Incentives for Academic Achievement: An Experimental Study

In recent years, educators and economists have experimented with a number of innovations to improve academic outcomes of students in developing countries. Providing cash rewards to students based on academic achievement may be a cost effective approach to achieve the goal. However, psychologists contend that external rewards undermines students' internal motivation to learn. To test these hypotheses, I am conducting a field experiment among eighth graders in public schools in the suburbs of Kathmandu, Nepal. Students receive cash reward at the end of each of three semesters based on their grades. Each exam is worth 100 points, and each point is worth 5 rupees (approximately 7 US cents). Therefore, each student can earn up to 500 rupees per semester. From a pool of 33 schools, the incentive scheme is offered to students in 11 randomly selected schools while the remaining 22 schools serve as the comparison group. At the end of the year, students take a district level examination. Scores of incentive recipients will be compared to that of non-recipients to gauge the impact of cash rewards on outcomes. Preliminary analysis shows that recipients have higher score than non-recipients in some subjects, the scores are similar in other subjects, and lower in yet other subjects. However, final conclusion can only be made after analyzing the scores from district level exam. Survey responses of students shows that that external rewards has had no noticeable impact on students' intrinsic motivation to learn.Cash Incentives, Intrinsic Motivation, Multitasking., Institutional and Behavioral Economics, International Development, Labor and Human Capital, D03, I20,

Confronting the concordance model of cosmology with Planck data

We confront the concordance (standard) model of cosmology, the spatially flat $\Lambda$CDM Universe with power-law form of the primordial spectrum with Planck CMB angular power spectrum data searching for possible smooth deviations beyond the flexibility of the standard model. The departure from the concordance cosmology is modeled in the context of Crossing statistic and statistical significance of this deviation is used as a measure to test the consistency of the standard model to the Planck data. Derived Crossing functions suggest the presence of some broad features in angular spectrum beyond the expectations of the concordance model. Our results indicate that the concordance model of cosmology is consistent to the Planck data only at 2 to 3$\sigma$ confidence level if we allow smooth deviations from the angular power spectrum given by the concordance model. This might be due to random fluctuations or may hint towards smooth features in the primordial spectrum or departure from another aspect of the standard model. Best fit Crossing functions indicate that there are lack of power in the data at both low-$\ell$ and high-$\ell$ with respect to the concordance model. This hints that we may need some modifications in the foreground modeling to resolve the significant inconsistency at high-$\ell$. However, presence of some systematics at high-$\ell$ might be another reason for the deviation we found in our analysis.Comment: 16 pages, 7 figures, 2 tables, matches final version published in JCA

Learning to Fly by Crashing

How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid obstacles? One approach is to use a small dataset collected by human experts: however, high capacity learning algorithms tend to overfit when trained with little data. An alternative is to use simulation. But the gap between simulation and real world remains large especially for perception problems. The reason most research avoids using large-scale real data is the fear of crashes! In this paper, we propose to bite the bullet and collect a dataset of crashes itself! We build a drone whose sole purpose is to crash into objects: it samples naive trajectories and crashes into random objects. We crash our drone 11,500 times to create one of the biggest UAV crash dataset. This dataset captures the different ways in which a UAV can crash. We use all this negative flying data in conjunction with positive data sampled from the same trajectories to learn a simple yet powerful policy for UAV navigation. We show that this simple self-supervised model is quite effective in navigating the UAV even in extremely cluttered environments with dynamic obstacles including humans. For supplementary video see: https://youtu.be/u151hJaGKU