Yasha Rohwer

Many environmental ethicists think evolutionary processes are good or, put differently, that they are morally valuable. Furthermore, many claim this value can be compromised when humans disrupt or cause a break in these processes. In this paper, I argue this account is mistaken. Evolution is not good. Furthermore, evolution cannot be “broken” by mere human involvement. There is no preordained trajectory in evolution; randomness, genetic drift, and historical contingency influence all evolutionary histories. Additionally, to think humans necessarily undermine so-called “natural” processes and turn them artificial is to ignore Vogel (2011, 2015), and insist on pre-Darwinian dualism. There is no morally meaningful distinction between natural selection and artificial selection; they are both simply selection. Furthermore, animals shape their own evolutionary trajectories, their progenies’, and those of other organisms through their intentions and choices—as is illustrated in the theory of niche construction. Human involvement in evolutionary processes does not “break” them nor does it necessarily reduce the value of the end products of those processes.

In this paper, we argue that rather than exclusively focusing on trying to determine if an idealized model fits a particular account of scientific explanation, philosophers of science should also work on directly analyzing various explanatory schemas that reveal the steps and justification involved in scientists’ use of highly idealized models to formulate explanations. We develop our alternative methodology by analyzing historically important cases of idealized statistical modeling that use a three-step explanatory schema involving idealization, mathematical operation, and explanatory interpretation.

Highly idealized models, such as the Hawk-Dove game, are pervasive in biological theorizing. We argue that the process and motivation that leads to the introduction of various idealizations into these models is not adequately captured by Michael Weisberg’s taxonomy of three kinds of idealization. Consequently, a fourth kind of idealization is required, which we call hypothetical pattern idealization. This kind of idealization is used to construct models that aim to be explanatory but do not aim to be explanations.

Shapiro and Sober claim that Walsh, Ariew, Lewens, and Matthen give a mistaken, a priori defense of natural selection and drift as epiphenomenal. Contrary to Shapiro and Sober’s claims, we first argue that WALM’s explanatory doctrine does not require a defense of epiphenomenalism. We then defend WALM’s explanatory doctrine by arguing that the explanations provided by the modern genetical theory of natural selection are ‘autonomous-statistical explanations’ analogous to Galton’s explanation of reversion to mediocrity and an explanation of the diffusion ofgases. We then argue that whereas Sober’s theory of forces is an adequate description of Darwin’s theory, WALM’s explanatory doctrine is required to understand how themodern genetical theory of natural selection explains large-scale statistical regularities. 1 Introduction2 Shapiro and Sober’s ‘Epiphenomenalism Do’s and Don’ts’3 WALM’s Explanatory Doctrine4 Galton’s Autonomous-Statistical Explanation5 A Second Example: The Statistical Explanation of the Diffusion of Gases6 Distinguishing Two Theories of Evolution by Natural Selection7 A Possible Objection: Are Statistical Laws Sufficient for Explanation?8 Conclusion

Part of what makes the environment valuable is its autonomy. There are some who think that any human influence on an environment is necessarily autonomy-compromising because it is a form of human control. In this article, I will assume human influence on the environment necessarily undermines autonomy. However, I will argue, even given this assumption, it is still possible for the intervention to enable autonomy in the long run. My focus is on genetic intervention into organisms, because some might think human influence in these cases cannot dissipate. I argue this is mistaken and, borrowing a concept from botany, I argue genes, even genes inserted into a genome by humans, can “naturalize.” Furthermore, they can function in ways that are autonomy-enabling to the individual and to the system to which the organism belongs.

Each year, over 200 million people are infected with the malaria parasite, nearly half a million of whom succumb to the disease. Emerging genetic technologies could, in theory, eliminate the burden of malaria throughout the world by intentionally eradicating the mosquitoes that transmit the disease. In this paper, we offer an ethical examination of the intentional eradication of Anopheles gambiae, the main malaria vector of sub-Saharan Africa. In our evaluation, we focus on two main considerations: the benefit of alleviating the malaria burden, and the loss of value that would accompany the eradication of the species. We outline a typology of the different ways in which species are valued or could be valuable, then use that typology to appraise the value of the species in question. We argue that Anopheles gambiae has minor (and redundant) instrumental value, little final subjective value and no objective final value.

Within the modeling literature, there is often an implicit assumption about the relationship between a given model and a scientific explanation. The goal of this article is to provide a unified framework with which to analyze the myriad relationships between a model and an explanation. Our framework distinguishes two fundamental kinds of relationships. The first is metaphysical, where the model is identified as an explanation or as a partial explanation. The second is epistemological, where the model produces understanding that is related to the explanation of interest. Our analysis reveals that the epistemological relationships are not always dependent on the metaphysical relationships, contrary to what has been assumed by many philosophers of science. Moreover, we identify several importantly different ways that scientific models instantiate these relationships. We argue that our framework provides novel insights concerning the nature of models, explanation, idealization, and understanding.

