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 rationality can only properly be assessed for pairs of learning and decision processes. We thus present a formal framework for modeling such pairs of cognitive processes, and thereby assessing their adaptive rationality relative to the environment and the agent’s goals. We then use this high-level formal framework on specific cases by demonstrating how natural formal constraints on decision-making can lead to substantive predictions about adaptively rational learning and representation; and characterizing adaptively rational learning for fast-and-frugal one-reason decision-making. (shrink)

According to Bayesian epistemology, rational learning from experience is consistent learning, that is learning should incorporate new information consistently into one's old system of beliefs. Simon M. Huttegger argues that this core idea can be transferred to situations where the learner's informational inputs are much more limited than Bayesianism assumes, thereby significantly expanding the reach of a Bayesian type of epistemology. What results from this is a unified account of probabilistic learning in the tradition of (...) Richard Jeffrey's 'radical probabilism'. Along the way, Huttegger addresses a number of debates in epistemology and the philosophy of science, including the status of prior probabilities, whether Bayes' rule is the only legitimate form of learning from experience, and whether rational agents can have sustained disagreements. His book will be of interest to students and scholars of epistemology, of game and decision theory, and of cognitive, economic, and computer sciences. (shrink)

Rational Rules argues that moral learning can be understood in terms of general-purpose rational learning procedures. Nichols provides statistical learning accounts of some fundamental aspects of moral development, combining aspects of traditional empiricist and rationalist approaches.

ABSTRACTJames Woodward and John Allman [2007, 2008] and Peter Railton [2014, 2016] argue that our moral intuitions are products of sophisticated rational learning systems. I investigate the implications that this discovery has for intuition-based philosophical methodologies. Instead of vindicating the conservative use of intuitions in philosophy, I argue that what I call the rational learning strategy fails to show philosophers are justified in appealing to their moral intuitions in philosophical arguments without giving reasons why those intuitions (...) are trustworthy. Despite the fact that our intuitions are outputs of surprisingly sophisticated learning mechanisms, we do not have reason to unreflectively trust them when offering arguments in moral philosophy. (shrink)

This article proposes a new model of human concept learning that provides a rational analysis of learning feature‐based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well‐known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7‐feature concepts—a more (...) natural setting in several ways—and again finds that the model explains human performance. (shrink)

The issue whether psychopathic offenders are practically rational has attracted philosophical attention. The problem is relevant in theoretical discussions on moral psychology and in those concerning the appropriate social response to the crimes of these individuals. We argue that classical and current experiments concerning the instrumental learning in psychopaths cannot directly support the conclusion that they have impaired instrumental rationality, construed as the ability for transferring the motivation by means-ends reasoning. In fact, we defend the different claim that (...) these experiments appear to show that psychopaths in certain circumstances are not aware of the relevant means for their ends. Moreover, we suggest how further empirical research could help to settle the issue. (shrink)

Game theory is often used to explain behavior. Such explanations often proceed by demonstrating that the behavior in question is a Nash equilibrium. Agents are in Nash equilibrium if each agent’s strategy maximizes her payoff given her opponents’ strategies. Nash equilibriums are fundamentally static, but it is usually assumed that equilibriums will be the outcome of a dynamic process of learning or evolution. This article demonstrates that, even in the most simple setting, this need not be true. In two-strategy (...) games with just a single equilibrium, a family of imitative learning dynamics does not lead to equilibrium. (shrink)

Stanovich & West's target article undervalues the power of implicit learning (particularly reinforcement learning). Implicit learning may allow the learning of more rational responses–and sometimes even generalisation of knowledge–in contexts where explicit, abstract knowledge proves only of limited value, such as for economic decision-making. Four other comments are made.

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape pattern (...) features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis. (shrink)

Purpose: Ernst von Glasersfeld’s question concerning the relationship between scientific/ rational knowledge and the domain of wisdom and how these forms of knowledge come about is the starting point. This article aims at developing an epistemological as well as methodological framework that is capable of explaining how profound change can be brought about in various contexts, such as in individual cultivation, in organizations, in processes of radical innovation, etc. This framework is based on the triple-loop learning strategy and (...) the U-theory approach, which opens up a perspective on how the domains of scientific/rational knowledge, constructivism, and wisdom could grow together more closely. Design/Structure: This article develops a strategy which is referred to as “tripleloop learning,” which is not only the basis for processes of profound change, but also brings about a new dimension in the field of learning and knowledge dynamics: the existential realm and the domain of wisdom. A concrete approach that puts into practice the tripleloop learning strategy is presented. The final section shows, how these concepts can be interpreted in the context of the constructivist approach and how they might offer some extensions to this paradigm. Findings: The process of learning and change has to be extended to a domain that concerns existential issues as well as questions of wisdom. Profound change can only happen if these domains are taken into consideration. The tripleloop learning strategy offers a model that fulfills this criterion. It is an “epistemo-existential strategy” for profound change on various levels. Conclusions: The (cognitive) processes and attitudes of receptivity, suspension, redirecting, openness, deep knowing, as well as “profound change/innovation from the interior” turn out to be core concepts in this process. They are compatible with constructivist concepts. Von Glasersfeld’s concept of functional fitness is carried to an extreme in the suggested approach of profound change and finds an extension in the existential domain. Key words: Double-loop learning, individual cultivation, (radical) innovation, knowledge creation, knowledge society, personality development, presencing, profound change, triple-loop learning, U-theory, wisdom. (shrink)

This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued (...) that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems. (shrink)

The rational-decision approach is superior to the associative-learning approach of Cook et al. at explaining why mirror neurons (MNs) fire or do not fire – even when the stimulus is the same. The rational-decision approach is superior because it starts with the analysis of the intention of the organism, i.e., with the identification of the specific objective or goal that the organism is trying to maximize.

