Why should moral philosophers, moral psychologists, and machine ethicists care about computational complexity? Debates on whether artificial intelligence (AI) can or should be used to solve problems in ethical domains have mainly been driven by what AI can or cannot do in terms of human capacities. In this paper, we tackle the problem from the other end by exploring what kind of moral machines are possible based on what computational systems can or cannot do. To do so, we analyze normative (...) ethics through the lens of computational complexity. First, we introduce computational complexity for the uninitiated reader and discuss how the complexity of ethical problems can be framed within Marr’s three levels of analysis. We then study a range of ethical problems based on consequentialism, deontology, and virtue ethics, with the aim of elucidating the complexity associated with the problems themselves (e.g., due to combinatorics, uncertainty, strategic dynamics), the computational methods employed (e.g., probability, logic, learning), and the available resources (e.g., time, knowledge, learning). The results indicate that most problems the normative frameworks pose lead to tractability issues in every category analyzed. Our investigation also provides several insights about the computational nature of normative ethics, including the differences between rule- and outcome-based moral strategies, and the implementation-variance with regard to moral resources. We then discuss the consequences complexity results have for the prospect of moral machines in virtue of the trade-off between optimality and efficiency. Finally, we elucidate how computational complexity can be used to inform both philosophical and cognitive-psychological research on human morality by advancing the moral tractability thesis. (shrink)

Much of our thinking focuses on deciding what to do in situations where the space of possible options is too large to evaluate exhaustively. Previous work has found that people do this by learning the general value of different behaviors, and prioritizing thinking about high-value options in new situations. Is this good-action bias always the best strategy, or can thinking about low-value options sometimes become more beneficial? Can people adapt their thinking accordingly based on the situation? And how do we (...) know what to think about in novel events? Here, we developed a block-puzzle paradigm that enabled us to measure people's thinking plans and compare them to a computational model of rational thought. We used two distinct response methods to explore what people think about—a self-report method, in which we asked people explicitly to report what they thought about, and an implicit response time method, in which we used people's decision-making times to reveal what they thought about. Our results suggest that people can quickly estimate the apparent value of different options and use this to decide what to think about. Critically, we find that people can flexibly prioritize whether to think about high-value options (Experiments 1 and 2) or low-value options (Experiments 3, 4, and 5), depending on the problem. Through computational modeling, we show that these thinking strategies are broadly rational, enabling people to maximize the value of long-term decisions. Our results suggest that thinking plans are flexible: What we think about depends on the structure of the problems we are trying to solve. (shrink)

Predictable polarization is everywhere: we can often predict how people’s opinions, including our own, will shift over time. Extant theories either neglect the fact that we can predict our own polarization, or explain it through irrational mechanisms. They needn’t. Empirical studies suggest that polarization is predictable when evidence is ambiguous, that is, when the rational response is not obvious. I show how Bayesians should model such ambiguity and then prove that—assuming rational updates are those which obey the value of evidence—ambiguity (...) is necessary and sufficient for the rationality of predictable polarization. The main theoretical result is that there can be a series of such updates, each of which is individually expected to make you more accurate, but which together will predictably polarize you. Polarization results from asymmetric increases in accuracy. This mechanism is not only theoretically possible, but empirically plausible. I argue that cognitive search—searching a cognitively accessible space for a particular item—often yields asymmetrically ambiguous evidence, I present an experiment supporting its polarizing effects, and I use simulations to show how it can explain two of the core causes of polarization: confirmation bias and the group polarization effect. (shrink)

Theories of rational decision making often abstract away from computational and other resource limitations faced by real agents. An alternative approach known as resource rationality puts such matters front and center, grounding choice and decision in the rational use of finite resources. Anticipated by earlier work in economics and in computer science, this approach has recently seen rapid development and application in the cognitive sciences. Here, the theory of rationality plays a dual role, both as a framework for normative assessment (...) and as a source of scientific hypotheses about how mental processes in fact work. The latter project, often called rational analysis, depends for its success on a fine-grained characterization of the computational problem facing a decision maker, which may in turn depend on realistic assumptions about what the relevant agent is like. As a consequence, resource rationality involves a delicate, but often fruitful interplay between the normative and the descriptive. (shrink)

Whereas most commentators agree about the centrality of narratives in decision-making, the commentaries revealed little consensus about the nature of radical uncertainty. Here we consider thirteen objections to our views, including our characterization of the uncertain decision environment and associated cognitive, affective, and social processes. We conclude that under radical uncertainty, narratives rather than probabilities are the currency of thought.