The concept of explanation is central to scientific practice. However, scientists explain phenomena in very different ways. That is, there are many different kinds of explanation; e.g. causal, mechanistic, statistical, or equilibrium explanations. In light of the myriad kinds of explanation identified in the literature, most philosophers of science have adopted some kind of explanatory pluralism. While pluralism about explanation seems plausible, it faces a dilemma Explanation beyond causation, Oxford University Press, Oxford, pp 39–56, 2018). Either there is nothing that unifies all instances of scientific explanation that makes them count as explanations, or there is some set of unifying features, which seems incompatible with explanatory pluralism. Different philosophers have adopted different horns of this dilemma. Some argue that no unified account of explanation is possible. Others suggest that there is a set of necessary features that can unify all explanations under a single account Explanation beyond causation, Oxford University Press, Oxford, pp 74–95, 2018; Strevens in Depth: an account of scientific explanation, Harvard University Press, Cambridge, 2008). In this paper, we argue that none of the features identified by existing accounts of explanation are necessary for all explanations. However, we argue that a unified account can still be provided that accommodates pluralism. This can be accomplished, we argue, by reconceiving of scientific explanation as a cluster concept: there are multiple subsets of features that are sufficient for providing an explanation, but no single feature is necessary for all explanations. Reconceiving of explanation as a cluster concept not only accounts for the diversity of kinds of explanations, but also accounts for the widespread disagreement in the explanation literature and enables explanatory pluralism to avoid Pincock’s dilemma.

Highly idealized models, such as the Hawk-Dove game, are pervasive in biological theorizing. We argue that the process and motivation that leads to the introduction of various idealizations into these models is not adequately captured by Michael Weisberg’s taxonomy of three kinds of idealization. Consequently, a fourth kind of idealization is required, which we call hypothetical pattern idealization. This kind of idealization is used to construct models that aim to be explanatory but do not aim to be explanations.

We argue that the de-extinction of the mammoth cannot be ethically grounded by duties to the extinct mammoth, to ecosystem health or to individual organisms in ecosystems missing the mammoth. However, the action can be shown to be morally permissible via the goods it will afford humans, including advances in scientific knowledge, valuable experiences of awe and pleasure, and perhaps improvements to our moral character or behaviour—if and only if suffering is minimal. Finally, we call for empirical research into how the possibility of the technology affects our behaviour or our character to help the debate transcend duelling intuitions.

In this article I argue that humans have a pro tanto duty to cognitively enhance some animals threatened with extinction. I will use as a case study a particular set of animals: smaller Australian marsupials. Many of these animals are on the brink of extinction thanks to the introduction of the fox and the domestic cat to the continent of Australia. Ecologists conjecture that these marsupials do not have the behavioural flexibility to cope with these introduced predators. By introducing predators, humans performed a wrong action because it led to the extinction of species and continues to threaten many marsupials species such as woylies, Gilbert's potoroos, numbats and mountain pygmy possums. This wrong action gives rise to an obligation to intervene to prevent further species loss. Traditional means of conservation do not seem sufficient to address this obligation; therefore, there is a duty to cognitively enhance these creatures as soon as the technology is sufficiently researched and safe.

Game theory has played a critical role in elucidating the evolutionary origins of social behavior. Sober and Wilson model altruism as a prisoner's dilemma and claim that this model indicates that altruism arose from group selection pressures. Sober and Wilson also suggest that the prisoner's dilemma model can be used to characterize punishment; hence, punishment too originated from group selection pressures. However, empirical evidence suggests that a group selection model of the origins of altruistic punishment may be insufficient. I argue that examining dominance hierarchies and coalition formation in chimpanzee societies suggests that the origins of altruistic punishment may be best captured by individual selection models. I suggest that this shows the necessity of coupling of game-theoretic models with a conception of what our actual social structure may have been like to best model the origins of our own behavior. ‡I would like to thank Zachary Ernst and Emma Marris for their many helpful comments which greatly improved this paper. †To contact the author, please write to: Department of Philosophy, University of Maryland, Skinner Building, College Park, MD 20742; e-mail: [email protected]

Some conservation biologists invoke the concept of ‘genetic integrity,’ which they generally assume is a good worth preserving without explicit justification. We examine the question of whether or not there is a prima facie duty to preserve genetic integrity in conservation biology. We examine several possible justifications for the potential duty found in the conservation biology literature. We argue, contra a dominant trend of thought in conservation biology, that there is no prima facie duty to preserve genetic integrity and that any moral obligation to preserve genetic integrity in specific cases is merely derivative of other possible duties.

Can one still have understanding in situations that involve the kind of epistemic luck that undermines knowledge? Kvanvig (The value of knowledge and the pursuit of understanding, 2003; in: Haddock A, Miller A, Pritchard D (eds) Epistemic value, 2009a; in: Haddock A, Miller A, Pritchard D (eds) Epistemic value, 2009b) says yes, Prichard (Grazer Philos Stud 77:325–339, 2008; in: O’Hear A (ed) Epistemology, 2009; in: Pritchard D, Millar A, Haddock A (eds) The nature and value of knowledge: three investigations, 2010) say sometimes, DePaul and Grimm (Philos Phenomenol Res 74:498–514, 2007) and Grimm (Br J Philos Sci 57:515–535, 2006; in: Bernecker S, Pritchard D (eds) The Routledge companion to epistemology, 2011), Kvanvig’s critics, say no. The cases put forth by Kvanvig’s critics share a common feature, which seems to drive the intuition that understanding can’t be lucky: the fact that the information that makes up the individual’s understanding comes exclusively from a bad source. I formulate a case that lacks this feature, drawing on the fact that understanding produced from scientific inquiry is often produced by collaboration. I argue that my case provides good evidence that understanding is not a species of knowledge