processes which then maintain the system’s equilibrium

Many daisy, rabbit, and fox types were first brought together by Lovelock to create a numerical model for biodiversity. In the real world, biological systems are continually being perturbed by the cycles of day and night, the turn of the seasons, changes in the climate, and innumerable other factors. When a Daisy-world in equilibrium is perturbed by the introduction of a herbivore or a sudden change in solar input, a transient burst of different daisy types appears until the system restabilizes, with new types dominant

Periods of normal science are interrupted when anomalies between observations and the expectations suggested by the paradigm begin to demonstrate the paradigm’s weakness.

In game theory, a focal point (or Schelling point) is a solution that people tend to choose by default in the absence of communication. The concept was introduced by the American economist Thomas Schelling in his book The Strategy of Conflict (1960).[1] Schelling states that "(p)eople can often concert their intentions or expectations with others if each knows that the other is trying to do the same" in a cooperative situation (at page 57), so their action would converge on a focal point which has some kind of prominence compared with the environment. However, the conspicuousness of the focal point depends on time, place and people themselves. It may not be a definite solution.

Brains – especially younger ones, since they’re changing so much – really do need to anneal regularly to pay down their ‘technical debt’, and if they don’t, they grow brittle and neurotic. (Technical debt in the brain builds up as we twist our existing brain networks to accommodate new facts; this debt is ‘paid down’ when we enter high-energy states and let new brain networks which fit these constraints self-organize)

heterogeneous networks have been found to be effective promoters of the evolution of cooperation, since there are advantages to being a cooperator when you are a hub, and hubs tend to stabilize networks in equilibriums where levels of cooperation are high (Ohtsuki et al. 2006), (Pacheco et al. 2006), (Lieberman et al. 2005), (Santos and Pacheco 2005).

McCloskey is attempting, that is, to correct an imbalance that he sees

Both phenomena help to show how the universe abhors balance. In the former case, we see a natural flight away from a 50/50 split of competing phenomena. A 51/49 split is inherently unstable and tends to gravitate towards a 95/5, 99/1 or even 100/0 split. Equality ends in dominance: that is one of the messages of chaos theory. The 80/20 Principle’s message is different yet complementary It tells us that, at any one point, a majority of any phenomenon will be explained or caused by a minority of the actors participating in the phenomenon. 80 per cent of the results come from 20 per cent of the causes. A few things are important; most are not.

“throwing seniors under the bus” (to use the words of one senator) by keeping interest rates low.

Some may believe that all of the deep, difficult work has already been done in Computer Science