Agent-based models (ABMs) are widely used to study how individual interactions shape collective behaviors. Critics argue that ABMs are often too simplistic to capture real-world complexities. We address this by integrating artificial neural networks into ABMs, focusing on enhancing the Hegselmann–Krause (HK) model. By using multilayer perceptrons as agents, we create more realistic ABMs that better reflect actual agents. This approach yields multiple models, as core elements of the HK model can be defined in various ways. We conduct two computational studies to compare these models with each other and with traditional individual-learning paradigms.

Absolute and relative outcome measures measure a treatment’s effect size, purporting to inform treatment choices. I argue that absolute measures are at least as good as, if not better than, relative ones for informing rational decisions across choice scenarios. Specifically, this dominance of absolute measures holds for choices between a treatment and a control group treatment from a trial and for ones between treatments tested in different trials. This distinction has hitherto been neglected, just like the role of absolute and baseline risks in rational decision making that my analysis reveals. Recognizing both aspects advances the discussion on reporting outcome measures.

Extant accounts of trust in science focus on reconciling scientific and public value judgments, but neglect the challenge of learning audience values. I argue that for scientific experts to be epistemically trustworthy, they should adopt a cooperative approach to learning about the values of their audience. A cooperative approach, in which expert and nonexpert inquirers iteratively refine value judgments, better achieves important second-order epistemic dimensions of trustworthiness. I suggest that strong objectivity in the feminist standpoint theoretic sense is sometimes a prerequisite for trustworthiness.

Psychology is a discipline that has a high number of failed replications, which has been characterized as a “crisis” on the assumption that failed replications are indicative of untrustworthy research. This article uses Chang’s concept of epistemic iteration to show how a research program can advance epistemic goals despite many failed replications. It illustrates this by analyzing an ongoing large-scale replication attempt of Southgate et al.’s work exploring infants’ understanding of false beliefs. It concludes that epistemic iteration offers a way of understanding the value of replications—both failed and successful—that contradicts the narrative centered around distrust.

This article develops an axiomatic theory of induction that speaks to the recent debate on Bayesian orgulity. It shows the exact principles associated with the belief that data can corroborate universal laws. We identify two types of disbelief about induction: skepticism that the existence of universal laws of nature can be determined empirically, and skepticism that the true law of nature, if it exists, can be successfully identified. We formalize and characterize these two dispositions toward induction by introducing novel axioms for subjective probabilities. We also relate these dispositions to the (controversial) axiom of σ-additivity.

Despite its potential implications for the objectivity of scientific knowledge, the claim that “scientific instruments are perspectival” has received little critical attention. I show that this claim is best understood as highlighting the dependence of instruments on different perspectives. When closely analyzed, instead of constituting a novel epistemic challenge, this dependence can be exploited to mount novel strategies for resolving two old epistemic problems: conceptual relativism and theory-ladeness. The novel content of this article consists in articulating and developing these strategies by introducing two fine-grained notions of perspectives as the key units of analysis: “broad perspectives” and “narrow perspectives.”

This article addresses problems with a defensive turn in discussions of science and Indigenous ways of knowing, being, and doing. Philosophers and practitioners of science have focused recent discussions on coarse-grained questions of demarcation, epistemic parity, and identity—asking questions such as “Is Indigenous knowledge science?” Using representative examples from Aotearoa New Zealand, we expose rampant ambiguities in these arguments, and show that this combative framing can overlook what is at stake. We provide a framework for analyzing these problems and suggest better ways forward.

Recently, Lisa Feldman Barrett and colleagues have introduced the Theory of Constructed Emotions (TCE), in which emotions are constituted by a process of categorizing the self as being in an emotional state. The view, however, has several counterintuitive implications: for instance, a person can have multiple distinct emotions at once. Further, the TCE concludes that emotions are constitutively social phenomena. In this article, I explicate the TCE*, which, while substantially similar to the TCE, makes several distinct claims aimed at avoiding the counterintuitive implications plaguing the TCE. Further, because of the changes that comprise the TCE*, emotions are not constitutively social phenomena.

Approximate coherentism suggests that imperfectly rational agents should hold approximately coherent credences. This norm is intended as a generalization of ordinary coherence. I argue that it may be unable to play this role by considering its application under learning experiences. While it is unclear how imperfect agents should revise their beliefs, I suggest a plausible route is through Bayesian updating. However, Bayesian updating can take an incoherent agent from relatively more coherent credences to relatively less coherent credences, depending on the data observed. Thus, comparative rationality judgments among incoherent agents are unduly sensitive to luck.

At the beginning of the twentieth century, the French philosopher of science Edmond Goblot wrote three prescient papers on function and teleology. He advanced the remarkable thesis that functions are, as a matter of conceptual analysis, selected effects. He also argued that “selection” must be understood broadly to include both evolutionary natural selection and intelligent design. Here, I do three things. First, I give an overview of Goblot’s thought. Second, I identify his core thesis about function. Third, I argue that, despite its ingenuity, Goblot’s expansive construal of function cannot be right. Still, Goblot deserves long-overdue credit for his work.

