In this article, I present the immunological account of physiological individuality courtesy of Thomas Pradeu and the evolutionary account of biological individuality from Ellen Clarke. I argue that in combination, the logic of these two accounts implies that all physiological individuals are capable of undergoing evolution by natural selection. The main objection to this view is the case of holobionts. Here I argue that this objection is unjustified and that holobionts meet basic criteria for evolutionary individuality. As such, this supports the view that physiological individuals are also evolutionary individuals.

As technology fosters connections among like-minded individuals, concerns about the effects of homogeneous clusters—often criticized as ideological bubbles and echo chambers—have intensified. While these clusters are commonly seen as obstacles to independent thought and progress, this paper argues that they can, under certain conditions, drive significant advancements. By revising computational models of collective problem-solving and examining historical cases, I demonstrate that clusters, particularly among minority groups with superior ideas, can overcome dominant resistance and promote progress. However, this clustering introduces trade-offs, including slower consensus formation.

It is plausible that a witness’s credibility is lowered if he testifies to an outcome that has low probability, but this is not always true. If the outcome has a low prior due only to its specificity (as in a fair lottery), witness credibility is not lowered by attesting to it. Even when witness credibility does drop as a result of the testimony, it can substantially confirm its contents, and agreement between independent witnesses can “give back” credibility that one witness lost. Formal modeling and analysis shed light on when each of these situations obtains.

This article examines whether characteristic modeling practices in economics lead to progress, that is, improved explanations and predictions. We question the widespread assumption that progress in modeling occurs solely through successive vertical refinements toward a single “best” model. Instead, we propose that progress can occur through multiple pathways. Specifically, we identify five distinct horizontal paths to progress: increased isolation of causal factors, differentiation of targets and purposes, derivation of robust theorems, multiplication of inconsistent perspectival models, and exploration of novel possibilities. We argue that these pathways, when properly constrained, increase explanatory and predictive power and therefore lead to scientific progress.

I give an account of ecology’s subject matter and generalizations in terms of functional roles. Functional roles are functionally defined kinds which include multiple species or general abiotic factors as members and occur in generalizations which hold across different ecological systems. Functional roles include central objects of study in ecology, like predator, parasite, and producer. I use functional roles to interpret and reorient major controversies in philosophy of ecology, including the metaphysics of ecological systems and the concept of “function.”

We propose an approach to the evolution of joint agency and cooperative behavior that contrasts with views that take joint agency to be a uniquely human trait. We argue that there is huge variation in cooperative behavior and that while much human cooperative behavior may be explained by invoking cognitively rich capacities, there is cooperative behavior that does not require such explanation. On both comparative and theoretical grounds, complex cognition is not necessary for forms of joint action or the evolution of cooperation. As a result, promising evolutionary approaches to cooperative behavior should explain how it arises across many contexts.

Mixture of experts is a prediction aggregation method in machine learning that aggregates the predictions of specialized experts. This method often outperforms Bayesian methods despite the Bayesian having stronger inductive guarantees. We argue that this is due to the greater functional capacity of mixture of experts. We prove that in a limiting case, mixture of experts will have greater capacity than equivalent Bayesian methods, which we vouchsafe through experiments on non-limiting cases. Finally, we conclude that mixture of experts is a type of abductive reasoning in the Peircean sense of hypothesis construction.

One cannot justifiably presuppose the physical salience of structures derived via decoherence theory based upon an entirely uninterpreted use of the quantum formalism. Non-probabilistic accounts of the emergence of probability via decoherence are unconvincing. An alternative account of the emergence of probability involves the combination of a partially interpreted decoherence model and an averaging of observables with respect to a positive-definite quasi-probability function and neglect of terms of $O\left( \hbar \right)$. Our analysis delimits the context in which the combination of decoherence and a semi-classical averaging allows us to recover a classical probability model within an emergent coarse-grained description.

Some authors maintain that we can use causal Bayes nets to infer whether $X \to Y$ or $X \leftarrow Y$ by consulting a probability distribution defined over some exogenous source of variation for $X$ or $Y$. We raise a problem for this approach. Specifically, we point out that there are cases where an exogenous cause of $X$ (${E_x}$) has no probabilistic influence on $Y$ no matter the direction of causation—namely, cases where ${E_x} \to X \to Y$ and ${E_x} \to X \leftarrow Y$ are probabilistically indistinguishable. We then assess the philosophical significance of this problem and discuss some potential solutions.

Advances in animal sentience research, neural organoids, and artificial intelligence reinforce the relevance of justifying attributions of consciousness to nonstandard systems. Clarifying the argumentative structure behind these attributions is important for evaluating their validity. This article addresses this issue, concluding that analogical abduction—a form of reasoning combining analogical and abductive elements—is the strongest method for extrapolating consciousness from humans to nonstandard systems. We argue that the argument from analogy and inference to the best explanation, individually taken, do not meet the criteria for successful extrapolations, whereas analogical abduction offers a promising approach despite limitations in current consciousness science.

Theories of ecological function often appeal to the liberality criterion to evaluate a theory’s adequacy. The liberality criterion requires that a functional description successfully differentiates between functions and nonfunctions. A functional description is considered inadequate if it is excessively liberal or excessively restrictive. I criticize the deference that is shown to the liberality criterion. I present a hypothetical case to illustrate how the application of the liberality criterion needlessly problematizes the four general theories of ecological function. Instead, a theory’s functional description should be considered adequate as long as it produces a better understanding of the complex phenomenon under study.

Trevor Griffith and Adrian Kind argue that we should reject a standard interpretation of pain asymbolia, according to which asymbolics experience pain even though their pain lacks the affective-motivational element that typical pains possess. We make the case that Griffith and Kind’s reasons for rejecting the standard interpretation are relatively weak. We end by arguing that debates between the standard interpretation and alternative interpretations cannot be resolved without addressing the issue of how we should taxonomize pain asymbolia as a neurological condition.

Jha et al. (2024) offer several objections to Lange’s account of “distinctively mathematical” scientific explanations (DMEs). This article argues that these objections fail. Jha et al.’s arguments fail to show that Lange is inconsistent in characterizing DMEs—that, by Lange’s lights, every causal explanation involving mathematical facts is a DME. Jha et al.’s arguments fail to suggest that on Lange’s account, DME’s are too common or explanatorily insubstantial to underwrite any philosophical lessons about explanation. Jha et al.’s arguments also fail to show that Lange relies on underhanded manipulations of the explananda targeted by DMEs.