Online research in philosophy

Extended algorithmic logic (EAL) as introduced in [18] is a modified version of extended +-valued algorithmic logic. Only two-valued predicates and two-valued propositional variables occur in EAL. The role of the +-valued logic is restricted to construct control systems (stacks) of pushdown algorithms whereas their actions are described by means of the two-valued logic. Thus EAL formalizes a programming theory with recursive procedures but without the instruction CASE.The aim of this paper is to discuss EAL and prove the completeness theorem. A complete formalization of EAL was announced in [20] but no proof of the completeness theorem was given.

There is significant interest in type-free systems that allow flexible self-application. Such systems are of interest in property theory, natural language semantics, the theory of truth, theoretical computer science, the theory of classes, and category theory. While there are a variety of proposed type-free systems, there is a particularly natural type-free system that we believe is prototypical: the logic of recursive algorithms. Algorithmic logic is the study of basic statements concerning algorithms and the algorithmic rules of inference between such statements. As shown in [1], the threat of paradoxes, such as the Curry paradox, requires care in implementing rules of inference in this context. As in any type-free logic, some traditional rules will fail. The first part of the paper develops a rich collection of inference rules that do not lead to paradox. The second part identifies traditional rules of logic that are paradoxical in algorithmic logic, and so should be viewed with suspicion in type-free logic generally.

There is significant interest in type-free systems that allow flexible self-application. Such systems are of interest in property theory, natural language semantics, the theory of truth, theoretical computer science, the theory of classes, and category theory. While there are a variety of proposed type-free systems, there is a particularly natural type-free system that we believe is prototypical: the logic of recursive algorithms. Algorithmic logic is the study of basic statements concerning algorithms and the algorithmic rules of inference between such statements. As shown in [1], the threat of paradoxes, such as the Curry paradox, requires care in implementing rules of inference in this context. As in any type-free logic, some traditional rules will fail. The first part of the paper develops a rich collection of inference rules that do not lead to paradox. The second part identifies traditional rules of logic that are paradoxical in algorithmic logic, and so should be viewed with suspicion in type-free logic generally.

In this paper we propose a theoretical model of protein folding and protein evolution in which a polypeptide (sequence/structure) is assumed to behave as a Maxwell Demon or Information Gathering and Using System (IGUS) that performs measurements aiming at the construction of the native structure. Our model proposes that a physical meaning to Shannon information (H) and Chaitin's algorithmic information (K) parameters can be both defined and referred from the IGUS standpoint. Our hypothesis accounts for the interdependence of protein folding and protein evolution through mutual influencing relationships mediated by the IGUS. In brief, IGUS activity in protein folding determines long term tendencies that emerge at the evolutionary time-scale.Thus, protein evolution is a consequence of measurements executed by proteins at the cellular level, where the IGUS imposes a tendency to attain a highly unique stable native form that promotes the updating of the information content. The folding kinetics observed is, thus, the outcome of an evolutionary process where the polypeptide-IGUS drives the evolution of its linear sequence. Finally, we describe protein evolution as an entropic process that tends to increase the content of mutual algorithmic information between the sequence and the structure. This model enables one: 1. To comprehend that full determination of the three-dimensional structure by the linear sequence is a tendency where satisfaction is only possible at thermodynamic equilibrium .2. To account for the observed randomness of the amino acid sequences. 3. To predict an alternation of periods of selection and neutral diffusion during protein evolutionary time.

We present a critical discussion of the claim (most forcefully propounded by Chaitin) that algorithmic information theory sheds new light on Gödel's first incompleteness theorem.

Hi everybody! It's a great pleasure for me to be back here at the new, improved Santa Fe Institute in this spectacular location. I guess this is my fourth visit and it's always very stimulating, so I'm always very happy to visit you guys. I'd like to tell you what I've been up to lately. First of all, let me say what algorithmic information theory is good for, before telling you about the new version of it I've got.

We review briefly the attempts to define random sequences $(\S0)$ . These attempts suggest two theorems: one concerning the number of subsequence selection procedures that transform a random sequence into a random sequence ( $\S\S1-3$ and 5); the other concerning the relationship between definitions of randomness based on subsequence selection and those based on statistical tests $(\S4)$.

In Darwin’s Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett’s analysis, evolutionary processes are trivially algorithmic because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements this poses for natural evolutionary processes if they are to be computational.

Algorithmic information theory, or the theory of Kolmogorov complexity, has become an extraordinarily popular theory, and this is no doubt due, in some part, to the fame of Chaitin’s incompleteness results arising from this field. Actually, there are two rather different results by Chaitin: the earlier one concerns the finite limit of the provability of complexity (see Chaitin, 1974a, 1974b, 1975a); and the later is related to random reals and the halting probability (see Chaitin, 1986, 1987a, 1987b, 1988, 1989.

According to a traditional view, scientific laws and theories constitute algorithmic compressions of empirical data sets collected from observations and measurements. This article defends the thesis that, to the contrary, empirical data sets are algorithmically incompressible. The reason is that individual data points are determined partly by perturbations, or causal factors that cannot be reduced to any pattern. If empirical data sets are incompressible, then they exhibit maximal algorithmic complexity, maximal entropy and zero redundancy. They are therefore maximally efficient carriers of information about the world. Since, on algorithmic information theory, a string is algorithmically random just if it is incompressible, the thesis entails that empirical data sets consist of algorithmically random strings of digits. Rather than constituting compressions of empirical data, scientific laws and theories pick out patterns that data sets exhibit with a certain noise.