Tacit Understanding Game (TUG): Predicting Interpersonal Compatibility

TL;DR

This paper introduces TUG, a privacy-preserving online game that uses minimal word association data to predict interpersonal compatibility, offering a new approach for relationship matching without invasive questionnaires.

Contribution

The study presents TUG, a novel game-based method that leverages large language models to predict compatibility from simple word choices, enhancing ecological validity and privacy.

Findings

TUG can predict compatibility with minimal data.

Synthetic data generated via language models supports the approach.

Privacy-preserving signals are effective for relationship matching.

Abstract

Research on relationship quality often relies on lengthy questionnaires or invasive textual corpora, limiting ecological validity and user privacy. We ask whether a sequence of single-word choices made in a playful setting can reveal personality and predict interpersonal compatibility. We introduce the Tacit Understanding Game (TUG), a two-player online word association game. We collect word choice traces, annotate a subset with psychological ground truth scales, and bootstrap a larger synthetic corpus via large language model simulation. TUG demonstrates that minimal, privacy preserving signals can support relationship matching, offering new design space for social platforms.