Bounded rationality gets a bad rap in epistemology. It is argued that theories of bounded rationality are overly context‐sensitive; conventionalist; or dependent on ordinary language (Carr, 2022; Pasnau, 2013). In this paper, I have three aims. The first is to set out and motivate an approach to bounded rationality in epistemology inspired by traditional theories of bounded rationality in cognitive science. My second aim is to show how this approach can answer recent challenges raised for theories of bounded rationality. My (...) third aim is to clarify the role of rational ideals in bounded rationality. (shrink)

Perception solves computationally demanding problems at lightning fast speed. It recovers sophisticated representations of the world from degraded inputs, often in a matter of milliseconds. Any theory of perception must be able to explain how this is possible; in other words, it must be able to explain perception's computational tractability. One of the few attempts to move toward such an explanation has been the information encapsulation hypothesis, which posits that perception can be fast because it keeps computational costs low by (...) forgoing access to information stored in cognition. I argue that we have no compelling reason to believe that encapsulation explains (or even contributes to an explanation of) perceptual tractability, and much reason to doubt it. This is because there exist much deeper computational challenges for perception than information access, and these threaten to make the costs of access irrelevant. If this is right, it undermines a core computational motivation for encapsulation and sends us back to the drawing board for explanations of perceptual tractability. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

According to evidentialist views, credence in a proposition p should be proportional to the degree of evidential support that one has in favor of p. However, empirical evidence suggests that our credences are systematically sensitive to practical factors. In this article, I provide a cost–benefit analysis of credences' practical sensitivity. The upshot of this analysis is that credences sensitive to practical factors fare better than practically insensitive ones along several dimensions. All things considered, our credences should be sensitive to practical (...) factors. (shrink)

The psychology of reasoning uses norms to categorize responses to reasoning tasks as correct or incorrect in order to interpret the responses and compare them across reasoning tasks. This raises the arbitration problem: any number of norms can be used to evaluate the responses to any reasoning task and there doesn’t seem to be a principled way to arbitrate among them. Elqayam and Evans have argued that this problem is insoluble, so they call for the psychology of reasoning to dispense (...) with norms entirely. Alternatively, Stupple and Ball have argued that norms must be used, but the arbitration problem should be solved by favouring norms that are sensitive to the context, constraints, and goals of human reasoning. In this paper, I argue that the design of reasoning tasks requires the selection of norms that are indifferent to the factors that influence human responses to the tasks—which aren’t knowable during the task design phase, before the task has been given to human subjects. Moreover, I argue that the arbitration problem is easily dissolved: any well-designed task will contain instructions that implicitly or explicitly specify a single determinate norm, which specifies what would count as a solution to the task—independently of the context, constraints, and goals of human reasoning. Finally, I argue that discouraging the use of these a priori task norms may impair the design of novel reasoning tasks. (shrink)

Perceptual representations pick out individuals and attribute properties to them. This paper considers the role of perceptual attribution in determining or guiding perceptual reference to objects. We consider three extant models of the relation between perceptual attribution and perceptual reference–all attribution guides reference, no attribution guides reference, or a privileged subset of attributions guides reference–and argue that empirical evidence undermines all three. We then defend a flexible-attributives model, on which the range of perceptual attributives used to guide reference shifts adaptively (...) with context. This model underscores the remarkable and dynamic intelligence of our perceptual capacities. We elucidate implications of the model for the boundary between perception and propositional thought. (shrink)

