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Forthcoming articles
  1. Keith Allen (forthcoming). Colour Physicalism, Naïve Realism, and the Argument From Structure. Minds and Machines:1-20.
    Colours appear to instantiate a number of structural properties: for instance, they stand in distinctive relations of similarity and difference, and admit of a fundamental distinction into unique and binary. Accounting for these structural properties is often taken to present a serious problem for physicalist theories of colour. This paper argues that a prominent attempt by Byrne and Hilbert to account for the structural properties of the colours, consistent with the claim that colours are types of surface spectral reflectance, is (...)
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  2. John Danaher (forthcoming). Why AI Doomsayers Are Like Sceptical Theists and Why It Matters. Minds and Machines:1-16.
    An advanced artificial intelligence (a “superintelligence”) could pose a significant existential risk to humanity. Several research institutes have been set-up to address those risks. And there is an increasing number of academic publications analysing and evaluating their seriousness. Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies represents the apotheosis of this trend. In this article, I argue that in defending the credibility of AI risk, Bostrom makes an epistemic move that is analogous to one made by so-called sceptical theists in the debate (...)
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  3. Gerhard Schurz & Paul D. Thorn (forthcoming). The Revenge of Ecological Rationality: Strategy Selection by Meta-Induction Within Changing Environments. Minds and Machines:1-29.
    ccording to the paradigm of adaptive rationality, successful inference and prediction methods tend to be local and frugal. As a complement to work within this paradigm, we investigate the problem of selecting an optimal combination of prediction methods from a given toolbox of such local methods, in the context of changing environments. These selection methods are called meta-inductive (MI) strategies, if they are based on the success-records of the toolbox-methods. No absolutely optimal MI strategy exists—a fact that we call the (...)
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  4. Stuart Armstrong, Anders Sandberg & Nick Bostrom (forthcoming). Thinking Inside the Box: Using and Controlling an Oracle AI. Minds and Machines.
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  5. Mark Jm Bishop & C. Sdrolia (forthcoming). Rethinking Construction. On Luciano Floridi's 'Against Digital Ontology. Minds and Machines.
  6. Derek H. Brown (forthcoming). Colour Layering and Colour Relationalism. Minds and Machines:1-15.
    Colour Relationalism asserts that colours are non-intrinsic or inherently relational properties of objects, properties that depend not only on a target object but in addition on some relation that object bears to other objects. The most powerful argument for Relationalism infers the inherently relational character of colour from cases in which one’s experience of a colour contextually depends on one’s experience of other colours. Experienced colour layering—say looking at grass through a tinted window and experiencing opaque green through transparent grey—demands (...)
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  7. Jamie Byrom, Christine Consell, Michael Gorman, Michael Riley & Andrew Wrenn (forthcoming). Think Through History. Minds and Machines.
     
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  8. Carol Cleland (forthcoming). Effective Procedures and Causal Processes. Minds and Machines.
     
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  9. K. Dautenhahn (forthcoming). Socially Intelligent Agents-Towards a Science of Social Minds. Submitted To. Minds and Machines.
  10. Till Grüne-Yanoff & Ralph Hertwig (forthcoming). Nudge Versus Boost: How Coherent Are Policy and Theory? Minds and Machines:1-35.
    If citizens’ behavior threatens to harm others or seems not to be in their own interest , it is not uncommon for governments to attempt to change that behavior. Governmental policy makers can apply established tools from the governmental toolbox to this end . Alternatively, they can employ new tools that capitalize on the wealth of knowledge about human behavior and behavior change that has been accumulated in the behavioral sciences . Two contrasting approaches to behavior change are nudge policies (...)
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  11. K. Laskey (forthcoming). Quantum Physical Symbol Systems. Submitted to Special Issue Of. Minds and Machines.
  12. Hansjörg Neth, Chris R. Sims & Wayne D. Gray (forthcoming). Rational Task Analysis: A Methodology to Benchmark Bounded Rationality. Minds and Machines:1-24.
    How can we study bounded rationality? We answer this question by proposing rational task analysis —a systematic approach that prevents experimental researchers from drawing premature conclusions regarding the rationality of agents. RTA is a methodology and perspective that is anchored in the notion of bounded rationality and aids in the unbiased interpretation of results and the design of more conclusive experimental paradigms. RTA focuses on concrete tasks as the primary interface between agents and environments and requires explicating essential task elements, (...)
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  13. M. I. T. OpenCourseWare (forthcoming). MIT OpenCourseWare. Minds and Machines.
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  14. O. Shagrir & I. Pitowsky (forthcoming). The Church-Turing Thesis and Hyper-Computation. Minds and Machines.
  15. Peter M. Todd & Henry Brighton (forthcoming). Building the Theory of Ecological Rationality. Minds and Machines:1-22.
    While theories of rationality and decision making typically adopt either a single-powertool perspective or a bag-of-tricks mentality, the research program of ecological rationality bridges these with a theoretically-driven account of when different heuristic decision mechanisms will work well. Here we described two ways to study how heuristics match their ecological setting: The bottom-up approach starts with psychologically plausible building blocks that are combined to create simple heuristics that fit specific environments. The top-down approach starts from the statistical problem facing the (...)
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  16. Sarah Wellen & David Danks (forthcoming). Adaptively Rational Learning. Minds and Machines:1-16.
    Research on adaptive rationality has focused principally on inference, judgment, and decision-making that lead to behaviors and actions. These processes typically require cognitive representations as input, and these representations must presumably be acquired via learning. Nonetheless, there has been little work on the nature of, and justification for, adaptively rational learning processes. In this paper, we argue that there are strong reasons to believe that some learning is adaptively rational in the same way as judgment and decision-making. Indeed, overall adaptive (...)
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