The afternoon extends the morning’s method beyond elections. The same architecture — unobtrusively harvesting someone’s public posts, repeatedly interviewing an LLM “digital twin” built from them, and applying panel econometrics to the result — turns scattered, self-selected commentary into a balanced, forward-looking panel of on-demand forecasters. We work through three applications. In financial markets, we build digital twins of “finfluencers” and interview them daily, recovering their stock-level beliefs even on days they post nothing, and show that these signals predict the cross-section of S&P 500 returns without look-ahead bias (drawing on our Talking to Digital Twins study, Bowles et al., 2026). We then turn to forecasting and prediction-market applications, and to a measurement problem the method is unusually suited to: eliciting views on sensitive topics people are reluctant to volunteer — treating silence itself as a belief state. Participants build a twin, run a repeated-interview protocol, assemble the panel, and evaluate a simple forecast.

Aimed at financial economists, quantitative and survey researchers, and data scientists comfortable with R; the morning session is helpful but not required.