Environmental public good provision under robust decision making

We study public good provision in a two-country dynamic setup with environmental externalities. In this framework, we examine robust decision making under potential misspecification of the process that describes the evolution of the environmental public good. Robust policies, arising from fear of model misspecification, help to correct for the inefficiencies associated with free riding and thus increase the provision of the public good. As a result, there can be welfare gains from robust policies even when the fear of model misspecification proves to be unfounded

Scale-Based Monotonicity Analysis in Qualitative Modelling with Flat Segments

Qualitative models are often more suitable than classical quantitative models in tasks such as Model-based Diagnosis (MBD), explaining system behavior, and designing novel devices from first principles. Monotonicity is an important feature to leverage when constructing qualitative models. Detecting monotonic pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper presents scale-based monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. sensor data or simulation results, can be partitioned into quasi-monotonic segments, i.e. segments monotonic with respect to a scale, in linear time. A novel segmentation algorithm is introduced along with a scale-based definition of "flatness"