Virtual objects, such as online shops, the elements that go to make up virtual life in computer games, virtual maps, e-books, avatars, cryptocurrencies, chatbots, holograms, etc., are a phenomenon we now encounter at every turn: they have become a part of our life and our world. Philosophers—and ontologists in particular—have sought to answer the question of what, exactly, they are. They fall into two camps: some, pointing to the chimerical character of virtuality, hold that virtual objects are like dreams, illusions (...) and fictions, while others, citing the real impact of virtuality on our world, take them to be real—an actual part of the real world, just like other real objects. In this article, we defend the thesis that both sides are wrong. Using Roman Ingarden’s phenomenological ontology, we advocate a position according to which a virtual object is a computationally grounded intentional object that has its existential foundation in computational processes, which are compliant with a certain model of computation. We point out that virtuality is framed by some kind of ideal mathematical objects: i.e., mathematical models of computation, which in turn fall, each of them, under their respective ideas. We also refer to the idea of natural computation, which in conjunction with the ontological analysis carried out leads to the thesis that an object can be more or less virtual. (shrink)

The reviewers Rapaport, Stephanou, Angius, Primiero, and Bringsjord of Turner cover a broad range of topics in the philosophy of computer science. They either challenge the positions outlined in Turner or offer a more refined analysis. This article is a response to their challenges.

Many philosophical accounts of scientific models fail to distinguish between a simulation model and other forms of models. This failure is unfortunate because there are important differences pertaining to their methodology and epistemology that favor their philosophical understanding. The core claim presented here is that simulation models are rich and complex units of analysis in their own right, that they depart from known forms of scientific models in significant ways, and that a proper understanding of the type of model simulations (...) are fundamental for their philosophical assessment. I argue that simulation models can be distinguished from other forms of models by the many algorithmic structures, representation relations, and new semantic connections involved in their architecture. In this article, I reconstruct a general architecture for a simulation model, one that faithfully captures the complexities involved in most scientific research with computer simulations. Furthermore, I submit that a new methodology capable of conforming such architecture into a fully functional, computationally tractable computer simulation must be in place. I discuss this methodology—what I call recasting—and argue for its philosophical novelty. If these efforts are heading towards the right interpretation of simulation models, then one can show that computer simulations shed new light on the philosophy of science. To illustrate the potential of my interpretation of simulation models, I briefly discuss simulation-based explanations as a novel approach to questions about scientific explanation. (shrink)

I critically review Raymond Turner’s Computational Artifacts – Towards a Philosophy of Computer Science by placing beside his position a rather different one, according to which computer science is a branch of, and is therefore subsumed by, immaterial formal logic.

The reviewers Rapaport, Stephanou, Angius, Primiero, and Bringsjord of Turner cover a broad range of topics in the philosophy of computer science. They either challenge the positions outlined in Turner or offer a more refined analysis. This article is a response to their challenges.

Turner argues that computer programs must have purposes, that implementation is not a kind of semantics, and that computers might need to understand what they do. I respectfully disagree: Computer programs need not have purposes, implementation is a kind of semantic interpretation, and neither human computers nor computing machines need to understand what they do.

The core entities of computer science include formal languages, spec-ifications, models, programs, implementations, semantic theories, type inference systems, abstract and physical machines. While there are conceptual questions concerning their nature, and in particular ontological ones (Turner 2018), our main focus here will be on the relationships between them. These relationships have an extensional aspect that articulates the propositional connection between the two entities, and an intentional one that fixes the direction of governance. An analysis of these two aspects will drive (...) our investigation; an investigation that will touch upon some of the central concerns of the philosophy of computer science (Turner 2017). (shrink)

Insofar as disagreement may in principle regard most of (maybe all) facets of information system ontologies’ [ISOs] debate, it may also produce a plurality of views – sometimes inconsistent with each other – on ISOs’ development and design. This paper analyzes a view that makes the recognition of – and provides a theoretical foundation for – such a plurality of views a trademark: perspectivism (on ISOs). The aim is to show what exactly endorsing perspectivism consists of, and how perspectivism differs (...) from different, competing views. Section 2 introduces the main claims of perspectivism, and remarks that perspectivism mainly deals with ISOs’ development and design. As for ISOs’ development, Sect. 3 considers domain’s partition and systematization, by distinguishing perspectivism from realism and relativism. Section 3 also shows that perspectivism implies some sort of variantism on ISOs’ representational primitives, about which perspectivism may not differ from its rivals. As for the ISOs’ design, Sect. 4 points out that despite perspectivism grants the possibility to use any procedural approach, principle, and ontological language, it is not committed to uphold that all those approaches, principles, and languages are legitimate. Finally, Sect. 5 focuses on both perspectivism’s weaknesses and (theoretical) contribution to ISOs’ debate. (shrink)

This review of Turner’s “Computational Artifacts” focuses on one of the key novelties of the book, namely the proposal to understand the nature of computer programs as a “trinity” of specification, symbolic program, and physical process, replacing the traditional dualist views of programs as functional/structural or as symbolic/physical. This trinitarian view is found to be robust and helpful to solve typical issues of dualist views. Drawing comparisons with Simon’s view of the artifact as an interface, the author suggests that this (...) trinitarian view may characterize not only computational artifacts but also artifacts in general. One ambiguity is however noticed on the denotation of what Turner actually calls the “physical process.”. (shrink)

We present a review of Raymond Turner’s Book Computational Artifacts – Towards a Philosophy of Computer Science, focusing on three main topics: Design, Malfunction, and Validity.

