Drawing on recent advances in evolutionary biology, prominent scholars return to the question posed in a pathbreaking book: how evolution itself evolved.

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)

This paper describes a pattern of explanation prevalent in the biological sciences that I call a ‘lineage explanation’. The aim of these explanations is to make plausible certain trajectories of change through phenotypic space. They do this by laying out a series of stages, where each stage shows how some mechanism worked, and the differences between each adjacent stage demonstrates how one mechanism, through minor modifications, could be changed into another. These explanations are important, for though it is widely accepted (...) that there is an ‘incremental constraint’ on evolutionary change, in an important class of cases it is difficult to see how to satisfy this constraint. I show that lineage explanations answer important questions about evolutionary change, but do so by demonstrating differences between individuals rather than invoking population processes, such as natural selection. Introduction Turning a ‘Scale’ into a ‘Plume’ Lineage Explanations in Biology 3.1 The evolution of eyes 3.2 The evolution of feathers The Two Dimensions of a Lineage Explanation 4.1 The production dimension 4.2 The continuity dimension 4.3 The dual role of the parts Constraining the Explanations Operational and Generative Lineages Explaining Change Without Populations Conclusion. (shrink)

This review of Wimsatt’s book Re-engineering Philosophy for Limited Beings focuses on analysing his use of robustness, a central theme in the book. I outline a family of three distinct conceptions of robustness that appear in the book, and look at the different roles they play. I briefly examine what underwrites robustness, and suggest that further work is needed to clarify both the structure of robustness and the relation between it various conceptions.

This collection reports on the latest research on an increasingly pivotal issue for evolutionary biology: cooperation. The chapters are written from a variety of disciplinary perspectives and utilize research tools that range from empirical survey to conceptual modeling, reflecting the rich diversity of work in the field. They explore a wide taxonomic range, concentrating on bacteria, social insects, and, especially, humans. -/- Part I (“Agents and Environments”) investigates the connections of social cooperation in social organizations to the conditions that make (...) cooperation profitable and stable, focusing on the interactions of agent, population, and environment. Part II (“Agents and Mechanisms”) focuses on how proximate mechanisms emerge and operate in the evolutionary process and how they shape evolutionary trajectories. Throughout the book, certain themes emerge that demonstrate the ubiquity of questions regarding cooperation in evolutionary biology: the generation and division of the profits of cooperation; transitions in individuality; levels of selection, from gene to organism; and the “human cooperation explosion” that makes our own social behavior particularly puzzling from an evolutionary perspective. (shrink)

I use some recent formal work on measuring causation to explore a suggestion by James Woodward: that the notion of causal specificity can clarify the distinction in biology between permissive and instructive causes. This distinction arises when a complex developmental process, such as the formation of an entire body part, can be triggered by a simple switch, such as the presence of particular protein. In such cases, the protein is said to merely induce or "permit" the developmental process, whilst the (...) causal "instructions" for guiding that process are already prefigured within the cells. I construct a novel model that expresses in a simple and tractable way the relevant causal structure of biological development and then use a measure of causal specificity to analyse the model. I show that the permissive-instructive distinction cannot be captured by simply contrasting the specificity of two causes as Woodward proposes, and instead introduce an alternative, hierarchical approach to analysing the interaction between two causes. The resulting analysis highlights the importance of focusing on gene regulation, rather than just the coding regions, when analysing the distinctive causal power of genes. (shrink)

Like Laland et al., I think Mayr’s distinction is problematic, but I identify a further problem with it. I argue that Mayr’s distinction is a false dichotomy, and obscures an important question about evolutionary change. I show how this question, once revealed, sheds light on some debates in evo-devo that Laland et al.’s analysis cannot, and suggest that it provides a different view about how future integration between biological disciplines might proceed.

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)

Understanding how cooperation evolves is central to explaining some core features of our biological world. Many important evolutionary events, such as the arrival of multicellularity or the origins of eusociality, are cooperative ventures between formerly solitary individuals. Explanations of the evolution of cooperation have primarily involved showing how cooperation can be maintained in the face of free-riding individuals whose success gradually undermines cooperation. In this paper I argue that there is a second, distinct, and less well explored, problem of cooperation (...) that I call the generation of benefit. Focusing on how benefit is generated within a group poses a different problem: how is it that individuals in a group can (at least in principle) do better than those who remain solitary? I present several different ways that benefit may be generated, each with different implications for how cooperation might be initiated, how it might further evolve, and how it might interact with different ways of maintaining cooperation. I argue that in some cases of cooperation, the most important underlying “problem” of cooperation may be how to generate benefit, rather than how to reduce conflict or prevent free-riding. (shrink)

