Adapting a Formal Model Theory to Applications in Augmented Personalized Medicine

The goal of this paper is to advance an extensible theory of living systems using an approach to biomathematics and biocomputation that suitably addresses self-organized, self-referential and anticipatory systems with multi-temporal multi-agents. Our first step is to provide foundations for modelling of emergent and evolving dynamic multi-level organic complexes and their sustentative processes in artificial and natural life systems. Main applications are in life sciences, medicine, ecology and astrobiology, as well as robotics, industrial automation and man-machine interface. Since 2011 over 100 scientists from a number of disciplines have been exploring a substantial set of theoretical frameworks for a comprehensive theory of life known as Integral Biomathics. That effort identified the need for a robust core model of organisms as dynamic wholes, using advanced and adequately computable mathematics. The work described here for that core combines the advantages of a situation and context aware multivalent computational logic for active self-organizing networks, Wandering Logic Intelligence (WLI), and a multi-scale dynamic category theory, Memory Evolutive Systems (MES), hence WLIMES. This is presented to the modeller via a formal augmented reality language as a first step towards practical modelling and simulation of multi-level living systems. Initial work focuses on the design and implementation of this visual language and calculus (VLC) and its graphical user interface. The results will be integrated within the current methodology and practices of theoretical biology and (personalized) medicine to deepen and to enhance the holistic understanding of life.Comment: 56 pages, 18 figures, technical application pape

A Field Synopsis, Systematic Review, and Meta-analyses of Cophylogenetic Studies: What Is Affecting Congruence between Phylogenies?

We conducted a field synopsis and systematic meta-analysis of studies that carried out cophylogenetic analyses using algorithms and available software. We evaluated the influence of three factors—namely, cophylogenetic method, association, and ecosystem type—on the outcome of the analyses, that is, the degree of congruence between phylogenies of interacting species. The published papers were identified using 4 different databases and 13 keywords; we included all studies for which statistical approaches to compare phylogenies (cophylogenetic analyses) of interacting lineages were used. After the initial screening, 296 studies were selected to extract response variable (outcome of the cophylogenetic analyses, i.e., congruent, incongruent, or both) and coded information of the three selected factors (method of analyses, association, and ecosystem type). The final dataset included 485 entries. The data were analyzed using the chi-square test and regression techniques. We provided evidence for the outcome to be strongly dependent on the method; in particular, we are confident in expecting that phylogenies in mutualistic associations are congruent when using global-fit methods and in parasitic associations are incongruent when using event-based methods. Using a mixed-model approach, the most parsimonious model includes a non-nested structure of two factors (method and association), with a higher probability for parasites, herbivores, and pollinators to provide incongruent results. We discuss the use of an alternative theoretical framework, the Stockholm paradigm (SP), to reanalyze published raw data, and the integration of the cophylogenetic analyses into a workbench (DAMA protocol, the policy extension of SP) aimed to anticipate emerging infectious diseases

A PHYSIOCRATIC SYSTEMS FRAMEWORK FOR OPEN SOURCE AGRICULTURAL RESEARCH AND DEVELOPMENT

This dissertation presents a new participatory approach to agricultural research and development. It surveys the biological, sociological, economic, and technical landscape and proposes a framework for adaptive management based on the 18th century Physiocratic school of land-based economics. Industrial specialization and heavy emphasis on deductive approaches to science have contributed to the disconnection of large portions of the population from natural systems. Conventional agriculture and agricultural research methods following this pattern have created expensive social, environmental, and economic external costs, while adaptive management and resilient agricultural systems have been hindered by the cost and complexity of quantifying environmental services. However, the convergence of low cost computing, sensors, memory, and resulting data analytic methods, combined with new collaborative tools and social media, have created an exciting open source environment with the potential to engage more people in analyzing and managing our natural environment

The role of representation in Bayesian reasoning: Correcting common misconceptions

