We investigate the notion of definability with respect to a full satisfaction class σ for a model M of Peano arithmetic. It is shown that the σ-definable subsets of M always include a class which provides a satisfaction definition for standard formulas. Such a class is necessarily proper, therefore there exist recursively saturated models with no full satisfaction classes. Nonstandard extensions of overspill and recursive saturation are utilized in developing a criterion for nonstandard definability. Finally, these techniques yield some information (...) concerning the extendibility of full satisfaction classes from one model to another. (shrink)

We show that IE1 proves that every element greater than 1 has a unique factorization into prime powers, although we have no way of recovering the exponents from the prime powers which appear. The situation is radically different in Bézout models of open induction. To facilitate the construction of counterexamples, we describe a method of changing irreducibles into powers of irreducibles, and we define the notion of a frugal homomorphism into Ẑ = ΠpZp, the product of the p-adic integers for (...) each prime p. (shrink)

We prove results about nonstandard formulas in models of Peano arithmetic which complement those of Kotlarski, Krajewski, and Lachlan in [KKL] and [L]. This enables us to characterize both recursive saturation and resplendency in terms of statements about nonstandard sentences. Specifically, a model M of PA is recursively saturated iff M is nonstandard and M-logic is consistent.M is resplendent iff M is nonstandard, M-logic is consistent, and every sentence φ which is consistent in M-logic is contained in a full satisfaction (...) class for M. Thus, for models of PA, recursive saturation can be expressed by a (standard) Σ 1 1 -sentence and resplendency by a ▵ 1 2 -sentence. (shrink)

We show that $\mathrm{IE}_1$ proves that every element greater than 1 has a unique factorization into prime powers, although we have no way of recovering the exponents from the prime powers which appear. The situation is radically different in Bezout models of open induction. To facilitate the construction of counterexamples, we describe a method of changing irreducibles into powers of irreducibles, and we define the notion of a frugal homomorphism into $\hat\mathbb{Z} = \Pi_p\mathbb{Z}_p$, the product of the $p$-adic integers for (...) each prime $p$. (shrink)

This is a review of Arnon Levy and Peter Godfrey-Smith's book, The Scientific Imagination.

Pancreatitis is a complex, progressively destructive inflammatory disorder. Alcohol was long thought to be the primary causative agent, but genetic contributions have been of interest since the discovery that rare PRSS1, CFTR and SPINK1 variants were associated with pancreatitis risk. We now report two associations at genome-wide significance identified and replicated at PRSS1-PRSS2 and X-linked CLDN2 through a two-stage genome-wide study. The PRSS1 variant likely affects disease susceptibility by altering expression of the primary trypsinogen gene. The CLDN2 risk allele is (...) associated with atypical localization of claudin-2 in pancreatic acinar cells. The homozygous CLDN2 genotype confers the greatest risk, and its alleles interact with alcohol consumption to amplify risk. These results could partially explain the high frequency of alcohol-related pancreatitis in men. © 2012 Nature America, Inc. All rights reserved. (shrink)

This paper focuses on the issue of justice as a challenge facing business and society. I advance a simple deductive argument based on two premises. The first emerges out of theories of justice and holds that fairness, as a foundational basis for justice, demands impartiality or the avoidance of bias. The second emerges out of fiduciary law and holds that the duty of loyalty owed by managers to serve the interests of investors is fundamentally partial or biased. The conclusion is (...) the troubling fact that the fiduciary duty of loyalty owed by managers to serve the interests of investors appears to be incompatible with the demands of justice. Having presented this, I describe the impartiality tools of Rawls’ veil of ignorance and Adam Smith’s impartial spectator and discuss how these might be applied in this context. I speculate that while Smith’s impartial spectator is absolutely incommensurable with managers’ fiduciary duty of loyalty, Rawls’ veil of ignorance might be used to imagine a synthesis between this duty of loyalty and the impartiality demands of justice – in theory at least. And finally, as a parting shot, I wonder whether the real “Leadership Challenge that Matters” isn’t the gap between theory and reality. (shrink)

Tennenbaum's Theorem yields an elegant characterisation of the standard model of arithmetic. Several authors have recently claimed that this result has important philosophical consequences: in particular, it offers us a way of responding to model-theoretic worries about how we manage to grasp the standard model. We disagree. If there ever was such a problem about how we come to grasp the standard model, then Tennenbaum's Theorem does not help. We show this by examining a parallel argument, from a simpler model-theoretic (...) result. (shrink)

Many have claimed that ceteris paribus (CP) laws are a quite legitimate feature of scientific theories, some even going so far as to claim that laws of all scientific theories currently on offer are merely CP. We argue here that one of the common props of such a thesis, that there are numerous examples of CP laws in physics, is false. Moreover, besides the absence of genuine examples from physics, we suggest that otherwise unproblematic claims are rendered untestable by the (...) mere addition of the CP operator. Thus, “CP all Fs are Gs” when read as a straightforward statement of fact, cannot be the stuff of scientific theory. Rather, we suggest that when ``ceteris paribus'' appears in scientific works it plays a pragmatic role of pointing to more respectable claims. (shrink)

