The cost of information

We develop an axiomatic theory of information acquisition that captures the idea of constant marginal costs in information production: the cost of generating two independent signals is the sum of their costs, and generating a signal with probability half costs half its original cost. Together with a monotonicity and a continuity conditions, these axioms determine the cost of a signal up to a vector of parameters. These parameters have a clear economic interpretation and determine the difficulty of distinguishing states. We argue that this cost function is a versatile modeling tool that leads to more realistic predictions than mutual information.Comment: 52 pages, 4 figure

Stable Matching under Forward-Induction Reasoning

A standing question in the theory of matching markets is how to define stability under incomplete information. The crucial obstacle is that a notion of stability must include a theory of how beliefs are updated in a blocking pair. This paper proposes a novel epistemic approach. Agents negotiate through offers. Offers are interpreted according to the highest possible degree of rationality that can be ascribed to their proponents, in line with the principle of forward-induction reasoning. This approach leads to a new definition of stability. The main result shows an equivalence between this notion and “incomplete-information stability”, a cooperative solution concept recently put forward by Liu, Mailath, Postlewaite and Samuelson (2014), for markets with one-sided incomplete information. The result implies that forward-induction reasoning leads to efficient matchings under standard supermodularity conditions. In addition, it provides an epistemic foundation for incomplete-information stability. The paper also shows new connections and distinctions between the cooperative and the epistemic approaches in matching markets

Testable Forecasts

Predictions about the future are commonly evaluated through statistical tests. As shown by recent literature, many known tests are subject to adverse selection problems and cannot discriminate between forecasters who are competent and forecasters who are uninformed but predict strategically. We consider a framework where forecasters' predictions must be consistent with a paradigm, a set of candidate probability laws for the stochastic process of interest. The paper presents necessary and sufficient conditions on the paradigm under which it is possible to discriminate between informed and uninformed forecasters. We show that optimal tests take the form of likelihood-ratio tests comparing forecasters' predictions against the predictions of a hypothetical Bayesian outside observer. In addition, the paper illustrates a new connection between the problem of testing strategic forecasters and the classical Neyman-Pearson paradigm of hypothesis testing

Luigi Bobbio: A Mentor Between Fieldwork and Public Action

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An Axiomatic Theory of Inductive Inference

This article develops an axiomatic theory of induction that speaks to the recent debate on Bayesian orgulity. It shows the exact principles associated with the belief that data can corroborate universal laws. We identify two types of disbelief about induction: skepticism that the existence of universal laws of nature can be determined empirically, and skepticism that the true law of nature, if it exists, can be successfully identified. We formalize and characterize these two dispositions toward induction by introducing novel axioms for subjective probabilities. We also relate these dispositions to the (controversial) axiom of σ-additivity

Assessment and Mapping Green Areas Ecosystem Services and Socio-Demographic Characteristics in Turin Neighborhoods (Italy)

The ecosystem services (ES) and human well-being are keywords that guide the Italian strategy on urban greening. The development of ES priorities linked to specific land uses help to guide the drafting of management plans. The aim of the research was to assess and map green areas ecosystem services and socio-demographic characteristics in Turin neighborhoods in order to identify where to improve the provision of ecosystem services and the socio-demographic conditions. The Preliminary Assessment Method (PAM) was used for the assessment of provision and regulating services based on land use. The Species-specific Air Quality index (S-AQI) was used to assess the regulating services provided by trees. Three socio-demographic characteristics were analyzed at the neighborhood level—age index, housing density, and % of economically assisted citizens. PAM results show that Turin provides more ecosystem services in peripheral areas of the city. Trees with high S-AQI values represent 21% of the censed trees. Not recommended trees are 18%. The neighborhoods with higher S-AQI values are not always characterized by a higher number of trees/km2 or species richness. Results show that the northern part of the city is characterized by higher values of ES and socio-demographic conditions than the central-southern part. This aspect is related to the conspicuous presence of agricultural land uses and water bodies, together with the presence of tree species with a high S-AQI values and high or medium socio-demographic conditions. 57% of the neighborhoods present low results for both aspects. Actions to improve the quality of green spaces in those neighborhoods could have great effects on liveability. Future management and planning strategies for increasing citizens’ well-being through urban greening should consider the proposed approach