Modeling for post-growth: Backcasting radical futures
本文指出经济模型嵌入伦理和政治假设，限制了探索后增长激进叙事的能力，提出回溯法结合定性叙事与定量模型，并基于戴尔手段-目的谱系对模型分类，呼吁建模界采取批判态度。
Economic models are more than technical instruments: they are lenses that shape our understanding of reality and our ability to address contemporary challenges. They embed ethical commitments, political assumptions, and institutional framings that influence their outputs. We illustrate how normative and positive modeling approaches anchor their projections in existing economic structures through what we call structural induction. This effect is further reinforced by the way models frame scenarios. In the context of post-growth, these framing effects limit the capacity to explore radical narratives. To overcome these limitations, we advocate a backcasting approach, beginning with detailed qualitative narratives of proposed futures and using models iteratively to check their consistency and explore them quantitatively. In this way, models are used as exploratory tools, probing uncertain terrain rather than producing projections. This approach calls for the development of comprehensive and shared post-growth narratives. We then propose a conceptual classification of models, based on Daly's ends-means spectrum, to cover the range of post-growth concerns: need-oriented modeling, social and physical provisioning systems modeling, and biophysical modeling. Reviewing recent modeling exercises through this lens, we observe that biophysical modeling is gaining increasing attention, while need-oriented modeling remains underexplored despite its central importance for representing post-growth futures. Finally, we discuss the need for the modeling community to adopt a critical stance toward models and to explicitly define criteria for their validity.