systems-artificial decision makers that are not paralyzed by the need for vast amounts of knowledge or extensive computational power. (Location 105)
Note: As comp power has got cheaper does this matter?
Is it better to use heuristics rather then massive inputs? (noise to signal)
frugal heuristics that use little information and (Location 136)
Simon’s (1956a) classic example concerns foraging organisms that have a single need, food. One organism lives in an environment in which little heaps of food are randomly distributed; it can get away with a simple heuristic, that is, run around randomly until a heap of food is found. For this, the organism needs some capacity for vision and movement, but it does not need a capacity for learning. A second organism lives in an environment where food is not distributed randomly but comes in hidden patches whose locations can be inferred from cues. This organism can use more sophisticated strategies, such as learning the association between cues and food, and a memory for storing this information. (Location 223)
The general point is that to understand which heuristic an organism employs, and when and why the heuristic works well, one needs to look at the structure of the information in the environment. (Location 226)
computations or knowledge to figure out where (Location 279)
Such one-reason decision making does not need to weight or combine cues, and so no common currency between cues need be determined. (Location 288)
Laplace’s superintelligence would never overfit because it does not have to make uncertain predictions. But models of inference that try to be like a Laplacean superintelligence are doomed to overfitting, when they swallow more data than they can digest. (Location 343)
Note: The problem of overfitting in big data?
There is a point where too much information and too much information processing can hurt. (Location 351)
As a measure of the success of a heuristic, we compare its performance with the actual requirements of its environment, which can include making accurate decisions, in a minimal amount of time, and using a minimal amount of information. (Location 376)
As this useful Scottish verb helps to demonstrate, recognition and recall memory can break apart. (Location 564)
Recognition memory is vast, automatic, and save for deja vu, reliable. (Location 593)
We conjecture that the limits of recognition memory cannot be exceeded in a laboratory experiment, and perhaps not in the lifetime of a human being. (Location 600)
If any group of investors was consistently better than average in forecasting stock price, they would bring the present price closer to the true value. Conversely, investors who were worse than average in forecasting ability would carry less and less weight. If this process worked well enough, the present price would reflect the best information about the future. (p. 80)Despite early empirical challenges (e.g., Rozef & Kinney, 1976; Special Issue, Journal of Financial Economics, 1978), the EMH has been fully incorporated in the leading normative models, such as the widespread Capital Asset Pricing Model (e.g., Sharpe, 1964)-itself constituting the basis for modern portfolio (management) theory. (Location 862)
