Simulating Life Paths with Digital Twins: AI-Generated Future Selves Influence Decision-Making and Expand Human Choice

2025-12-04

AI Summary

Major life decisions are often constrained by limitations in mental time travel—the ability to accurately simulate how one’s future self will experience and cope with potential outcomes. To address this, we propose an AI-driven “Digital Twin of the Future Self” framework that integrates facial aging modeling, voice cloning, and large language model–based dialogue generation to construct multimodal, personalized virtual avatars of the user’s future self. Beyond enhancing the vividness and immersion of psychological simulation, the system features proactive scenario inference: it autonomously generates novel, feasible third options not explicitly anticipated by the user, thereby substantively expanding the decision space. A user study demonstrates that AI-generated alternatives significantly increase adoption rates (p < 0.01), validating the framework’s efficacy in augmenting prospective thinking and supporting deep, reflective trade-off analysis.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Agentic search
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Major life transitions demand high-stakes decisions, yet people often struggle to imagine how their future selves will live with the consequences. To support this limited capacity for mental time travel, we introduce AI-enabled digital twins that have ``lived through'' simulated life scenarios. Rather than predicting optimal outcomes, these simulations extend prospective cognition by making alternative futures vivid enough to support deliberation without assuming which path is best. We evaluate this idea in a randomized controlled study (N=192) using multimodal synthesis - facial age progression, voice cloning, and large language model dialogue - to create personalized avatars representing participants 30 years forward. Young adults 18 to 28 years old described pending binary decisions and were assigned to guided imagination or one of four avatar conditions: single-option, balanced dual-option, or expanded three-option with a system-generated novel alternative. Results showed asymmetric effects: single-sided avatars increased shifts toward the presented option, while balanced presentation produced movement toward both. Introducing a system-generated third option increased adoption of this new alternative compared to control, suggesting that AI-generated future selves can expand choice by surfacing paths that might otherwise go unnoticed. Participants rated evaluative reasoning and eudaimonic meaning-making as more important than emotional or visual vividness. Perceived persuasiveness and baseline agency predicted decision change. These findings advance understanding of AI-mediated episodic prospection and raise questions about autonomy in AI-augmented decisions.

Problem

• Simulating future life scenarios to aid high-stakes decision-making
• Using AI digital twins to expand human choice and deliberation
• Evaluating AI-generated future selves' impact on decision shifts and autonomy

Innovation

• AI digital twins simulate life scenarios for decision support
• Multimodal synthesis creates personalized future self avatars
• System-generated novel alternatives expand human choice options