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With the proliferation of information and communication technologies, especially with recent developments in Artificial Intelligence, social robots at home and the workplace are no longer being treated as lifeless and emotionless, leading to proposals which aim at incorporating affective elements within agents. Advances in areas such as affective decision-making and affective computing drive this interest. Our motivation in this paper is to use affection as a basic element within a decision-making process to facilitate robotic agents providing more seemingly human responses. We use earlier research in cognitive science and psychology to provide a model for an autonomous agent that makes decisions partly influenced by affective factors when interacting with humans and other agents. The factors included are emotions, mood, personality traits, and activation sets in relation with impulsive behavior. We describe several simulations with our model to study and compare its performance when facing various types of users. Through them, we essentially showcase that our model allows for a powerful agent design mechanism regulating its behavior and provides greater decision-making adaptivity when compared to emotionless agents and simpler emotional models. We conclude describing potential uses of our model in several application areas., The research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie Grant Agreement No. 713673. Si Liu has received the financial support through the “la Caixa” INPhINIT Fellowship Grant for Doctoral studies at Spanish Research Centres of Excellence. The project that gave rise to these results received the support of a fellowship from la Caixa Foundation (ID 100010434). The fellowship code is LCF/BQ/IN17/11620052. The work of David Rios Insua is supported by the Spanish Ministry of Economy and Innovation program MTM2017-86875-C3-1-R and the AXA-ICMAT Chair on Adversarial Risk Analysis.

Starting in the late 80s Bayesian methods have gained increasing attention in the reliability literature. The focus of most of the earlier Bayesian work in reliability involved statistical inference and thus the main emphasis was on modeling and analysis. Advances in Bayesian computing after the 90’s have significantly contributed not only to the use of Bayesian inference and prediction but also to the implementation of Bayesian decision-theoretic approaches in reliability problems. In this review we present an overview of Bayesian methods to solve decision problems in reliability some of which involve two or more decision makers with conflicting objectives. We consider problems in areas such as design, life testing, preventive maintenance, reliability certification, or warranty policies. In doing so, we present key aspects of the decision problems, give a brief review of earlier methods and finally discuss recent advances in Bayesian approaches to solve them.

Many reliability problems involve two or more agents with conflicting interests whose decisions affect the performance of the system at hand. Examples of such problems relevant in management practice abound and include acceptance sampling, life testing, software testing, optimal maintenance, reliability demonstration, warranties and insurance. Most earlier attempts in such problems have focused on game theoretic approaches based on Nash equilibria and related concepts. However, these require strong common knowledge assumptions which do not frequently hold in practice. We provide an alternative framework based on adversarial risk analysis to deal with such problems which avoids the strong common knowledge assumptions of game theory. We illustrate the framework through acceptance sampling and life testing problems., The work of DRI is supported by the Spanish Ministry of Economy and Innovation program MTM2014-56949-C3-1-R, and the AXA-ICMAT Chair on Adversarial Risk Analysis. DRI and FR are grateful to the support of the ESF-COST Action IS1304 on Expert Judgement. FR was supported by a Catedra de Excelencia program of the Government of Madrid.

The work of DRI has been supported by the Spanish Ministry of Economy and Innovation program MTM2014-56949-C3-1-R, the AXA-ICMAT Chair on Adversarial Risk Analysis and the ESF Cost Action IS1304 on Expert Judgement. This project has received funding from the European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement 285223. We are grateful to the transportation experts and stakeholders participating at validation workshops for fruitful discussions.

International audience; This paper presents an approach aimed at assessing multiple criteria spatial risk, where several methods are used for decision aiding purposes, with an application on marine nuclear releases. The case study simulates a post-accident analysis evaluating the impact upon the bay of Toulon of an accidental nuclear release. The problem is characterized by the presence of spatial features, multiple criteria describing the involved assets and uncertainties represented through multiple release scenarios and their corresponding probabilities.

We provide an introduction to Bayesian methods in conservation biology, illustrating inferences, prediction and decision making issues. After presenting the basic framework with a recovery plan evaluation problem, we illustrate more complex issues related to forecasting trends of structured populations using matrix population models and, finally, describe relevant topics in spatial and logistic regression problems. Computational and other implementation difficulties are also discussed.