Some computational phenomena rely essentially on pragmatic considerations, and seem to undermine the independence of the specification from the implementation. These include software development, deviant uses, esoteric languages and recent data-driven applications. To account for them, the interaction between pragmatics, epistemology and ontology in computational artefacts seems essential, indicating the need to recover the role of the language metaphor. We propose a User Levels (ULs) structure as a pragmatic complement to the Levels of Abstraction (LoAs)-based structure defining the ontology and (...) epistemology of computational artefacts. ULs identify a flexible hierarchy in which users bear their own semantic and normative requirements, possibly competing with the logical specification. We formulate a notion of computational act intended in its pragmatic sense, alongside pragmatic versions of implementation and correctness. (shrink)

This paper provides an epistemological and methodological analysis of the recent practice of using neural language models to simulate brain language processing. It is argued that, on the one hand, this practice can be understood as an instance of the traditional simulative method in artificial intelligence, following a mechanistic understanding of the mind; on the other hand, that it modifies the simulative method significantly. Firstly, neural language models are introduced; a study case showing how neural language models are being applied (...) in cognitive neuroscience for simulative purposes is then presented; after recalling the main epistemological features of the simulative method in artificial intelligence, it is finally highlighted how the epistemic opacity of neural language models is tackled by using the brain itself to simulate the neural language model and to test hypotheses about it, in what is called here a co-simulation. (shrink)

This paper offers a review of Giuseppe Primero’s (2020) book “On the foundations of computing”_._ Mathematical, engineering, and experimental foundations of the science of computing are examined under the light of the notions of formal, physical, and experimental computational validity provided by the author. It is challenged the thesis that experimental computational validity can be defined only for the algorithmic method and not for the software development process. The notions of computational hypothesis and computational experiment provided by Primiero (2020) are (...) extended to the case of software development. Finally, it is highlighted how the hypothetical-deductive method is involved in the practice of using models to corroborate computational hypotheses in software testing. As a concluding remark, it is underlined how defining experimental computational validity in the context of software development offers a sound experimental foundation to the science of computing. (shrink)

This paper provides a review of Raymond Turner’s book Computational Artefacts. Towards a Philosophy of Computer Science. Focus is made on the definition of program correctness as the twofold problem of evaluating whether both the symbolic program and the physical implementation satisfy a set of specifications. The review stresses how these are not two separate problems. First, it is highlighted how formal proofs of correctness need to rely on the analysis of physical computational processes. Secondly, it is underlined how software (...) testing requires considering the formal relations holding between the specifications and the symbolic program. Such a mutual dependency between formal and empirical program verification methods is finally shown to influence the debate on the epistemological status of computer science. (shrink)

This paper contributes to the computer ethics debate on software ownership protection by examining the ontological, methodological, and ethical problems related to property right infringement that should come prior to any legal discussion. The ontological problem consists in determining precisely what it is for a computer program to be a copy of another one, a largely neglected problem in computer ethics. The methodological problem is defined as the difficulty of deciding whether a given software system is a copy of another (...) system. And the ethical problem corresponds to establishing when a copy constitutes, or does not constitute, a property right infringement. The ontological problem is solved on the logical analysis of abstract machines, and the latter are argued to be the appropriate level of abstraction for software at which the methodological and the ethical problems can be successfully addressed. (shrink)

This paper sheds light on the shift that is taking place from the practice of ‘coding’, namely developing programs as conventional in the software community, to the practice of ‘curing’, an activity that has emerged in the last few years in Deep Learning (DL) and that amounts to curing the data regime to which a DL model is exposed during training. Initially, the curing paradigm is illustrated by means of a study-case on autonomous vehicles. Subsequently, the shift from coding to (...) curing is analysed taking into consideration the epistemological notions, central in the philosophy of computer science, of function, implementation, and correctness. First, it is illustrated how, in the curing paradigm, the functions performed by the trained model depend much more on dataset curation rather than on the model algorithms which, in contrast with the coding paradigm, do not comply with requested specifications. Second, it is highlighted how DL models cannot be considered implementations according to any of the available definitions of implementation that follow an intentional theory of functions. Finally, it is argued that DL models cannot be evaluated in terms of their correctness but rather in their experimental computational validity. (shrink)

What is a computer? What distinguishes computers from other artificial or natural systems with alleged computational capacities? What does use of a physical system for computation entail, and what distinguishes such use from otherwise identical transformation of that same system when it is not so used? This paper addresses such questions through a theory of information processing artifacts, the class of technical artifacts with physical capacities that enable agents to use them as means to their computational ends. Function ascription, use (...) plan requirements, malfunction, and efficacy of IPAs are all addressed in this theory, with emphasis on artifacts that can be used—reliably or otherwise—for digital computation. By explicitly distinguishing physically grounded computational capacities from user-ascribed computational functions, and by recognizing the distinct roles of each for the implementation of computations in artifacts, this theory clearly distinguishes the use of physical systems for computation from the transformations of physical system states that enable such use. As such, it provides a rigorous basis for distinguishing “computers” from other artificial and natural systems—a distinction whose nature and legitimacy faces ever-evolving challenges from multiple disciplines. This theory, and the associated “instrumental” view of computation in artifacts, naturally accommodates the openminded but scrupulous consideration of radically unconventional physical systems as potential substrates for future computers. (shrink)