The convexity-cone approach to comparative risk and downside risk.

Based on Jewitt (1986) we try to find a characterization of comparative downside risk aversion and love. The desired characterizations involve the decomposition of the dual of the intersection of two convexity cones. The decomposition holds in the case of downside risk love, but not in the case of downside risk aversion. A counterexample is provided.Convexity cones; risk; downside risk; risk aversion; dual cones

The convexity-cone approach to comparative risk and downside risk

Based on Jewitt (1986) we try to find a characterization of comparative downside risk aversion and love. The desired characterizations involve the decomposition of the dual of the intersection of two convexity cones. The decomposition holds in the case of downside risk love, but not in the case of downside risk aversion. A counterexample is provided.Based on Jewitt (1986) we try to find a characterization of comparative downside risk aversion and love. The desired characterizations involve the decomposition of the dual of the intersection of two convexity cones. The decomposition holds in the case of downside risk love, but not in the case of downside risk aversion. A counterexample is provided.Non-Refereed Working Papers / of national relevance onl

Selection and Gratitude: Anonymity and gratitude

What kind of candidate is selected into a job when the principal has to appoint a committee to measure the candidate's ability and select a winner through a call specifying a wage for the job? In a model where the principal fixes the wage anticipating the committee's choice, under a rather natural assumption about the committee's objective we find that if the committee takes into account the candidate's gratitude a candidate with less than first best ability will be selected in equilibrium. First best selection is achieved if the committee is anonymous to the candidates. If the committee could also set the wage the first best candidate would be selected, but the principal would be worse off hence he would not implement full delegation

Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively

Damned If You Do and Damned If You Don’t: Two Masters

We study common agency problems in which principals (groups) make costly commit- ments to incentives that are conditioned on imperfect signals of the agent’s action. Our framework allows for incentives to be either rewards or punishments and an equilibrium al- ways exists. For our canonical example with two principals we obtain a unique equilibrium, which typically involves randomization by both principals. Greater similarity between prin- cipals leads to more aggressive competition. The principals weakly prefer punishment to rewards, sometimes strictly. With rewards an agent voluntarily joins both groups; with pun- ishment it depends on whether severe punishments are feasible and cheap for the principals. We study whether introducing an attractive compromise reduces competition between prin- cipals. Our framework of imperfect monitoring offers a natural perturbation of the standard common agency model, which results in sharper equilibrium predictions. The limit equilib- rium prediction provides support to both truthful equilibria and the competing notion of natural equilibria, which unlike the former may be inefficient

Trasformazione del paesaggio, sistemi insediativi e borghi rurali

Introduction at section 1.3 Landscape TransformationsIntroduzione alla sezione 1.3 Le trasformazioni del paesaggi

Evolving to the Impatience Trap: The Example of the Farmer-Sheriff Game

The literature on the evolution of impatience, focusing on one-person decision problems, finds that evolutionary forces favor the more patient individuals. This paper shows that in the context of a game, this is not necessarily the case. In particular, it offers a twopopulation example where evolutionary forces favor impatience in one group while favoring patience in the other. Moreover, not only evolution but also efficiency may prefer impatient individuals. In our example, it is efficient for one population to evolve impatience and for the other to develop patience. Yet, evolutionary forces move the wrong populations.Impatience, evolution