A large body of research has examined the relationship between moral intensity (MI) and the four‐component model of morality, typically, by separating MI into its constituent dimensions and regressing them individually against the four‐component model. This approach, however, violates the definition of MI as a single construct. To correct this problem, we develop and test a model of the impact of MI as a single, 6‐item formative construct. We find that when MI is taken into account, moral recognition is not (...) significant. MI does, however, serve as a strong trigger for judgment. We also determine that splitting MI into multiple factors does not significantly affect the results, confirming the assertion that MI is a single, coherent construct. (shrink)

Categorization and decision making are combined in a task with photorealistic faces. Two different types of face stimuli were assigned probabilistically into one of two fictitious groups; based on the category, faces were further probabilistically assigned to be hostile or friendly. In Part I, participants are asked to categorize a face into one of two categories, and to make a decision concerning interaction. A Markov model of categorization followed by decision making provides reasonable fits to Part I data. A Markov (...) model predicting decision making followed by categorization is rejected. In Part II, a no-parameter model predicts decisions using categorization and decision responses collected in separate trials, suggesting that Part 1 results are not an artifact of the presentation of categorization and decision questions within a single trial. Decisions concerning interaction (defensive/friendly) appear to be based on information from the category decision, and not from the face stimuli alone. (shrink)

Can an ugly experiment be a good experiment? Philosophers have identified many beautiful experiments and explored ways in which their beauty might be connected to their epistemic value. In contrast, the present chapter seeks out (and celebrates) ugly experiments. Among the ugliest are those being designed by AI algorithms. Interestingly, in the contexts where such experiments tend to be deployed, low aesthetic value correlates with high epistemic value. In other words, ugly experiments can be good. Given this, we should conclude (...) that beauty is not generally necessary or sufficient for epistemic value, and increasing beauty will not generally tend to increase epistemic value. (shrink)

As Nelson Goodman highlighted, there are two main senses of “abstract” that can be found in discussions about abstract art. On the one hand, a representation is abstract if it leaves out certain features of its target. On the other hand, something can be abstract to the extent that it does not represent a concrete subject. The first sense of “abstract” is well-known in philosophy of science. For example, philosophers discuss mathematical models of physical, biological, and economic systems as being (...) abstract in this sense. However, it is the second sense that dominates discussions of abstract art in aesthetics. For example, abstract art was (and is) considered revolutionary precisely for being non-figurative. Through an analysis of artists including Kandinsky, Malevich, and Mondrian, we develop a reading of this second sense, which we call “generative abstraction,” as opposed to “subtractive” abstraction. Generative abstraction is a process in which a new artifact is created which does not represent the initial concrete target system that inspired it (if there was one), where the artifact’s features are explored for their own sake, and where the “language” of the new artifact is in some way more “universal.” Focusing on this sense of abstraction is helpful in revealing the complexity of the process of crafting an abstract artifact, in problematizing the notion that abstraction can always be un-done (or concretized), as well as revealing new ways for abstractions to be epistemically (un)successful. (shrink)

While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...) robot blame, namely the folk's willingness to ascribe inculpating mental states or "mens rea" to robots. In a vignette-based experiment (N=513), we presented participants with a situation in which an agent knowingly runs the risk of bringing about substantial harm. We manipulated agent type (human v. group agent v. AI-driven robot) and outcome (neutral v. bad), and measured both moral judgment (wrongness of the action and blameworthiness of the agent) and mental states attributed to the agent (recklessness and the desire to inflict harm). We found that (i) judgments of wrongness and blame were relatively similar across agent types, possibly because (ii) attributions of mental states were, as suspected, similar across agent types. This raised the question - also explored in the experiment - whether people attribute knowledge and desire to robots in a merely metaphorical way (e.g., the robot "knew" rather than really knew). However, (iii), according to our data people were unwilling to downgrade to mens rea in a merely metaphorical sense when given the chance. Finally, (iv), we report a surprising and novel finding, which we call the inverse outcome effect on robot blame: People were less willing to blame artificial agents for bad outcomes than for neutral outcomes. This suggests that they are implicitly aware of the dangers of overattributing blame to robots when harm comes to pass, such as inappropriately letting the responsible human agent off the moral hook. (shrink)

Imagination is important for many things in science: solving problems, interpreting data, designing studies, etc. Philosophers of imagination typically account for the productive role played by imagination in science by focusing on how imagination is constrained, e.g., by using self-imposed rules to infer logically, or model events accurately. But the constraints offered by these philosophers either constrain too much, or not enough, and they can never account for uses of imagination that are needed to break today’s constraints in order to (...) make progress tomorrow. Thus, epistemology of imagination needs to make room for an element of epistemological anarchy. (shrink)

