Sublime

Scientific models that seek to predict the consequences of human actions with some reasonable accuracy—such as game theoretical models of economic behavior—for the most part ignore human individuality in favor of aggregated outcomes.

Over the course of several summers in the late 1960s, Baum and Lloyd Welch, an information theorist working down the hall, developed an algorithm to analyze Markov chains, which are sequences of events in which the probability of what happens next depends only on the current state, not past events.

programmers often increase the conceptual complexity of a program in an effort to reduce its computational complexity.

there do exist a number of efficient strategies for solving the continuous versions of these problems,

with causal diagrams.

that people—or, if you like, automata, algorithms—can and do act in situations that are not well defined.

Keep in mind that the most efficient algorithm is not always the algorithm of choice.

social complexity ultimately emerges from people behaving in terms of the body-state imagery of their shared social metaphors. (If you habitually imagine your community as a family, and you have experienced loving parents, then surely your leaders have your best interests at heart.) In social

instead of representing probability in huge tables, as was previously done, let’s represent it with a network of loosely coupled variables. If we only allow each variable to interact with a few neighboring variables, then we might overcome the computational hurdles that had caused other probabilists to stumble.