Online research in philosophy

It has been suggested that biological theories differ from physical theories because the subject matter of biology differs from the subject matter of physics especially in the fact that living bodies are more complex than nonliving bodies. It is shown that the interactional complexity of living bodies can only be expressed by invoking biological theories. The claim that living bodies are complex is, therefore, ultimately a claim about the nature of scientific theories rather than a claim about the nature of the subject matter of biology resting upon a presystematic judgement.

Both Popper and van Fraassen have used evolutionary analogies to defend their views on the aim of science, although these are diametrically opposed. By employing Price's equation in an illustrative capacity, this paper considers which view is better supported. It shows that even if our observations and experimental results are reliable, an evolutionary analogy fails to demonstrate why conjecture and refutation should result in: (1) the isolation of true theories; (2) successive generations of theories of increasing truth-likeness; (3) empirically adequate theories; or (4) successive generations of theories of increasing proximity to empirical adequacy. Furthermore, it illustrates that appeals to induction do not appear to help. It concludes that an evolutionary analogy is only sufficient to defend the notion that the aim of science is to isolate a particular class of false theories, namely those that are empirically inadequate.

We compare the elementary theories of Shannon information and Kolmogorov complexity, the extent to which they have a common purpose, and wherethey are fundamentally different. We discuss and relate the basicnotions of both theories: Shannon entropy, Kolmogorov complexity, Shannon mutual informationand Kolmogorov (``algorithmic'') mutual information. We explainhow universal coding may be viewed as a middle ground betweenthe two theories. We consider Shannon's rate distortion theory, whichquantifies useful (in a certain sense) information.We use the communication of information as our guiding motif, and we explain howit relates to sequential question-answer sessions.

Cultural and dual-inheritance models of evolution present ambiguities not typically present in biological evolution. Criteria and the ability to specify the adaptive value of a trait or cultural practice become less clear. When niche construction is added, additional challenges and ambiguities arise. Its dynamic nature increases the difficulty of identifying adaptations, tracing the causal path between a trait and its function, and identifying the links between environmental demands and the development of adaptations.

Most standard results on structure identification in first order theories depend upon the correctness and completeness (in the limit) of the data, which are provided to the learner. These assumption are essential for the reliability of inductive methods and for their limiting success (convergence to the truth).The paper investigates inductive inference from (possibly) incorrect and incomplete data. It is shown that such methods can be reliable not in the sense of truth approximation, but in the sense that the methods converge to empirically adequate theories, i.e. theories, which are consistent with all data (past and future) and complete with respect to a given complexity class of L-sentences. Adequate theories of bounded complexity can be inferred uniformly and effectively by polynomial-time learning algorithms. Adequate theories of unbounded complexity can be inferred pointwise by less efficient methods.

Complexity theories are on the way to establish a new worldview—processes instead of objects, history and uniqueness of everything instead of repetition and lawlikeness are the elements. These theories from deterministic chaos via the dissipative structures, the theory of catastrophes, self organization and synergetics are mathematical models, connected with a new understanding of science. They are characterized by new fundamental commitments of sciences. But at the same time, they are characterized by epistemic boundaries.

The formal theory of the Model of Hierarchical Complexity is presented. Complexity theories generally exclude the concept of hierarchical complexity; Developmental Psychology has included it for over 20 years. It also applies to social systems and non-human systems. Formal axioms for the Model are outlined. The model assigns an order of hierarchical complexity to every task, using natural numbers, establishing a quantal notion of stage and stages of performance. This formalizes properties of stage theories in psychology. The formal theory of the model enables extending the concept of hierarchical complexity to any field where tasks and their performances exist.

The theories of complexity comprise a system of great breadth. But what is included under this umbrella? Here we attempt a portrait of complexity theory, seen through the lens of complexity theory itself. That is, we portray the subject as an evolving complex dynamical system, or social network, with bifurcations, emergent properties, and so on. This is a capsule history covering the twentieth century. Extensive background data may be seen at www.visual-chaos.org/complexity.

In this article, the author argues that complexity theories have limited use in the study of society, and that social processes are too complex and particular to be rigorously modeled in complexity terms. Theories of social complexity are shown to be inadequately developed, and typical weaknesses in the literature on social complexity are discussed. Two stronger analyses, of Luhmann and of Harvey and Reed, are also critically considered. New considerations regarding social complexity are advanced, on the lines that simplicity, complexity that can be modeled, and incondensible complexity permeate society simultaneously. The difficulty of establishing complexity models for processes involving ongoing interpretation is discussed. It is argued that the notions of system and environment need recasting in social studies. Existing social studies and literature, it is argued, reflect a polymorphous, contextual, contingent, labyrinthine, dramatic and political face to social complexity. Students of social complexity must be literate in such studies.

In the past two or three decades, complexity not only has been a hot research topic but has caught the popular imagination. Terms such as chaos and bifurcation become so common they find their way into Hollywood movies. What is complexity? What is the theory of complexity or the science of complexity? I do not think there is such a thing as the theory of complexity. Not even a rigid definition of complexity exists in the natural sciences. There are many theories trying to address various complex systems. What I try to do is to extract some general ideas that are implicit in these theories, and more generally, in the way that scientists face and think about complicated situations.