Sublime

At Harvard in the early 1960s, Daniel Ellsberg observed what he called ‘ambiguity aversion’ – people might prefer certainty to maximising subjective expected utility.

previous decisions. Only 22 percent voted for option C, while 78 percent chose option D, the risky strategy. Most doctors were now acting just like Frank: they were rejecting a guaranteed gain in order to participate in a questionable gamble. Of course, this is a ridiculous shift in preference. The two different questions examine identical dilemmas

Two University of Zurich researchers were equally curious: The Swiss nuclear incentive study, titled “The Cost of Price Incentives: An Empirical Analysis of Motivation Crowding-Out,” was conducted by Bruno S. Frey and Felix Oberholzer-Gee. It was published in the American Economic Review 87 (1997): 746–55. forty students sat with number 2 pencils:

His model could explain Anthony’s risk aversion, but it cannot explain Betty’s risk-seeking preference for the gamble, a behavior that is often observed in entrepreneurs and in generals when all their options are bad.

on average, we tend to be quicker to credit ourselves for positive outcomes, and blame external forces for negative ones (sometimes called attribution bias).

The different choices in the two frames fit prospect theory, in which choices between gambles and sure things are resolved differently, depending on whether the outcomes are good or bad. Decision makers tend to prefer the sure thing over the gamble (they are risk averse) when the outcomes are good. They tend to reject the sure thing and accept the

If people were perfectly rational—if they made decisions solely by crunching the numbers—then subjects would always choose to invest, since the expected overall value on each round is higher if one invests ($1.25, or $2.50 multiplied by the 50 percent chance of getting tails on the coin toss) than if one does not ($1). In fact, if a person invests

However, the videos were carefully designed to be uninformative; they provided no reason to suspect that the individuals would be either more or less helpful than a randomly chosen student. In the absence of useful new information, the Bayesian solution is to stay with the base rates.

In this view, people often (but not