What caught my attention most during training is that the ITIL library writers, in my opinion, correctly identified economic value as a combination of both (marginal) utility and warranty (irreversibility).  Somewhere along the line, I/T practitioners discovered what few economists (save for some, like Hernando de Soto Polar) bothered to factor into so many economic formulations: utility is fine, but if the economic actor fails to perceive that their utility is theirs to keep, then the sense of economic value falls.  While property rights (de Soto) alone do not economic value make, they are necessary prerequisites for any functioning economy.  In information technology a service like Google provides great utility, but if it were perceived as an unreliable service its overall economic value would drop through the floor.

Of course, the ITIL “utility + warranty” model is itself a little simplistic.   Max Neef breaks up utility further:

• affection
• creation
• freedom
• identity
• leisure
• participation
• protection (security, warranty)
• subsistence
• understanding

Max Neef provides a nice balance of qualities, certainly, but I feel that protection/security/warranty/irreversibility plays a very specific role in economic transactions because of the way our brains are built.  I believe it remains useful to break out qualities associated with irreversibility (security, protection, warranty) into a separate, analyzable category of study.  For me, ITIL’s “utility + warranty” description of economic value is a great model to use.

One can derive an ethic by first understanding what beliefs, behaviors and other factors thwart rational thinking in yourself, and then second determine, by experiment, the principles and standards which allow one to manage one’s roadblocks to rationality. Ethics has nothing to say about the content of your rational thought, for that is the realm of morality.

http://en.wikipedia.org/wiki/Ethics

http://thefuture101.blogspot.com/2011/08/and-now-lets-move-to-jobs.html

Technology is automating more and more jobs.  We software-oriented architects are the “grunts” that are helping to usher this process along.  Indeed, we are working to automate ourselves out of traditional employment.  We have been creating conditions which favor permanent entrepreneurship for every one of us, and which do not favor traditional employment for any of us.

From a Coasean economics perspective, information technology is helping to reduce general transaction costs worldwide such that transaction costs internal to firms and external to them are approaching parity.  In other words, it is increasingly nonsensical for any company to bother hiring employees.  This does not mean however, that companies do not need people, nor does it mean that future consumers do not need the products of your hard work!  Read Michael’s article for his detailed analysis of this phenomenon.

How can I write a book on a “theory of I/T architecture”, of the philosophy and science of I/T architecture, without addressing this trend?  I can’t.  I need to discuss where we have been as professionals, where we are, and where were are going.  I must play the futurist and make predictions.  Of course, some of my predictions will be shown to have been correct over time, some wrong, but stick my neck out I must!  There is no way I can write such a book, sit on the side lines, and simply throw up my arms and say, “I have no idea what to do next.”  If I am not attempting to help my readers make critical decisions about their personal futures, then what good would I be as an author?  Why should you bother to read what I have to write?

http://edge.org/conversation/the-marvels-and-flaws-of-intuitive-thinking

We ended up studying something that we call “heuristics and biases”. Those were shortcuts, and each shortcut was identified by the biases with which it came. The biases had two functions in that story. They were interesting in themselves, but they were also the primary evidence for the existence of the heuristics. If you want to characterize how something is done, then one of the most powerful ways of characterizing the way the mind does anything is by looking at the errors that the mind produces while it’s doing it because the errors tell you what it is doing. Correct performance tells you much less about the procedure than the errors do.

If it weren’t for Nature’s “cheap and dirty tricks” of the mind, we would not be alive today.  On the other side of the coin is the science of information saturation in complex adaptive systems, as told by Geoffrey West, also at Edge.org,

http://edge.org/conversation/geoffrey-west

The work I got involved in was to try to understand these scaling laws. And to make it a very short story, what was proposed apart from the thinking was, look, this is universal. It cuts across the design of organisms. Whether you are insects, fish, mammals or birds, you get the same scaling laws. It is independent of design. Therefore, it must be something that is about the structure of the way things are distributed.

