The Case for Compression

What Do We Mean by Compression?

The Warm Up

Compression is intended to expand our thinking about the big squeeze on both us and the planet. There’s a dichotomy here: expand thinking; compress our use of resources – and much else. We use “compression” as an analogy in an uncommon sense, so first let’s tour several definitions of physical Compression.

Mechanical compression is deformation of any material imperceptibly to totally from a pressing force. Compression is push; tension is a pull. Release a compressed mechanical spring and almost perfect mechanical resilience returns the spring to shape. When squeezed, less resilient materials distort – from a little to totally smashed. Poke into explanations using materials science, a highly cross-disciplinary field, and a simple concept explodes in detail, everything seemingly affected by everything else.

In biology, resilience refers to the ability of an organism or an ecosystem to overcome all kinds of disruption and keep going, or even to thrive. Indeed, resilience to pressure is one way to describe causes of biological evolution. We don’t just live; we evolve.

Elasticity is a common term in materials science. There, elasticity is the degree to which a material is mechanically resilient – returns to shape after compression or tension. But economists long ago borrowed elasticity to mean how responsive one economic variable is to another – or to multiple others. From there economic elasticity has come to denote flexibility (or resilience) of a system. For example, in the digital age, elasticity in cloud computing refers to the ability of overloaded servers to offload to other servers. “Future proofing” a company means making it resilient to change — adaptable.

In computing data compression reduces the number of bits to be stored or transmitted by encoding redundancies; then decoding them for interpretation. For example, instead of transmitting the datum “yellow pixel” 500 times, encode it as “500 yellow pixels” and send it once. We do this all the time when we zip and unzip documents.

But data compression also factors into algorithmic efficiency in coding. Inefficient code wastes huge amounts of energy, human time, and other resources. Creating algorithmic efficiency is a space-time-complexity trade off. Furthermore, the codes to compress and decompress data are one way to project a future state from a past one. They are among the basic learning tools for developing artificial intelligence.

Compression of a mechanical spring is a simple, observable phenomenon. Take offs and analogies from it become abstract. Direct learning is from observing tangible, real phenomena, like a spring, but now we must learn more and more from abstract data. Our thinking must be conscious of logic loops, incomplete knowledge, and inconsistent interpretation of symbols. We struggle with “complexification” while a basic question remains: how much can we abuse the “spring” called earth before it loses resilience, unable to spring back. On the other hand, if we can increase the resilience of the global ecology, it will regenerate – increase its resilience and start to expand, rather than human development expanding, sapping its size and resilience.

Environmental and Human Compression

The earth’s environment is being squeezed; anyone observant has no doubt. Some changes you can sense directly. Others are abstract, learned only by ability to interpret the veracity of reports by others citing evidence of global changes:

(Click on the links below to read more)

  1. Human Population
  2. Resource Limits
  3. Biodiversity
  4. Information Overload and Limited Human Bandwidth
  5. Psychology
  6. Complexity
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