. For that reason it is worth s[pending more time understanding the nature of cause in our complex real world. Read on and I'll share some more of what this entails.
By far the the most difficult problem with scrapping the almost useless direct cause mindset is that it fits so well with reductionist thinking. If the world were closer to the reductionist model you could break it down to its parts and look at the way parts interact a few at a time. What could possibly be wrong with that? The most obvious answer is that the world is not constructed that way nor does it work as if it were. I am taking ideas here from Rosen's books Anticipatory Systems, Life Itself and Essays on Life Itself. I am also responding to difficulties some of our Kossites have expressed with the newness of these ideas to them.
The issues we are discussing can not be broken apart just like any complex system is unable to be understood that way... The way we can get around that is by repetition and multiple diverse examples. So here we go!
Why complex systems are not computable is tied to the inability to reduce them to material parts.
This is a big step. It requires seeing that ideas expressed here as possible ways we might compute these systems miss the mark. It also requires seeing why the idea that physics can deal with these issues is just not right. So how can we say this with the assurance we do? The answer is simple once you get rid of some baggage you are probably carrying from courses in school, things in the media, etc. At the level of the biochemical charts and the physiology textbooks we are confronted with a myriad of detail that defies being put into simple algorithms. If that is true we can not build a big model of the cell or any other living system. (My apologies to those breaking their special parts trying to do this!) How do I come about the right to speak about this? My 76th birthday is Friday and I spent a significant part of my life constructing some very impressive computer models of some very complicated living systems from the molecular level on up to the ecosystem level. Now maybe I'm just not smart enough? Well, from the record I seem to have been looked on as smarter than most who made similar efforts. Ok then what made me stop these attempts to get at useful ways of dealing with my subject matter by modeling details on the computer? (At this point I have to tell you that I once introduced my self when giving a talk in this way-"Hello, My name is Don Mikulecky and I am a recovering reductionist. Maybe we need a 12 step program here?) Now the answer is simple. The important relationships neither exist at that level nor can they be formulated at this level. Rosen made an important breakthrough to solve this problem. He simple stopped looking at the parts and the physics they carried as baggage and he began to talk about something akin to "organization."
That may sound simple enough, but it is a big step. The organization he found we needed to focus on was the very organization of the system that gave answers to causal questions about entailment. Entailment is sort of a fancy way of talking about the answers to"why?". (Forgive me for a very big oversimplification.) Those answers do not come from looking at the way the molecules are organized they come from looking at what the system is doing, its function! Not only that but the function is only there when the system is actively doing its thing! Dead systems are totally without the essential information. you can stain them, look in microscopes, etc. but what you need to know is long gone!
Rosen's next move was as brilliant as the others he devised a modeling method for function based on what we see when the system is doing its thing. He called these functional components. These functional components were the "actors" in his Metabolism, Repair (M,R) models. Their causal relationships were then shown to effect closure in causal models in distinction to the way functional components relate in a machine.
I hope this helps you understand why organisms can not be computed. It is because the functional components (which do not map 1:1 to the material parts) are involved in closed loops of causality and these can never be reduced to algorithms for computation. Beyond that fundamental restriction is the inability to find algorithms that relate the functional components to the material parts since these relationships are not 1:1. (Think about the war on drugs example above).
I'm going to stop here for now and let this be something to chew on. I'll get back to it in the next diaries. Let me warn you that Friday's installment will be a digression because I always do a special diary on my birthday. I really don't feel like 76 when I'm writing. I did working in the yard today. Here's a link to the last diary and through it you can link you way back through the series:Reading Ramblings: "Relational Biology" has its roots in "Topology". What does this mean?