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 (...) organism and a set of principles, such as the bias– variance tradeoff, that can explain when and why heuristics work in uncertain environments, and then shows how effective heuristics can be built by biasing and simplifying more complex models. We conclude with challenges these approaches face in developing a psychologically realistic perspective on human rationality. (shrink)

Cognitive scientists have revealed systematic errors in human reasoning. There is disagreement about what these errors indicate about human rationality, but one upshot seems clear: human reasoning does not seem to fit traditional views of human rationality. This concern about rationality has made its way through various fields and has recently caught the attention of philosophers. The concern is that if philosophers are prone to systematic errors in reasoning, then the integrity of philosophy would be threatened. In this paper, I (...) present some of the more famous work in cognitive science that has marshaled this concern. Then I present reasons to think that those with training in philosophy will be less prone to certain systematic errors in reasoning. The suggestion is that if philosophers could be shown to be less prone to such errors, then the worries about the integrity of philosophy could be constrained. Then I present evidence that, according to performance on the CRT (Frederick 2005), those who have benefited from training and selection in philosophy are indeed less prone to one kind of systematic error: irrationally arbitrating between intuitive and reflective responses. Nonetheless, philosophers are not entirely immune to this systematic error, and their proclivity for this error is statistically related to their responses to a variety of philosophical questions. So, while the evidence herein puts constraints on the worries about the integrity of philosophy, it by no means eliminates these worries. The conclusion, then, is that the present evidence offers prima facie reasons to ascribe a mitigated privilege to philosophers' ability to rationally arbitrate between intuitive and reflective responses. (shrink)

Although it makes some progress, McKay & Dennett's (M&D's) proposal is limited because (1) the argument for adaptive misbelief is not new, (2) arguments overextend the evidence provided, and (3) the alleged sufficient conditions are not as prohibitive as suggested. We offer alternative perspectives and evidence, including individual differences research, indicating that adaptive misbeliefs are likely much more widespread than implied.

Surrogates’ decisions to withhold or withdraw life-sustaining treatments (LSTs) are pervasive. However, the factors influencing surrogates’ decisions to initiate LSTs are relatively unknown. We present evidence from two experiments indicating that some surrogates’ decisions about when to initiate LSTs can be predictably manipulated. Factors that influence surrogate decisions about LSTs include the patient’s cognitive state, the patient’s age, the percentage of doctors not recommending the initiation of LSTs, the percentage of patients in similar situations not wanting LSTs, and default treatment (...) settings. These results suggest that some people may use heuristics when making these important life-and-death decisions. These findings may have important moral implications for improving surrogate decisions about LSTs and reconsidering paternalism. (shrink)

When proponents of cognitive externalism (CE) turn to empirical studies in cognitive science to put the framework to use and to assess its explanatory success, they typically refer to perception, memory, or motor coordination. In contrast, not much has been said about reasoning. One promising avenue to explore in this respect is the theory of bounded rationality (BR). To clarify the relationship between CE and BR, we criticize Andy Clark's understanding of BR, as well as his claim that BR does (...) not fit his version of CE. We then propose and defend a version of CE—“scaffolded cognition”—that is not committed to constitutive claims about the mind, but still differs from mainstream internalism. Finally, we analyze BR from our own CE perspective, thereby clarifying its vague appeals to the environment, and argue that cognitive internalism cannot explain important aspects of the BR program. (shrink)

Abstract:The effectiveness of heuristics has received contradicting interpretations in the behavioural sciences. We study the policy implications of two programmes that dispute the effectiveness of heuristics – the biases and heuristics and the fast and frugal heuristics programmes. While the first blames heuristics for most errors in judgement, the second posits heuristics as simple mental algorithms that work well in a range of environments. We argue that the fast and frugal programme is less paternalistic insofar as it models humans as (...) effective decision-makers in a range of environments. However, in the rapidly changing environments of the 21st century, both are needed to inform evidence-based policies. (shrink)

Libertarian paternalism is a weak form of paternalism that recommends nudges rather than bans, restrictions, or other strong interventions. Nudges influence people’s choice by modifying contextual factors (the “choice architecture”). This paper explores the possibility of an epistemic analogue of libertarian paternalism. What I call “epistemic libertarian paternalism” is a weak form of epistemic paternalism that recommends “epistemic nudges” rather than stronger paternalistic interventions. Epistemic nudges influence people’s beliefs and judgments by modifying contextual factors (the “epistemic choice architecture”). The main (...) aim of this paper is to defend epistemic libertarian paternalism from the “irrationality problem”, which I take to be the most urgent problem for epistemic libertarian paternalism; given how epistemic nudges work (i.e. they typically co-opt psychological biases), nudged beliefs are irrational. In response to the irrationality problem, I admit that nudged beliefs are often (not always, though) irrational, but insist that there are conditions in which epistemic nudging can be justifiable nonetheless. I will propose two conditions that are jointly sufficient for justifiable epistemic nudging: “Veridicality Condition” (which says that nudged beliefs are more likely to be true than non-nudged beliefs) and “Not-More-Irrationality Condition” (which says that nudged beliefs are not more likely to be irrational than non-nudged beliefs). (shrink)