Penguins jumping off a cliff, economic forecasters and financial advisors speculating against a currency, and farmers using traditional methods in India are all practising social learning. Such learning from the behavior of others may and does lead to herds, crashes, and booms. These issues have become, over the last ten years, an exciting field of research in theoretical and applied economics, finance, and in other social sciences. This book provides both an informal introduction and in-depth insights into the (...) subject. Each chapter is devoted to a separate issue: individuals learn from the observations of actions, the outcomes of these actions, and from what others say. They may delay or make an immediate decision; they may compete against others or gain from cooperation; they make decisions about investment, crop choices, and financial investments. The book highlights the similarities and the differences between the various cases. (shrink)

Journal of Philosophy of Education, EarlyView.

W D Hudson; Learning to be Rational, Journal of Philosophy of Education, Volume 11, Issue 1, 30 May 2006, Pages 39–56, https://doi.org/10.1111/j.1467-9752.1977.

My aim in this paper is fairly modest. I obviously do not claim that there has never been or could never be an instance of irrational or fallacious appeals to quotations from canonical sources in the marxist tradition. Instead, I claim that the practice of using quotations from canonical sources is not, as such, irrational. If we understand the epistemological infrastructure of the practice -- the rational underpinnings of it -- we can grasp how these citations appeal to the (...) presumptive authoritativeness of formulations that condense or concisely convey the core of insights that emerged from learning processes that the intellectual tradition of marxism has already traversed. The rational underpinnings of the practice include, first, the neo-Hegelian idea that we can justify a view we now hold by rationally reconstructing the insight-motivated learning process from which it emerged, and second, the convention that these rational reconstructions do not have to be elaborated in each insistence, but can be invoked in a short-form way, such as by citing the name of a decision or a concise statement of it (like a short quotation), as is common in the epistemologically similar practice of precedential reasoning in common law juridical justification. No claim is true just because of who made it; but a claim might be presumptively authoritative in cases where it was made at the end of a learning process, which has not been overtaken by a change in the circumstances or further learning. (shrink)

Literature on consumers’ ethical decision making is rooted in a rationalist perspective that emphasizes the role of moral reasoning. However, the view of ethical consumption as a thorough rational and conscious process fails to capture important elements of human cognition, such as emotions and intuitions. Based on moral psychology and microsociology, this paper proposes a holistic and integrated framework showing how emotive and intuitive information processing may foster ethical consumption at individual and social levels. The model builds on social (...) intuitionism to show how consumers’ a priori affect-laden intuitive moral judgments impact their post hoc reflective moral reasoning. Symbolic interactionism is used to interpret consumers as interdependent and socially embedded agents that self-construct their social identity through interactions with other consumers. The proposed social intuitionist framework of consumers’ ethical decision making shows that other-oriented moral emotions—such as elevation, gratitude, and empathy—interact with persuasion and social influence in ethical consumption. Consequently, moral emotions and intuition drive interpersonal persuasion among ethical consumers. Theoretical propositions and implications for consumer ethics theory and practice are discussed. (shrink)

We challenge Binz et al.'s claim of meta-learned model superiority over Bayesian inference for large world problems. While comparing Bayesian priors to model-training decisions, we question meta-learning feature exclusivity. We assert no special justification for rational Bayesian solutions to large world problems, advocating exploring diverse theoretical frameworks beyond rational analysis of cognition for research advancement.

In order to account for how children can generalize words beyond a very limited set of labeled examples, Bloom's proposal of word learning requires two extensions: a better understanding of the “general learning and memory abilities” involved, and a principled framework for integrating multiple conflicting constraints on word meaning. We propose a framework based on Bayesian statistical inference that meets both of those needs.

In this contribution we analyze how the actions of rational agents feed back on their beliefs. We present two agent-based computer simulations studying complex social interactions in which agents that follow utility maximizing strategies thereby deteriorate their own long-term quality of beliefs. We take these results as a starting point to discuss the complex relationship between rational action couched in terms of maximizing utility and the emergence of informational inequalities.

