Each node is a group of sentences discussing rationality in a similar way, identified within a given decade. Node size reflects how many sentences belong to that group. Colors indicate broader thematic clusters that persist across decades — nodes sharing the same color address rationality in comparable ways across time. See the paper for the full methodology. Two visualization modes are available. Static mode positions clusters using a force-directed layout that reflects overall semantic proximity. Temporal mode groups clusters by decade along the x-axis (1900–1919, 1920–1929, …, 2000–2009), with thematic clusters separated along the y-axis, making it easier to follow how topics evolve over time.
Click a node in the network to explore its content.
TF-IDF highlights words that are specific to a group of data in comparison to the entire dataset. In this context, it emphhasiezs terms specific to the sentences within the selected cluster in comparison to all sentences across clusters.
Sentences assigned to this cluster, ranked by semantic proximity. Two metrics of similarity are provided: (1) similarity to the cluster centroid gives the cluster sentences most representative of the cluster as a whole; (2) similarity to the representative vector gives cluster sentences most representative of rationality discussion at this period. You can also filter to only show sentences mentioning 'rational' or 'rationality' (since not all sentences in the cluster may explicitly mention these terms).
Articles with the most sentences in this cluster.
Most cited references by articles which have sentences in this cluster. The user can filter to only show references with a known Web of Science ID.
Each node is an article. Articles are connected when they cite similar references — a proxy for shared intellectual background. Colors indicate communities of articles that recurrently cluster together across consecutive 8-year time windows. Uncolored nodes belong to communities that are too small or too short-lived to be considered substantive. The spatial layout positions articles with similar citation profiles in close proximity. See the paper for the full methodology. Use the time window selector to explore how the citation structure changes across periods.
Click a cluster label to explore its content.
Share of articles per cluster within the selected time window.
TF-IDF highlights terms that are specific to a cluster compared to all articles across all time windows.
Sentences most representative of the cluster content for the period.
Most cited references among articles in the selected cluster.
Origins show where articles came from (t-1), destinies show where they go (t+1).
This article demonstrates how unsupervised quantitative methods can enrich the history of economic thought. Using the largest English-language corpus ever assembled for the field—nearly 290,000 economics journal articles from 1900 to 2009 with citation data—we analyze the evolution of the concept of rationality. Combining large language model–based semantic analysis with bibliometric and network methods, we identify and cluster discussions of rationality across time and scales, such as the circulation of bounded rationality and the emergence of behavioral economics. We provide an open-source interactive tool to support transparency and reuse.