Bayesian epistemologists support the norms of probabilism and conditionalization using Dutch book and accuracy arguments. These arguments assume that rationality requires agents to maximize practical or epistemic value in every doxastic state, which is evaluated from a subjective point of view (e.g., the agent’s expectancy of value). The accuracy arguments also presuppose that agents are opinionated. The goal of this paper is to discuss the assumptions of these arguments, including the measure of epistemic value. I have designed AI agents based (...) on the Bayesian model and a nonmonotonic framework and tested how they achieve practical and epistemic value in conditions in which an alternative set of assumptions holds. In one of the tested conditions, the nonmonotonic agent, which is not opinionated and fulfills neither probabilism nor conditionalization, outperforms the Bayesian in the measure of epistemic value that I argue for in the paper (α-value). I discuss the consequences of these results for the epistemology of rationality. (shrink)

Ethical decision-making is inherent to the research ethics committee deliberation process. While ethical codes, regulations, and research standards are indispensable in guiding this process,...

How do mushroom foragers make safe and efficient decisions under high degrees of uncertainty, or deal with the genuine risks of misidentification and poisoning? This article is an inquiry into ecological rationality, heuristics, perception, and decision-making in mushroom foraging. By surveying 894 Finnish mushroom foragers with a total of 22,304 years of foraging experience, this article illustrates how socially learned rules of thumb and heuristics are used in mushroom foraging. It illustrates how traditional foraging cultures have evolved precautionary principles to (...) deal with uncertainties and poisonous species, and how foragers leverage both simple heuristics and complex cognitive strategies in their search for, and identification of, mushrooms. Foragers also develop selective attention through experience. The results invite us to consider whether other human foraging cultures might use heuristics similarly, how and why such traditions have culturally evolved, and whether early hunter-gatherers might have used fast and frugal heuristics to deal with uncertainty. (shrink)

ABSTRACTBehavioral economics may be considered as neoclassical behavioral economics, which adopts a neoclassical normative model of rationality and explains bias a...

This article is a case study that describes the natural and human history of the gaze heuristic. The gaze heuristic is an interception heuristic that utilizes a single input repeatedly as a task is performed. Its architecture, advantages, and limitations are described in detail. A history of the gaze heuristic is then presented. In natural history, the gaze heuristic is the only known technique used by predators to intercept prey. In human history the gaze heuristic was discovered accidentally by Royal (...) Air Force fighter command just prior to World War II. As it was never discovered by the Luftwaffe, the technique conferred a decisive advantage upon the RAF throughout the war. After the end of the war in America, German technology was combined with the British heuristic to create the Sidewinder AIM9 missile, the most successful autonomous weapon ever built. There are no plans to withdraw it or replace its guiding gaze heuristic. The case study demonstrates that the gaze heuristic is a specific heuristic type that takes a single best input at the best time. Its use is an adaptively rational response to specific, rapidly evolving decision environments that has allowed those animals/humans/machines who use it to survive, prosper, and multiply relative to those who do not. (shrink)

When economists perform experiments, they do so typically in one of two traditions: cognitive psychology experiments in the heuristics and biases tradition (H&B-experiments) and experimental economics in the tradition of Vernon Smith. What sets these two traditions apart? In this paper, I offer a novel conceptualization of their pervasive disagreements. Focusing on how each camp approaches preferences, one of the most fundamental concepts in economics, I argue that experimental economics can be reconstructed as holding that the constituents of preferences can (...) be partially located in agents’ environments, while H&B-experiments implicitly assume that the constituents of preferences are entirely located within agents’ bodies. The paper (i) outlines how my reconstruction can account for the disagreement between the two paradigms, (ii) defends the plausibility of this reconstruction, and (iii) highlights its implications for the debate about the nature of preferences in economics. (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)

