Online research in philosophy

In Darwin's Black Box: The BiochemicalChallenge to Evolution I argued thatpurposeful intelligent design, rather thanDarwinian natural selection, better explainssome aspects of the complexity that modernscience has discovered at the molecularfoundation of life. In the five years since itspublication the book has been widely discussedand has received considerable criticism. Here Irespond to what I deem to be the mostfundamental objections. In the first part ofthe article I address empirical criticismsbased on experimental studies alleging eitherthat biochemical systems I discussed are notirreducibly complex or that similar systemshave been demonstrated to be able to evolve byDarwinian processes. In the remainder of thearticle I address methodological concerns,including whether a claim of intelligent designis falsifiable and whether intelligent designis a permissible scientific conclusion.

This article describes a brief overview of systems design concepts, and provides an example of the use of one very simple framework for utilizing systems design. Its purpose is to demonstrate the value of even the simplest of systems design models in clarifying the issues behind what are often perceived to be organizational conflicts. The example provided is that of a medical function within an industrial organization, but the implications apply to almost any support function or department found within a large organization.

In this essay I examine the ways in which the Belousov–Zhabotinsky (BZ) reaction is being used by biologists to model a variety of biological systems and processes. The BZ reaction is characterized as a functional model of biological phenomena. It is able to play this role because, though based on very different substrates, the model and system modeled are examples of the same type of excitable medium. Lessons are drawn from this case about the relationships between the sciences of chemistry and biology.

Recent work on self organization promises an explanation of complex order which is independent of adaptation. Self-organizing systems are complex systems of simple units, projecting order as a consequence of localized and generally nonlinear interactions between these units. Stuart Kauffman offers one variation on the theme of self-organization, offering what he calls a ``statistical mechanics'' for complex systems. This paper explores the explanatory strategies deployed in this ``statistical mechanics,'' initially focusing on the autonomy of statistical explanation as it applies in evolutionary settings and then turning to Kauffman's analysis. Two primary morals emerge as a consequence of this examination: first, the view that adaptation and self-organization should be seen as competing theories or models is misleading and simplistic; and second, while we need a synthesis treating self-organization and adaptation as geared toward different problems, at different levels of organization, and deploying different methods, we do not yet have such a synthesis.

Based on an analysis of the origins and characteristics of Intelligent Design (ID), this essay discusses the related issues of probability and irreducible complexity. From the viewpoint of complex systems theory, I suggest that Intelligent Design is not, as certain advocates claim, the only reasonable approach for dealing with the current difficulties of evolutionary biology.

Complexly organized systems include biological and cognitive systems, as well as many of the everyday systems that form our environment. They are both common and important, but are not well understood. A complex system is, roughly, one that cannot be fully understood via analytic methods alone. An organized system is one that shows spatio-temporal correlations that are not determined by purely local conditions, though organization can be more or less localizable within a system. Organization and complexity can vary independently to some extent, but they are interconnected: organisation requires some complexity, but complexity cannot be maximum in an organized system. I will define complexity and organization more precisely, and show how these definitions imply the above properties. Next I will discuss how organized complexity can be modelled, with an eye to limitations on the tractability of both the models and the modelling process. I will finish with some remarks on the limits of our possible understanding of complexly organized systems. Keywords: complexity, organization, modelling, holism, information theory..

Complex systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex systems. In this thesis I propose a methodology to aid engineers in the design and control of complex systems. This is based on the description of systems as self-organizing. Starting from the agent metaphor, the methodology proposes a conceptual framework and a series of steps to follow to find proper mechanisms that will promote elements to find solutions by actively interacting among themselves. The main premise of the methodology claims that reducing the “friction” of interactions between elements of a system will result in a higher “satisfaction” of the system, i.e. better performance. A general introduction to complex thinking is given, since designing self-organizing systems requires a non-classical thought, while practical notions of complexity and self-organization are put forward. To illustrate the methodology, I present three case studies. Self-organizing traffic light controllers are proposed and studied with multi-agent simulations, outperforming traditional methods. Methods for improving communication within self-organizing bureaucracies are advanced, introducing a simple computational model to illustrate the benefits of self-organization. In the last case study, requirements for self-organizing artifacts in an ambient intelligence scenario are discussed. Philosophical implications of the conceptual framework are also put forward.

Introduction to complexity and complex systems -- Introduction to large linear systems -- Introduction to biochemical oscillators and nonlinear biochemical systems -- Modularity, redundancy, degeneracy, pleiotropy and robustness in complex biological systems -- The evolution of biological complexity; invertebrate immune systems -- Irreducible and specified complexity in living systems -- The complex adaptive and innate human immune systems -- Complexity in quasispecies : microRNAs -- Introduction to complexity in economic systems -- Complexity in quasispecies : micrornas -- Dealing with complexity.

In this essay we take creationist biochemist Michael Behe to task for failing to make an evidentially grounded case for the supernatural intelligent design of biochemical systems. In our earlier work on Behe we showed that there were dimensions to biochemical complexity---redundant complexity---that he appeared to have ignored. Behe has recently replied to that work. We show here that his latest arguments contain fundamental flaws.

Biological systems exhibit complexity at all levels of organization. It has recently been argued by Michael Behe that at the biochemical level a type of complexity exists--irreducible complexity--that cannot possibly have arisen as the result of natural, evolutionary processes and must instead be the product of (supernatural) intelligent design. Recent work on self-organizing chemical reactions calls into question Behe's analysis of the origins of biochemical complexity. His central interpretative metaphor for biochemical complexity, that of the well-designed mousetrap that ceases to function if critical parts are absent, is undermined by the observation that typical biochemical systems exhibit considerable redundancy and overlap of function. Real biochemical systems, we argue, manifest redundant complexity--a characteristic result of evolutionary processes.