Can Confirmation Bias Improve Group Learning?

Confirmation bias has been widely studied for its role in failures of reasoning. Individuals exhibiting confirmation bias fail to engage with information that contradicts their current beliefs, and, as a result, can fail to abandon inaccurate beliefs. But although most investigations of confirmation bias focus on individual learning, human knowledge is typically developed within a social structure. We use network models to show that moderate confirmation bias often improves group learning. However, a downside is that a stronger form of confirmation bias can hurt the knowledge producing capacity of the community

On Dieks against the Received View

Dennis Dieks addressed some criticisms of the so-called Received View (RV) of non-individual quantum objects in a series of papers. His main con- cern is that the RV doesn’t fit the practice of physics since in some situations the physicist assumes that quantum objects can be treated individually, im- itating standard objects (individuals) in classical physics. In this paper, we revise his argumentation, showing that it involves some misunderstand- ings regarding the objectives of the RV. Dieks also proposes an Alternative View (AV) which he thinks is more in accordance with the way physicists proceed. We argue that the AV is not conflating the RV, but is complementary to it, namely, substitutes it when quantum objects are sufficiently apart and can be treated as obeying classical logic. Thus, from the point of view of the practice of physics, in most cases, we can opt for the Alternative View, but the RV is more adequate when we are looking for logical and foundational analyses

Outline for an externalist psychiatry (1): or, how to realise the biopsychosocial model

The biopsychosocial model in psychiatry has come under fire for being too vague to be of any practical use in the clinic. For many, its central flaw consists in lack of scientific validity and philosophical coherence: the model never specified how biological, psychological and social factors causally integrate with one another. Recently, advances in the cognitive sciences have made great strides towards meeting this very ‘integration challenge’. The paper begins by illustrating how enactivist and predictive processing frameworks propose converging accounts of biopsychosocial integration that are far superior to those of previous theories. It argues, however, that the main problem of implementing the biopsychosocial model has less to do with integration than with the lack of a social aetiology. Psychiatric practice leans heavily towards ‘bio’ and ‘psycho’ approaches, without an equally developed set of explanatory and therapeutic resources for dealing with the ‘social’ dimension of illness. This leaves psychiatry essentially internalist in orientation. As illustrated most poignantly by conditions such as functional neurological disorders, internalism comes with the risks of stigma and the curtailment of therapeutic possibilities. The paper argues that the answer to the failings of the biopsychosocial model lies in combining the integration challenge with the development of an ‘externalist psychiatry’, which casts both causes and treatment of psychiatric illness onto the social environment. The following two papers explore the conditions that might make this idea a reality

Reliability and Interpretability in Science and Deep Learning

In recent years, the question of the reliability of Machine Learning (ML) methods has acquired significant importance, and the analysis of the associated uncertainties has motivated a growing amount of research. However, most of these studies have applied standard error analysis to ML models---and in particular Deep Neural Network (DNN) models---which represent a rather significant departure from standard scientific modelling. It is therefore necessary to integrate the standard error analysis with a deeper epistemological analysis of the possible differences between DNN models and standard scientific modelling and the possible implications of these differences in the assessment of reliability. This article offers several contributions. First, it emphasises the ubiquitous role of model assumptions (both in ML and traditional Science) against the illusion of theory-free science. Secondly, model assumptions are analysed from the point of view of their (epistemic) complexity, which is shown to be language-independent. It is argued that the high epistemic complexity of DNN models hinders the estimate of their reliability and also their prospect of long-term progress. Some potential ways forward are suggested. Thirdly, this article identifies the close relation between a model's epistemic complexity and its interpretability, as introduced in the context of responsible AI. This clarifies in which sense---and to what extent---the lack of understanding of a model (black-box problem) impacts its interpretability in a way that is independent of individual skills. It also clarifies how interpretability is a precondition for assessing the reliability of any model, which cannot be based on statistical analysis alone. This article focuses on the comparison between traditional scientific models and DNN models. However, Random Forest (RF) and Logistic Regression (LR) models are also briefly considered