Declan Smithies’ The Epistemic Role of Consciousness is a defense of “Phenomenal Mentalism” according to which, necessarily, which propositions X has epistemic justification to believe at any given time is determined solely by X’s phenomenally individuated mental states at that time. Smithies offers two kinds of arguments for Phenomenal Mentalism: the ones that appeal to particular cases such as blindsight and the ones that appeal to general epistemic principles such as the JJ principle. My focus is on the former. More (...) precisely, I focus on a particular argument from below in Chapter 3, which I call “Argument from Blindsight”. According to this argument, the cases of blindsight show that consciousness is necessary for perceptual justification. In response, I raise two worries about Argument from Blindsight: first, it is difficult to find a plausible interpretation of “full rationality” according to which the premises are true and, second, the argument oscillates between empirical and stipulative discussions of blindsight in a potentially problematic manner. (shrink)

As modern deep networks become more complex, and get closer to human‐like capabilities in certain domains, the question arises as to how the representations and decision rules they learn compare to the ones in humans. In this work, we study representations of sentences in one such artificial system for natural language processing. We first present a diagnostic test dataset to examine the degree of abstract composable structure represented. Analyzing performance on these diagnostic tests indicates a lack of systematicity in representations (...) and decision rules, and reveals a set of heuristic strategies. We then investigate the effect of training distribution on learning these heuristic strategies, and we study changes in these representations with various augmentations to the training set. Our results reveal parallels to the analogous representations in people. We find that these systems can learn abstract rules and generalize them to new contexts under certain circumstances—similar to human zero‐shot reasoning. However, we also note some shortcomings in this generalization behavior—similar to human judgment errors like belief bias. Studying these parallels suggests new ways to understand psychological phenomena in humans as well as informs best strategies for building artificial intelligence with human‐like language understanding. (shrink)

To develop more effective ethics education for cross-cultural and international engineering, a study was conducted to determine what Chinese engineering students have learned and think about ethics. Recent research shows traditional approaches to ethics education are potentially ineffective, but also points towards ways of improving ethical behaviors. China is the world’s most populous country, graduating and employing the highest number of STEM majors, although little empirical research exists about the ethical knowledge and perspectives of Chinese engineering students. When compared to (...) engineering students in the US, Chinese engineering students received less ethics education; the form of the education they did receive stressed virtue ethics or the development of moral character; conceive of ethics in contradistinction to the law, where ethics deals with matters of right and wrong not covered by legality. Based on these findings and research in moral psychology and behavioral ethics, recommendations are made for improving engineering ethics education both in China and abroad. (shrink)

We propose another positive illusion that fits with McKay & Dennett's (M&D's) criteria for adaptive misbeliefs. This illusion is pervasive in adult reasoning but we focus on its prevalence in children's developing theories. It is a strongly held conviction arising from normal functioning of the doxastic system that confers adaptive advantage on the individual.

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)

Animals depleting one patch of resources must decide when to leave and switch to a fresh patch. Foraging theory has predicted various decision mechanisms; which is best depends on environmental variation in patch quality. Previously we tested whether these mechanisms underlie human decision making when foraging for external resources; here we test whether humans behave similarly in a cognitive task seeking internally generated solutions. Subjects searched for meaningful words made from random letter sequences, and as their success rate declined, they (...) could opt to switch to a fresh sequence. As in the external foraging context, time since the previous success and the interval preceding it had a major influence on when subjects switched. Subjects also used the commonness of sequence letters as a proximal cue to patch quality that influenced when to switch. Contrary to optimality predictions, switching decisions were independent of whether sequences differed little or widely in quality. (shrink)

Naive physics beliefs can be systematically mistaken. They provide a useful test-bed because they are common, and also because their existence must rely on some adaptive advantage, within a given context. In the second part of the commentary we also ask questions about when a whole family of misbeliefs should be considered together as a single phenomenon.

While action selection strategies in well‐defined domains have received considerable attention, little is yet known about how people choose what to do next in ill‐defined tasks. In this contribution, we shed light on this issue by considering everyday tasks, which in many cases have a multitude of possible solutions (e.g., it does not matter in which order the items are brought to the table when setting a table) and are thus categorized as ill‐defined problems. Even if there are no hard (...) constraints on the ordering of subtasks in everyday activities, our research shows that people exhibit specific preferences. We propose that these preferences arise from bounded rationality, that is, people only have limited knowledge and processing power available, which results in a preference to minimize the overall physical and cognitive effort. In the context of everyday activities, this can be achieved by (a) taking properties of the spatial environment into account to use them to one's advantage, and (b) employing a stepwise‐optimal action selection strategy. We present the Opportunistic Planning Model as an explanatory cognitive model, which instantiates these assumptions, and show that the model is able to generalize to new everyday tasks, outperforming machine learning models such as neural networks during generalization. (shrink)