How effectively do people learn from a variety of different opinions?

This paper presents experimental evidence about how effectively individuals learn from information coming from heterogeneous sources. In the experiment, Thai subjects observed information that came from Americans and from other Thais that they could use to help them answer a series of questions. Despite listening too little to either group, subjects demonstrated a significant amount of statistical sophistication in how they weighed observed American information relative to observed Thai information. The data indicate that subjects understood that outside information has extra value because people from the same group tend to make the same kinds of mistakes. The results illustrate the importance of forming diverse groups to solve problems

Do Firms Have Short Memories? Evidence From Major League Baseball

When deciding what salary to offer an employee, a firm needs to predict that employee’s future productivity. One piece of information that a firm can use to predict productivity is the employee’s past performance record. Classical theory predicts that firms will effectively use the available information to choose an appropriate salary offer. Evidence from baseball contracts indicates, however, that memory-based biases influence salary offers. Consistent with insights from psychology and behavioral economics, salaries are affected too much by recent performance compared with past performance. All organizations do not suffer equally from short memories. The teams that achieve the most with the money that they spend also use past performance data most effectively

Predicting SMT solver performance for software verification

The approach Why3 takes to interfacing with a wide variety of interactive and automatic theorem provers works well: it is designed to overcome limitations on what can be proved by a system which relies on a single tightly-integrated solver. In common with other systems, however, the degree to which proof obligations (or “goals”) are proved depends as much on the SMT solver as the properties of the goal itself. In this work, we present a method to use syntactic analysis to characterise goals and predict the most appropriate solver via machine-learning techniques. Combining solvers in this way - a portfolio-solving approach - maximises the number of goals which can be proved. The driver-based architecture of Why3 presents a unique opportunity to use a portfolio of SMT solvers for software verification. The intelligent scheduling of solvers minimises the time it takes to prove these goals by avoiding solvers which return Timeout and Unknown responses. We assess the suitability of a number of machinelearning algorithms for this scheduling task. The performance of our tool Where4 is evaluated on a dataset of proof obligations. We compare Where4 to a range of SMT solvers and theoretical scheduling strategies. We find that Where4 can out-perform individual solvers by proving a greater number of goals in a shorter average time. Furthermore, Where4 can integrate into a Why3 user’s normal workflow - simplifying and automating the non-expert use of SMT solvers for software verification

Predicting SMT solver performance for software verification

The approach Why3 takes to interfacing with a wide variety of interactive and automatic theorem provers works well: it is designed to overcome limitations on what can be proved by a system which relies on a single tightly-integrated solver. In common with other systems, however, the degree to which proof obligations (or “goals”) are proved depends as much on the SMT solver as the properties of the goal itself. In this work, we present a method to use syntactic analysis to characterise goals and predict the most appropriate solver via machine-learning techniques. Combining solvers in this way - a portfolio-solving approach - maximises the number of goals which can be proved. The driver-based architecture of Why3 presents a unique opportunity to use a portfolio of SMT solvers for software verification. The intelligent scheduling of solvers minimises the time it takes to prove these goals by avoiding solvers which return Timeout and Unknown responses. We assess the suitability of a number of machinelearning algorithms for this scheduling task. The performance of our tool Where4 is evaluated on a dataset of proof obligations. We compare Where4 to a range of SMT solvers and theoretical scheduling strategies. We find that Where4 can out-perform individual solvers by proving a greater number of goals in a shorter average time. Furthermore, Where4 can integrate into a Why3 user’s normal workflow - simplifying and automating the non-expert use of SMT solvers for software verification

The minor author and the major editor: a case study in determining the canon

This dissertation explores the relationship between a literary work and its printed edition in the production of reputation--the editor as gatekeeper of the reputation of a “minor” poet. That relationship is demonstrated through a case study on the effects of the nineteenth-century edition of the works of the fifteenth-century poet Thomas Hoccleve and an analysis of the lingering effects of the Foucauldian “editor-function.” The number of surviving manuscripts indicates that Hoccleve’s work was well-regarded during the early fifteenth century, but his reputation fell with that of other non-Chaucerian medieval poets as later critics lost linguistic familiarity with Middle English. The Victorian-era work of the Early English Text Society was intended to reclaim the positive reception for medieval works; however, the EETS offerings achieved just the opposite result for Hoccleve’s poetry and perpetuated the negative reputation the poet had acquired. Frederick J. Furnivall’s EETS “standard” Hoccleve editions, still in print, are largely unfavorable in the crucial prefatory matter, even though it is rife with transparent Victorian prejudices. Furnivall’s text itself is haphazardly irregular, frequently producing--not reproducing--the same flaws the forewords criticize. As these blemished editions have remained the standard for over a century, Furnivall’s editorial irresponsibility undoubtedly slowed the critical re-evaluation of Hoccleve, which began at the end of the twentieth century

How Do People Learn by Listening to Others? Experimental Evidence from Thailand

This paper presents experimental evidence about how individuals learn from information that comes from inside versus outside their ethnic group. In the experiment, Thai subjects observed information that came from Americans and other Thais that they could use to help them answer a series of questions. Two main findings emerge. First, subjects display overconfidence in their own opinions and place too low a value on the information that they observe. Second, conditional on this overconfidence, subjects weigh American information relative to Thai information in a nearly optimal way. The data also indicates that subjects appear to understand that outside information has extra value because people from different groups know different things and so have an opportunity to learn from each other.laboratory experiment, economic development, Bayesian updating, behavioral economics, learning

A psychological bias helps to explain why voters focus on the election-year economy

Voters have long displayed the perplexing inclination to reelect presidential candidates based on the performance of the economy during election years—a tendency that can turn elections into a game of chance based on the arc of the business cycle, rather than a careful review of the candidates’ performances. Andrew Healy and Gabriel Lenz examine this trend, concluding that voters actually prefer to evaluate candidates based on their entire terms. However, without the necessary information at their fingertips, voters simplify and use conditions at the end to represent the whole term

Myopic Voters and Natural Disaster Policy

Do voters effectively hold elected officials accountable for policy decisions? Using data on natural disasters, government spending, and election returns, we show that voters reward the incumbent presidential party for delivering disaster relief spending, but not for investing in disaster preparedness spending. These inconsistencies distort the incentives of public officials, leading the government to underinvest in disaster preparedness, thereby causing substantial public welfare losses. We estimate that $1 spent on preparedness is worth about$15 in terms of the future damage it mitigates. By estimating both the determinants of policy decisions and the consequences of those policies, we provide more complete evidence about citizen competence and government accountabilit

THE EFFECTS OF A UNILATERAL GLUTEAL ACTIVATION PROTOCOL ON SINGLE LEG DROP JUMP PERFORMANCE

This study examined the effects of a gluteal activation protocol (GA) on the performance of drop jumps performed on a force sledge apparatus. Fifteen sprinters performed 10 single-leg drop jumps on three days with a unilateral GA performed within the warm up on day 2. Ground contact time (CT), height jumped (HJ), maximum vertical ground reaction force (GRFmax) and vertical leg-spring stiffness (Kvert) were calculated on all three days. A repeated measures ANOVA was used to examine mean differences on all variables across days. The results show significant differences on all variables between days 1 and 2 and on HJ and Kvert between days 1 and 3 but no differences in any varables between days 2 and 3. This suggests that the improvements in day 2 were due to a practice/learning effect rather than the GA protocol