Bell's Theorem Begs the Question

I demonstrate that Bell's theorem is based on circular reasoning and thus a fundamentally flawed argument. It unjustifiably assumes the additivity of expectation values for dispersion-free states of contextual hidden variable theories for non-commuting observables involved in Bell-test experiments, which is tautologous to assuming the bounds of ±2 on the Bell-CHSH sum of expectation values. Its premises thus assume in a different guise the bounds of ±2 it sets out to prove. Once this oversight is ameliorated from Bell's argument by identifying the impediment that leads to it and local realism is implemented correctly, the bounds on the Bell-CHSH sum of expectation values work out to be ±2√2 instead of ±2, thereby mitigating the conclusion of Bell's theorem. Consequently, what is ruled out by any of the Bell test experiments is not local realism but the linear additivity of expectation values, which does not hold for non-commuting observables in any hidden variable theories to begin with

A new theory of causation based on probability distribution determinism

The concept of causation is essential for understanding relationships among various phenomena, yet its fundamental nature and the criteria for establishing it continue to be debated. This paper presents a new theory of causation through a quasi-axiomatic approach. The core of this framework is Probability Distribution Determinism (PDD), which updates traditional determinism by representing states of affairs as probability distributions, with the if-then function serving as its foundational definition. Based on PDD, by merely using appropriate naming strategies, it is possible to derive systems in which the structural characteristics of relationships among things closely resemble those in the real world, such as having various forms of nested hierarchies. Additionally, there are two related yet distinctly different contexts about relationships in PDD: one emphasizes the potential influence of conditions on outcomes in the general sense, while the other focuses on attributing responsibility for the state changes in specific scenarios. The formula for determining the relationship in the general sense is established as S(Y |S1(X), Ψ) ̸≡ S(Y |S2(X), Ψ). Subsequently, within the PDD framework, the paper clarifies the legitimate use of a series of concepts related to causation in those two contexts, thus encompassing the entire detailed connotation of the concept of causation. The comparison with other theories of causation and the analysis of case applications demonstrate that the new theory applies not only to situations where other theories are competent but also to situations where they are not. This suggests that, although certain aspects within the new framework may require further analysis, it provides a highly promising direction for a deeper understanding of causation

Science and Values: A Two-way Direction

In the science and values literature, scholars have shown how science is influenced and shaped by values, often in opposition to the ‘value free’ ideal of science. In this paper, we aim to contribute to the science and values literature by showing that the relation between science and values flows not only from values into scientific practice, but also from (allegedly neutral) science to values themselves. The extant literature in the ‘science and values’ field focuses by and large on reconstructing, post hoc, how values have influenced science; our reconstruction of the case studies, instead, aims to show that scientific concepts and methods, because of specific identifiable characteristics, can promote some values rather than (or at the expense of) others. We explain this bidirectional relation in analogy to debates on the normativity of technical artifacts and on feminist approaches in science, and we illustrate our claims with cases from the health sciences and machine learning. While our arguments in this paper also draw on post hoc reconstructions, we intend to show where we should engage not only with the question of whether a practice is value-laden, but also how specific conceptual and methodological choices can influence values down the road. All in all, these considerations expand the ways in which philosophers can contribute to more value-aware scientific practices

On the geometric trinity of gravity, non-relativistic limits, and Maxwell gravitation

We show that the dynamical common core of the recently-discovered non-relativistic geometric trinity of gravity is Maxwell gravitation. Moreover, we explain why no analogous distinct dynamical common core exists in the case of the better-known relativistic geometric trinity of gravity

Why the global phase is not real

In this paper, I present a new analysis of the meaning of the phase in quantum mechanics. First, I give a simple but rigorous proof that the global phase is not real in $\psi$-ontic quantum theories. Next, I argue that a similar strategy cannot be used to prove the reality of the global phase due to the existence of the tails of the wave function. Finally, I argue that the relative phase is not a nonlocal property of two regions together, and adding a relative phase to one local branch of a superposition only changes the local properties at the boundary of the region of the branch

Affective artificial agents as sui generis affective artifacts

AI-based technologies are increasingly pervasive in a number of contexts. Our affective and emotional life makes no exception. In this article, we analyze one way in which AI based technologies can affect them. In particular, our investigation will focus on affective artificial agents, namely AI-powered software or robotic agents designed to interact with us in affectively salient ways. We build upon the existing literature on affective artifacts with the aim of providing an original analysis of affective artificial agents and their distinctive features. We argue that, unlike comparatively low-tech affective artifacts, affective artificial agents display a specific form of agency, which prevents them from being perceived by their users as extensions of their selves. In addition to this, we claim that their functioning crucially depends on the simulation of human-like emotion-driven behavior and requires a distinctive form of transparency – we call it emotional transparency – that might give rise to ethical and normative tensions