Keynes's Animal Spirits Explained

In these days economics is popular again doing what it does best, that is, analyzing dismal. One related catchphrase is animal spirits, meaning something like a force behind panic action. This phrase is famous for being used by John Maynard Keynes in the General Theory:

"Even apart from the instability due to speculation, there is the instability due to the characteristic of human nature that a large proportion of our positive activities depend on spontaneous optimism rather than mathematical expectations, whether moral or hedonistic or economic. Most, probably, of our decisions to do something positive, the full consequences of which will be drawn out over many days to come, can only be taken as the result of animal spirits - a spontaneous urge to action rather than inaction, and not as the outcome of a weighted average of quantitative benefits multiplied by quantitative probabilities." (161-162)

As I see it, Mr. Keynes argues that decisions are not always based on rational optimization, but on an urge to take some available alternative instead of doing nothing. Explicitly, Keynes separates this urge from the instability due to speculation. As Jeff Frankel discusses, only bandwagon speculation can cause instable speculative bubbles. 

Usually there is nothing to be found in semantic interpretations, but here is an interesting similarity. Namely, Keynes's 'animal spirits' is very close to the concept of greed in computer science.

Most computer scientists are familiar with so-called greedy algorithms that "follow the problem solving metaheuristic of making the locally optimum choice at each stage". This means taking the best available alternative from a finite candidate set and quitting when reaching the first peak of the hill.

Greedy algorithms are usually fast but fail to find the globally optimal solution. As in mountain ranges, there are lower and higher peaks. You can't be sure that the first one is the highest, especially if you are climbing blindfolded. On the other hand, globally optimal algorithms are exhaustive and unusable in practice. No one has conquered every peak of the Himalayas just in order to find Mt. Everest. Between these strategies lies a whole lot of mystery and scientific effort.