People draw subtle distinctions in the normative domain. But it remains unclear exactly what gives rise to such distinctions. On one prominent approach, emotion systems trigger non-utilitarian judgments. The main alternative, inspired by Chomskyan linguistics, suggests that moral distinctions derive from an innate moral grammar. In this article, we draw on Bayesian learning theory to develop a rational learning account. We argue that the ‘size principle’, which is implicated in word learning, can also explain how children (...) would use scant and equivocal evidence to interpret candidate rules as applying more narrowly than utilitarian rules. (shrink)

This paper identifies the major failings of mainstream economics and the rational choice theory it relies upon. These failures were identified by the four figures mentioned in the title: economics treats agents as rational fools; by the time the long … More ›.

We have analyzed the long-term success of various social learning heuristics. Specifically, we have examined their ability to persist and to replace other heuristics, and we have done this in two broadly different kinds of environments: environments in which the optimal behavior varies across space, or through time. Because each social learning heuristic also shapes its environment as individuals use it, our analysis has been at the same time ecological, game-theoretic, and evolutionary: The performance of each social (...) class='Hi'>learning heuristic depends on assumptions about the environment and population structure in which it is used. The use of a particular social learning heuristic will affect the success of this and other social learning heuristics over time. Therefore, our analysis is directed at the long-term survival and reproduction of each social learning heuristic. (shrink)

Rationalization occurs when a person has performed an action and then concocts the beliefs and desires that would have made it rational. Then, people often adjust their own beliefs and desires to match the concocted ones. While many studies demonstrate rationalization, and a few theories describe its underlying cognitive mechanisms, we have little understanding of its function. Why is the mind designed to construct post hoc rationalizations of its behavior, and then to adopt them? This may accomplish an important (...) task: transferring information between the different kinds of processes and representations that influence our behavior. Human decision making does not rely on a single process; it is influenced by reason, habit, instinct, norms, and so on. Several of these influences are not organized according to rational choice. Rationalization extracts implicit information – true beliefs and useful desires – from the influence of these non-rational systems on behavior. This is a useful fiction – fiction, because it imputes reason to non-rational psychological processes; useful, because it can improve subsequent reasoning. More generally, rationalization belongs to the broader class of representational exchange mechanisms, which transfer information between many different kinds of psychological representations that guide our behavior. Representational exchange enables us to represent any information in the manner best suited to the particular tasks that require it, balancing accuracy, efficiency, and flexibility in thought. The theory of representational exchange reveals connections between rationalization and theory of mind, inverse reinforcement learning, thought experiments, and reflective equilibrium. (shrink)

How do people learn multisensory, or amodal, representations, and what consequences do these representations have for perceptual performance? We address this question by performing a rational analysis of the problem of learning multisensory representations. This analysis makes use of a Bayesian nonparametric model that acquires latent multisensory features that optimally explain the unisensory features arising in individual sensory modalities. The model qualitatively accounts for several important aspects of multisensory perception: (a) it integrates information from multiple sensory sources in (...) such a way that it leads to superior performances in, for example, categorization tasks; (b) its performances suggest that multisensory training leads to better learning than unisensory training, even when testing is conducted in unisensory conditions; (c) its multisensory representations are modality invariant; and (d) it predicts ‘‘missing” sensory representations in modalities when the input to those modalities is absent. Our rational analysis indicates that all of these aspects emerge as part of the optimal solution to the problem of learning to represent complex multisensory environments. (shrink)

Research on human causal learning has largely focused on strength learning, or on computational-level theories; there are few formal algorithmic models of how people learn causal structure from covariations. We introduce a model that learns causal structure in a local manner via prediction-error learning. This local learning is then integrated dynamically into a unified representation of causal structure. The model uses computationally plausible approximations of rational learning, and so represents a hybrid between the associationist (...) and rational paradigms in causal learning research. We conclude by showing that the model provides a good fit to data from a previous experiment. (shrink)

A historical theory of rational norms claims that, if we are supposed to think rationally, this is because it is biologically normal for us to do so. The historical theorist is committed to the view that we are supposed to think rationally only if, in the past, adult humans sometimes thought rationally. I consider whether there is any plausible model of rational norms that can be adopted by the historical theorist that is compatible with the claim that adult (...) human beings are subject to rational norms, given certain plausible empirical assumptions about our history and capabilities. I suggest that there is one such model: this model centres on the idea that a procedure is rational if it has been endorsed (or at least not rejected) by mechanisms that have the function to ensure that the subject learns to reason in a way that approaches a certain kind of optimality. (shrink)

Accuracy-first accounts of rational learning attempt to vindicate the intuitive idea that, while rationally-formed belief need not be true, it is nevertheless likely to be true. To this end, they attempt to show that the Bayesian's rational learning norms are a consequence of the rational pursuit of accuracy. Existing accounts fall short of this goal, for they presuppose evidential norms which are not and cannot be vindicated in terms of the single-minded pursuit of accuracy. I (...) propose an alternative account, according to which learning experiences rationalize changes in the way you value accuracy, which in turn rationalize changes in belief. I show that this account is capable of vindicating the Bayesian's rational learning norms in terms of the single-minded pursuit of accuracy, so long as accuracy is rationally valued. (shrink)

Reflection and martingale principles are central to models of rational learning. They can be justified in a variety of ways. In what follows we study martingale and reflection principles in the con...