Common-sense (or folk) psychology holds that (generally) we do what we do for the reasons we have. This common-sense approach is embodied in claims like ‘I went to the kitchen because I wanted a drink’ and ‘She took a coat because she thought it might rain and hoped to stay dry’. However, the veracity of these common-sense psychological explanations has been challenged by experimental evidence (primarily from behavioural economics and social psychology) which appears to show that individuals are systematically irrational—that (...) often we do not do what we do because of the reasons we have. Recently, some of the same experimental evidence has also been used to level a somewhat different challenge at the common-sense view, arguing that the overarching aim of reasoning is not to deliver better or more reason-governed decisions for individual reasoners, but to improve group decision making or to protect an individual’s sense of self. This paper explores the range of challenges that experimental work has been taken to raise for the common-sense approach and suggests some potential responses. Overall, I argue that the experimental evidence surveyed should not lead us to a rejection of individual rationality. (shrink)

Many scientists routinely generalize from study samples to larger populations. It is commonly assumed that this cognitive process of scientific induction is a voluntary inference in which researchers assess the generalizability of their data and then draw conclusions accordingly. We challenge this view and argue for a novel account. The account describes scientific induction as involving by default a generalization bias that operates automatically and frequently leads researchers to unintentionally generalize their findings without sufficient evidence. The result is unwarranted, overgeneralized (...) conclusions. We support this account of scientific induction by integrating a range of disparate findings from across the cognitive sciences that have until now not been connected to research on the nature of scientific induction. The view that scientific induction involves by default a generalization bias calls for a revision of the current thinking about scientific induction and highlights an overlooked cause of the replication crisis in the sciences. Commonly proposed interventions to tackle scientific overgeneralizations that may feed into this crisis need to be supplemented with cognitive debiasing strategies against generalization bias to most effectively improve science. (shrink)

My aim in this paper is to develop a unified solution to two paradoxes of bounded rationality. The first is the regress problem that incorporating cognitive bounds into models of rational decisionmaking generates a regress of higher-order decision problems. The second is the problem of rational irrationality: it sometimes seems rational for bounded agents to act irrationally on the basis of rational deliberation. I review two strategies which have been brought to bear on these problems: the way of weakening which (...) responds by weakening rational norms, and the way of indirection which responds by letting the rationality of behavior be determined by the rationality of the deliberative processes which produced it. Then I propose and defend a third way to confront the paradoxes: the way of level separation. (shrink)

We do not all make choices, reason, interpret our experience, or respond to our environment in the same way. A recent surge of scientific interest has thrust these individual differences into the spotlight: researchers in cognitive psychology and neuroscience are now devoting increasing attention to cognitive variation. The philosophical dimensions of this research, however, have yet to be systematically explored. Here I make an initial foray by considering how cognitive variation is characterized. I present a central dilemma facing descriptions of (...) individual differences, discuss several distinctions used to categorize variation, and show that these distinctions require further elaboration. Finally, by canvassing several philosophical topics for which the characterization of cognitive variation may have significant implications, I argue that philosophers should take note of how and why our minds differ. (shrink)

We argue that the concept of practical wisdom is particularly useful for organizing, understanding, and improving human-machine interactions. We consider the relationship between philosophical analysis of wisdom and psychological research into the development of wisdom. We adopt a practical orientation that suggests a conceptual engineering approach is needed, where philosophical work involves refinement of the concept in response to contributions by engineers and behavioral scientists. The former are tasked with encoding as much wise design as possible into machines themselves, as (...) well as providing sandboxes or workspaces to help various stakeholders build practical wisdom in systems that are sufficiently realistic to aid transferring skills learned to real-world use. The latter are needed for the design of exercises and methods of evaluation within these workspaces, as well as ways of empirically assessing the transfer of wisdom from workspace to world. Systematic interaction between these three disciplines (and others) is the best approach to engineering wisdom for the machine age. (shrink)

