Analysing partner selection through exchange values

Dynamic and resource-constrained environments raise interesting issues for partnership formation and multi-agent systems. In a scenario in which agents interact with each other to exchange services, if computational resources are limited, agents cannot always accept a request, and may take time to find available partners to delegate their needed services. Several approaches are available to solve this problem, which we explore through an experimental evaluation in this paper. In particular, we provide a computational implementation of Piaget's exchange-values theory, and compare its performance against alternatives

Evolution of Helping and Harming in Viscous Populations When Group Size Varies

Funding: Balliol College and the Royal Society.Recent years have seen huge interest in understanding how demographic factors mediate the evolution of social behavior in viscous populations. Here we study the impact of variation in group size on the evolution of helping and harming behavior. Although variation in group size influences the degree of relatedness and the degree of competition between groupmates, we find that these effects often exactly cancel, so as to give no net impact of variation in group size on the evolution of helping and harming. Specifically, (1) obligate helping and harming are never mediated by variation in group size, (2) facultative helping and harming are not mediated by variation in group size when this variation is spatial only, (3) facultative helping and harming are mediated by variation in group size only when this variation is temporal or both spatial and temporal, and (4) when there is an effect of variation in group size, facultative helping is favored in big groups and facultative harming is favored in little groups. Moreover, we find that spatial and temporal heterogeneity in individual fecundity may interact with patch-size heterogeneity to change these predictions, promoting the evolution of harming in big patches and of helping in little patches.Publisher PDFPeer reviewe

Learning from medical data streams: an introduction

Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference

Seasonal Unit Root Tests under Structural Breaks

In this paper, several seasonal unit root tests are analysed in the context of structural breaks at known time and a new break corrected test is suggested. We show that the widely used HEGY test as well as an LM variant thereof are asymptotically robust to seasonal mean shifts of finite magnitude. In finite samples, however, experiments reveal that such tests suffer from severe size distortions and power reductions when breaks are present. Hence, a new break corrected LM test is proposed in order to overcome this problem. Importantly, the correction for seasonal mean shifts bears no consequence on the limiting distributions thereby maintaining the legitimacy of canonical critical values. Moreover, although this test assumes a breakpoint a priori, it is robust in terms of misspecification of the time of the break. This asymptotic property is well reproduced in finite samples. Based on a Monte Carlo study, our new test is compared with other procedures suggested in the literature and shown to hold superior finite sample properties.Structural Breaks, Unit Roots, Seasonal Unit Root Tests

Conditional tests for elliptical symmetry using robust estimators

This paper presents a procedure for testing the hypothesis that the underlying distribution of the data is elliptical when using robust location and scatter estimators instead of the sample mean and covariance matrix. Under mild assumptions that include elliptical distributions without first moments, we derive the test statistic asymptotic behaviour under the null hypothesis and under special alternatives. Numerical experiments allow to compare the behaviour of the tests based on the sample mean and covariance matrix with that based on robust estimators, under various elliptical distributions and different alternatives. This comparison was done looking not only at the observed level and power but we rather use the size-corrected relative exact power which provides a tool to assess the test statistic skill to detect alternatives. We also provide a numerical comparison with other competing tests.Comment: In press in Communications in Statistics: Theory and Methods, 201

Tourism Demand in Portugal: Market Perspectives

Tourism has experienced different levels of development in the different regions of Portugal. To frame this development, several panel data models were estimated. The main objective is to explain the evolution of overnight stays by nationality in each region. Secondary data from 2000 to 2010 was used. The analysis includes the main tourism markets, such as the United Kingdom, Germany, the Netherlands, Ireland, France and Spain. Tourism literature suggests that, among others, the main determinants of tourism demand are Income (GDP), population, tourist´s income by place of residence, households’ consumption, unemployment rate, inflation rate, compensation of employees, comparative prices and households’ investment rate. It is observed that, although significant, the explanatory power of these variables varies according to the origin and the destination region considered