Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

The Origins of Self

The Origins of Self explores the role that selfhood plays in defining human society, and each human individual in that society. It considers the genetic and cultural origins of self, the role that self plays in socialisation and language, and the types of self we generate in our individual journeys to and through adulthood. Edwardes argues that other awareness is a relatively early evolutionary development, present throughout the primate clade and perhaps beyond, but self-awareness is a product of the sharing of social models, something only humans appear to do. The self of which we are aware is not something innate within us, it is a model of our self produced as a response to the models of us offered to us by other people. Edwardes proposes that human construction of selfhood involves seven different types of self. All but one of them are internally generated models, and the only non-model, the actual self, is completely hidden from conscious awareness. We rely on others to tell us about our self, and even to let us know we are a self

Géosimulation multi-niveau de phénomènes complexes basés sur les multiples interactions spatio-temporelles de nombreux acteurs : développement d'un outil générique d'aide à la décision pour la propagation des zoonoses

Nous proposons dans cette thèse une nouvelle approche de géosimulation multi-niveau permettant de simuler la propagation d’une zoonose (maladie infectieuse qui se transmet des animaux aux humains) à différents niveaux de granularité. Cette approche est caractérisée entre autres par l’utilisation d’un modèle théorique original que nous avons nommé MASTIM (Multi-Actor Spatio-Temporal Interaction Model) permettant de simuler des populations contenant un nombre considérable d’individus en utilisant des modèles compartimentaux enrichis. MASTIM permet de spécifier non seulement l’évolution de ces populations, mais également les aspects relatifs aux interactions spatio-temporelles de ces populations incluant leurs déplacements dans l’environnement de simulation géoréférencé. Notre approche de géosimulation multi-niveau est caractérisée également par l’utilisation d’un environnement géographique virtuel informé (IVGE) qui est composé d’un ensemble de cellules élémentaires dans lesquelles les transitions des différents stades biologiques des populations concernées, ainsi que leurs interactions peuvent être plausiblement simulées. Par ailleurs, nous avons appliqué nos travaux de recherche au développement d’outils d’aide à la décision. Nous avons acquis une première expérience avec le développement d’un outil (WNV-MAGS) dont l’objectif principal est de simuler les comportements des populations de moustiques (Culex) et des oiseaux (corneilles) qui sont impliquées dans la propagation du Virus du Nil Occidental (VNO). Nous avons par la suite participé au développement d’un outil générique (Zoonosis-MAGS) qui peut être utilisé pour simuler la propagation d'une variété de zoonoses telles que la maladie de Lyme et le VNO. Ces outils pourraient fournir des informations utiles aux décideurs de la santé publique et les aider à prendre des décisions informées. En outre, nous pensons que nos travaux de recherche peuvent être appliqués non seulement au phénomène de la propagation des zoonoses, mais également à d’autres phénomènes faisant intervenir des interactions spatio-temporelles entre différents acteurs de plusieurs types.We propose in this thesis a new multi-level geosimulation approach to simulate the spread of a zoonosis (infectious disease transmitted from animals to humans) at different levels of granularity. This approach is characterized by using an original theoretical model named MASTIM (Multi-Actor Spatio-Temporal Interaction Model) which can be applied to simulate populations containing a huge number of individuals using extended compartmental models. MASTIM may specify not only the evolution of these populations, but also the aspects related to their spatio-temporal interactions, including their movements in the simulated georeferenced environment. Our multi-level geosimulation approach take advantage of an informed virtual geographic environment (IVGE) composed of a set of elementary cells in which the transitions of the different biological stages of the involved populations, as well as their interactions can be simulated plausibly. Furthermore, this approach has been applied to develop decision support tools. We got a first experience with the development of WNV-MAGS, a tool whose main purpose is to simulate the populations’ behavior of mosquitoes (Culex) and birds (crows), which are involved in the spread of West Nile Virus (WNV). We subsequently participated in the development of a generic tool (Zoonosis-MAGS) that can be used to simulate the spread of a variety of zoonoses such as Lyme disease and WNV. These tools may provide useful information to help public health officers to make informed decisions. Besides, we believe that this research can be applied not only to the spread of zoonoses, but also to other phenomena involving spatio-temporal interactions between different actors of different types