This paper argues against the view that the standard explanation of phase transitions in statistical mechanics may be considered a causal explanation, a distortion that can nevertheless successfully represent causal relations.

Evo-Devo exhibits a plurality of scientific “cultures” of practice and theory. When are the cultures acting—individually or collectively—in ways that actually move research forward, empirically, theoretically, and ethically? When do they become imperialistic, in the sense of excluding and subordinating other cultures? This chapter identifies six cultures – three /styles/ (mathematical modeling, mechanism, and history) and three /paradigms/ (adaptationism, structuralism, and cladism). The key assumptions standing behind, under, or within each of these cultures are explored. Characterizing the internal structure of (...) the cultures is necessary for understanding how they collaborate or compete, and how they are fragmented or integrated, in the rich interdisciplinary /trading zone/ (Galison 1997) of Evo-Devo. Evo-Devo is an important example of how science can progress through a radical plurality of perspectives and cultures. (shrink)

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Because evolution in natural systems happens so slowly, it is dif- ficult to design inquiry-based labs where students can experiment and observe evolution in the way they can when studying other phenomena. New research in evolutionary computation and artificial life provides a solution to this problem. This paper describes a new A-Life software environment – Avida-ED – in which undergraduate students can test evolutionary hypotheses directly using digital organisms that evolve on their own through the very mechanisms that Darwin discovered.

Microbial model systems have a long history of fruitful use in fields that include evolution and ecology. In order to develop further insight into modelling practice, we examine how the competitive exclusion and coexistence of competing species have been modelled mathematically and materially over the course of a long research history. In particular, we investigate how microbial models of these dynamics interact with mathematical or computational models of the same phenomena. Our cases illuminate the ways in which microbial systems and (...) equations work as models, and what happens when they generate inconsistent findings about shared targets. We reveal an iterative strategy of comparative modelling in different media, and suggest reasons why microbial models have a special degree of epistemic tractability in multimodel inquiry. (shrink)

Intelligent design advocate William Dembski has introduced a measure of information called “complex specified information”, or CSI. He claims that CSI is a reliable marker of design by intelligent agents. He puts forth a “Law of Conservation of Information” which states that chance and natural laws are incapable of generating CSI. In particular, CSI cannot be generated by evolutionary computation. Dembski asserts that CSI is present in intelligent causes and in the flagellum of Escherichia coli, and concludes that neither have (...) natural explanations. In this paper, we examine Dembski’s claims, point out significant errors in his reasoning, and conclude that there is no reason to accept his assertions. (shrink)

In the 2005 Kitzmiller v Dover Area School Board case, a federal district court ruled that Intelligent Design creationism was not science, but a disguised religious view and that teaching it in public schools is unconstitutional. But creationists contend that it is illegitimate to distinguish science and religion, citing philosophers Quinn and especially Laudan, who had criticized a similar ruling in the 1981 McLean v. Arkansas creation-science case on the grounds that no necessary and sufficient demarcation criterion was possible and (...) that demarcation was a dead pseudo-problem. This article discusses problems with those conclusions and their application to the quite different reasoning between these two cases. Laudan focused too narrowly on the problem of demarcation as Popper defined it. Distinguishing science from religion was and remains an important conceptual issue with significant practical import, and philosophers who say there is no difference have lost touch with reality in a profound and perverse way. The Kitzmiller case did not rely on a strict demarcation criterion, but appealed only to a “ballpark” demarcation that identifies methodological naturalism as a “ground rule” of science. MN is shown to be a distinguishing feature of science both in explicit statements from scientific organizations and in actual practice. There is good reason to think that MN is shared as a tacit assumption among philosophers who emphasize other demarcation criteria and even by Laudan himself. (shrink)

