Autoethnography Reveals Human Dimensions of Energy Optimization

Optimization models are frequently presented as neutral, objective tools that generate clear, rational answers to complex decision-making problems in energy systems, logistics, and infrastructure planning. However, a new study published in Energy Informatics argues that modeling is not merely a technical activity but a highly interpretive process shaped by values such as efficiency, fairness, and accountability. The research, conducted by Chiara Bordin, utilizes an autoethnographic approach to explore how abstract optimization models gain meaning in practice, particularly within energy informatics applications like electric vehicle charging, battery systems, and sustainable logistics. By drawing on ten years of interdisciplinary experience across academia, industry, and education, the study proposes a reflexive framework that conceptualizes optimization as a meaning-making practice rather than a purely computational exercise.

The central finding of the study is that optimization models do not generate purely objective solutions. Instead, their outputs acquire meaning and authority through processes of framing, storytelling, and interaction with stakeholders. Ethical tensions, such as those between technical elegance and social responsibility, or between abstraction and contextual complexity, emerge as central features of modeling practice. These tensions are often managed through informal negotiation and compromise, highlighting the sociotechnical nature of optimization work. The study emphasizes that what constitutes an “optimal” solution depends not only on data and algorithms but also on how problems are framed, which objectives are prioritized, and whose perspectives are included or ignored.

Mathematical optimization, rooted in operations research and management science, translates real-world problems into formal models constituted by objective functions and constraints. These models are typically solved using linear or non-linear algorithms to determine optimal or sub-optimal solutions. In the energy sector, these models support decisions related to system planning, operation, and control, including renewable integration, demand response, and storage management. Despite their widespread use, the dominant paradigm often abstracts away from the broader contexts in which models are developed, treating objectives as given and constraints as fully capturing system realities. This approach can obscure the fact that many energy-related decision problems are characterized by ambiguity, contested priorities, and evolving stakeholder expectations.

The study identifies a significant gap in the existing literature, which has largely focused on extending models to include additional social or ethical variables while paying less attention to the interpretive processes through which modelers frame problems and assign meaning to results. While interdisciplinary research in science and technology studies has highlighted that models are sociotechnical artifacts embedding assumptions and values, there remains a lack of systematic understanding of how meaning is generated and negotiated in everyday modeling practice. The author addresses this gap by providing an insider account of the interpretive, ethical, and relational dimensions of modeling that are often neglected in traditional technical studies.

Using an autoethnographic methodology, the researcher combined self-reflection, narrative, and ethnographic description to examine her own experiences as both an insider and observer within the field of energy system modeling. Data were collected over approximately ten years through reflective field notes, informal conversations, personal journals, and project documentation. The analysis employed an inductive thematic approach, identifying recurring patterns directly from the empirical material without applying predefined categories. This process allowed themes such as fairness, local knowledge, narrative, and reflexivity to emerge organically, reflecting the lived complexity of modeling practice rather than imposing external analytical structures.

One recurring theme identified in the findings is the tension between the mathematical ideal of efficiency and real-world needs for fairness or equity. In a logistics project aimed at optimizing delivery routes for cost and energy efficiency, warehouse staff and union representatives challenged the model’s output, noting that the “optimal” routes resulted in significantly longer and more dangerous workdays for drivers. This example illustrates how optimization models embed assumptions about what matters, and when real-life fairness challenges these assumptions, modelers must navigate ethical conflicts. The model’s focus on fuel usage and time ignored the labor conditions and dignity of the workers, revealing a blind spot in the optimization framework that required ongoing negotiation and reframing.

Another key theme is the role of local and experiential knowledge in challenging model abstractions. In a master’s thesis project on optimizing waste collection routes for a northern municipality, the model produced routes that were technically efficient but included steep, icy roads that drivers actively avoided in winter. The drivers’ pushback revealed that practical, hands-on knowledge was missing from the model, which focused on a narrow kind of logic that ignored real-world risks and seasonal experience. This case underscores that optimization is never fully neutral; excluding local knowledge risks exporting hidden dangers under the justification of efficiency. Modeling in such environments requires humility and the willingness to treat lived experience as part of the model itself.

The study also highlights the importance of narrative and storytelling in making optimization results meaningful to non-technical audiences. In a participatory modeling project in Longyearbyen, an Arctic community undergoing an energy transition, residents engaged with an interactive model that allowed them to explore energy system choices. Despite clear economic signals favoring the cheapest options, many residents prioritized solutions that reflected their values and community visions, even when these choices were substantially more expensive. This dynamic illustrates how narrative transforms optimization from a purely technical exercise into a participatory process grounded in lived experience. Storytelling serves as a bridge between technical models and community meaning-making, humanizing abstract trade-offs and inviting diverse stakeholder engagement.

