Social Norms, Information and Trust among Strangers: Theory and Evidence

Can a social norm of trust and reciprocity emerge among strangers? We investigate this question by examining behavior in an experiment where subjects repeatedly play a two-player binary ―trust‖ game. Players are randomly and anonymously paired with one another in each period. The main questions addressed are whether a social norm of trust and reciprocity emerges under the most extreme information restriction (anonymous community-wide enforcement) or whether trust and reciprocity require additional, individual-specific information about a player’s past history of play and whether that information must be provided freely or at some cost. In the absence of such reputational information, we find that a social norm of trust and reciprocity is difficult to sustain. The provision of reputational information on past individual decisions significantly increases trust and reciprocity, with longer histories yielding the best outcomes. Importantly, we find that making reputational information available at a small cost may also lead to a significant improvement in trust and reciprocity, despite the fact that most subjects do not choose to purchase this information.Social Norms, Trust Game, Random Matching, Trust and Reciprocity, Information, Reputational Mechanisms, Experimental Economics.

Fitting magnetic field gradient with Heisenberg-scaling accuracy

We propose a quantum fitting scheme to estimate the magnetic field gradient with $N$-atom spins preparing in W state, which attains the Heisenberg-scaling accuracy. Our scheme combines the quantum multi-parameter estimation and the least square linear fitting method to achieve the quantum Cram\'{e}r-Rao bound (QCRB). We show that the estimated quantity achieves the Heisenberg-scaling accuracy. In single parameter estimation with assumption that the magnetic field is strictly linear, two optimal measurements can achieve the identical Heisenberg-scaling accuracy. Proper interpretation of the super-Heisenberg-scaling accuracy is presented. The scheme of quantum metrology combined with data fitting provides a new method in fast high precision measurements.Comment: 7 pages, 2 figure

Measuring Bridge Construction Efficiency Using the Wireless Real-Time Video Monitoring System

To enhance the efficiency of bridge construction, the wireless real-time video monitoring system (WRITE) was developed. Utilizing the advanced technologies of computer vision and artificial neural networks, the developed system first wirelessly acquired a sequence of images of work face operations. Then human pose analyzing algorithms processed these images in real time to generate human poses associated with construction workers who performed the operations. Next, a portion of the human poses were manually classified into three categories as effective work, contributory work, and ineffective work and were used to train the built-in artificial neural networks (ANN). Finally, the trained neural networks were employed to decide the ongoing laborers’ working status by comparing the in coming images to the developed human poses. The developed system was tested for accuracy on a bridge construction project. Results of the test showed that efficiency measurements by the system were reasonably accurate when compared to the measurements produced by the manual method. Thus, the success of this research indicates promise for enabling project managers to quickly identify work-face operation problems and to take actions immediately to address these problems

Development of Human Poses for the Determination of On-site Construction Productivity in Real-time

To enhance the capability of rapid construction, an automated on-site productivity measurement system is developed. Employing the concepts of Computer Vision and Artificial Intelligence, the developed system wirelessly acquires a sequence of images of construction activities. It first processes these images in real-time to generate human poses that are associated with construction activities at a project site. The human poses are classified into three categories as effective work, ineffective work, and contributory work. Then, a built-in neural network determines the working status of a worker by comparing in-coming images to the developed human poses. The labor productivity is determined from the comparison statistics. This system has been tested for accuracy on a bridge construction project. The results of analyses were accurate as compared to the results produced by the traditional productivity measurement method. This research project made several major contributions to the advancement of construction industry. First, it applied advanced image processing techniques for analyzing construction operations. Second, the results of this research project made it possible to automatically determine construction productivity in real-time. Thus, an instant feedback to the construction crew was possible. As a result, the capability of rapid construction was improved using the developed technology