Land-use decision-making processes have a long history of producing globally
pervasive systemic equity and sustainability concerns. Quantitative,
optimization-based planning approaches, e.g. Multi-Objective Land Allocation
(MOLA), seemingly open the possibility to improve objectivity and transparency
by explicitly evaluating planning priorities by the type, amount, and location
of land uses. Here, we show that optimization-based planning approaches with
generic planning criteria generate a series of unstable "flashpoints" whereby
tiny changes in planning priorities produce large-scale changes in the amount
of land use by type. We give quantitative arguments that the flashpoints we
uncover in MOLA models are examples of a more general family of instabilities
that occur whenever planning accounts for factors that coordinate use on- and
between-sites, regardless of whether these planning factors are formulated
explicitly or implicitly. We show that instabilities lead to regions of
ambiguity in land-use type that we term "gray areas". By directly mapping gray
areas between flashpoints, we show that quantitative methods retain utility by
reducing combinatorially large spaces of possible land-use patterns to a small,
characteristic set that can engage stakeholders to arrive at more efficient and
just outcomes.

通过优化规划确定的基于具有广泛可行性的规划准则的多目标土地分配方法，揭示了不稳定的 “闪点” 和 “灰区”，这些区域的微小变化会导致土地利用类型的大规模变化。

土地利用规划中的潜在不稳定性信号

Flashpoints Signal Hidden Inherent Instabilities in Land-Use Planning

This work proposes an optimization-based manipulation planning framework
where the objectives are learned functionals of signed-distance fields that
represent objects in the scene. Most manipulation planning approaches rely on
analytical models and carefully chosen abstractions/state-spaces to be
effective. A central question is how models can be obtained from data that are
not primarily accurate in their predictions, but, more importantly, enable
efficient reasoning within a planning framework, while at the same time being
closely coupled to perception spaces. We show that representing objects as
signed-distance fields not only enables to learn and represent a variety of
models with higher accuracy compared to point-cloud and occupancy measure
representations, but also that SDF-based models are suitable for
optimization-based planning. To demonstrate the versatility of our approach, we
learn both kinematic and dynamic models to solve tasks that involve hanging
mugs on hooks and pushing objects on a table. We can unify these quite
different tasks within one framework, since SDFs are the common object
representation. Video: this https URL

本文提出了一种基于优化的操作规划框架，其中目标是学习表示场景中物体的符号距离场的学习功能。我们通过代表物体的符号距离场来展示表示对象的方法不仅可以学习和表示多种模型，而且比点云和占用度量表示具有更高的准确性，并适用于基于优化的规划。我们通过学习运动学和动力学模型来解决在钩子上挂杯子和在桌子上推动物体等任务，这些任务都可以在相同的研究框架下解决，因为符号距离场是对象的共同表示。