TY - GEN ID - cogprints6551 UR - http://cogprints.org/6551/ A1 - Mukherjee, Mr. Debaprasad TI - Complexity, BioComplexity, the Connectionist Conjecture and Ontology of Complexity Y1 - 2009/05/29/ N2 - This paper develops and integrates major ideas and concepts on complexity and biocomplexity - the connectionist conjecture, universal ontology of complexity, irreducible complexity of totality & inherent randomness, perpetual evolution of information, emergence of criticality and equivalence of symmetry & complexity. This paper introduces the Connectionist Conjecture which states that the one and only representation of Totality is the connectionist one i.e. in terms of nodes and edges. This paper also introduces an idea of Universal Ontology of Complexity and develops concepts in that direction. The paper also develops ideas and concepts on the perpetual evolution of information, irreducibility and computability of totality, all in the context of the Connectionist Conjecture. The paper indicates that the control and communication are the prime functionals that are responsible for the symmetry and complexity of complex phenomenon. The paper takes the stand that the phenomenon of life (including its evolution) is probably the nearest to what we can describe with the term ?complexity?. The paper also assumes that signaling and communication within the living world and of the living world with the environment creates the connectionist structure of the biocomplexity. With life and its evolution as the substrate, the paper develops ideas towards the ontology of complexity. The paper introduces new complexity theoretic interpretations of fundamental biomolecular parameters. The paper also develops ideas on the methodology to determine the complexity of ?true? complex phenomena. AV - public KW - Complexity; Totality; Conjecture; BioComplexity; Connectionism; Network; Ontology; Information; Computation; Randomness; Evolution; Emergence; Criticality; Symmetry; Taxon; Species; Signaling; Communication; Control; Protein; Families; Domains; Algorithm; Clustering; Dimensions; Spaces; Nonlinearity; Methodology ER -