The thesis discusses the role of narratives for the determination of system boundaries of scientific modeling. In two case studies, narratives are used to distinguish perspectives, and to identify and weight factors involved (Further Methods: Simulation and Data Analysis in Python Notebooks and OpenLCA).
Theory: The interplay of model and narrative is intended to show how technology assessment is guided by the implicit assumptions of narratives and thus produces practically useful results.
Case 1: Biogas plants are in their environmental impact compared with battery storage as flexibility options, rather than being compared with PV and wind in the framing of renewable energy production.
Case 2: In a study on nutritional vision of microalgae, statistical methods are used to develop different narratives for comparing possible innovation paths.
The Idea: Computational Narratives
“Computers are good at consuming, producing and processing data. Humans, on the other hand, process the world through narratives. Thus, in order for data, and the computations that process and visualize that data, to be useful for humans, they must be embedded into a narrative - a computational narrative - that tells a story for a particular audience and context.” (Perez and Granger 2015, p. 1)
Theory: Models, Narratives and Self-Reference for the Assessment of Knowledge
Models have become a central epistemological tool in many scientific disciplines. They represent target systems and the scientists aim to learn something about the target system by learning something about the model. In our view, the models connect narratively highlighted, distinct aspects. In our narrated imagination, models serve as tangible worlds for the common assessment of narrative aspects on the basis of authorized props (see e.g. Walton, Frigg, Morgan, Wise).
The concept of narrative self-reference incorporates selected aspects of literary theory into the theory of self-referential systems (see e.g. Luhmann, MacIntyre, Bruner). Narrative self-reference is the simplified narrative self-image that reflects the system-environment relationship and thereby stabilizes the system. Narratives provide system boundaries for orientation, collaboration, and a referable common ground for the assessment of knowledge.
Case Study 1: Biogas as flexibility option
The German energy transition is characterized by a growing share of intermittent renewables resulting in an increased demand for flexibility. The provision of flexibility services through biogas power plants is a promising alternative in line with e.g. grid expansion and storage. The provision of “flexibility” is considered as a qualitative property with no specific unit, but rather the capability of a technology to provide certain power grid services and demand-based production. This work aims to provide a way to compare the environmental effects of flexibility services provided by modern biogas plants and Li-Ion battery storage units. In simulation of time-shift scenarios, both technologies aim to sell energy at high prices with different strategies and are optimized for the same hypothetical turnover which serves as an indicator for this specific flexibility service. The ecological impacts of both flexibility scenarios are compared in LCA.
Case Study 2: Visions of a microalgae based nutrition
Microalgae produce biomass with high quality ingredients that can be made usable for human nutrition. The study on nutritional visions of microalgae highlights different meanings of the technology: micro-algae as a sustainability technology, a high-priced health and wellness product, a value-priced and functional food, and a regional product of greater independence. Starting points were current topics of microalgae research, nutritional scenarios and technical visions. Based on a Delphi survey, the clustering was analyzed in addition to the weighting of these aspects. The narrativized interpretation of the results was questioned in a second round regarding desirability and feasibility. The four narratives emphasize the meaning of the aspects involved and serve to guide possible innovation paths.
Perez, Fernando; Granger, Brian E. (2015): Project Jupyter: Computational Narratives as the Engine of Collaborative Data Science. Helmsley Trust; Gordon and Betty Moore Foundation; Alfred P. Sloan Foundation.
Online at http://archive.ipython.org/JupyterGrantNarrative-2015.pdf, last check 09.11.2017.