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on Neuroeconomics |
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Issue of 2009‒06‒17
four papers chosen by |
| By: | Moreira, Bruno; Matsushita, Raul; Da Silva, Sergio |
| Abstract: | A recent neurobiology study showed that monkeys systematically prefer risky targets in a visual gambling task. We set a similar experiment with preschool children to assess their attitudes toward risk and found the children, like the monkeys, to be risk seeking. This suggests that adult humans are not born risk averse, but become risk averse. Our experiment also suggests that this behavioral change may be due to learning from negative experiences in their risky choices. We also showed that though emotional states and predetermined prenatal testosterone can influence children’s preferences toward risk, these factors could not override learning experiences. |
| Keywords: | Risk; Children |
| JEL: | D81 C92 D87 |
| Date: | 2008–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:15516&r=neu |
| By: | Anna MAFFIOLETTI; Michele SANTONI |
| Abstract: | This paper presents the results of two experiments testing reaction to risk and uncertainty of a sample of 66 Italian university students. Risky prospects were based on games of chance, while uncertain lotteries were based on the forthcoming results of either the May 2001 Italian general political election or the June 2004 election for the European Parliament. We computed decision weights for risk and uncertainty; we also collected data as regards the subjects’ degree of belief, expressed by probability judgements, for the same uncertain events. Our results show that the subjects’ behaviour is consistent with expected utility theory as regards risk, but not under uncertainty. In particular, our subjects show a strong superadditivity in the decision weights and the possibility effect (lower subadditivity) is stronger than the certainty effect (upper subadditivity). There is also evidence that emotions, actual competence and confidence positively affect the possibility effect, whereas they do not have any influence on the certainty effect, reinforcing the lack of symmetry between the two effects |
| Keywords: | Uncertainty, Subadditivity, Emotions, Competence, Confidence |
| JEL: | D81 |
| Date: | 2007–09–18 |
| URL: | http://d.repec.org/n?u=RePEc:mil:wpdepa:2007-31&r=neu |
| By: | Stefanie Behncke |
| Abstract: | This paper investigates the extent to which test performance is affected by shocks to noncognitive skills. 440 students took a low stakes mathematics test. About half of them were exposed to positive affirmation while being given test instructions, whereas the other half served as controls. The students were allocated to 14 tutorials and randomisation was conducted at the tutorial level. Mean comparisons suggest that test scores were raised by the intervention. In particular, students with low maths grades and with self-assessed difficulties in maths gained from the positive affirmation. Results suggest that teachers might increase their students' performance by interventions to their non-cognitive skills. Inference is obtained by four different methods that take into account that randomisation was clustered at the tutorial group level. These methods are evaluated in a Monte Carlo study for data generating processes which resemble actual data. We find that randomisation inference followed by the wild cluster bootstrap have superior size properties compared to conventional approaches. |
| Keywords: | test scores, non-cognitive skills, cluster randomised trial, wild cluster bootstrap, randomisation inference |
| JEL: | C15 C21 C93 I20 |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:usg:dp2009:2009-11&r=neu |
| By: | Davide LA TORRE; Edward R. VRSCAY; Mehran EBRAHIMI; Michael F. BARNSLEY |
| Abstract: | We construct a complete metric space (Y,dY) of measure-valued images, μ:X→M(Rg), where X is the base or pixel space and M(Rg) is the set of probability measures supported on the greyscale range Rg. Such a formalism is well-suited to nonlocal image processing, i.e., the manipulation of the value of an image function u(x) based upon values u(yk) elsewhere in the image. In fact there are situations in which it is useful to consider the greyscale value of an image u at a point x as a random variable that can assume a range of values Rg of R. One example is the characterization of the statistical properties of a class of images, e.g., MRI brain scans, for a particular application, say image compression. Another example is statistical image processing as applied to the problem of image restoration (denoising or deblurring).Of course, it is not enough to know the greyscale values that may be assumed by an image u at a point x: one must also have an idea of the probabilities (or frequencies) of these values. As such, it may be more useful to represent images by measure-valued functions.We then show how the space (Y,dY) can be employed with a general model of affine self-similarity of images that includes both same-scale as well as cross-scale similarity. We focus on two particular applications: nonlocal-means denoising (same-scale) and multiparent block fractal image coding (cross-scale).In order to accomodate the latter, a new method of fractal transforms is formulated over the metric space (Y,dY).Nonlocal image processing has recently received a great deal of attention, fuelled in part by the exceptional success of the nonlocal means image denoising method. Fractal image coding is another example of a nonlocal image processing method. Both of these methods, which will be described briefly below, may be viewed under the umbrella of a more general model of affine image selfsimilarity, in which subblocks of an image are approximated by other sublocks of the image. Indeed, a number of other image processing methods that exploit self-similarity and the various example-based methods, also fit naturally under this nonlocal, self-similar framework. |
| Keywords: | Measure-valued images, multifunctions, nonlocal image processing, self-similarity, nonlocal-means denoising, fractal transforms, iterated function systems |
| Date: | 2008–12–22 |
| URL: | http://d.repec.org/n?u=RePEc:mil:wpdepa:2008-45&r=neu |