Abstract
This paper's top-level goal is to provide an overview of research conducted in the many academic domains concerned with forecasting. By providing a summary encompassing these domains, this survey connects them, establishing a common ground for future discussions. To this end, we survey literature on human judgement and quantitative forecasting as well as hybrid methods that involve both humans and algorithmic approaches. The survey starts with key search terms that identified more than 280 publications in the fields of computer science, operations research, risk analysis, decision science, psychology and forecasting. Results show an almost 10-fold increase in the application-focused forecasting literature between the 1990s and the current decade, with a clear rise of quantitative, data-driven forecasting models. Comparative studies of quantitative methods and human judgement show that (1) neither method is universally superior, and (2) the better method varies as a function of factors such as availability, quality, extent and format of data, suggesting that (3) the two approaches can complement each other to yield more accurate and resilient models. We also identify four research thrusts in the human/machine-forecasting literature: (i) the choice of the appropriate quantitative model, (ii) the nature of the interaction between quantitative models and human judgement, (iii) the training and incentivization of human forecasters, and (iv) the combination of multiple forecasts (both algorithmic and human) into one. This review surveys current research in all four areas and argues that future research in the field of human/machine forecasting needs to consider all of them when investigating predictive performance. We also address some of the ethical dilemmas that might arise due to the combination of quantitative models with human judgement.
Highlights
To predict an uncertain quantity or to determine its distribution, people often seek the advice of human experts and/or apply algorithmic procedures
Combining the two fields could mean that their inherent shortcomings balance each other out, thereby increasing forecasting accuracy and reliability. We review both human judgement and quantitative forecasting methods as well as methods to combine their outputs
We identified the main goal of the study, which was to review the literature on human judgement, quantitative forecasting models and their combination
Summary
To predict an uncertain quantity or to determine its distribution, people (and organizations) often seek the advice of human experts and/or apply algorithmic procedures. Mostly found in the operations research and computer science communities, is that with the wide availability of data and advances in computing technology, algorithmic forecasts offer the opportunity to support humans by mining large datasets and learning patterns and trends from data. Critics of this view point out that the use of machine learning or ‘big data methods’—such as stepwise regression and neural nets—that use statistical procedures to discover apparent patterns without recourse in theory and prior knowledge are akin to alchemy These range from linear combinations to Bayesian updating of the decision maker’s prior belief
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