Online research in philosophy

Emergence is a universal phenomenon that can be defined mathematically in a very general way. This is useful for the study of scientifically legitimate explanations of complex systems, here defined as hyperstructures. A requirement is that the observation mechanisms are considered within the general framework. Two notions of emergence are defined, and specific examples of these are discussed.

We investigate the behavioral patterns of a population of agents, each controlled by a simple biologically motivated neural network model, when they are set in competition against each other in the Minority Model of Challet and Zhang. We explore the effects of changing agent characteristics, demonstrating that crowding behavior takes place among agents of similar memory, and show how this allows unique `rogue' agents with higher memory values to take advantage of a majority population. We also show that agents' analytic capability is largely determined by the size of the intermediary layer of neurons. In the context of these results, we discuss the general nature of natural and artificial intelligence systems, and suggest intelligence only exists in the context of the surrounding environment (embodiment). Source code for the programs used can be found at http://neuro.webdrake.net/.

The view that posterior brain systems engaged in lower-order perceptual functions are activated during sustained retention is challenged by fMRI data, which show consistent retention-related activation of higher-order memory representations for a variety of working-memory materials. Sustained retention entails the dynamic link of these higher-order memories with schemata for goal-oriented action housed by the frontal lobes.

In this article, I develop a higher-order interpretation of Leibniz's theory of consciousness according to which memory is constitutive of consciousness. I offer an account of Leibniz's theory of memory on which his theory of consciousness may be based, and I then show that Leibniz could have developed a coherent higher-order account. However, it is not clear whether Leibniz held (or should have held) such an account of consciousness; I sketch an alternative that has at least as many advantages as the higher-order theory. This analysis provides an important antecedent to the contemporary discussions of higher-order theories of consciousness.

In the natural sciences higher order structures often occur. There seems to be a need for good methods of describing what we mean by higher order structures in various contexts. This is what hyperstructures are intended to do. We motivate and introduce this new concept. Next we illustrate how it can be applied in various types of genomic analysis—particular the correlations between single nucleotide polymorphisms and diseases. The suggested structure is quite general and may be applied to a variety of situations. Finally we discuss how data sets (f. ex. genomic) may lead to topological spaces, giving new invariants and lead to the prediction of hyperstructures.

As a significant extension of our previous calculus of logical differentials and moving logic, we propose here a mathematical diagram for specifying the emergence of novelty, through the construction of some “differentials” related to cohomological computations. Later we intend to examine how to use these “differentials” in the analysis of anticipation or evolution schemes. This proposal is given as a consequence of our comments on the Ehresmann–Vanbremeersch’s work on memory evolutive systems (MES), from the two points of view which are characterization of life and use of categorical modeling. It would not be possible to conceive of the “differentials” outside the frame of a MES (and specifically the description of the part played in a MES by the “co-regulators” and their “landscapes” for anticipation and emergence). Furthermore, we hope that this diagram will be useful to exhibit the “emergence of sense” in discourses, and this idea is supported here by a brief examination of how a discourse could be seen as a living system (and then could be studied as a MES).

We comment on the preceding reviews of our book “ Memory Evolutive Systems ”, discussing the improvements proposed by some of the reviewers and answering to critics of others, in particular on the use of category theory for modeling living systems.

This is a review of the book ‘Memory Evolutive Systems; Hierarchy, Emergence, Cognition’, by A. Ehresmann and J.P. Vanbremeersch. I welcome the use of category theory and the notion of colimit as a way of describing how complex hierarchical systems can be organised, and the notion of categories varying with time to give a notion of an evolving system. In this review I also point out the relation of the notion of colimit to ideas of communication; the necessity of communications to be symbolic representations; and the use of an analogy with mathematics to spell out some of the necessities of such a mode of communication to be powerful, robust and efficient.

In a series of preceding papers, the authors have developed the theory of Memory Evolutive Systems which represents a mathematical model (based on Category theory) for natural open self-organizing systems, such as biological, sociological or neural systems. In these systems, the dynamics is modulated by the cooperative or/and competitive interactions between the global system and a net of internal more or less specialized Centers of Regulation (CR) with a differential access to a central hierarchical Memory. Each CR operates at its own complexity level and time-scale, but their strategies are competitive, whence a 'dialectics between heterogeneous CRs which is at the root of higher order cognition. The problem tackled in the present paper is the emergence of a Semantics in the MES modeling a cognitive system; it relies on the detection of specific invariances by the CRs that leads to classify objects according to their main attributes, and form new formal units representing their invariance classes. The idea is that a (lower) CR, say E, classifies two objects B and C as having 'the same shape' if they activate the same pattern of its actors; however this classification remains implicit for E itself and it can be apprehended only by a higher Ievel CR which may memorize the invariance class by a higher object, called a 'E-concept'. The concepts with respect to the various CRs form the semantic memory which gives more flexibility in the evaluation, selection and memorization of appropriate strategies, as well as in internal or external communications.

Robert Rosen has proposed several characteristics to distinguish “simple” physical systems (or “mechanisms”) from “complex” systems, such as living systems, which he calls “organisms”. The Memory Evolutive Systems (MES) introduced by the authors in preceding papers are shown to provide a mathematical model, based on category theory, which satisfies his characteristics of organisms, in particular the merger of the Aristotelian causes. Moreover they identify the condition for the emergence of objects and systems of increasing complexity. As an application, the cognitive system of an animal is modeled by the “MES of cat-neurons” obtained by successive complexifications of his neural system, in which the emergence of higher order cognitive processes gives support to Mario Bunge’s “emergentist monism.”.