You recognize what the problem is. You have ten¹⁴ cells. You have this problem. You’ve got to sustain them, roughly speaking, democratically and efficiently. And however natural selection solved it, it solved it by evolving hierarchical networks.

There is a very simple way of doing it. You take something macroscopic, you go through a hierarchy and you deliver them to very microscopic sites, like for example, your capillaries to your cells and so on. And so the idea was, this is true at all scales. It is true of an ecosystem; it is true within the cell. And what these scaling laws are manifesting are the generic, universal, mathematical, topological properties of networks.

Read the whole article, especially the part about network saturation along S-curves, and about singularity/collapse of those networks.  Also note his discovery about the growth curve of companies, which is a semi-vindication of Coasean economics.

http://lesswrong.com/lw/7e5/the_cognitive_science_of_rationality/

I particularly like the discussion of error types.

These modern models of cognitive science are great, but they only explain the mechanisms used to desaturate our neural networks.  What is missing is a good method to differentiate phenomena as a function of whether they are a result of network saturation or desaturation.  At this time, I have no reliable means of differentiating the two.  For instance, is autism a problem of heavy saturation or of excessive desaturation?

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2526142/?tool=pubmed

http://www.physorg.com/news/2011-08-people-biased-creative-ideas.html

This study documents the various ways people react negatively to creative ideas.  Indeed, creative people have had a sense of this throughout history.  Nothing new, really.  This study attempts to quantify what we have already known.

We feel good when we collapse, compress or otherwise irreversibly jettison information in our brain.  The transformation of thought into word is a compression event, which makes us feel good (or at least relieved).  The “Eureka” or “ah-hah” moments are certainly exciting.  It feels good to sleep and let the day’s entropy slowly evaporate away.

http://en.wikipedia.org/wiki/Harrison_Bergeron

Recently, scientists have discovered that class of cells, “K” (keniocellular) cells, at the end of optic nerve in primates which produce a sleep-like pulsing rhythm to the gateway of the optical cortex,

http://medicalxpress.com/news/2011-08-scientists-window-brain.html

In previous postings, I have suggested that “pulsing” behavior in neural networks in the brain is one of nature’s “cheap and dirty tricks” to “stir the pot” as it were, or to prevent our neural networks from remaining saturated and otherwise unresponsive to continued adaptation to an animal’s ever-changing environment.  The rhythmic pulsing of the “K” optic layer might to induce a regular series of George Bergeron moments in the optical networks which would prevent easy saturation.

Neural networks probably rely on multiple methods for reducing saturation effects.  I am sure other network methods of de-saturation will be found along the visual pathway over time.

http://medicalxpress.com/news/2011-08-ready-brain-scans.html

In short, when the parahippocampal cortex (PHC) is saturated, it is less likely to allow new long term learning.  What these researchers ought to do next is track down the “cheap and dirty tricks” which de-saturate the PHC.  How does the cortical region become calm once again?  What are the mechanisms which reset the network so that it can be “open for business” once again?

That a homogeneous neural network saturates itself and becomes immune to change or is otherwise dysfunctional is not new.  Nature’s real magic lies in how de-saturation takes place, and, actually, that de-saturation happens at all!

Also related is an interesting theory about humor.  Humor, it seems, is also one of nature’s cheap and dirty tricks for not only de-saturating our neural networks, but also for describing what seems like a rewarding experience we receive when it occurs!

http://www.slate.com/content/slate/blogs/browbeat/2011/05/13/5_leading_theories_for_why_we_laugh_and_the_jokes_that_prove_them_wrong.html

Phwew, that was a mouthful!  What does that mean?

Memory is the ability of matter to change state and maintain that state for a non-zero period of time.  At the smallest scales of existence, atoms have memory when, for instance, chemical changes influence the electron configuration of those atoms.  The ability of paper to hold graphite markings throughout its lifetime is also a form of memory.

An event is a directional transfer of energy from one memory component to another, from source to target, in a way that induces a state change in the target which lasts for a non-zero period of time.  An event is an event if it alters the memory configuration of its target.  An event map is a set of source/target associations.  Causality is the study of the effects of event maps upon their state-absorbing targets.

