Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way¿relative to its role, meaning, and relationship with participants in decision making¿because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water managemen

A unified framework for building ontological theories with application and testing in the field of clinical trials

The objective of this research programme is to contribute to the establishment of the emerging science of Formal Ontology in Information Systems via a collaborative project involving researchers from a range of disciplines including philosophy, logic, computer science, linguistics, and the medical sciences. The re­searchers will work together on the construction of a unified formal ontology, which means: a general framework for the construction of ontological theories in specific domains. The framework will be constructed using the axiomatic-deductive method of modern formal ontology. It will be tested via a series of applications relating to on-going work in Leipzig on medical taxonomies and data dictionaries in the context of clinical trials. This will lead to the production of a domain-specific ontology which is designed to serve as a basis for applications in the medical field

The Pondicherry interpretation of quantum mechanics: An overview

An overview of the Pondicherry interpretation of quantum mechanics is presented. This interpretation proceeds from the recognition that the fundamental theoretical framework of physics is a probability algorithm, which serves to describe an objective fuzziness (the literal meaning of Heisenberg's term "Unschaerfe," usually mistranslated as "uncertainty") by assigning objective probabilities to the possible outcomes of unperformed measurements. Although it rejects attempts to construe quantum states as evolving ontological states, it arrives at an objective description of the quantum world that owes nothing to observers or the goings-on in physics laboratories. In fact, unless such attempts are rejected, quantum theory's true ontological implications cannot be seen. Among these are the radically relational nature of space, the numerical identity of the corresponding relata, the incomplete spatiotemporal differentiation of the physical world, and the consequent top-down structure of reality, which defies attempts to model it from the bottom up, whether on the basis of an intrinsically differentiated spacetime manifold or out of a multitude of individual building blocks.Comment: 18 pages, 1 eps figure, v3: with corrections made in proo

Knowledge Graph Embedding with Iterative Guidance from Soft Rules

Embedding knowledge graphs (KGs) into continuous vector spaces is a focus of current research. Combining such an embedding model with logic rules has recently attracted increasing attention. Most previous attempts made a one-time injection of logic rules, ignoring the interactive nature between embedding learning and logical inference. And they focused only on hard rules, which always hold with no exception and usually require extensive manual effort to create or validate. In this paper, we propose Rule-Guided Embedding (RUGE), a novel paradigm of KG embedding with iterative guidance from soft rules. RUGE enables an embedding model to learn simultaneously from 1) labeled triples that have been directly observed in a given KG, 2) unlabeled triples whose labels are going to be predicted iteratively, and 3) soft rules with various confidence levels extracted automatically from the KG. In the learning process, RUGE iteratively queries rules to obtain soft labels for unlabeled triples, and integrates such newly labeled triples to update the embedding model. Through this iterative procedure, knowledge embodied in logic rules may be better transferred into the learned embeddings. We evaluate RUGE in link prediction on Freebase and YAGO. Experimental results show that: 1) with rule knowledge injected iteratively, RUGE achieves significant and consistent improvements over state-of-the-art baselines; and 2) despite their uncertainties, automatically extracted soft rules are highly beneficial to KG embedding, even those with moderate confidence levels. The code and data used for this paper can be obtained from https://github.com/iieir-km/RUGE.Comment: To appear in AAAI 201

Combining Luhmann and Actor-Network Theory to see Farm Enterprises as Self-organizing Systems

From a rural, sociological point of view no social theories have so far been able to grasp the ontological complexity and special character of a farm enterprise as an entity in a really satisfying way. The contention of this paper is that a combination of Luhmann’s theory of social systems and actor-network theory (ANT) of Latour, Callon, and Law offers a new and radical framework for understanding a farm as a self-organizing, heterogeneous system. Luhmann’s theory offers an approach to understand a farm as a self-organizing system (operating in meaning) that must produce and reproduce itself through demarcation from the surrounding world by selection of meaning. The meaning of the system is expressed through the goals, values, and the logic of the farming processes. His theory, however, is less useful when studying the heterogeneous character of a farm as a mixture of biology, sociology, technology, and economy. ANT offers an approach to focus on the heterogeneous network of interactions of human and non-human actors such as knowledge, technology, money, farmland, animals, plants, etc., and as to how these interactions depend on both the quality of the actors and the network context of interaction, but the theory is weak when it comes to explaining the self-organizing character of a farm enterprise