Abstract: We use evidence of human activity intervals (IAIs) to discover statistical regularities in four new data sets. The main regularity is that the relation between IAIs of length τ and their relative frequencies P(τ) are well-approximated by a power law that is linear in a log-log plot. However, there are two related limits to the quality of the power-law relation: Statistical tests reject the power law specification unless low values of τ are excluded, and there is substantial evidence that the underlying distribution P(τ) is closer to log-normal than to a power law. These limits notwithstanding, regularities can be compared with different plausible "reduced-form" processes which are mathematically equivalent to certain predicted regularities (e.g., they will predict specific slopes in a regression). The more challenging goal is to link the reduced-form processes to different basic constructs from social science which have traditionally been used to understanding human habit and behavior change. Preliminary investigation suggests a two-process "neural autopilot" model of behavior toggling between maximization and repetitive habit can approximate the near-power laws in the data

Abstract: One of the most prevailing theories about female underrepresentation in academia, particularly in STEM, is the existence of recruiting bias against women, and gender-blind evaluations are often recommended to prevent such discrimination. Meanwhile, after a reform of Taiwan’s college admission, more programs implemented non-gender-blind evaluations (including application portfolio reviews, interviews, and others). Taking the adoption of non-gender-blind evaluations as a natural experiment, this study examines its impact on female college admission. The empirical result indicates that moving from fully gender-blind evaluations to fully non-gender-blind evaluations raises the female percentage of admitted applicants by 5.54 percentage points. Moreover, this pro-women effect surges up to a 10.06 percentage point increase in majors not directly linked to subjects taught in high school. This can be due to the interaction between non-gender-blind evaluations and gender differences in learning styles for unfamiliar subjects. This research contributes to the debate over which interventions can be a remedy to enhance women’s participation in fields where they are outnumbered, by shifting away from one-size-fits-all solutions and focusing on uncovering the most effective approach in particular circumstances.

Abstract: Nash equilibrium in mixed strategies incorporates equalized payoff and serial independence for undominated strategies.While laboratory experiments reveal deviations from theoretical predictions, an increasing number of studies examine mixed strategies using empirical data from sports. This study shows strong evidence that pitchers in Major League Baseball do not follow mixed strategies in pitch-type selection. Moreover, overconfidence and fatigue, rather than experience, are two underlying mechanisms explaining such deviations. To address the selection bias, we perform propensity score matching and confirm our results persisted. In conclusion, we contribute additional empirical evidence and enrich the discourse on real-life mixed strategy applicability.

Abstract: This paper describes analyses of data from the Zearn K-5 math educational learning platform that were used to inform/motivate/inspire causal interventions, which were then tested with many other interventions in a mega-study. These analyses purely correlate student outcomes with teacher Zearn activity but are then hypothesized to possibly have causal effects. One analysis reduces the many kinds of teacher activity to a smaller number of dimensions (1-3) using principal-component analysis. The most diagnostic dimension is how often teachers absorb student failures and successes, which we term “empathy”. A second analysis finds that when teachers access Zearn on Fridays their students’ learning is higher in the future. An intervention in the mega-study based on empathy, from the principal-component analysis, is one of the most successful of many interventions. The Friday engagement-prompt works positively but less well. We draw conclusions from this example about how candidate nudges can be motivated by careful analysis of data in advance of nudge design.

Abstract coming soon