Book Pick: Information & Entropy

Entropy is the part of relevant data not mapped by the representation.
Excerpt from Enterprise Architecture Fundamentals:


As understood in thermodynamics, the entropy within a system is the quantum of energy that cannot be converted into mechanical work. Formalized by Shannon in terms of leakage along communication channels, entropy has become a pillar of information theory.

Translated to economics, entropy could be understood as unexplained data or, to mirror energy in thermodynamics, as the part of data that cannot be translated into useful information. Yet, that transposition faces two hurdles.

To begin with, the second law of thermodynamics states that entropy as a whole is uniform and constant: the gains in one system are balanced by the losses in another. Economics laws, if there are such a thing, beg to differ: as far as business environments are concerned, data is not a given and uniform resource, free to be picked up. Quite the contrary, it is an ephemeral resource with very specific relevance.

Moreover, in contrast to the binary dimension of digits in communication channels, business information combines an open-ended number of interdependent dimensions: physical (e.g., demographics, climate), socio-economic (e.g., income, education), and symbolic (e.g., religion, politics, culture).

It ensues that for enterprises in competitive environments, Shannon’s understanding of entropy occurring during communication is irrelevant: the issue is what happens before and after communication; namely, how the exchanged information affects the internal states of enterprises. On that point, as noted above, conditions are drastically modified by digital osmosis and homeostasis, and by their leveraging effect on entropy across the whole of architectures.

In principle, the leveraging effect of digital osmosis and homeostasis can play in both directions: rounding out the rough edges of external disorder or speeding up improvements across architectures. In practice, the level of entropy will be determined by the self-improvement capability of decision-making processes (cf. chapter 10) and their ability to cope with complexity.


(From Chapter 15)
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