This paper attempts to elucidate three characteristics of causal relationships that are important in biological contexts. Stability has to do with whether a causal relationship continues to hold under changes in background conditions. Proportionality has to do with whether changes in the state of the cause “line up” in the right way with changes in the state of the effect and with whether the cause and effect are characterized in a way that contains irrelevant detail. Specificity is connected both to (...) David Lewis’ notion of “influence” and also with the extent to which a causal relation approximates to the ideal of one cause–one effect. Interrelations among these notions and their possible biological significance are also discussed. (shrink)

Several authors have argued that causes differ in the degree to which they are ‘specific’ to their effects. Woodward has used this idea to enrich his influential interventionist theory of causal explanation. Here we propose a way to measure causal specificity using tools from information theory. We show that the specificity of a causal variable is not well-defined without a probability distribution over the states of that variable. We demonstrate the tractability and interest of our proposed measure by measuring the (...) specificity of coding DNA and other factors in a simple model of the production of mRNA. (shrink)

The semantic concept of information is one of the most important, and one of the most problematical concepts in biology. I suggest a broad definition of biological information: a source becomes an informational input when an interpreting receiver can react to the form of the source (and variations in this form) in a functional manner. The definition accommodates information stemming from environmental cues as well as from evolved signals, and calls for a comparison between information‐transmission in different types of inheritance (...) systems—the genetic, the epigenetic, the behavioral, and the cultural‐symbolic. This comparative perspective highlights the different ways in which information is acquired and transmitted, and the role that such information plays in heredity and evolution. Focusing on the special properties of the transfer of information, which are very different from those associated with the transfer of materials or energy, also helps to uncover interesting evolutionary effects and suggests better explanations for some aspects of the evolution of communication. (shrink)

The role played by the concept of genetic coding in biology is discussed. I argue that this concept makes a real contribution to solving a specific problem in cell biology. But attempts to make the idea of genetic coding do theoretical work elsewhere in biology, and in philosophy of biology, are probably mistaken. In particular, the concept of genetic coding should not be used (as it often is) to express a distinction between the traits of whole organisms that are coded (...) for in the genes, and the traits that are not. (shrink)

There is ongoing controversy as to whether the genome is a representing system. Although it is widely recognised that DNA carries information, both correlating with and coding for various outcomes, neither of these implies that the genome has semantic properties like correctness or satisfaction conditions, In the Scope of Logic, Methodology, and the Philosophy of Sciences, Vol. II. Kluwer, Dordrecht, pp. 387–400). Here a modified version of teleosemantics is applied to the genome to show that it does indeed have semantic (...) properties – there is representation in the genome. The account differs in three respects from previous attempts to apply teleosemantics to genes. It emphasises the role of the consumer of representations. It rejects the standard assumption that genetic representation can be used to explain the course of an organism’s development. And it identifies the explanatory role played by representational properties of the genome. A striking consequence of this account is that other inheritance systems could also be representational. Thus, a version of the parity thesis is accepted. However, the criteria for being an inheritance system are demanding, so semantic properties are not ubiquitous. (shrink)

Griffiths et al. (2015) have proposed a quantitative measure of causal specificity and used it to assess various attempts to single out genetic causes as being causally more specific than other cellular mechanisms, for example, alternative splicing. Focusing in particular on developmental processes, they have identified a number of important challenges for this project. In this discussion note, I would like to show how these challenges can be met.

We provide a new perspective on the relation between the space of description of an object and the appearance of novelties. One of the aims of this perspective is to facilitate the interaction between mathematics and historical sciences. The definition of novelties is paradoxical: if one can define in advance the possibles, then they are not genuinely new. By analyzing the situation in set theory, we show that defining generic (i.e., shared) and specific (i.e., individual) properties of elements of a (...) set are radically different notions. As a result, generic and specific definitions of possibilities cannot be conflated. We argue that genuinely stating possibilities requires that their meaning has to be made explicit. For example, in physics, properties playing theoretical roles are generic; then, generic reasoning is sufficient to define possibilities. By contrast, in music, we argue that specific properties matter, and generic definitions become insufficient. Then, the notion of new possibilities becomes relevant and irreducible. In biology, among other examples, the generic definition of the space of DNA sequences is insufficient to state phenotypic possibilities even if we assume complete genetic determinism. The generic properties of this space are relevant for sequencing or DNA duplication, but they are inadequate to understand phenotypes. We develop a strong concept of biological novelties which justifies the notion of new possibilities and is more robust than the notion of changing description spaces. These biological novelties are not generic outcomes from an initial situation. They are specific and this specificity is associated with biological functions, that is to say, with a specific causal structure. Thus, we think that in contrast with physics, the concept of new possibilities is necessary for biology. (shrink)

