The article "Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning" examines how decision-making models tailored to individuals, particularly in critical contexts such as vaccine uptake, often diverge from predictions based on broader population data (F1). It emphasizes the significance of personal factors, including numerical attributes and linguistic influences, in shaping individual choices (F2). Grounded in Utility Theory, the work explores how these theoretical foundations can be expanded to better capture individual decision processes (F3). Additionally, the article highlights the potential role of Large Language Models (LLMs) in enhancing decision-making by integrating textualized-symbolic reasoning alongside traditional utility optimization approaches (F4). This perspective aligns with recent research trends that investigate the application of LLMs to complex decision-making tasks. By combining quantitative and qualitative factors, the approach aims to provide a more nuanced understanding of individual behavior than population-level models typically allow. The integration of LLMs represents a promising avenue for advancing personalized decision support systems.