Overconfidence in Search

In a standard search model I relax the assumption that agents know the distribution of offers and characterize the behavioral and welfare consequences of overconfidence. Optimistic individuals search longer if they are equally stubborn and high offers are good news. Otherwise, the pessimists search longer. The welfare of unbiased individuals is larger than that of overconfident decision makers if the latter's biases are large and searchers stubborn. Otherwise, the overconfident may be better off. Finally, I give a testable implication of overconfidence and discuss applications and policy issues.

Information Revelation in Auctions.

Auction theory has emphasized the importance of private information to the profits of bidders. However, the theory has failed to consider the question of whether or not bidders will be able to keep their information private. We show that in a variety of contexts bidders will reveal all their information, even if this information revelation is (ex ante) detrimental to them. Similarly, a seller may reveal all her information even when this revelation lowers revenues. We also show that bidders may be harmed by private information.WINNERS CURSE; LINKAGE PRINCIPLE; REVELATION BY BIDDERS

APPARENT BIAS: WHAT DOES ATTITUDE POLARIZATION SHOW?

Many, though not all, experiments have found that exposing groups of subjects who disagree to the same evidence may cause their initial attitudes to strengthen and move further apart, or polarize. Some have concluded that findings of attitude polarization show that people process information so as to support their initial views. We argue that, on the contrary, polarization is often what we should expect to find in an unbiased Bayesian population, in the context of the experiments that find polarization

Adaptive optics imaging of inherited retinal diseases.

Adaptive optics (AO) ophthalmoscopy allows for non-invasive retinal phenotyping on a microscopic scale, thereby helping to improve our understanding of retinal diseases. An increasing number of natural history studies and ongoing/planned interventional clinical trials exploit AO ophthalmoscopy both for participant selection, stratification and monitoring treatment safety and efficacy. In this review, we briefly discuss the evolution of AO ophthalmoscopy, recent developments and its application to a broad range of inherited retinal diseases, including Stargardt disease, retinitis pigmentosa and achromatopsia. Finally, we describe the impact of this in vivo microscopic imaging on our understanding of disease pathogenesis, clinical trial design and outcome metrics, while recognising the limitation of the small cohorts reported to date

RAC-CNN: multimodal deep learning based automatic detection and classification of rod and cone photoreceptors in adaptive optics scanning light ophthalmoscope images

Quantification of the human rod and cone photoreceptor mosaic in adaptive optics scanning light ophthalmoscope (AOSLO) images is useful for the study of various retinal pathologies. Subjective and time-consuming manual grading has remained the gold standard for evaluating these images, with no well validated automatic methods for detecting individual rods having been developed. We present a novel deep learning based automatic method, called the rod and cone CNN (RAC-CNN), for detecting and classifying rods and cones in multimodal AOSLO images. We test our method on images from healthy subjects as well as subjects with achromatopsia over a range of retinal eccentricities. We show that our method is on par with human grading for detecting rods and cones

Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia

Fast and reliable quantification of cone photoreceptors is a bottleneck in the clinical utilization of adaptive optics scanning light ophthalmoscope (AOSLO) systems for the study, diagnosis, and prognosis of retinal diseases. To-date, manual grading has been the sole reliable source of AOSLO quantification, as no automatic method has been reliably utilized for cone detection in real-world low-quality images of diseased retina. We present a novel deep learning based approach that combines information from both the confocal and non-confocal split detector AOSLO modalities to detect cones in subjects with achromatopsia. Our dual-mode deep learning based approach outperforms the state-of-the-art automated techniques and is on a par with human grading

Reliability and Repeatability of Cone Density Measurements in Patients With Stargardt Disease and RPGR-Associated Retinopathy

PURPOSE: To assess reliability and repeatability of cone density measurements by using confocal and (nonconfocal) split-detector adaptive optics scanning light ophthalmoscopy (AOSLO) imaging. It will be determined whether cone density values are significantly different between modalities in Stargardt disease (STGD) and retinitis pigmentosa GTPase regulator (RPGR)–associated retinopathy. METHODS: Twelve patients with STGD (aged 9–52 years) and eight with RPGR-associated retinopathy (aged 11–31 years) were imaged using both confocal and split-detector AOSLO simultaneously. Four graders manually identified cone locations in each image that were used to calculate local densities. Each imaging modality was evaluated independently. The data set consisted of 1584 assessments of 99 STGD images (each image in two modalities and four graders who graded each image twice) and 928 RPGR assessments of 58 images (each image in two modalities and four graders who graded each image twice). RESULTS: For STGD assessments the reliability for confocal and split-detector AOSLO was 67.9% and 95.9%, respectively, and the repeatability was 71.2% and 97.3%, respectively. The differences in the measured cone density values between modalities were statistically significant for one grader. For RPGR assessments the reliability for confocal and split-detector AOSLO was 22.1% and 88.5%, respectively, and repeatability was 63.2% and 94.5%, respectively. The differences in cone density between modalities were statistically significant for all graders. CONCLUSIONS: Split-detector AOSLO greatly improved the reliability and repeatability of cone density measurements in both disorders and will be valuable for natural history studies and clinical trials using AOSLO. However, it appears that these indices may be disease dependent, implying the need for similar investigations in other conditions

Reasons and Means to Model Preferences as Incomplete

Literature involving preferences of artificial agents or human beings often assume their preferences can be represented using a complete transitive binary relation. Much has been written however on different models of preferences. We review some of the reasons that have been put forward to justify more complex modeling, and review some of the techniques that have been proposed to obtain models of such preferences