The integration of biomedical terminologies is indispensable to the process of information integration. When terminologies are linked merely through the alignment of their leaf terms, however, differences in context and ontological structure are ignored. Making use of the SNAP and SPAN ontologies, we show how three reference domain ontologies can be integrated at a higher level, through what we shall call the OBR framework (for: Ontology of Biomedical Reality). OBR is designed to facilitate inference across the boundaries of domain ontologies (...) in anatomy, physiology and pathology. (shrink)

Scientists imagine constantly. They do this when generating research problems, designing experiments, interpreting data, troubleshooting, drafting papers and presentations, and giving feedback. But when and how do scientists learn how to use imagination? Across 6 years of ethnographic research, it has been found that advanced career scientists feel comfortable using and discussing imagination, while graduate and undergraduate students of science often do not. In addition, members of marginalized and vulnerable groups tend to express negative views about the strength of their (...) imaginations and the general usefulness of imagination in science. After introducing these findings and discussing the typical relationship between a scientist and their imagination across a career, we argue that reducing the number or power of active imaginations in science is epistemically counterproductive. We suggest several ways to bring imagination back into science in a more inclusive way, especially through courses on imagination for scientists, good role models, and exemplar-based learning. (shrink)

Scientists recognize the necessity of imagination for solving tough problems. But how does the cognitive faculty responsible for daydreaming help in solving scientific problems? Philosophers claim that imagination is informative only when it is constrained to be maximally realistic. However, using a case study from space science, we show that scientists use imagination intentionally to break reality-oriented constraints. To do this well, they first target low-confidence constraints, and then higher-confidence constraints, until a plausible solution is found. This paper exemplifies a (...) new approach to epistemology of imagination that focuses on sets of imaginings (rather than individual imaginings), and responsible (rather than reliable) imaginings. (shrink)

In Rethinking the Enlightenment, Dr. Stuart demonstrates that the three primary strategies employed during the Enlightenment -- conflict, engagement, and retreat -- are time-tested methods that should be employed in our own anti-Christian age"--The publisher.

Imagination is extremely important for science, yet very little is known about how scientists actually use it. Are scientists taught to imagine? What do they value imagination for? How do social and disciplinary factors shape it? How is the labor of imagining distributed? These questions should be high priority for anyone who studies or practices science, and this paper argues that the best methods for addressing them are qualitative. I summarize a few preliminary findings derived from recent interview-based and observational (...) qualitative studies that I have performed. These finding include: (i) imagination is only valued for use in addressing maximally specific problems, and only when all else fails; (ii) younger scientists and scientists who are members of underrepresented groups express less positive views about imagination in general, and have less confidence in their own imaginations; (iii) while scientists seem to employ various epistemological frameworks to evaluate imaginings, overall they appear to be epistemic consequentialists about imagination, and this holds also for their evaluations of the tools they use to extend the power of their imaginations. I close by discussing the epistemic and ethical consequences of these findings, and then suggesting a few research avenues that could be explored next as we move forward in the study of scientific imagination. (shrink)

v. 1. Nicomachean ethics. Politics. The Athenian Constitution. Rhetoric. On Poetics.--v. 2. Logic.--v. 3. Physics. Metaphysics. On the soul. Short physical treaties.--v. 4. On the heavens. On generation and corruption. Meteorology. Biological treatises.

We might think that thought experiments are at their most powerful or most interesting when they produce new knowledge. This would be a mistake; thought experiments that seek understanding are just as powerful and interesting, and perhaps even more so. A growing number of epistemologists are emphasizing the importance of understanding for epistemology, arguing that it should supplant knowledge as the central notion. In this chapter, I bring the literature on understanding in epistemology to bear on explicating the different ways (...) that thought experiments increase three important kinds of understanding: explanatory, objectual and practical. (shrink)

This is a book review of Karim Bschir and Jamie Shaw (eds.); Interpreting Feyerabend: critical essays. Cambridge: Cambridge University Press, 2021, 290 pp, $99.99 HB.

Sometimes we learn through the use of imagination. The epistemology of imagination asks how this is possible. One barrier to progress on this question has been a lack of agreement on how to characterize imagination; for example, is imagination a mental state, ability, character trait, or cognitive process? This paper argues that we should characterize imagination as a cognitive ability, exercises of which are cognitive processes. Following dual process theories of cognition developed in cognitive science, the set of imaginative processes (...) is then divided into two kinds: one that is unconscious, uncontrolled, and effortless, and another that is conscious, controlled, and effortful. This paper outlines the different epistemological strengths and weaknesses of the two kinds of imaginative process, and argues that a dual process model of imagination helpfully resolves or clarifies issues in the epistemology of imagination and the closely related epistemology of thought experiments. (shrink)

What role does the imagination play in scientific progress? After examining several studies in cognitive science, I argue that one thing the imagination does is help to increase scientific understanding, which is itself indispensable for scientific progress. Then, I sketch a transcendental justification of the role of imagination in this process.