Matthias Seifert

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate ...

Workplace engagement is an important determinant for the level of commitment and loyalty that employees show toward their respective organizations. Executives must understand what motivates employees to excel in their jobs to reduce the risk of brain drain and, ultimately, to create sustainable organizational success. One of the crucial prerequisites for workforce well-being is that employees feel they can trust their line managers. Trust in decision-making authorities fundamentally shapes employees expectations about how they will be treated in the future. Study findings suggest that employees who expect their line managers to be untrustworthy are more likely to be disloyal toward the organization and exhibit lower levels of motivation. The research generally confirmed the view that workers who show a high level of trust in management are more committed toward their organization than those who mistrust their superiors. The findings have clear implications for managing employee well...

We examine the decision maker’s external environment as a predictor of her judgmental effectiveness in detecting regime changes. Relying on two experimental studies, our main task employed is one of regime-change detection over time. Our first study looks at the effect of different task factors (signal diagnosticity, transition probability, and signal length) on judgmental accuracy, particularly with regards to over- and under-reaction. We find evidence of systematic over- and under-reaction, further supporting what is known as the system-neglect hypothesis (Massey & Wu 2005). We also establish that signal length is a significant component in a regime change detection task and show that increasing amounts of information are related to increasing conservatism. Yet what has not received adequate attention in studies of regime change detection is the effect of the strength or extremeness of evidence. We hypothesize that signals that are highly representative of a change having occurred...

The article notes political analyst Nate Silver's book "The Signal and the Noise" and focuses on research that examined human and computer predictions for hit songs on the Top 100 popular music charts in Great Britain and Germany. The study found statistics-based computer software was more accurate in forecasting hit songs than the 180 people in the study. It also found a mix of human and computer predictions produced the best forecasts. Research methods in the study are noted.

ABSTRACT We study the conditions that influence judgmental forecasting effectiveness when predicting demand in the context of fashion products. Human judgment is of practical importance in this setting. Our goal is to investigate what type of decision support, in particular historical and/or contextual predictors, should be provided to human forecasters to improve their ability to detect and exploit linear and nonlinear cue-criterion relationships in the task environment. Using a field experiment on new product forecasts in the music industry, our analysis reveals that when forecasters are concerned with predictive accuracy and only managerial judgments are employed, providing both types of decision support data is beneficial. However, if judgmental forecasts are combined with a statistical forecast, restricting the decision support provided to human judges to contextual anchors is beneficial. We identify two novel interactions demonstrating that the exploitation of nonlinearities is easiest for human judgment if contextual data are present but historical data are absent. Thus, if the role of human judgment is to detect these nonlinearities (and the linearities are taken care of by some statistical model with which judgments are combined), then a restriction of the decision support provided would make sense. Implications for the theory and practice of building decision support models are discussed.

When and to what extent should forecasts rely on linear model or human judgment? The judgmental forecasting literature suggests that aggregating model and judge using a simple 50:50 split tends to outperform the two inputs alone. However, current research disregards the important role that the structure of the task, judges’ level of expertise, and the number of individuals providing a forecasting judgment may play. 92 music industry professionals and 88 postgraduate students were recruited in a field experiment to predict chart entry positions of pop music singles in the UK and Germany. The results of a lens model analysis show how task structure and domain-specific expertise moderate the relative importance of model and judge. The study also delineates an upper boundary to which aggregating multiple judgments in model-expert combinations adds predictive accuracy. It is suggested that ignoring the characteristics of task and/or judge may lead to suboptimal forecasting performance.

Purpose – This article seeks to provide a theoretical framework for facilitating talent management decisions in the music industry. Design/methodology/approach – Strategic decision-making theory and the resource-based view of strategy are used to identify the talent-selection process as a core capability in the entertainment industry. Their original combination leads to the introduction of a framework aimed at facilitating the selection