Shefrin and Thaler show that plausible assumptions about mental accounting for wealth predict important deviations from life-cycle savings theory. For example, the measured marginal propensities to consume (MPC) an extra dollar of income from different income categories are very different. The MPC from housing equity is extremely
low (people don’t see their house as a pile of cash). On the other hand, the MPC
from windfall gains is substantial and often close to 1 (the MPC from one-time
tax cuts is around 1/3–2/3). (查看原文)
These assumptions can be considered “procedurally rational” (Herbert Simon’s term) because they posit functional heuristics for solving problems that are often so complex that they cannot be solved exactly by even modern computer algorithms. (查看原文)
The standard principles used in economics to model probability judgment in
economics are concepts of statistical sampling, and Bayes’s rule for updating
probabilities in the face of new evidence. Bayes’s rule is unlikely to be correct descriptively because it has several features that are cognitively unrealistic. First,
Bayesian updating requires a prior.6 Second, Bayesian updating requires a separation
between previously judged probabilities and evaluations of new evidence.
But many cognitive mechanisms use previous information to filter or interpret
what is observed, violating this separability. (查看原文)
One is “hindsight bias”: Because events that actually occurred are easier to imagine than counterfactual events that did not, people often overestimate the probability
they previously attached to events that later happened. This bias leads to “second
guessing”. (查看原文)
A more general bias is called the “curse of knowledge”—people who know a lot find it hard to imagine how little others know. The development psychologist Jean Piaget suggested that the difficulty of teaching is caused by this curse. (For example, why is it so hard to explain something “obvious” like consumer indifference curves or Nash equilibrium to your undergraduate students? ) (查看原文)
Here is an example from the business world: When its software engineers refused to believe that everyday folks were having trouble learning to use their opaque, buggy software, Microsoft installed a test room with a one-way mirror so that the engineers could see people struggling before their very eyes (Heath, Larrick, and Klayman 1998) (查看原文)
Barberis, Shleifer, and Vishny (1998) adopt such a quasi-Bayesian approach to explain why the stock market underreacts to information in the short-term and
overreacts in the long-term. In their model, earnings follow a random walk but investors
believe, mistakenly, that earnings have positive momentum in some
regimes and regress toward the mean in others. After one or two periods of good
earnings, the market can’t be confident that momentum exists and hence expects
mean-reversion; but since earnings are really a random walk, the market is too
pessimistic and is underreacting to good earnings news. After a long string of
good earnings, however, the market believes momentum is building. Since it isn’t,
the market is too optimistic and overreacts. (查看原文)
作者: Colin F. Camerer, George Loewenstein, Matthew Rabin isbn: 0691116822 书名: Advances in Behavioral Economics 页数: 768 定价: USD 67.50 出版社: Princeton University Press 出版年: 2003 装帧: Paperback
