Stevens, J.R., Polzkill Saltzman, A., Rasmussen, T., & Soh, L.-K. (2020). Improving measurements of similarity judgments with machine-learning algorithms. *Journal of Computational Social Science*. https://doi.org/10.1007/s42001-020-00098-1 Abstract: Intertemporal choices involve assessing options with different reward amounts available at different time delays. The similarity approach to intertemporal choice focuses on judging how similar amounts and delays are, yet we do not fully understand the cognitive process of how these judgments are made. Here, we use machine-learning algorithms to predict similarity judgments to (1) investigate which algorithms best predict similarity judgments, (2) assess which predictors are most useful in predicting participants' similarity judgments, and (3) determine the minimum number of judgments required to accurately predict future judgments. We applied eight algorithms to similarity judgments made by participants in two data sets. We found that neural network, random forest, and support vector machine algorithms generated the highest predictive accuracy. Though neural networks and support vector machines offer little clarity in terms of a possible process for making similarity judgments, random forest algorithms generate decision trees that can mimic the cognitive computations of human judgment-making. We also found that the numerical difference between amount values or delay values was the most important predictor of similarity judgments, replicating previous work. Finally, we found that the best performing algorithms such as random forest can make highly accurate predictions of judgments with relatively small sample sizes (~15), which will help minimize the numbers of judgments required to extrapolate to new value pairs and aid in determining how future data collection studies can be designed. In summary, machine-learning algorithms provide both theoretical improvements to our understanding of the cognitive computations involved in similarity judgments and intertemporal choices as well as practical improvements in designing better ways of collecting data. Keywords: algorithm, classification, decision making, intertemporal choice, judgment, machine learning, similarity