What is the definition of life? Artificial life environments provide an interesting test case for this classical question. Understanding what such systems can tell us about biological life requires negotiating the tricky conceptual boundary between virtual and real life forms. Drawing from Wittgenstein’s analysis of the concept of a game and a Darwinian insight about classification, I argue that classifying life involves both causal and pragmatic elements. Rather than searching for a single, sharp definition, these considerations suggest that life is (...) a cluster concept with fuzzy boundaries and that there are multiple legitimate ways to make the notion precise for different scientific purposes. This pluralist, realist account avoids unnecessary border disputes by emphasizing how science negotiates such questions in relation to theory and evidence. I also discuss several objections to this approach, including a “moral hesitation” some have to allowing broader application of the concept of life to include artificial life. (shrink)

The leading Intelligent Design theorist William Dembski (Rowman & Littlefield, Lanham MD, 2002) argued that the first No Free Lunch theorem, first formulated by Wolpert and Macready (IEEE Trans Evol Comput 1: 67–82, 1997), renders Darwinian evolution impossible. In response, Dembski’s critics pointed out that the theorem is irrelevant to biological evolution. Meester (Biol Phil 24: 461–472, 2009) agrees with this conclusion, but still thinks that the theorem does apply to simulations of evolutionary processes. According to Meester, the theorem shows (...) that simulations of Darwinian evolution, as these are typically set in advance by the programmer, are teleological and therefore non-Darwinian. Therefore, Meester argues, they are useless in showing how complex adaptations arise in the universe. Meester uses the term teleological inconsistently, however, and we argue that, no matter how we interpret the term, a Darwinian algorithm does not become non-Darwinian by simulation. We show that the NFL theorem is entirely irrelevant to this argument, and conclude that it does not pose a threat to the relevance of simulations of biological evolution. (shrink)

What is artificial life? Much has been said about this interesting collection of efforts to artificially simulate and synthesize lifelike behavior and processes, yet we are far from having a robust philosophical understanding of just what Alifers are doing and why it ought to interest philosophers of science, and philosophers of biology in particular. In this paper, I first provide three introductory examples from the particular subset of artificial life I focus on, known as ‘soft Alife’ (s-Alife), and follow up (...) with a more in-depth review of the Avida program, which serves as my case study of s-Alife. Next, I review three well-known accounts of thought experiments, and then offer my own synthesized account, to make the argument that s-Alife functions as thought experimentation in biology. I draw a comparison between the methodology of the thought-experimental world that yields real-world results, and the s-Alife research that informs our understanding of natural life. I conclude that the insights provided by s-Alife research have the potential to fundamentally alter our understanding of the nature of organic life and thus deserve the attention of both philosophers and natural scientists. (shrink)

Complex, multigenic biological traits are shaped by the emergent interaction of proteins being the main functional units at the molecular scale. Based on a phenomenological approach, algorithms for quantifying two different aspects of emergence were introduced (Wegner and Hao in Progr Biophys Mol Biol 161:54–61, 2021) describing: (i) pairwise reciprocal interactions of proteins mutually modifying their contribution to a complex trait (denoted as weak emergence), and (ii) formation of a new, complex trait by a set of n ‘constitutive’ proteins at (...) concentrations exceeding individual threshold values (strong emergence). The latter algorithm is modified here to take account of protein redundancy with respect to a complex trait (‘full redundancy’). Irreducibility is considered a necessary and sufficient criterion for strong biological emergence; if one constitutive protein is missing, or its concentration drops below the threshold the trait is lost. A definition based on ‘unpredictability’ is dismissed, because this criterion is irrelevant for the evolution of a complex trait, and apparent unpredictability may rather reflect our basic deficits in understanding unless we can provide an unequivocal proof for it. The phenomenological approach advocated here allows to identify hidden rules according to which strongly emergent traits may be organized. This is of high value for understanding the evolution of complex traits which seems to require the saltational advent of all constitutive proteins ‘in one turn’ to arrive at a functional trait providing for an improved fitness of the organism. Rather than being a purely random process, it may be guided by fundamental structural principles. (shrink)

We investigate the evolution of memory usage in environments where information about past experience is required for optimal decision making. For this study, we use digital organisms, which are self-replicating computer programs that are subject to mutations and natural selection. We place the digital organisms in a range of experimental environments: simple ones where environmental cues indicate that a specific action should be taken (e.g., turn left to find food) as well as slightly more complex ones where cues refer to (...) prior experience (e.g., repeat the action indicated by the previous cue). We demonstrate that flexible behaviors evolve in each of these environments, often leading to clever survival strategies. Additionally, memory usage evolves only when it provides a significant advantage and organisms will often employ surprisingly successful strategies that do not use memory. However, the most powerful strategies we found all made effective use of memory. (shrink)