In this paper we contrast bounded and ecological rationality with a proposed alternative, generative rationality. Ecological approaches to rationality build on the idea of humans as “intuitive statisticians” while we argue for a more generative conception of humans as “probing organisms.” We first highlight how ecological rationality’s focus on cues and statistics is problematic for two reasons: the problem of cue salience, and the problem of cue uncertainty. We highlight these problems by revisiting the statistical and cue-based logic that underlies (...) ecological rationality, which originate from the misapplication of concepts in psychophysics. We then work through the most popular experimental task in the ecological rationality literature—the city size task—to illustrate how psychophysical assumptions have informally been linked to ecological rationality. After highlighting these problems, we contrast ecological rationality with a proposed alternative, generative rationality. Generative rationality builds on biology—in contrast to ecological rationality’s focus on statistics. We argue that in uncertain environments cues are rarely given or available for statistical processing. Therefore we focus on the psychogenesis of awareness rather than psychophysics of cues. For any agent or organism, environments “teem” with indefinite cues, meanings and potential objects, the salience or relevance of which is scarcely obvious based on their statistical or physical properties. We focus on organism-specificity and the organism-directed probing that shapes awareness and perception. Cues in teeming environments are noticed when they serve as cues-for-something, requiring what might be called a “cue-to-clue” transformation. In this sense, awareness toward a cue or cues is actively “grown.” We thus argue that perception might more productively be seen as the presentation of cues and objects rather than their representation. This generative approach not only applies to relatively mundane organism interactions with their environments—as well as organism-object relationships and their embodied nature—but also has significant implications for understanding the emergence of novelty in economic settings. We conclude with a discussion of how our arguments link with—but modify—Herbert Simon’s popular “scissors” metaphor, as it applies to bounded rationality and its implications for decision making in uncertain, teeming environments. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 602-620, July 2022.

Topics in Cognitive Science, Volume 14, Issue 3, Page 451-466, July 2022.

Recent years have seen a Bayesian revolution in cognitive science. This should be of interest to metaphysicians of science, whose naturalist project involves working out the metaphysical implications of our leading scientific accounts, and in advancing our understanding of those accounts by drawing on the metaphysical frameworks developed by philosophers. Toward these ends, in this paper I develop a metaphysics of the Bayesian mind. My central claim is that the Bayesian approach supports a novel empirical argument for normativism, the thesis (...) that belief has a normative essence, with the norms in question being those of rationality. The argument I develop draws in part on the causal powers subset account of realization defended by Wilson and Shoemaker, with the link being that Bayesians often appeal to Marr’s framework of levels of analysis, which carries with it multiple realizability claims that can be understood in terms of the subset model. I then use the view developed, with its appeal to normativism and subset realization, to explain how Bayesians can accommodate empirical findings of irrationality that are sometimes thought to pose a problem for their view. (shrink)

While the existence of human cognitive and behavioral diversity is now widely recognized, it is not yet well established how to explain this diversity. In particular, it is still unclear how to determine whether any given instance of human cognitive and behavioral diversity is due to a common psychology that is merely “triggered” differently in different bio-cultural environments, or whether it is due to deeply and fundamentally different psychologies. This paper suggests that, to answer this question, we need to employ (...) subtle theoretical considerations of theory choice—especially the consideration of the complexity-weighted differential predictive successes of the two accounts. To make this clearer, the paper develops a novel analysis of the observed differences in human sharing dispositions. (shrink)

The flow construct has been influential within positive psychology, sport psychology, the science of consciousness, the philosophy of agency, and popular culture. In spite of its longstanding influence, it remains unclear [a] how the constituents of the flow state ‘hang together’—how they relate to each other causally and functionally—[b] in what sense flow is an ‘optimal experience,’ and [c] how best to describe the unique phenomenology of the flow state. As a result, difficulties persist for a clear understanding of the (...) flow state’s structure and function. After explicating the standard view of the flow construct, I articulate several basic questions regarding its nature and functional roles, and I argue that these questions are best answered by integrating flow within broader streams of research on the dynamics of thought, on cognitive control resource allocation, and on creative thought. (shrink)

