Entropy and Consciousness, Part 2

This is a follow up to a previous article on consciousness and entropy:  https://birkdalecomputing.com/2020/05/03/entropy-and-consciousness/

We have entered into the age of uber-prediction.  No I don’t mean guessing when your hired ride will arrive, but an age when nearly everything is predicted.  Humans have, of course, always been predicting the outcomes of activities and events.  Predicting the future has been called an emergent behavior of intelligence. Our ancestors needed it to tell them the most likely places that the alpha predator might be hiding, as well as telling them which route would most likely be taken by the prey they are trying to catch.

There is a natural feedback loop between predicted outcomes and expected outcomes. If a predicted outcome is perceived to be within a certain margin of error of an expected outcome the prediction is said to have “worked” and this positive assessment (i.e. that the prediction worked) when it occurs tends to reinforce the use of predictive behavior in the future.  In other words it increases the occurrence of predictions and simultaneously increases the amount of data that can be used for future predictions.

In the past we did not have as much data or as much computing power to process the data as we have today. This had always acted as a constraint on, not only, the aspects of life that could be predicted (i.e. not enough data), but also on how quickly prediction worked in respect to the aspect of life being predicted (i.e. not enough processing power). Predictability now tends to “work” better than it ever did before because there is more data to use and faster ways to use it.  The success of prediction creates a virtuous cycle that reinforces the desire for more prediction.

The state of the world around us seems to be increasing in its predictability.  This leads me to believe that we must be pushing back more and more against entropy, which is defined by Claude Shannon in Information Theory as a state of zero predictability where all outcomes are equally likely. This means you need less new information to predict an outcome because the amount of ambient information is constantly increasing.  To make information work for you, often requires discovery of predictions made by others. Consequently you need to ask less questions to obtain a workable prediction. The less entropic a system, the less new information it contains.

Information is measured in the bit, or single unit of surprise. The more bits a system has the more possible surprises it can have and the more entropic it is. So it follows that the more information there is in a system, the more units of surprise it potentially has and the less likely it is to work as a predicted.

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