Dialog Systems based on Markov decision processes over two real tasks

In this work the state of the art of the automatic dialogue strategy management using Markov decision processes (MDP) with reinforcement learning (RL) is described. Partially observable Markov decision processes (POMDP) are also described. To test the validity of these methods, two spoken dialogue systems have been developed. The first one is a spoken dialogue system for weather forecast providing, and the second one is a more complex system for train information. With the first system, comparisons between a rule-based system and an automatically trained system have been done, using a real corpus to train the automatic strategy. In the second system, the scalability of these methods when used in larger systems has been tested.

Multirating decision model validation: the relevance of the quality of the securitization issues

Rating enables the information asymmetry existing in the issuer-investor relationship to be reduced, particularly for issues with a high degree of complexity, as is the case of securitizations. However, there may be a serious conflict of interest between the issuer’s choice and remuneration of the agency and the credit rating awarded, resulting in lower quality and information power of the published rating. In this paper, we propose an explicative model of the number of ratings requested, by analyzing the relevance of the number of ratings to measure the reliability, where multirating is shown to be associated to the quality, size, liquidity and the degree of information asymmetry relating to the issue. Thus, we consider that the regulatory changes that foster the widespread publication of simultaneous ratings could help to alleviate the problem of rating model arbitrage and the crisis of confidence in credit ratings in general and in the securitization issues, in particular.

Group size and decision rules in legislative bargaining

We conduct experiments to investigate the effects of different majority requirements on bargaining outcomes in small and large groups. In particular, we use a Baron-Ferejohn protocol and investigate the effects of decision rules on delay (number of bargaining rounds needed to reach agreement) and measures of "fairness" (inclusiveness of coalitions, equality of the distribution within a coalition). We find that larger groups and unanimity rule are associated with significantly larger decision making costs in the sense that first round proposals more often fail, leading to more costly delay. The higher rate of failure under unanimity rule and in large groups is a combination of three facts: (1) in these conditions, a larger number of individuals must agree, (2) an important fraction of individuals reject offers below the equal share, and (3) proposers demand more (relative to the equal share) in large groups.

Design and implementation of a cloud computing adoption decision tool: Generating a cloud road

Migrating to cloud computing is one of the current enterprise challenges. This technology provides a new paradigm based on "on-demand payment" for information and communication technologies. In this sense, the small and medium enterprise is supposed to be the most interested, since initial investments are avoided and the technology allows gradual implementation. However, even if the characteristics and capacities have been widely discussed, entry into the cloud is still lacking in terms of practical, real frameworks. This paper aims at filling this gap, presenting a real tool already implemented and tested, which can be used as a cloud computing adoption decision tool. This tool uses diagnosis based on specific questions to gather the required information and subsequently provide the user with valuable information to deploy the business within the cloud, specifically in the form of Software as a Service (SaaS) solutions. This information allows the decision makers to generate their particular Cloud Road. A pilot study has been carried out with enterprises at a local level with a two-fold objective: To ascertain the degree of knowledge on cloud computing and to identify the most interesting business areas and their related tools for this technology. As expected, the results show high interest and low knowledge on this subject and the tool presented aims to readdress this mismatch, insofar as possible. Copyright: © 2015 Bildosola et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Elements of Decision under Uncertainty with Applications

This work presents the basic elements for the analysis of decision under uncertainty: Expected Utility Theory and its citicisms and risk aversion and its measurement. The concepts of certainty equivalent, risk premium, absolute risk aversion and relative risk aversion, and the "more risk averse than" relation are discussed. The work is completed with several applications of decision making under uncertainty to different economic problems: investment in risky assets and portfolio selection, risk sharing, investment to reduce risk, insurance, taxes and income underreporting, deposit insurance and the value of information.