Creative and insightful researches

Seeing a number of papers presented at the ASSA meetings, David Laibson's work on hyperbolic discounting provides a better illustration of how the new technology can enhance the field of economics. In this article in Science, he and his co-authors show:


When humans are offered the choice between rewards available at different points in time, the relative values of the options are discounted according to their expected delays until delivery. Using functional magnetic resonance imaging, we examined the neural correlates of time discounting while subjects made a series of choices between monetary reward options that varied by delay to delivery. We demonstrate that two separate systems are involved in such decisions. Parts of the limbic system associated with the midbrain dopamine system, including paralimbic cortex, are preferentially activated by decisions involving immediately available rewards. In contrast, regions of the lateral prefrontal cortex and posterior parietal cortex are engaged uniformly by intertemporal choices irrespective of delay. Furthermore, the relative engagement of the two systems is directly associated with subjects’ choices, with greater relative fronto-parietal activity when subjects choose longer term options.

The research shows that two different neural systems, which evolved for very different purposes in the human brain, deal with the two decisions. When a part of the brain is activated during a particular decision, we can infer that the decision is similar to other choices or behaviors that activate that part of the brain. The more primitive part of the brain is activated with the near-term choice. This is what gives Laibson's argument credibility. We have already learned from observation of individual choices that behavior departed from the classical model. Without the brain imaging, there could have been a number of competing theories for why this is so, many of which would not cause us to dramatically rethink the underlying model. With the brain imaging, we give substantially greater weight to the theories like Laibson's that are predicated on different decision frameworks for different types of intertemporal choices.

Andrew Samwick, www.adamsmith.org/blog













T Pott Comment:
Produce only to consume. Otherwise, why produce? Consume and so produce. Otherwise we starve. Modernity rides on the twin horses of mass production and mass consumption. Massive surplus from production lifts the sight above sweat and grind, and mass media veer that instrument of labour, the body, towards glorification and gratification. Modern marriage (it is numbers sensitive) has more economic value for being less permanent�€”conceptually at least. Who stands to gain? Everyone who is on the economic wagon. Who loses? The products of sexual unions until the young generation start to make sense of it all if not themselves. But if life is all economics, then it is worthwhile to bear in mind the scientific principle that nature naturally behaves to conserve maximum energy possible. Here marriage or family is in line with the principle. But it is good to think we have a choice.

The age of hedonic marriage, Economist, 18 Jan.





Statistical Degrees of Freedom

Early in this century it was shown by W.S. Gossett, writing under the name of ‘Student’, that the mean of a sample from a Normal distribution with unknown variance has distribution that is similar to, but not quite the same as, a Normal distribution. He called it the t distribution, and we still refer to it as Student’s t distribution. As the sample size increases the sampling distribution of the mean become closer to the Normal distribution. We use the t distribution for estimation and hypothesis testing relating to the means of one or two samples. Although we can use the Normal distribution for large samples there is little point in doing so, since for large samples the methods give virtually identical answers and it is simpler to use the same method regardless of the sample size.

The t distribution has one parameter, a quantity called the degrees of freedom. The concept of degrees of freedom is one of the more elusive statistical ideas. In general the degrees of freedom are calculated as the sample size minus the number of estimated parameters. The degrees of freedom for the t distribution relate to the estimated standard deviation, which is calculated as variation around the estimated mean. Hence, for a single sample of n observations we have n-1 degrees of freedom.

Altman D., (Oxford Univ.'s Medical Statistics), Practical statistics for medical research, p. 181