The interventionist account of causation offers a criterion to distinguish causes from non-causes. It also aims at defining various desirable properties of causal relationships, such as specificity, proportionality and stability. Here we apply an information-theoretic approach to these properties. We show that the interventionist criterion of causation is formally equivalent to non-zero specificity, and that there are natural, information-theoretic ways to explicate the distinction between potential and actual causal influence. We explicate the idea that the description of causes should be (...) proportional to that of their effects. Then we draw a distinction between two ideas in the existing literature, the range of invariance of a causal relationship and its stability. The range of invariance is related to specificity and range of causal values. Stability concerns the effect of additional variables on the relationship between some focal pair of cause and effect variables. We show how to distinguish and measure the direct influence of background variables on the effect variable, and their influence on the relationship between the focal cause and the effect variable. Finally, we discuss the limitations of the information-theoretic approach, and offer prospects for complementary approaches. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks—from the way monkeys in a troop communicate to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this article, we argue there is a tension between how Skyrms talks of signalling networks and his formal measure (...) of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts; they must be difference-makers for acts. _1_ Signalling, Evolution, and Information _2_ Skyrms’s Measure of Information _3_ Carrying Information versus Information Flow _3.1_ Example 1 _3.2_ Example 2 _3.3_ Example 3 _4_ Signalling Networks Are Causal Networks _4.1_ Causal specificity _4.2_ Formalizing causal specificity _5_ Information Flow as Causal Control _5.1_ Example 1 _5.2_ Examples 2 and 3 _5.3_ Average control implicitly ‘holds fixed’ other pathways _6_ How Does Evolution Create Information? _7_ Conclusion Appendix >. (shrink)

Recent work by Brian Skyrms offers a very general way to think about how information flows and evolves in biological networks — from the way monkeys in a troop communicate, to the way cells in a body coordinate their actions. A central feature of his account is a way to formally measure the quantity of information contained in the signals in these networks. In this paper, we argue there is a tension between how Skyrms talks of signalling networks and his (...) formal measure of information. Although Skyrms refers to both how information flows through networks and that signals carry information, we show that his formal measure only captures the latter. We then suggest that to capture the notion of flow in signalling networks, we need to treat them as causal networks. This provides the formal tools to define a measure that does capture flow, and we do so by drawing on recent work defining causal specificity. Finally, we suggest that this new measure is crucial if we wish to explain how evolution creates information. For signals to play a role in explaining their own origins and stability, they can’t just carry information about acts: they must be difference-makers for acts. (shrink)

In "The Indeterministic Character of Evolutionary Theory: No 'Hidden Variables Proof' But No Room for Determinism Either," Brandon and Carson (1996) argue that evolutionary theory is statistical because the processes it describes are fundamentally statistical. In "Is Indeterminism the Source of the Statistical Character of Evolutionary Theory?" Graves, Horan, and Rosenberg (1999) argue in reply that the processes of evolutionary biology are fundamentally deterministic and that the statistical character of evolutionary theory is explained by epistemological rather than ontological considerations. In (...) this paper I focus on the topic of mutation. By focusing on some of the theory and research on this topic from early to late, I show how quantum indeterminism hooks up to point mutations (via tautomeric shifts, proton tunneling, and aqueous thermal motion). I conclude with a few thoughts on some of the wider implications of this topic. (shrink)

An assessment is offered of the recent debate on information in the philosophy of biology, and an analysis is provided of the notion of information as applied in scientific practice in molecular genetics. In particular, this paper deals with the dependence of basic generalizations of molecular biology, above all the ‘central dogma’, on the so-called ‘informational talk’ (Maynard Smith [2000a]). It is argued that talk of information in the ‘central dogma’ can be reduced to causal claims. In that respect, the (...) primary aim of the paper is to consider a solution to the major difficulty of the causal interpretation of genetic information: how to distinguish the privileged causal role assigned to nucleic acids, DNA in particular, in the processes of replication and protein production. A close reading is proposed of Francis H. C. Crick's On Protein Synthesis (1958) and related works, to which we owe the first explicit definition of information within the scientific practice of molecular biology. Introduction 1.1 The basic questions of the information debate 1.2 The causal interpretation (CI) of biological information and Crick's ‘central dogma’ Crick's definitions of genetic information The main requirement for (CI) Types of causation in molecular biology 4.1 Structural causation in molecular biology 4.2 Nucleic acids as correlative causal factors The ‘central dogma’ without the notion of information Concluding remarks This is a new version of this article as there were errors in the abstract and full text in the previous version. (shrink)

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. (shrink)

This chapter contains section titled: Introduction General Scenario for the Transmission of Information and Its Application to Genetics Semiotic Dimensions of Biological Information Syntactic Dimension I: Measuring the Statistical Entropy of Signals and Messages Syntactic Dimension II: Estimating the Algorithmic Complexity of Signals and Messages Semantic Dimension: Classifying the Mutual Complexity of Transmitters and Receivers Acknowledgment References Further Reading.