Facticity as the amount of self-descriptive information in a data set

Using the theory of Kolmogorov complexity the notion of facticity {\phi}(x) of a string is defined as the amount of self-descriptive information it contains. It is proved that (under reasonable assumptions: the existence of an empty machine and the availability of a faithful index) facticity is definite, i.e. random strings have facticity 0 and for compressible strings 0 < {\phi}(x) < 1/2 |x| + O(1). Consequently facticity measures the tension in a data set between structural and ad-hoc information objectively. For binary strings there is a so-called facticity threshold that is dependent on their entropy. Strings with facticty above this threshold have no optimal stochastic model and are essentially computational. The shape of the facticty versus entropy plot coincides with the well-known sawtooth curves observed in complex systems. The notion of factic processes is discussed. This approach overcomes problems with earlier proposals to use two-part code to define the meaningfulness or usefulness of a data set.Comment: 10 pages, 2 figure

Information as Power: Democratizing Environmental Data

Environmental data systems have largely escaped scrutiny in the past decades. But these systems are the foundations for evaluating environmental priorities, making management decisions, and deciding which perspectives to value. Information is the foundation of effective regulation. The decisions regulators make about gathering, assimilating, and sharing information are, in many cases, determinative of the outcomes they reach. This is certainly true in the case of the environment. This paper looks at how current environmental regulation has created data systems that undermine scientific legitimacy and systematically prevent stakeholder participation in environmental decision-making. These data systems concentrate power within federal and state agencies that are often ill-equipped to use this data effectively. New calls to open environmental data have the potential to shift these norms, but they will not be successful without fundamental restructuring in the regulatory treatment of environmental data. This paper uses fisheries management as a case study to expose how outdated data perceptions and architectures are at the root of many current environmental management failures. Technological innovation is challenging many of these norms, creating opportunities for better management that can only be achieved if agencies fundamentally rethink environmental data management. I argue that federal agencies can support better regulatory outcomes by creating Environmental Data Offices and open data systems

Data uncertainty and the role of money as an information variable for monetary policy

In this study, we perform a quantitative assessment of the role of money as an indicator variable for monetary policy in the euro area. We document the magnitude of revisions to euro area-wide data on output, prices, and money, and find that monetary aggregates have a potentially significant role in providing information about current real output. We then proceed to analyze the information content of money in a forward-looking model in which monetary policy is optimally determined subject to incomplete information about the true state of the economy. We show that monetary aggregates may have substantial information content in an environment with high variability of output measurement errors, low variability of money demand shocks, and a strong contemporaneous linkage between money demand and real output. As a practical matter, however, we conclude that money has fairly limited information content as an indicator of contemporaneous aggregate demand in the euro area

Data collection methods for task-based information access in molecular medicine

An important area of improving access to health information is the study of task-based information access in the health domain. This is a significant challenge towards developing focused information retrieval (IR) systems. Due to the complexities of this context, its study requires multiple and often tedious means of data collection, which yields a lot of data for analysis, but also allows triangulation so as to increase the reliability of the findings. In addition to traditional means of data collection, such as questionnaires, interviews and observation, there are novel opportunities provided by lifelogging technologies such as the SenseCam. Together they yield an understanding of information needs, the sources used, and their access strategies. The present paper examines the strengths and weaknesses of the traditional and the more novel means of data collection and addresses the challenges in their application in molecular medicine, which intensively uses digital information sources