Using Qualitative Hypotheses to Identify Inaccurate Data

Identifying inaccurate data has long been regarded as a significant and difficult problem in AI. In this paper, we present a new method for identifying inaccurate data on the basis of qualitative correlations among related data. First, we introduce the definitions of related data and qualitative correlations among related data. Then we put forward a new concept called support coefficient function (SCF). SCF can be used to extract, represent, and calculate qualitative correlations among related data within a dataset. We propose an approach to determining dynamic shift intervals of inaccurate data, and an approach to calculating possibility of identifying inaccurate data, respectively. Both of the approaches are based on SCF. Finally we present an algorithm for identifying inaccurate data by using qualitative correlations among related data as confirmatory or disconfirmatory evidence. We have developed a practical system for interpreting infrared spectra by applying the method, and have fully tested the system against several hundred real spectra. The experimental results show that the method is significantly better than the conventional methods used in many similar systems.Comment: See http://www.jair.org/ for any accompanying file

Reconfiguring Household Management in Times of Discontinuity as an Open System: The Case of Agro-food Chains

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This article is based upon a heterodox approach to economics that rejects the oversimplification made by closed economic models and the mainstream concept of ‘externality.’ This approach re-imagines economics as a holistic evaluation of resources versus human needs, which requires judgement based on understanding of the complexity generated by the dynamic relations between different systems. One re-imagining of the economic model is as a holistic and systemic evaluation of agri-food systems’ sustainability that was performed through the multi-dimensional Governance Assessment Matrix Exercise (GAME). This is based on the five capitals model of sustainability, and the translation of qualitative evaluations into quantitative scores. This is based on the triangulation of big data from a variety of sources. To represent quantitative interactions, this article proposes a provisional translation of GAME’s qualitative evaluation into a quantitative form through the identification of measurement units that can reflect the different capital dimensions. For instance, a post-normal, ecological accounting method, Emergy is proposed to evaluate the natural capital. The revised GAME re-imagines economics not as the ‘dismal science,’ but as one that has potential leverage for positive, adaptive and sustainable ecosystemic analyses and global ‘household’ management. This article proposes an explicit recognition of economics nested within the social spheres of human and social capital which are in turn nested within the ecological capital upon which all life rests and is truly the bottom line. In this article, the authors make reference to an on-line retailer of local food and drink to illustrate the methods for evaluation of the five capitals model

A quantum theoretical explanation for probability judgment errors

A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction, disjunction, inverse, and conditional fallacies, as well as unpacking effects and partitioning effects. Quantum probability theory is a general and coherent theory based on a set of (von Neumann) axioms which relax some of the constraints underlying classic (Kolmogorov) probability theory. The quantum model is compared and contrasted with other competing explanations for these judgment errors including the representativeness heuristic, the averaging model, and a memory retrieval model for probability judgments. The quantum model also provides ways to extend Bayesian, fuzzy set, and fuzzy trace theories. We conclude that quantum information processing principles provide a viable and promising new way to understand human judgment and reasoning