What is the relationship between the concepts of the predictive processing theory of brain functioning and the everyday concepts with which people conduct and explain their mental lives? To answer this question, we focus on predictive processing explanations of mental disorder that appeal to false inference. After distinguishing two concepts of false inference, we survey four ways of understanding the relationship between explanations of mental phenomena at the personal and sub-personal level. We then argue that if predictive processing accurately accounts (...) for psychological and socio-cultural dynamics, then personal and sub-personal concepts must display a looping relationship that can impact the nature of at least some mental phenomena. In particular, explaining the phenomenon of delusion in terms of the sub-personal concept of false inference should change the nature of delusional experiences themselves. (shrink)

Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration. -/- Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science. (shrink)

It would be good to have a Bayesian decision theory that assesses our decisions and thinking according to everyday standards of rationality — standards that do not require logical omniscience (Garber 1983, Hacking 1967). To that end we develop a “fragmented” decision theory in which a single state of mind is represented by a family of credence functions, each associated with a distinct choice condition (Lewis 1982, Stalnaker 1984). The theory imposes a local coherence assumption guaranteeing that as an agent's (...) attention shifts, successive batches of "obvious" logical information become available to her. A rule of expected utility maximization can then be applied to the decision of what to attend to next during a train of thought. On the resulting theory, rationality requires ordinary agents to be logically competent and to often engage in trains of thought that increase the unification of their states of mind. But rationality does not require ordinary agents to be logically omniscient. (shrink)

Several authors have investigated the question of whether canonical logic-based accounts of belief revision, and especially the theory of AGM revision operators, are compatible with the dynamics of Bayesian conditioning. Here we show that Leitgeb's stability rule for acceptance, which has been offered as a possible solution to the Lottery paradox, allows to bridge AGM revision and Bayesian update: using the stability rule, we prove that AGM revision operators emerge from Bayesian conditioning by an application of the principle of maximum (...) entropy. In situations of information loss, or whenever the agent relies on a qualitative description of her information state - such as a plausibility ranking over hypotheses, or a belief set - the dynamics of AGM belief revision are compatible with Bayesian conditioning; indeed, through the maximum entropy principle, conditioning naturally generated AGM revision operators. This mitigates an impossibility theorem of Lin and Kelly for tracking Bayesian conditioning with AGM revision, and suggests an approach to the compatibility problem that highlights the information loss incurred by acceptance rules in passing from probabilistic to qualitative representations of beliefs. (shrink)

Which rules should guide our reasoning? Human reasoners often use reasoning shortcuts, called heuristics, which function well in some contexts but lack the universality of reasoning rules like deductive implication or inference to the best explanation. Does it follow that human reasoning is hopelessly irrational? I argue: no. Heuristic reasoning often represents human reasoners reaching a local rational maximum, reasoning more accurately than if they try to implement more “ideal” rules of reasoning. I argue this is a genuine rational achievement. (...) Our ideal rational advisors would advise us to reason with heuristic rules, not more complicated ideal rules. I argue we do not need a radical new account of epistemic norms to make sense of the success of heuristic reasoning. (shrink)

The empirical study of belief is emerging at a rapid clip, uniting work from all corners of cognitive science. Reliance on belief in understanding and predicting behavior is widespread. Examples can be found, inter alia, in the placebo, attribution theory, theory of mind, and comparative psychological literatures. Research on belief also provides evidence for robust generalizations, including about how we fix, store, and change our beliefs. Evidence supports the existence of a Spinozan system of belief fixation: one that is automatic (...) and independent of belief rejection. Independent research supports the existence of a system of fragmented belief storage: one that relies on large numbers of causally isolated, context-sensitive stores of belief in memory. Finally, empirical and observational data support at least two systems of belief change. One system adheres, mostly, to epistemological norms of updating; the other, the psychological immune system, functions to guard our most centrally held beliefs from potential inconsistency with newly formed beliefs. Refining our under- standing of these systems can shed light on pressing real-world issues, such as how fake news, propaganda, and brainwashing exploit our psychology of belief, and how best to construct our modern informational world. (shrink)

