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Modern natural language models such as the GPT-2/GPT-3 contain tremendousamounts of information about human belief in a consistently testable form. Ifthese models could be shown to accurately reflect the underlying beliefs of thehuman beings that produced the data used to train these models, then suchmodels become a powerful sociological tool in ways that are distinct fromtraditional methods, such as interviews and surveys. In this study, We train aversion of the GPT-2 on a corpora of historical chess games, and then "launch"clusters of synthetic agents into the model, using text strings to createcontext and orientation. We compare the trajectories contained in the textgenerated by the agents/model and compare that to the known ground truth of thechess board, move legality, and historical patterns of play. We find that thepercentages of moves by piece using the model are substantially similar fromhuman patterns. We further find that the model creates an accurate latentrepresentation of the chessboard, and that it is possible to plot trajectoriesof legal moves across the board using this knowledge.

Navigating Human Language Models with Synthetic Agents