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Forecasting of geopolitical events is a notoriously difficult task, with experts failing to significantly outperform a random baseline across many types of forecasting events. One successful way to increase the performance of forecasting tasks is to turn to crowdsourcing: leveraging many forecasts from non-expert users. Simultaneously, advances in machine learning have led to models that can produce reasonable, although not perfect, forecasts for many tasks. Recent efforts have shown that forecasts can be further improved by "hybridizing" human forecasters: pairing them with the machine models in an effort to combine the unique advantages of both. In this demonstration, we present Synergistic Anticipation of Geopolitical Events (SAGE), a platform for human/computer interaction that facilitates human reasoning with machine models.

Over the past several decades, there has been considerable interest in the theoretical causes of work-family conflict (WFC). Most studies have focused on situational determinants, often ignoring the role of personal factors such as disposition and heritable elements. We increase understanding of person versus situation influences on WFC through estimation of the relationship between role demands and WFC after controlling for genetic confounding, measured personality traits, family confounds, and other stable dispositions. Based on twin data from the National Survey of Midlife Development in the United States (MIDUS), we examine the role of genetic factors in explaining variation in WFC (both work interference with family [WIF] and family interference with work [FIW]). Results support WFC has an additive genetic component, accounting for 31% [95% CI 18%, 45%] and 16% [95% CI 2%, 30%] of the variance in WIF and FIW, respectively. In addition, we test two competing hypotheses with regard to the relationship between role demands and WFC. Results support the phenotypic causal relationship for WIF, consistent with the notion the relationship between work demands and WIF reflect situational processes. However, results support the genetic confounding hypothesis for FIW, indicating observed relationships between family demands and FIW are primarily due to genetic factors. Our results provide new insights into the nature of WFC relationships and underscore that ignoring the influence of heritability can bias estimates of role demand effects in WFC research.

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