Happiness: A Theory — The Probabilistic Machine

If our essential functionality is probabilistic – any of an infinite number of synaptic connections may lead to one event or another within us – then our happiness must, in some measure, accommodate this chance-based essence. See, e.g., Gary Lynch (Big Brain).

We analogize to computers in conceiving of the brain, but, so far, this is a poor analogy. We demand consistency from our computers, and, under ideal circumstances, consistency is what we get. We never get consistency from our brains. Rather, we get a constantly shifting array of possible outcomes, which helps us contend with a constantly shifting array of stimuli.

Our greatest drawback is also our greatest talent. By being so unpredictable, we are attuned to an unpredictable milieu. The emergent behavior is so hard to copy, it is derived from such complexity and chance, that it is a root frustration of robot scientists who seek to duplicate humans. But it also means that feeling happy, feeling attuned to our own essence, is highly unlikely to be, in its essence, consistent.

Our happiness may, in some measure, depend upon appreciation of whatever happens, because, no less daunting than our incapacity to govern our milieu is our incapacity to govern ourselves. We are, very much, passengers, spectators, possessed of a well-honed delusion as to our powers.

“The brain is like a Ferrari – pedigreed and difficult to keep balanced. ” – Louis Cozalino

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