Reflexivity emerges as a critical ethical practice in managing these tensions. The author’s reflections reveal moments of doubt and negotiation, demonstrating that ethical awareness is an ongoing, active process embedded in modeling work. For instance, in a project on electric vehicle charging, the model used price signals to manage grid capacity, assuming users would adapt their behavior. However, the discussion revealed that people who lack flexibility, such as shift workers or single parents, might be disproportionately penalized by high prices during peak hours. This ethical blind spot highlighted that demand response is not just about incentives but about lived constraints. Reflexive modeling demands attention not just to accuracy, but to justice, asking who wins, who loses, and why.

Based on these insights, the study proposes a reflexive framework consisting of two interacting components: modeling moments and ethical-interpretive dimensions. Modeling moments are recurring activities in optimization practice where meaning is negotiated, including problem framing, objective and constraint formulation, data selection, solution interpretation, and communication. Ethical-interpretive dimensions include fairness and equity, local and experiential knowledge, narrative and framing, power and voice, and the reflexivity of the modeler. This framework does not prescribe a sequence or fixed principles but provides a structured lens for modelers to recognize, articulate, and reflect upon the ethical and interpretive work that accompanies optimization in practice.

The framework is designed to be reusable in practice through three complementary modes: reflective diagnosis, anticipatory reflexivity, and structured dialogue. Reflective diagnosis involves using the framework retrospectively to analyze completed projects and identify where tensions arose. Anticipatory reflexivity uses the framework prospectively as a scaffold during model development to prompt questions about assumptions and excluded knowledge. Structured dialogue positions the framework as a boundary object for interdisciplinary contexts, providing a shared vocabulary to distinguish between technical disagreements, ethical tensions, and power-related concerns. These modes support ethical attentiveness and transparent communication without undermining technical rigor.

The study also discusses the implications for modeling education, arguing that technical proficiency alone is insufficient. Modeling education must cultivate the ability to critically reflect on how models are constructed, communicated, and applied in real-world contexts. The reflexive framework can serve as a pedagogical tool to guide students beyond purely formal representations, supporting the development of interpretive awareness. By engaging with vignettes and dialogic examples, students can better grasp the broader implications of modeling choices and consider whose perspectives are represented within a model. This approach helps shift the focus from obtaining a single “optimal” solution to understanding the plurality of possible interpretations and trade-offs.

Limitations of the study include its reliance on autoethnography, which provides a unique insider perspective but may not be generalizable to all modeling contexts. The empirical material is drawn from the author’s accumulated experience over ten years, and while the analysis is systematic, it is inherently subjective. The study does not claim to replace technical analysis but to complement it by making visible the human and interpretive dimensions of modeling work. Future research could extend the analysis through collaborative or multi-perspective autoethnographies, enabling comparative insights across contexts and disciplines. The study contributes to ongoing discussions on responsible modeling by offering a grounded, practice-oriented perspective that acknowledges the ethical, interpretive, and pedagogical work involved in transforming mathematical formulations into actionable decisions.

Ultimately, the research challenges the operations research and energy system modeling communities to reconsider the role of optimization models. Rather than viewing them as neutral problem-solving tools, the study advocates for recognizing them as socially situated practices of ethical work and meaning-making. By providing a reflexive framework grounded in empirical experience, the study supports a shift toward more reflective, context-aware, and socially responsible approaches to modeling. This perspective emphasizes that technical excellence must be complemented by critical reflection on whose values are represented, how decisions are shaped, and what futures are made possible in the optimization of energy systems.

Subject of Research: Autoethnographic analysis of ethical and interpretive dimensions in energy system optimization modeling

Article Title: From models to meaning – an autoethnographic path toward a reflexive framework for interpreting human and ethical dimensions in energy system optimization

Article References: Bordin, C. (2026). From models to meaning – an autoethnographic path toward a reflexive framework for interpreting human and ethical dimensions in energy system optimization. Energy Informatics, 9(1), Article 71. https://doi.org/10.1186/s42162-026-00674-1

Image Credits: AI Generated

DOI: 10.1186/s42162-026-00674-1

Keywords: energy informatics, optimization modeling, autoethnography, ethical dimensions, reflexive framework, operations research, sustainable logistics, stakeholder engagement, meaning-making, energy systems, models, meaning