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Forthcoming articles
  1.  17
    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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  2.  19
    Stuart Armstrong, Anders Sandberg & Nick Bostrom (forthcoming). Thinking Inside the Box: Using and Controlling an Oracle AI. Minds and Machines.
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  3.  4
    Mark Jm Bishop & C. Sdrolia (forthcoming). Rethinking Construction. On Luciano Floridi's 'Against Digital Ontology'. Minds and Machines.
  4. Jamie Byrom, Christine Consell, Michael Gorman, Michael Riley & Andrew Wrenn (forthcoming). Think Through History. Minds and Machines.
     
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  5.  2
    Arthur Charlesworth (forthcoming). A Theorem About Computationalism and “Absolute” Truth. Minds and Machines:1-22.
    This article focuses on issues related to improving an argument about minds and machines given by Kurt Gödel in 1951, in a prominent lecture. Roughly, Gödel’s argument supported the conjecture that either the human mind is not algorithmic, or there is a particular arithmetical truth impossible for the human mind to master, or both. A well-known weakness in his argument is crucial reliance on the assumption that, if the deductive capability of the human mind is equivalent to that of a (...)
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  6. Carol Cleland (forthcoming). Effective Procedures and Causal Processes. Minds and Machines.
     
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  7. Jason Dana & Clintin P. Davis-Stober (forthcoming). Rational Foundations of Fast and Frugal Heuristics: The Ecological Rationality of Strategy Selection Via Improper Linear Models. Minds and Machines:1-26.
    Research on “improper” linear models has shown that predetermined weighting schemes for the linear model, such as equally weighting all predictors, can be surprisingly accurate on cross-validation. We review recent advances that can characterize the optimal choice of an improper linear model. We extend this research to the understanding of fast and frugal heuristics, particularly to the ecologically rational goal of understanding in which task environments given heuristics are optimal. We demonstrate how to test this model using the Recognition Heuristic (...)
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  8. K. Dautenhahn (forthcoming). Socially Intelligent Agents-Towards a Science of Social Minds. Submitted To. Minds and Machines.
     
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  9.  4
    Wai-Tat Fu (forthcoming). The Central Role of Heuristic Search in Cognitive Computation Systems. Minds and Machines:1-21.
    This paper focuses on the relation of heuristic search and level of intelligence in cognitive computation systems. The paper begins with a review of the fundamental properties of a cognitive computation system, which is defined generally as a control system that generates goal-directed actions in response to environmental inputs and constraints. An important property of cognitive computations is the need to process local cues in symbol structures to access and integrate distal knowledge to generate a response. To deal with uncertainties (...)
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  10.  23
    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 and boost policies. (...)
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  11.  3
    Ralph Hertwig & Arthur Paul Pedersen (forthcoming). Finding Foundations for Bounded and Adaptive Rationality. Minds and Machines:1-8.
  12. K. Laskey (forthcoming). Quantum Physical Symbol Systems. Submitted to Special Issue Of. Minds and Machines.
     
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  13.  2
    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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  14. M. I. T. OpenCourseWare (forthcoming). MIT OpenCourseWare. Minds and Machines.
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  15.  1
    Ronald Ortner (forthcoming). Optimal Behavior is Easier to Learn Than the Truth. Minds and Machines:1-10.
    We consider a reinforcement learning setting where the learner is given a set of possible models containing the true model. While there are algorithms that are able to successfully learn optimal behavior in this setting, they do so without trying to identify the underlying true model. Indeed, we show that there are cases in which the attempt to find the true model is doomed to failure.
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  16. O. Shagrir & I. Pitowsky (forthcoming). The Church-Turing Thesis and Hyper-Computation. Minds and Machines.
  17.  5
    Patrick Suppes (forthcoming). Qualitative Axioms of Uncertainty as a Foundation for Probability and Decision-Making. Minds and Machines:1-18.
    Although the concept of uncertainty is as old as Epicurus’s writings, and an excellent quantitative theory, with entropy as the measure of uncertainty having been developed in recent times, there has been little exploration of the qualitative theory. The purpose of the present paper is to give a qualitative axiomatization of uncertainty, in the spirit of the many studies of qualitative comparative probability. The qualitative axioms are fundamentally about the uncertainty of a partition of the (...)
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  18.  3
    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 (...)
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  19.  1
    Paolo Vicig (forthcoming). A Note on the Equivalence of Coherence and Constrained Coherence. Minds and Machines:1-3.
    Constrained coherence is compared to coherence and its role in the behavioural interpretation of coherence is discussed. The equivalence of these two notions is proven for coherent conditional previsions, showing that the same course of reasoning applies to several similar concepts developed in the realm of imprecise probability theory.
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  20.  2
    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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