Towards Distributed and Adaptive Detection and Localisation of Network Faults

We present a statistical probing-approach to distributed fault-detection in networked systems, based on autonomous configuration of algorithm parameters. Statistical modelling is used for detection and localisation of network faults. A detected fault is isolated to a node or link by collaborative fault-localisation. From local measurements obtained through probing between nodes, probe response delay and packet drop are modelled via parameter estimation for each link. Estimated model parameters are used for autonomous configuration of algorithm parameters, related to probe intervals and detection mechanisms. Expected fault-detection performance is formulated as a cost instead of specific parameter values, significantly reducing configuration efforts in a distributed system. The benefit offered by using our algorithm is fault-detection with increased certainty based on local measurements, compared to other methods not taking observed network conditions into account. We investigate the algorithm performance for varying user parameters and failure conditions. The simulation results indicate that more than 95 % of the generated faults can be detected with few false alarms. At least 80 % of the link faults and 65 % of the node faults are correctly localised. The performance can be improved by parameter adjustments and by using alternative paths for communication of algorithm control messages

The adaptive problem of absent third-party punishment

Language is a uniquely human behaviour, which has presented unique adaptive problems. Prominent among these is the transmission of information that may affect an individual’s reputation. The possibility of punishment of those with a low reputation by absent third parties has created a selective pressure on human beings that is not shared by any other species. This has led to the evolution of unique cognitive structures that are capable of handling such a novel adaptive challenge. One of these, we argue, is the propositional theory of mind, which enables individuals to model, and potentially manipulate, their own reputation in the minds of other group members, by representing the beliefs that others have about the first party’s intentions and actions. Support for our theoretical model is provided by an observational study on tattling in two preschools, and an experimental study of giving under threat of gossip in a dictator game

The relationship between intra- and intergenerational ecological justice. Determinants of goal conflicts and synergies in sustainability policy

The guiding principle of sustainability is widely accepted in today´s international policies. The principle contains two seperate objectives of justice with regard to the conservation and use of ecosystems and their services: (1) global justice between different people of the present generation ("intragenerational justice"); and (2) justice between people of different generations ("intergenerational justice"). Three hypotheses about the relationship between these objectives are logically possible and are, in fact, held in the political and scientific discourse on sustainable development: independency, facilitation and rivalry. Applying the method of qualitative content analysis we evaluate political documents and the scientific literature on sustainable development by systematically revealing the lines of reasoning and determinants underlying the different hypotheses. These determinants are the quantity and quality of ecosystem services, population development, substitutability of ecosystem services by humanmade goods and services, technological progress, institutions and political restrictionssustainable development, ecosystem services, intragenerational justice, intergenerational justice, ecological justice, sustainability research

The Basic Principles of Uncertain Information Fusion. An organized review of merging rules in different representation frameworks

We propose and advocate basic principles for the fusion of incomplete or uncertain information items, that should apply regardless of the formalism adopted for representing pieces of information coming from several sources. This formalism can be based on sets, logic, partial orders, possibility theory, belief functions or imprecise probabilities. We propose a general notion of information item representing incomplete or uncertain information about the values of an entity of interest. It is supposed to rank such values in terms of relative plausibility, and explicitly point out impossible values. Basic issues affecting the results of the fusion process, such as relative information content and consistency of information items, as well as their mutual consistency, are discussed. For each representation setting, we present fusion rules that obey our principles, and compare them to postulates specific to the representation proposed in the past. In the crudest (Boolean) representation setting (using a set of possible values), we show that the understanding of the set in terms of most plausible values, or in terms of non-impossible ones matters for choosing a relevant fusion rule. Especially, in the latter case our principles justify the method of maximal consistent subsets, while the former is related to the fusion of logical bases. Then we consider several formal settings for incomplete or uncertain information items, where our postulates are instantiated: plausibility orderings, qualitative and quantitative possibility distributions, belief functions and convex sets of probabilities. The aim of this paper is to provide a unified picture of fusion rules across various uncertainty representation settings

Social-ecological analysis of climate induced changes in biodiversity – outline of a research concept

The interactions of changes in climate and biodiversity with societal actions, structures and processes are a priority topic within the international scientific debate – and thus, a relevant subject matter for BiKF’s work. This paper outlines a concept for transdisciplinary research within BiKF. It focuses on the analysis of social-ecological systems supporting society with biodiversity driven ecosystem services. Such research is considering different issues: defining sustainable societal adaptations to climate induced biodiversity changes; permitting adequate understanding of the social-ecological reproduction of ecosystem functions, including their conservation and restoration; analysing the societal values and socio-economic utilisation of ecosystem services. Gaining knowledge in these areas provides an improved basis for decision-making in biodiversity and resource management