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 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)

The Genotype-Phenotype (G-P) distinction was proposed in the context of Mendelian genetics, in the wake of late 19th century studies about heredity. In this paper, we provide a conceptual analysis that highlights that the G-P distinction was grounded on three pillars: observability, transmissibility, and causality. Originally, the genotype is the non-observable and transmissible cause of the phenotype, which is its observable and non-transmissible effect. We argue that the current developments of biology have called the validity of such pillars into question. (...) First, molecular biology has unveiled the putative material substrate of the genotype (qua DNA), making it an observable object. Second, numerous findings on nongenetic heredity suggest that some phenotypic traits can be directly transmitted. Third, recent organicist approaches to biological phenomena have emphasized the reciprocal causality between parts of a biological system, which notably applies to the relations between genotypes and phenotypes. As a consequence, we submit that the G-P distinction has lost its general validity, although it can still apply to specific situations. This calls for forging new frameworks and concepts to better describe heredity and development. (shrink)

In this paper, we apply the perspective of intra-organismal ecology by investigating a family of ecological models suitable to describe a gene therapy to a particular metabolic disorder, the adenosine deaminase deficiency (ADA-SCID). The gene therapy is modeled as the prospective ecological invasion of an organ (here, bone marrow) by genetically modified stem cells, which then operate niche construction in the cellular environment by releasing an enzyme they synthesize. We show that depending on the chosen order (a choice that cannot (...) be made on \textit{a priori} assumptions), different kinds of dynamics are expected, possibly leading to different therapeutic strategies. This drives us to discuss several features of the extension of ecology to intra-organismal ecology. (shrink)

A causal approach to biological information is outlined. There are two aspects to this approach: information as determining a choice between alternative objects and information as determining the construction of a single object. The first aspect has been developed in earlier work to yield a quantitative measure of biological information that can be used to analyze biological networks. This article explores the prospects for a measure based on the second aspect and suggests some applications for such a measure. These two (...) aspects are not suggested to exhaust all the facets of biological information. (shrink)

In this paper, we discuss the perspective of intra-organismal ecology by investigating a family of ecological models. We consider two types of models. First order models describe the population dynamics as being directly affected by ecological factors (here understood as nutrients, space, etc). They might be thought of as analogous to Aristotelian physics. Second order models describe the population dynamics as being indirectly affected, the ecological factors now affecting the derivative of the growth rate (that is, the population acceleration), possibly (...) through an impact on non-genetically inherited factors. Second order models might be thought of as analogous to Galilean physics. In the joint paper, we apply these ideas to a situation of gene therapy. (shrink)