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Recommender systems build user profiles using concept analysis of usage matrices. The concepts are mined as spectra and form Galois connections. Descent is a general method for spectral decomposition in algebraic geometry and topology which also leads to generalized Galois connections. Both recommender systems and descent theory are vast research areas, separated by a technical gap so large that trying to establish a link would seem foolish. Yet a formal link emerged, all on its own, bottom-up, against authors’ intentions and better judgment. Familiar problems of data analysis led to a novel solution in category theory. The present paper arose from a series of earlier efforts to provide a top-down account of these developments. 

Hilbert and Ackermann asked for a method to consistently extend incomplete theories to complete theories. Gödel essentially proved that any theory capable of encoding its own statements and their proofs contains statements that are true but not provable. Hilbert did not accept that Gödel’s construction answered his question, and in his late writings and lectures, Gödel agreed that it did not, since theories can be completed incrementally, by adding axioms to prove ever more true statements, as science normally does, with completeness as the vanishing point. This pragmatic view of validity is familiar not only to scientists who conjecture test hypotheses but also to real-estate agents and other dealers, who conjure claims, albeit invalid, as necessary to close a deal, confident that they will be able to conjure other claims, albeit invalid, sufficient to make the first claims valid. We study the underlying logical process and describe the trajectories leading to testable but unfalsifiable theories to which bots and other automated learners are likely to converge. 

We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. An artificial mind is deemed to be more objective than the human mind. After many years of science-fiction fantasies about even-minded androids, they are now sold as personal or expert assistants, as brand advocates, as policy or candidate supporters, as network influencers. Artificial agents have been stunningly successful in disseminating artificial causal beliefs among humans. As malicious artificial agents continue to manipulate human cognitive biases, and deceive human communities into ostensive but expansive causal illusions, the hope for defending us has been vested into developing benevolent artificial agents, tasked with preventing and mitigating cognitive distortions inflicted upon us by their malicious cousins. Can the distortions of human causal cognition be corrected on a more solid foundation of artificial causal cognition? In the present paper, we study a simple model of causal cognition, viewed as a quest for causal models. We show that, under very mild and hard to avoid assumptions, there are always self-confirming causal models, which perpetrate self-deception, and seem to preclude a royal road to objectivity. 

In this paper we generalise the notion of extensional (functional) equivalence of programs to abstract equivalences induced by abstract interpretations. The standard notion of extensional equivalence is recovered as the special case, induced by the concrete interpretation. Some properties of the extensional equivalence, such as the one spelled out in Rice’s theorem, lift to the abstract equivalences in suitably generalised forms. On the other hand, the generalised framework gives rise to interesting and important new properties, and allows refined, non-extensional analyses. In particular, since programs turn out to be extensionally equivalent if and only if they are equivalent just for the concrete interpretation, it follows that any non-trivial abstract interpretation uncovers some intensional aspect of programs. This striking result is also effective, in the sense that it allows constructing, for any non-trivial abstraction, a pair of programs that are extensionally equivalent, but have different abstract semantics. The construction is based on the fact that abstract interpretations are always sound, but that they can be made incomplete through suitable code ransformations. To construct these transformations, we introduce a novel technique for building incompleteness cliques of extensionally equivalent yet abstractly distinguishable programs: They are built together with abstract interpretations that produce false alarms. While programs are forced into incompleteness cliques using both control-flow and data-flow transformations, the main result follows from limitations of data-flow ransformations with respect to control-flow ones. A further consequence is that the class of incomplete programs for a non-trivial abstraction is Turing complete. The obtained results also shed a new light on the relation between the techniques of code obfuscation and the precision in program analysis. 

Enterprises, including military, law enforcement, medical, financial, and commercial organizations, must often share large quantities of data, some potentially sensitive, with many other enterprises. A key issue, the mechanics of data sharing, involves how to precisely and unambiguously specify which data to share with which partner or group of partners. This issue can be addressed through a system of formal data sharing policy definitions and automated enforcement. Several challenges arise when specifying enterprise-level data sharing policies. A first challenge involves the scale and complexity of data types to be shared. An easily understood method is required to represent and visualize an enterprise’s data types and their relationships so that users can quickly, easily, and precisely specify which data types and relationships to share. A second challenge involves the scale and complexity of data sharing partners. Enterprises typically have many partners involved in different projects, and there are often complex hierarchies among groups of partners that must be considered and navigated to specify which partners or groups of partners to include in a data sharing policy. A third challenge is that defining policies formally, given the first two challenges of scale and complexity, requires complex, precise language, but these languages are difficult to use by non-specialists. More useable methods of policy specification are needed. Our approach was to develop a software wizard that walks users through a series of steps for defining a data sharing policy. A combination of innovative and well known methods is used to address these challenges of scale, complexity, and usability.