The terms nested sets, partitive frequencies, inside-outside view, and dual processes add little but confusion to our original analysis (Gigerenzer & Hoffrage 1995; 1999). The idea of nested set was introduced because of an oversight; it simply rephrases two of our equations. Representation in terms of chances, in contrast, is a novel contribution yet consistent with our computational analysis - it uses exactly the same numbers as natural frequencies. We show that non-Bayesian reasoning in children, laypeople, and physicians follows multiple rules rather than a general-purpose associative process in a vaguely specified "System 1.” It is unclear what the theory in "dual process theory” is: Unless the two processes are defined, this distinction can account post hoc for almost everything. In contrast, an ecological view of cognition helps to explain how insight is elicited from the outside (the external representation of information) and, more generally, how cognitive strategies match with environmental structure

An Optimisation-based Framework for Complex Business Process: Healthcare Application

The Irish healthcare system is currently facing major pressures due to rising demand, caused by population growth, ageing and high expectations of service quality. This pressure on the Irish healthcare system creates a need for support from research institutions in dealing with decision areas such as resource allocation and performance measurement. While approaches such as modelling, simulation, multi-criteria decision analysis, performance management, and optimisation can – when applied skilfully – improve healthcare performance, they represent just one part of the solution. Accordingly, to achieve significant and sustainable performance, this research aims to develop a practical, yet effective, optimisation-based framework for managing complex processes in the healthcare domain. Through an extensive review of the literature on the aforementioned solution techniques, limitations of using each technique on its own are identified in order to define a practical integrated approach toward developing the proposed framework. During the framework validation phase, real-time strategies have to be optimised to solve Emergency Department performance issues in a major hospital. Results show a potential of significant reduction in patients average length of stay (i.e. 48% of average patient throughput time) whilst reducing the over-reliance on overstretched nursing resources, that resulted in an increase of staff utilisation between 7% and 10%. Given the high uncertainty in healthcare service demand, using the integrated framework allows decision makers to find optimal staff schedules that improve emergency department performance. The proposed optimum staff schedule reduces the average waiting time of patients by 57% and also contributes to reduce number of patients left without treatment to 8% instead of 17%. The developed framework has been implemented by the hospital partner with a high level of success

Refounding of Activity Concept ? Towards a Federative Paradigm for Modeling and Simulation

Journal : Simulation, Transactions of the Society for Modeling and Simulation InternationalInternational audienceCurrently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accord-ingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epis-temology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new simulation activity definition is first applied critically. Then, activity is discussed generally. In epistemology, activity is discussed, in a prospective way, as a possible framework in models of human beliefs and knowledge

A Promethean Philosophy of External Technologies, Empiricism, & the Concept: Second-Order Cybernetics, Deep Learning, and Predictive Processing

Beginning with a survey of the shortcoming of theories of organology/media-as-externalization of mind/body—a philosophical-anthropological tradition that stretches from Plato through Ernst Kapp and finds its contemporary proponent in Bernard Stiegler—I propose that the phenomenological treatment of media as an outpouching and extension of mind qua intentionality is not sufficient to counter the ̳black-box‘ mystification of today‘s deep learning‘s algorithms. Focusing on a close study of Simondon‘s On the Existence of Technical Objectsand Individuation, I argue that the process-philosophical work of Gilbert Simondon, with its critique of Norbert Wiener‘s first-order cybernetics, offers a precursor to the conception of second-order cybernetics (as endorsed byFrancisco Varela, Humberto Maturana, and Ricardo B. Uribe) and, specifically, its autopoietic treatment of information. It has been argued by those such as Frank Pasquale that neuro-inferential deep learning systems premised on predictive patterning, suchas AlphaGo Zero, have a veiled logic and, thus, are ̳black boxes‘. In detailing a philosophical-historical approach to demystify predictive patterning/processing and the logic of such deep learning algorithms, this paper attempts to shine a light on such systems and their inner workingsàla Simondon

The propositional nature of human associative learning

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research