“…Machine models outperform humans (including experts) in many tasks involving predictive or diagnostic judgments (Cowgill, 2017; Dawes, 1979; Grove et al, 2000; Meehl, 1954), particularly in stable environments (Kremer et al, 2011). However, developing formal forecasting models is challenging (e.g., because of having many features but little data, Cohen et al, 2022) and most existing approaches rely on heuristics, past experience, or trial and error (Lawrence et al, 2006). Furthermore, forecasts are not static but rather benefit from frequent updates (Atanasov et al, 2020; Mellers, Stone, Atanasov, et al, 2015; Mellers, Stone, Murray, et al, 2015), increasing the costs of using human crowds for forecasting in unstable contexts over longer time periods.…”