In search of our highest capacities, cognitive scientists aim to explain things like mathematics, language, and planning. But are these really our most sophisticated forms of knowing? In this paper, I point to a different pinnacle of cognition. Our most sophisticated human knowing, I think, lies in how we engage with each other, in our relating. Cognitive science and philosophy of mind have largely ignored the ways of knowing at play here. At the same time, the emphasis on discrete, rational (...) knowing to the detriment of engaged, human knowing pervades societal practices and institutions, often with harmful effects on people and their relations. There are many reasons why we need a new, engaged—or even engaging—epistemology of human knowing. The enactive theory of participatory sense-making takes steps towards this, but it needs deepening. Kym Maclaren’s idea of letting be invites such a deepening. Characterizing knowing as a relationship of letting be provides a nuanced way to deal with the tensions between the knower’s being and the being of the known, as they meet in the process of knowing-and-being-known. This meeting of knower and known is not easy to understand. However, there is a mode of relating in which we know it well, and that is: in loving relationships. I propose to look at human knowing through the lens of loving. We then see that both knowing and loving are existential, dialectic ways in which concrete and particular beings engage with each other. (shrink)

We consider the questions of whether or not large language models (LLMs) have beliefs, and, if they do, how we might measure them. First, we consider whether or not we should expect LLMs to have something like beliefs in the first place. We consider some recent arguments aiming to show that LLMs cannot have beliefs. We show that these arguments are misguided. We provide a more productive framing of questions surrounding the status of beliefs in LLMs, and highlight the empirical (...) nature of the problem. With this lesson in hand, we evaluate two existing approaches for measuring the beliefs of LLMs, one due to Azaria and Mitchell (The internal state of an llm knows when its lying, 2023) and the other to Burns et al. (Discovering latent knowledge in language models without supervision, 2022). Moving from the armchair to the desk chair, we provide empirical results that show that these methods fail to generalize in very basic ways. We then argue that, even if LLMs have beliefs, these methods are unlikely to be successful for conceptual reasons. Thus, there is still no lie-detector for LLMs. We conclude by suggesting some concrete paths for future work. (shrink)

This paper develops a novel approach to the question of the normativity of logic, which we reinterpret as a clash between two intuitions: the direct normativity intuition and the unfeasibility intuition. The standard response has been to dismiss the direct normativity intuition, bridging logic and reasoning via principles that relativize the normative import of logic to pragmatic and feasibility considerations. We argue that the standard response is misguided. Building upon theories of bounded rationality, our approach conceptualizes reasoning as constrained by (...) multiple, independent normative factors, logical and non-logical ones. These different factors can conflict with one another, to the effect that logically sound inferences might not coincide with what is feasible for an agent to infer. From this perspective, we will argue that logic gives us only prima facie, i.e. contributive and defeasible, obligations on what to believe, but that such obligations do not always coincide with what an agent ought to believe all things considered. These distinctions will dissolve the alleged clash between the direct normativity and the unfeasibility intuition. (shrink)

Since the early days of artificial intelligence (AI), many logics have been explored as tools for knowledge representation and reasoning. In the spirit of the Crossley Festscrift and recognizing John Crossley’s diverse interests and his legacy in both mathematical logic and computer science, I discuss examples from my own research that sit in the overlap of logic and AI, with a focus on supporting human–AI interactions.