Quantitative laws are more typical of science than are generalizations involving observational predicates, yet much discussion of scientific inference takes the confirmation of a universal generalization by its instances to be typical and paradigmatic. The important difference is that measurement necessarily involves error. It is argued that because of error laws can no more be refuted by observation than they can be verified by observation. Without much background knowledge, tests of a law mainly provide evidence for the distribution of errors of measurement of the quantities involved. With more background knowledge, the data may contribute either to our knowledge of the error distributions, or to the grounds we have for accepting or rejecting the law. With enough background knowledge, data may verify as well as refute laws.

This paper does not purport to present a fully developed argument. Rather, it intends to delineate, in broad strokes and in a synoptic manner, the total frame of reference of Scientific Empiricism as far as it is necessary for an integration of the several contributory clarifications of the complex, usually referred to as the psychophysical problem. Several considerations seem to justify such an attempt. It is desirable, from time to time, to free the results of logical and methodological analysis from their specialized contexts. Only thus can they be presented in a way which brings to the fore their continuity with more traditionally worded epistemological thought. By doing this such a presentation should also help to dissolve the current objections of formalism, scientism, and philosophical irrelevance so frequently based on misunderstanding. It is only fair to admit, however, that these misunderstandings have been facilitated by the preoccupation with the technical, and the brevity with regard to the nontechnical, which characterize the modern analysts.

I argue that the pessimistic meta-induction (PMI) seems to point an ontological priority of the relations over the objects of the scientific theories of the kind suggested by French and Ladyman (2003). My strategy will involve a critical examination of epistemic structural realism (ESR) and an historical case-study: the prediction of Zeeman's effect in Lorentz's theory of the electron.

I present a game-theoretic way to understand the situation describing Newcomb's Problem (NP) which helps to explain the intuition of both one-boxers and two-boxers. David Lewis has shown that the NP may be modelled as a Prisoner's Dilemma game (PD) in which ‘cooperating’ corresponds to ‘taking one box’. Adopting relevant results from game theory, this means that one should take just one box if the NP is repeated an indefinite number of times, but both boxes if it is a one-shot game. Causal decision theorists thus give the right answer for the one-shot situation, whereas the one-boxers’ solution applies to the indefinitely iterated case. Because Nozick's set-up of the NP is ambiguous between a one-shot and a repeated game, both of these solutions may appear plausible—depending on whether one conceives of the situation as one-off or repeated. If the players’ aim is to maximize their payoffs, the symmetric structure of the PD implies that the two players will behave alike both when the game is one-shot and when it is played repeatedly. Therefore neither the observed outcome of both players selecting the same strategy (in the PD) nor, correspondingly, the predictor's accurate prediction of this outcome (in the NP) is at all surprising. There is no need for a supernatural predictor to explain the NP phenomena.

Background theories in science are used both to prove and to disprove that theory choice is underdetermined by data. The alleged proof appeals to the fact that experiments to decide between theories typically require auxiliary assumptions from other theories. If this generates a kind of underdetermination, it shows that standards of scientific inference are fallible and must be appropriately contextualized. The alleged disproof appeals to the possibility of suitable background theories to show that no theory choice can be timelessly or noncontextually underdetermined: Foreground theories might be distinguished against different backgrounds. Philosophers have often replied to such a disproof by focussing their attention not on theories but on Total Sciences. If empirically equivalent Total Sciences were at stake, then there would be no background against which they could be differentiated. I offer several reasons to think that Total Science is a philosophers’ fiction. No respectable underdetermination can be based on it.

This paper is an assessment of an attempt, by James Greeno, to measure the explanatory power of statistical theories by means of the notion of transmitted information (It). It is argued that It has certain features that are inappropriate in a measure of explanatory power. In particular, given a statistical theory T with explanans variables Si and explanandum variables Mj, it is argued that no plausible measure of explanatory power should depend on the probability P(Si) of occurrence of initial conditions in the systems to which T applies or the magnitudes of the conditional probabilities P(Mj/Si), in the manner in which IT does.

After briefly presenting Ronald Giere's (1979, 1980) recent counterfactual characterization of population-level causation, I present two counterexamples to the characterization. The difficulty discussed stems from nonaccidental correlations that can obtain between causally effective and causally neutral factors.

Suppose that entities composed of two distinct components can be qualitatively ordered in two ways, such that each ordering relation satisfies the axioms of conjoint measurement. Without further assumptions nothing can be said about the relation between the pair of numerical scales constructed for each component. Axioms are stated that relate the two measurement theories, and that are sufficient to establish that the two conjoint scales on each component are linearly related.