Learning about Learning in Games through Experimental Control of Strategic Interdependence

We conduct experiments in which humans repeatedly play one of two games against a computer decision maker that follows either Roth and Erev's reinforcement learning algorithm or Camerer and Ho's EWA algorithm. The human/algorithm interaction provides results that can't be obtained from the analysis of pure human interactions or model simulations. The learning algorithms are more sensitive than humans in calculating exploitable opponent play. Learning algorithms respond to these calculated opportunities systematically; however, the magnitude of these responses are too weak to improve the algorithm's payoffs. Human play against various decision maker types does not significantly vary. These results demonstrate that humans and currently proposed models of their behavior differ in that humans do not adjust payoff assessments by smooth transition functions and that when humans detect exploitable play they are more likely to choose the best response to this belief.

Procurement Auctions for Differentiated Goods

We consider two mechanisms to procure differentiated goods: a request for quote and an English auction with bidding credits. In the request for quote, each seller submits a price and the inherent quality of his good. Then the buyer selects the seller who offers the greatest difference in quality and price. In the English auction with bidding credits, the buyer assigns a bidding credit to each seller conditional upon the quality of the seller’s good. Then the sellers compete in an English auction with the winner receiving the auction price and his bidding credit. Game theoretic models predict the request for quote is socially efficient but the English auction with bidding credits is not. The optimal bidding credit assignment under compensates for quality advantages, creating a market distortion in which the buyer captures surplus at the expense of the seller’s profit and social efficiency. In experiments, the request for quote is less efficient than the English auctions with bidding credits. Moreover, both the buyer and seller receive more surplus in the English auction with bidding credits.

Do We Detect and Exploit Mixed Strategy Play by Opponents?

We conducted an experiment in which each subject repeatedly played a game with a unique Nash equilibrium in mixed strategies against some computer-implemented mixed strategy. The results indicate subjects are successful at detecting and exploiting deviations from Nash equilibrium. However, there is heterogeneity in subject behavior and performance. We present a one variable model of dynamic random belief formation which rationalizes observed heterogeneity and other features of the data.best response correspondence, mixed strategy

The performance of reverse auctions versus request for quotes when procuring goods with quality differences

The use of dynamic auctions is a major component in many enterprises' e-procurement initiatives. In the case where suppliers offer goods and services of inherently different quality the traditional mechanism has been the request for quote. In a request for quote, suppliers submit a sealed bid and the fixed quality of their offering and then the buyer selects the seller who offers the greatest difference between quality and price. The winning seller receives a price equal to his submitted bid. The reverse auction has immerged as the most commonly adopted dynamic auction for this setting. In a reverse auction, suppliers first submit the qualities of their goods and then the suppliers participate in an auction with the same message space as an open outcry English auction (descending because this is a procurement auction.) However, the auction is only used to set each suppliers price. The last price a supplier submits in the auction becomes their actual submitted price. After, the auction the buyer selects the winning seller who offers the greatest difference between quality and actual submitted price. We provide a game theoretic analysis of both mechanisms. We also provide extensive experimental evaluation of the two mechanisms as wellReverse Auction, Request for Quote, procurement

Data-Driven Decisions: Business Intelligence (BI) Training Skills

Organizations are drowning in data and struggling to turn disparate facts into useful information. The technological capability to collect data has expanded faster than the ability to turn data points into useful information. Never before has so much data been available for users to leverage to make a decision. Data-driven metrics can help teams make informed decisions and provide a competitive advantage. When it comes to using data to answer business questions and make a data-driven decision, technology is only one part of the solution. Business Intelligence (BI) is a discipline that attempts to turn data into meaningful insights in order to make a better decision. In the last decade BI technology has evolved as the ability to process information has increased. There is a wealth of research about BI technology but less material on the human elements and skills needed to be successful developing BI solutions. BI skills and the best methods to teach those skills need to be further analyzed to improve data-driven decisions. Without a comprehensive BI strategy, data will continue to be used in a limited capacity and provide a fraction of its potential value. The purpose of this phenomenological study was to determine which BI skills professionals believe should be taught to enable better data-driven decisions. Five BI program components were categorized and analyzed during the course of this study. These components are: (a) data management, (b) calculation intelligence, (c) delivery output, (d) consumption device, and (e) business enablement. Within each of these components, skill variables were rated by importance and their effect on user adoption. Interviews and surveys were conducted to collect data and determine the skills that should be taught and the best practices on how to teach those skills. The findings highlight which skills are important and influence user adoption, ideal formats to teach the skills, and relationship dynamics between the skills that enable BI teams to be more successful

Zephyr extensibility in small workstation oriented computer networks

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.Includes bibliographical references.by Jason T. Hunter.M.Eng

Evidence for Correlated Titanium and Deuterium Depletion in the Galactic ISM

Current measurements indicate that the deuterium abundance in diffuse interstellar gas varies spatially by a factor of ~4 among sightlines extending beyond the Local Bubble. One plausible explanation for the scatter is the variable depletion of D onto dust grains. To test this scenario, we have obtained high signal-to-noise, high resolution profiles of the refractory ion TiII along seven Galactic sightlines with D/H ranging from 0.65 to 2.1x10^-5. These measurements, acquired with the recently upgraded Keck/HIRES spectrometer, indicate a correlation between Ti/H and D/H at the >95% c.l. Therefore, our observations support the interpretation that D/H scatter is associated with differential depletion. We note, however, that Ti/H values taken from the literature do not uniformly show the correlation. Finally, we identify significant component-to-component variations in the depletion levels among individual sightlines and discuss complications arising from this behavior.Comment: 4 pages; Accepted to Astrophysical Journal Letter

Person-Level Predictors Of Bullying And Bystander Behaviors Of Middle School Students

This research examined the ways in which person-level factors (social goals, self-efficacy for defending, moral disengagement, and empathy) influence bullying and bystander experiences of middle school students. Participants (N = 207) in grades 6 to 8 (ages 11- to 15-years-old) who were enrolled in a suburban Public School Academy (i.e., charter school) middle school located in Southeastern Michigan completed a self-report questionnaire on one occasion. Multivariate analysis of variance revealed gender and grade differences in person-level factors. Gender differences were found for victimization. Females experienced significantly more social victimization than males. Multiple regression analyses revealed a synergistic effect for some, but not all, person-level factors on bullying and bystander behavior. Agentic goals, self-efficacy for defending, moral disengagement were significant predictors. Individually, affective, but not cognitive, empathy was significant for overall, verbal, and social bullying. However, moderated multiple regression analyses revealed that gender significantly moderated the relationship between cognitive empathy and overall bullying, such that the relationship is significantly negative and stronger for males and not significant and weaker for females. Grade moderated the relationship between cognitive empathy and verbal bullying