To study a system is to study a well-defined, finite set of memory components and the causative event maps which affect those components.  For every system under study, there exists that which is outside of that system which we call the system’s environment.  Causative events flow from system to environment, and from environment to system, composing a causative event map called a feedback loop.

Entropy is the degree to which a system has been affected by its causative event map.  Low entropy implies that a system has “room” to absorb new state changes in an unambiguous way.  A set of aligned, untoppled dominoes has low entropy.  High positive entropy implies that a system has attained a degree of ambiguity with regard to its ability to absorb specific kinds of changes.  A set of toppled dominoes has a high degree of entropy relative to “toppling” events.  One can attempt to topple already-toppled dominoes, but the result is ambiguous in that it is more difficult to leave evidence of a toppling event (a finger push) than it was prior to toppling.  Negative entropy is a condition in which a system is to some degree “reset” so that it can once again, unambiguously, absorb more events than it could before.  To induce negative entropy into a system of toppled dominoes is to set them back up again to be retoppled.

All physical systems tend to increase in measures of entropy over time.  They do so because they have memory and exhibit hysteresis.  To memorize a change is to freeze that change in time.  Changes induced by previous events interfere with the ability of new events to be absorbed.  A thermodynamically hot system imparts kinetic events to cold systems they are connected to, at the cost of the energy stored in its own memory.  Slowly, the cold systems absorb the kinetic energy of the hot until a point is reached which the cold memory systems reach capacity, or become saturated.  Such a point of memory capacity saturation is called “equilibrium”.  If the cold system had no memory, for instance if it were a vacuum, it would never have increased in temperature and the hot system would have eventually become absolutely cold since it would be connected to systems with infinite capacities to absorb events.

As noted by Erwin Schrödinger, life in general has a “habit” of reversing entropy and in fact could be defined by this single, dominant habit.  Lifeless physical systems tend towards maximum positive entropy and tend to remain that way.  Life, on the other hand, does its damnedest to reverse entropy.  For life, it is not merely enough to keep entropy from increasing.  Like all systems, life which is saturated to its limit of information capacity can fail to adapt to a changing environment.  Life is a process through which its subsystems are continually de-saturated in order to make room for new information.  Life depends on entropy reversal.

This is not to say that entropy reversal does not happen to lifeless systems; entropy may be reversed here and there and for short periods of time.  Random, isolated reversals of entropy in any system however are always—even in the case of life—compensated for by an increase of entropy in the outer environment.  Ultimately, the Great Environment we call the Universe is continually losing more and more of its ability to unambiguously absorb new events.  The arrow of time since the Big Bang is the story of how the memory components of the Universe are reaching capacity saturation.

The metaphor of the economic transaction is useful for describing the flow of events leading to entropy reversal.  Financial transactions follow the same entropy build-up and subsequent decrease.  Even in the simplest of cases, financial participants form a “memory system” which saturates before it collapses.  Work is done between participants before money is exchanged.  The exchange of money allows the information of the transaction to “compress”, and entropy to reverse in the well-defined, temporary system of the particular transaction.  This entropy reversal occurs, of course, at the expense of the outer environment.  Quantum transactions also follow the same build-up and tear-down in terms of the memory capacities of participating elements of matter.

For true de-saturation to occur within a system, a system’s memory must be irreversibly erased.  If memory erasure were reversible, then memory would not have been erased and the system would have remained saturated.  “Reversible” memory loss is not true memory loss, but an illusion, a shuffling, a card trick.  Irreversibility however, comes at a price for a system.  One can shuffle sand in a sandbox from one side to another, but to truly increase the capacity of a sandbox one must expend energy to remove sand from it and returning that sand to the outer environment.  “Irreversibility” however, is not some separate, measurable feature of entropy reversal, but is a necessary part of its definition.  If a transaction is reversible, then entropy was not reversed.  If entropy has not been reversed, either partially or completely, then the transaction metaphor does not apply.  Irreversibility is a necessary test to determine the appropriateness of the transaction metaphor.