This paper explores how ethical, social, and interpretive dimensions shape the use of optimization in real-world decision-making with a focus on energy-related applications. By drawing on ten years of the author’s experience working on interdisciplinary projects across academia, industry, and education, the objective is to investigate how abstract models acquire meaning in real-world decision-making contexts relevant to energy informatics such as electric vehicle charging, battery systems, energy system planning, sustainable logistics. Using an autoethnographic approach the study shows that modeling is not just technical but deeply interpretive. Decisions are influenced by values like efficiency, fairness, and accountability, and by the need to balance technical simplicity with complex stakeholder needs. The findings highlight how models, rather than offering purely objective answers, are embedded in dynamic interpretive processes, gaining meaning and authority through storytelling, framing, and negotiation. Ethical tensions frequently emerge between technical elegance and social responsibility, or between model simplicity and stakeholder complexity, and are often managed informally, through persuasion and compromise. By positioning modeling as a situated, meaning-making practice, this paper connects operations research and ethnographic approaches, in a way that has never been done before, and it offers a novel insider account of the human and ethical efforts involved in turning mathematical formulations into actionable decisions.