Comparing engineering to evolution typically involves adaptationist thinking, where well-designed artifacts are likened to well-adapted organisms, and the process of evolution is likened to the process of design. A quite different comparison is made when biologists focus on evolvability instead of adaptationism. Here, the idea is that complex integrated systems, whether evolved or engineered, share universal principles that affect the way they change over time. This shift from adaptationism to evolvability is a significant move for, as I argue, we can (...) make sense of these universal principles without making any adaptationism claims. Furthermore, evolvability highlights important aspects of engineering that are ignored in the adaptationist debates. I introduce some novel engineering examples that incorporate these key neglected aspects, and use these examples to challenge some commonly cited contrasts between engineering and evolution, and to highlight some novel resemblances that have gone unnoticed. (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)

This paper is about mechanisms and models, and how they interact. In part, it is a response to recent discussion in philosophy of biology regarding whether natural selection is a mechanism. We suggest that this debate is indicative of a more general problem that occurs when scientists produce mechanistic models of populations and their behaviour. We can make sense of claims that there are mechanisms that drive population-level phenomena such as macroeconomics, natural selection, ecology, and epidemiology. But talk of mechanisms (...) and mechanistic explanation evokes objects with well-defined and localisable parts which interact in discrete ways, while models of populations include parts and interactions that are neither local nor discrete in any actual populations. This apparent tension can be resolved by carefully distinguishing between the properties of a model and those of the system it represents. To this end, we provide an analysis that recognises the flexible relationship between a mechanistic model and its target system. In turn, this reveals a surprising feature of mechanistic representation and explanation: it can occur even when there is a mismatch between the mechanism of the model and that of its target. Our analysis reframes the debate, providing an alternative way to interpret scientists’ mechanism-talk , which initially motivated the issue. We suggest that the relevant question is not whether any population-level phenomenon such as natural selection is a mechanism, but whether it can be usefully modelled as though it were a particular type of mechanism. (shrink)

Recent work on the evolution of signaling systems provides a novel way of thinking about genetic information, where information is passed between genes in a regulatory network. I use examples from evolutionary developmental biology to show how information can be created in these networks and how it can be reused to produce rapid phenotypic change.

Biologists frequently draw on ideas and terminology from engineering. Evolutionary systems biology—with its circuits, switches, and signal processing—is no exception. In parallel with the frequent links drawn between biology and engineering, there is ongoing criticism against this cross-fertilization, using the argument that over-simplistic metaphors from engineering are likely to mislead us as engineering is fundamentally different from biology. In this article, we clarify and reconfigure the link between biology and engineering, presenting it in a more favorable light. We do so (...) by, first, arguing that critics operate with a narrow and incorrect notion of how engineering actually works, and of what the reliance on ideas from engineering entails. Second, we diagnose and diffuse one significant source of concern about appeals to engineering, namely that they are inherently and problematically metaphorical. We suggest that there is plenty of fertile ground left for a continued, healthy relationship between engineering and biology. (shrink)

The notion of a genetic program has been widely criticized by both biologists and philosophers. But the debate has revolved around a narrow conception of what programs are and how they work, and many criticisms are linked to this same conception. To remedy this, I outline a modern and more apt idea of a program that possesses many of the features critics thought missing from programs. Moving away from over-simplistic conceptions of programs opens the way to a more fruitful interplay (...) of ideas between the complexity of biology and our most complex engineering discipline. (shrink)

According to Pigliucci and Kaplan, there is a revolution underway in how we understand fitness landscapes. Recent models suggest that a perennial problem in these landscapes—how to get from one peak across a fitness valley to another peak—is, in fact, non-existent. In this paper I assess the structure and the extent of Pigliucci and Kaplan’s proposed revolution and argue for two points. First, I provide an alternative interpretation of what underwrites this revolution, motivated by some recent work on model-based science. (...) Second, I show that the implications of this revolution need to carefully assessed depending on question being asked, for peak-shifting is not central to all evolutionary questions that fitness landscapes have been used to explore. (shrink)

I respond to recent criticism of my analysis of the permissive-instructive distinction and outline problems with the alternative analysis on offer. Amongst other problems, I argue that the use of formal measures is unclear and unmotivated, that the distinction is conflated with others that are not equivalent, and that no good reasons are provided for thinking the alternative model or formal measure tracks what biologists are interested in. I also clarify my own analysis where it has been misunderstood or ignored.

I respond to recent criticism of my analysis of the permissive-instructive distinction and outline problems with the alternative analysis on offer. Amongst other problems, I argue that the use of formal measures is unclear and unmotivated, that the distinction is conflated with others that are not equivalent, and that no good reasons are provided for thinking the alternative model or formal measure tracks what biologists are interested in. I also clarify my own analysis where it has been misunderstood or ignored.

We provide an account of mechanistic representation and explanation that has several advantages over previous proposals. In our view, explaining mechanistically is not simply giving an explanation of a mechanism. Rather, an explanation is mechanistic because of particular relations that hold between a mechanical representation, or model, and the target of explanation. Under this interpretation, mechanistic explanation is possible even when the explanatory target is not a mechanism. We argue that taking this view is not only coherent and plausible, it (...) gives a more sophisticated view of the relationship between mechanical models and their targets. This allows us to address some ambiguities within the mechanist framework, and delivers a more intuitive way to interpret scientists' use of the term "mechanism". (shrink)