Conviction Narrative Theory (CNT) is a theory of choice underradical uncertainty– situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people usenarratives– structured representations of causal, temporal, analogical, and valence relationships – rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, (...) narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels “right” to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We identify 12 propositions to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, and communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences. (shrink)

Practical quantum computing devices and their applications to AI in particular are presently mostly speculative. Nevertheless, questions about whether this future technology, if achieved, presents any special ethical issues are beginning to take shape. As with any novel technology, one can be reasonably confident that the challenges presented by "quantum AI" will be a mixture of something new and something old. Other commentators (Sevilla & Moreno 2019), have emphasized continuity, arguing that quantum computing does not substantially affect approaches to value (...) alignment methods for AI, although they allow that further questions arise concerning governance and verification of quantum AI applications. In this brief paper, we turn our attention to the problem of identifying as-yet-unknown discontinuities that might result from quantum AI applications. Wise development, introduction, and use of any new technology depends on successfully anticipating new modes of failure for that technology. This requires rigorous efforts to break systems in protected sandboxes, and it must be conducted at all stages of technology design, development, and deployment. Such testing must also be informed by technical expertise but cannot be left solely to experts in the technology because of the history of failures to predict how non-experts will use or adapt to new technologies. This interplay between experts and non-experts may be particularly acute for quantum AI because quantum mechanics is notoriously difficult to understand. (As Richard Feynman quipped, "Anyone who claims to understand quantum mechanics is either lying or crazy.") We will discuss the extent to which the difficulties in understanding the physics underlying quantum computing challenges attempts to anticipate new failure modes that might be introduced in AI applications intended for unsupervised operation in the public sphere. (shrink)

Topics in Cognitive Science, Volume 14, Issue 3, Page 508-527, July 2022.

Recent debates over adults' theory of mind use have been fueled by surprising failures of perspective-taking in communication, suggesting that perspective-taking may be relatively effortful. Yet adults routinely engage in effortful processes when needed. How, then, should speakers and listeners allocate their resources to achieve successful communication? We begin with the observation that the shared goal of communication induces a natural division of labor: The resources one agent chooses to allocate toward perspective-taking should depend on their expectations about the other's (...) allocation. We formalize this idea in a resource-rational model augmenting recent probabilistic weighting accounts with a mechanism for (costly) control over the degree of perspective-taking. In a series of simulations, we first derive an intermediate degree of perspective weighting as an optimal trade-off between expected costs and benefits of perspective-taking. We then present two behavioral experiments testing novel predictions of our model. In Experiment 1, we manipulated the presence or absence of occlusions in a director–matcher task. We found that speakers spontaneously modulated the informativeness of their descriptions to account for “known unknowns” in their partner's private view, reflecting a higher degree of speaker perspective-taking than previously acknowledged. In Experiment 2, we then compared the scripted utterances used by confederates in prior work with those produced in interactions with unscripted directors. We found that confederates were systematically less informative than listeners would initially expect given the presence of occlusions, but listeners used violations to adaptively make fewer errors over time. Taken together, our work suggests that people are not simply “mindblind”; they use contextually appropriate expectations to navigate the division of labor with their partner. We discuss how a resource-rational framework may provide a more deeply explanatory foundation for understanding flexible perspective-taking under processing constraints. (shrink)

A large body of research in cognitive psychology and neuroscience draws on Bayesian statistics to model information processing within the brain. Many theorists have noted that this research seems to be in tension with a large body of experimental results purportedly documenting systematic deviations from Bayesian updating in human belief formation. In response, proponents of the Bayesian brain hypothesis contend that Bayesian models can accommodate such results by making suitable assumptions about model parameters. To make progress in this debate, I (...) argue that it is fruitful to focus not on specific experimental results but rather on what I call the ‘sources of epistemic irrationality’ in human cognition. I identify four such sources and I explore whether and, if so, how Bayesian models can be reconciled with them: processing costs, evolutionary suboptimality, motivated cognition, and error management. 1 Introduction 2 The Bayesian Brain 3 The Problem of Epistemic Irrationality 3.1 Bayesian inference and rationality 3.2 Intuitive Bayesian inference 4 Sources of Epistemic Irrationality 4.1 Processing costs 4.2 Evolutionary suboptimality 4.3 Motivational influences 4.4 Error management 5 Conclusion. (shrink)

