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Kuhn, Michael Wiegand, Connor 2026-06-18T14:42:43Z 2026-06-18 https://hdl.handle.net/1794/32782 This dissertation studies forward-looking economic behavior in remote experimental settings, focusing on time preferences, measurement design, and trust in technologically mediated interaction. As experimental research increasingly relies on remote platforms, careful implementation is essential for credible inference about how individuals evaluate future outcomes. The first chapter examines the stability of time preferences using longitudinal experimental data. Repeated measurement reveals systematic within-individual variation in discounting behavior, suggesting that intertemporal preferences may fluctuate over relatively short horizons and that single-elicitation estimates may obscure temporal instability. The second chapter develops and evaluates a modified Multiple Lottery List (MLL) designed for remote, repeated use. The instrument reduces compound risk, eliminates multiple switching through an intuitive interface, and streamlines dense price lists to improve precision while minimizing participant burden. The design yields stable and internally consistent estimates and is well suited for multi-wave studies. The final chapter investigates how artificial intelligence systems influence communication and trust in an experimental exchange setting. AI-assisted messaging alters cooperative outcomes by shaping the content and credibility of communication. Together, the chapters combine substantive evidence on time preferences and trust with methodological advances in remote experimental design, demonstrating how carefully structured remote experiments can deepen our understanding of forward-looking economic behavior. en_US University of Oregon CC BY-NC-SA artificial intelligence behavioral economics experimental economics intertemporal choice time preferences trust Remote Experiments in Trust and Time Dissertation or thesis Ph.D. doctoral Department of Economics University of Oregonopen.access