We demonstrate the existence of altruism via kin selection in artificial life and explore its nuances. We do so in the Avida system through a setup that is based on the behavior of colicinogenic bacteria: Organisms can kill unrelated organisms in a given radius but must kill themselves to do so. Initially, we confirm!results found in the bacterial world: Digital organisms do sacrifice themselves for their kin—an extreme example of altruism— and do so more often in structured environments, where kin (...) are always nearby, than in well-mixed environments, where the location of kin is stochastically determined. Having shown that helping one’s kin is advantageous, we turn our attention to investigating the efficacy and implications of the strategies of kincheaters, those who receive help from kin but do not return it. Contrary to the expectations of current theory, we find that kin-cheaters outcompete kin-altruists. Our results cause us to question the stability of strategies that involve altruism between kin. Knowing that kin-altruism persists in biological systems, however, we search for, and find, conditions that allow!kin-based altruism to persist in evolving!systems despite the!presence of kin-cheaters. (shrink)

This paper addresses the question of whether animals have souls and the ability to experience God after death within the limitations of their nature. Plausible explanations for the natural origin of life and for the development of subsequent complexity are increasingly being advanced by molecular biologists. Christian tradition and scholasticism teach that the human body is animated by the soul which is the agent of vital activities. This teaching is incompatible with the claim for a natural origin for life. At (...) some stage in the evolution of chordates and cephalopods a sense of self-awareness and an ability to distinguish between pleasure and the absence of pleasure would have arisen permitting the potential for ensoulment. The premise that evolution was gradual but ensoulment was discontinuous predicates the irrational conclusion that for one generation the parents were animals without souls and their children humans, made in the image of God, and with souls. Biological gradualism is incompatible with a sudden ensoulment dichotomy both in the evolutionary history of humans and for a maturing foetus, human or animal. Gradualism must apply to both body and soul. A Christian interpretation of physicalism, however, provides an alternative to dualism and resolves the paradoxes and difficulties relating to animals. (shrink)

In the natural world, individual organisms can adapt as their environment changes. In most in silico evolution, however, individual organisms tend to consist of rigid solutions, with all adaptation occurring at the population level. If we are to use artificial evolving systems as a tool in understanding biology or in engineering robust and intelligent systems, however, they should be able to generate solutions with fitness-enhancing phenotypic plasticity. Here we use Avida, an established digital evolution system, to investigate the selective pressures (...) that produce phenotypic plasticity. We witness two different types of fitness-enhancing plasticity evolve: static- execution-flow plasticity, in which the same sequence of actions produces different results depending on the environment, and dynamic-execution-flow plasticity, where organisms choose their actions based on their environment. We demonstrate that the type of plasticity that evolves depends on the environmental challenge the population faces. Finally, we compare our results to similar ones found in vastly different systems, which suggest that this phenomenon is a general feature of evolution. (shrink)

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

William Dembski (No free lunch: why specified complexity cannot be purchased without intelligence, 2002) claimed that the NFL theorems from optimization theory render darwinian biological evolution impossible. Häggström (Biology and Philosophy 22:217–230, 2007) argued that the NFL theorems are not relevant for biological evolution at all, since the assumptions of the NFL theorems are not met. Although I agree with Häggström (Biology and Philosophy 22:217–230, 2007), in this article I argue that the NFL theorems should be interpreted as dealing with (...) an extreme case within a much broader context. This broader context is in fact relevant for scientific research of certain evolutionary processes; not in the sense that the theorems can be used to draw conclusions about any intelligent design inference, but in the sense that it helps us to interpret computer simulations of evolutionary processes. As a result of this discussion, I will argue that from simulations, we do not learn much about how complexity arises in the universe. This position is in contrast with certain claims in the literature that I will discuss. (shrink)