Over the past decades psychological theories have made significant headway into economics, culminating in the 2002 (partially) and 2017 Nobel prizes awarded for work in the field of Behavioral Economics. Many of the insights imported from psychology into economics share a common trait: the presumption that decision makers use shortcuts that lead to deviations from rational behaviour (the Heuristics-and-Biases program). Many economists seem unaware that this viewpoint has long been contested in cognitive psychology. Proponents of an alternative program (the Ecological-Rationality (...) program) argue that heuristics need not be irrational, particularly when judged relative to characteristics of the environment. We sketch out the historical context of the antagonism between these two research programs and then review more recent work in the Ecological-Rationality tradition. While the heuristics-and-biases program is now well-established in (mainstream neo-classical) economics via Behavioral Economics, we show there is considerable scope for the Ecological-Rationality program to interact with economics. In fact, we argue that there are many existing, yet overlooked, bridges between the two, based on independently derived research in economics that can be construed as being aligned with the tradition of the Ecological-Rationality program. We close the paper with a discussion of the open challenges and difficulties of integrating the Ecological Rationality program with economics. (shrink)

Why would decision makers adopt heuristics, priors, or in short “habits” that prevent them from optimally using pertinent information—even when such information is freely-available? One answer, Herbert Simon’s “procedural rationality” regards the question invalid: DMs do not, and in fact cannot, process information in an optimal fashion. For Simon, habits are the primitives, where humans are ready to replace them only when they no longer sustain a pregiven “satisficing” goal. An alternative answer, Daniel Kahneman’s “mental economy” regards the question valid: (...) DMs make decisions based on optimization. Kahneman understands optimization not differently from the standard economist’s “bounded rationality.” This might surprise some researchers given that the early Kahneman, along with Tversky, have uncovered biases that appear to suggest that choices depart greatly from rational choices. However, once we consider cognitive cost as part of the constraints, such biases turn out to be occasional failures of habits that are otherwise optimal on average. They are optimal as they save us the cognitive cost of case-by-case deliberation. While Kahneman’s bounded rationality situates him in the neoclassical economics camp, Simon’s procedural rationality echoes Bourdieu’s “habitus” camp. To abridge the fault line of the two camps, this paper proposes a “two problem areas hypothesis.” Along the neoclassical camp, habits satisfy wellbeing, what this paper calls “substantive satisfaction.” Along the Bourdieu camp, habits satisfy belonging, love, and bonding with one’s environment, what this paper calls “transcendental satisfaction.”. (shrink)

By synthesizing the argumentation theory of new rhetoric with research on heuristics and motivated reasoning, we develop a conceptual view of argumentation based on reasoning motivations that sheds new light on the morality of decision-making. Accordingly, we propose that reasoning in eristic argumentation is motivated by psychological (e.g., anxiety reduction) or material (e.g., vested interests) gains that do not depend on resolving the problem in question truthfully. Contrary to heuristic argumentation, in which disputants genuinely argue to reach a practically rational (...) solution, eristic argumentation aims to defeat the counterparty rather than seeking a reasonable solution. Eristic argumentation is susceptible to arbitrariness and power abuses; therefore, it is inappropriate for making moral judgments with the exception of judgments concerning moral taboos, which are closed to argumentation by their nature. Eristic argumentation is also problematic for strategic and entrepreneurial decision-making because it impedes the search for the right heuristic under uncertainty as an ecologically rational choice. However, our theoretical view emphasizes that under extreme uncertainty, where heuristic solutions are as fallible as any guesses, pretense reasoning by eristic argumentation may be instrumental for its adaptive benefits. Expanding the concept of eristic argumentation based on reasoning motivations opens a new path for studying the psychology of reasoning in connection to morality and decision-making under uncertainty. We discuss the implications of our theoretical view to relevant research streams, including ethical, strategic and entrepreneurial decision-making. (shrink)

How much would you pay to play a lottery with an “infinite expected payoff?” In the case of the century old, St. Petersburg Paradox, the answer is that the vast majority of people would only pay a small amount. The authors seek to understand this paradox by providing an explanation consistent with a broad, process‐level model of human decision‐making under risk.