Online research in philosophy

Many of the arguments against reductionism and fundamental theory as a method for explaining physical phenomena focus on the role of models as the appropriate vehicle for this task. While models can certainly provide us with a good deal of explanatory detail, problems arise when attempting to derive exact results from approximations. In addition, models typically fail to explain much of the stability and universality associated with critical point phenomena and phase transitions, phenomena sometimes referred to as "emergent." The paper examines the connection between theoretical principles like spontaneous symmetry breaking and emergent phenomena and argues that new ways of thinking about emergence and fundamentalism are required in order to account for the behavior of many phenomena in condensed matter and other areas of physics.

Artificial life uses computer models to study the essential nature of the characteristic processes of complex adaptive systems proceses such as self-organization, adaptation, and evolution. Work in the field is guided by the working hypothesis that simple computer models can capture the essential nature of these processes. This hypothesis is illustrated by recent results with a simple population of computational agents whose sensorimotor functionality undergo open-ended adaptive evolution. These might illuminate three aspects of complex adaptive systems in general: punctuated equilibrium dynamics of diversity, a transition separating genetic order and disorder, and a law of adaptive evolutionary activity.

Biochemical networks are often called upon to illustrate emergent properties of living systems. In this contribution, I question such emergentist claims by means of theoretical work on genetic regulatory models and random Boolean networks. If the existence of a critical connectivity Kc of such networks has often been coined “emergent” or “irreducible”, I propose on the contrary that the existence of a critical connectivity Kc is indeed mathematically explainable in network theory. This conclusion also applies to many other types of formal networks and weakens the emergentist claim attached to bio-molecular networks, and by extension to living systems.

The field of artificial life is enriching both the content and method of philosophy. One example of the impact of artificial life on the content of philosophy is the light it sheds on the perennial philosophical question of the nature of emergent pheonomena in general. Another second example is the way it highlights and promises to explain the suppleness of mental processes. Artificial life's computational thought experiments also provide philosophy with a methodological innovation. The limitations of the central arguments in Stephen Jay Gould's..

Lattice-gas cellular automaton (LGCA) and lattice Boltzmann (LB) models are promising models for studying emergent behaviour of transport and interaction processes in biological systems. In this chapter, we will emphasise the use of LGCA/LB models and the derivation and analysis of LGCA models ranging from the classical example dynamics of fluid flow to clotting phenomena in cerebral aneurysms and the invasion of tumour cells.

Computational modeling plays an increasingly important explanatory role in cases where we investigate systems or problems that exceed our native epistemic capacities. One clear case where technological enhancement is indispensable involves the study of complex systems.1 However, even in contexts where the number of parameters and interactions that define a problem is small, simple systems sometimes exhibit non-linear features which computational models can illustrate and track. In recent decades, computational models have been proposed as a way to assist us in understanding emergent phenomena.

Three principles of dialectical analysis are examined in terms of nonlinear dynamics models. The three principles are the transformation of quantity into quality, the interpenetration of opposites, and the negation of the negation. The first two of these especially are interpreted within the frameworks of catastrophe, chaos, and emergent dynamics complexity theoretic models, with the concept of bifurcation playing a central role. Problems with this viewpoint are also discussed.

To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation (Bedau 1996) gets support from its natural and compelling resolutions of the following four puzzles: (1) How are different forms of life at different levels of the vital hierarchy related? (2) Is there a continuum between life and non-life? (3) Does life essentially concern a living entity’s material composition or its form? (4) Are life and mind intrinsically connected?

To surmount the notorious difficulties of defining life, we should evaluate theories of life not by whether they provide necessary and sufficient conditions for our current preconceptions about life but by how well they explain living phenomena and how satisfactorily they resolve puzzles about life. On these grounds, the theory of life as supple adaptation (Bedau 1996) gets support from its natural and compelling resolutions of the following four puzzles: (1) How are different forms of life at different levels of the vital hierarchy related? (2) Is there a continuum between life and non-life? (3) Does life essentially concern a living entity’s material composition or its form? (4) Are life and mind intrinsically connected?

The nature and status of psychological laws are a long-standing controversy. I will argue that part of the controversy stems from the distinctive nature of an important subset of those laws, which I’ll call “supple laws.” An emergent-model strategy taken by the new interdisciplinary field of artificial life provides a strikingly successful understanding of analogously supple laws in biology. So, after reviewing the failures of the two evident strategies for understanding supple psychological laws, I’ll turn for inspiration to emergent-models explanations of supple laws in biology. I’ll conclude by inferring what an emergent model of supple laws in psychology should be like.