Exploring the absolutist vs relativist perception of poverty using a cross-country questionnaire survey

Questionnaires eliciting the absolutist vs relativist perception of poverty are administered to 1941 undergraduate students in eight countries - Bolivia, Brazil, Italy, Kenya, Laos, Sweden, Switzerland and the UK. We find that the perception of poverty expressed by a large fraction of respondents exhibits both absolutist and relativist concerns, with the former components prevailing over the latter. High-income countries exhibit a significantly more pronounced relativist attitude. Personal characteristics such as past experience of material hardship and relative standard of living play a germane role in shaping respondents’ views.     

Uncertainty and Risk in Financial Markets

This paper considers a general equilibrium model in which the distinction between uncertainty and risk is formalized by assuming agents have incomplete preferences over state‐contingent consumption bundles, as in Bewley (1986). Without completeness, individual decision making depends on a set of probability distributions over the state space. A bundle is preferred to another if and only if it has larger expected utility for all probabilities in this set. When preferences are complete this set is a singleton, and the model reduces to standard expected utility. In this setting, we characterize Pareto optima and equilibria, and show that the presence of uncertainty generates robust indeterminacies in equilibrium prices and allocations for any specification of initial endowments. We derive comparative statics results linking the degree of uncertainty with changes in equilibria. Despite the presence of robust indeterminacies, we show that equilibrium prices and allocations vary continuously with underlying fundamentals. Equilibria in a standard risk economy are thus robust to adding small degrees of uncertainty. Finally, we give conditions under which some assets are not traded due to uncertainty aversion.     

Using Expert Models in Human Reliability Analysis—A Dependence Assessment Method Based on Fuzzy Logic

In human reliability analysis (HRA), dependence analysis refers to assessing the influence of the failure of the operators to perform one task on the failure probabilities of subsequent tasks. A commonly used approach is the technique for human error rate prediction (THERP). The assessment of the dependence level in THERP is a highly subjective judgment based on general rules for the influence of five main factors. A frequently used alternative method extends the THERP model with decision trees. Such trees should increase the repeatability of the assessments but they simplify the relationships among the factors and the dependence level. Moreover, the basis for these simplifications and the resulting tree is difficult to trace. The aim of this work is a method for dependence assessment in HRA that captures the rules used by experts to assess dependence levels and incorporates this knowledge into an algorithm and software tool to be used by HRA analysts. A fuzzy expert system (FES) underlies the method. The method and the associated expert elicitation process are demonstrated with a working model. The expert rules are elicited systematically and converted into a traceable, explicit, and computable model. Anchor situations are provided as guidance for the HRA analyst's judgment of the input factors. The expert model and the FES‐based dependence assessment method make the expert rules accessible to the analyst in a usable and repeatable way, with an explicit and traceable basis.     

A Social‐Cognitive Model of Pandemic Influenza H1N1 Risk Perception and Recommended Behaviors in Italy

The outbreak of the pandemic influenza H1N1 2009 (swine flu) between March and April 2009 challenged the health services around the world. Indeed, misconceptions and worries have led the public to refuse to comply with precautionary measures. Moreover, there have been limited efforts to develop models incorporating cognitive, social‐contextual, and affective factors as predictors of compliance with recommended behaviors. The aim of this study was to apply a social‐cognitive model of risk perception and individual response to pandemic influenza H1N1 in a representative sample of Italian population. A sample of 1,010 Italians of at least 18 years of age took part in a telephone survey. The survey included measures of perceived preparedness of institutions, family members and friends’ levels of worry, exposure to media campaigns (social‐contextual factors), perceived coping efficacy, likelihood of infection, perceived seriousness, personal impact, and severity of illness (cognitive evaluations), affective response and compliance with recommended behaviors. Results demonstrated that affective response fully mediated the relationship between cognitive evaluations and social‐contextual factors (with the exception of exposure to media campaigns) and compliance with recommended behaviors. Perceived coping efficacy and preparedness of institutions were not related to compliance with recommended behaviors.     

A generalization of the adaptive rejection sampling algorithm

Rejection sampling is a well-known method to generate random samples from arbitrary target probability distributions. It demands the design of a suitable proposal probability density function (pdf) from which candidate samples can be drawn. These samples are either accepted or rejected depending on a test involving the ratio of the target and proposal densities. The adaptive rejection sampling method is an efficient algorithm to sample from a log-concave target density, that attains high acceptance rates by improving the proposal density whenever a sample is rejected. In this paper we introduce a generalized adaptive rejection sampling procedure that can be applied with a broad class of target probability distributions, possibly non-log-concave and exhibiting multiple modes. The proposed technique yields a sequence of proposal densities that converge toward the target pdf, thus achieving very high acceptance rates. We provide a simple numerical example to illustrate the basic use of the proposed technique, together with a more elaborate positioning application using real data.     

Robust inference for the stress–strength reliability

We address the problem of robust inference about the stress–strength reliability parameter R = P(X < Y), where X and Y are taken to be independent random variables. Indeed, although classical likelihood based procedures for inference on R are available, it is well-known that they can be badly affected by mild departures from model assumptions, regarding both stress and strength data. The proposed robust method relies on the theory of bounded influence M-estimators. We obtain large-sample test statistics with the standard asymptotic distribution by means of delta-method asymptotics. The finite sample behavior of these tests is investigated by some numerical studies, when both X and Y are independent exponential or normal random variables. An illustrative application in a regression setting is also discussed.     

Wholly proprietary versus wholly open knowledge strategies: some empirical evidences from Italian biotech firms

This paper focuses on biotechnologies and biotech companies. Our aim is to develop a model for assessing the openness degree of knowledge governance strategies, applying it to the biotech firms. Biotech production is described as an emerging meta-industry in the global economy, fostering through new products, many socio-economic fields. Its main features are: a close relationship among knowledge, innovation and competitiveness; the need for collaborative research; the importance of small firms. Our paper begins with a brief characterization of the biotech meta-sector. We then review the literature regarding the advantages and disadvantages of ??wholly proprietary?? versus ??wholly open?? knowledge strategies. Next we propose a model and report the main results of its application to biotech companies in Italy. In conclusion, we evaluate the degree of openness of the company knowledge strategies based upon empirical evidence from our research in the field.