This paper argues that emotions can play an epistemic role as justifiers of evaluative beliefs. It also presents the heuristics theory of emotion as an empirically informed explanation of how emotions can play such a role and why they in practice usefully complement non-affective evaluative judgments. As such, the heuristics theory represents a form of moderate rationalism: it acknowledges that emotions can be epistemically valuable, even privileged in some sense, but denies that they would be uniquely privileged. I argue that (...) judgments and emotional responses pick out different but correlated kinds of evaluative properties and therefore emotional responses and non-affective evaluative judgments play mutually complementary rather than mutually exclusive roles. It follows that emotional responses can be legitimately drawn upon to support evaluative beliefs, but they lack supreme epistemic authority. (shrink)

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)

We explore and provide an account for a recently identified judgment anomaly, i.e., an order effect that changes the strength of intentionality ascriptions for some side effects (e.g., when a chairman's pursuit of profits has the foreseen but unintended consequence of harming the environment). Experiment 1 replicated the previously unanticipated order effect anomaly controlling for general individual differences. Experiment 2 revealed that the order effect was multiply determined and influenced by factors such as beliefs (i.e., that the same actor was (...) involved in bringing about both good and bad side effects) and philosophical training (i.e., more training was associated with smaller differences in judgment when harm followed help). Results provide more evidence that the folk's philosophically relevant intuitions are predictably fragmented and depend on the dynamic interplay between persons, process, and environments. Methodological and theoretical implications are discussed. (shrink)

Locked and unlocked strategies are at the center of this article, as ways of shedding new light on the cognitive aspects of deep learning machines. The character and the role of these cognitive strategies, which are occurring both in humans and in computational machines, is indeed strictly related to the generation of cognitive outputs, which range from weak to strong level of knowledge creativity. I maintain that these differences lead to important consequences when we analyze computational AI programs, such as (...) AlphaGo, which aim at performing various kinds of abductive hypothetical reasoning. In these cases, the programs are characterized by _locked_ abductive strategies: they deal with weak (even if sometimes amazing) kinds of hypothetical creative reasoning, because they are limited in what I call eco-cognitive openness, which instead qualifies human cognizers who are performing higher kinds of abductive creative reasoning, where cognitive strategies are instead _unlocked_. (shrink)

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)

The lottery paradox involves a set of judgments that are individually easy, when we think intuitively, but ultimately hard to reconcile with each other, when we think reflectively. Empirical work on the natural representation of probability shows that a range of interestingly different intuitive and reflective processes are deployed when we think about possible outcomes in different contexts. Understanding the shifts in our natural ways of thinking can reduce the sense that the lottery paradox reveals something problematic about our concept (...) of knowledge. However, examining these shifts also raises interesting questions about how we ought to be thinking about possible outcomes in the first place. (shrink)

AI has become the poster child for a certain kind of thinking which holds that some technologies can become objective, independent and emergent entities which can evolve beyond the control of their creators. This thinking is not new however. It is a product of certain philosophical ideas such as materialism, a common-sense world of objective and independent objects, a correspondence theory of truth, and so forth, which are centered around the pre-eminence of science, epistemology, and logical reasoning, among others, as (...) the supremely valid modes of engaging experience. This paper aims to critically examine the synthesis of the development program for AI to be found in the writings of computer scientist John McCarthy (1927–2011). McCarthy has been called one of the founding fathers of AI. First, some of the main themes which recur in his writings from as early as the mid nineteen fifties in the Dartmouth proposal, up to his late writings, are considered. A discussion of the goals that such a program implies, follows, in terms of society’s relation with AI based technology and the nature and purpose of AI itself. These implications are arguably related to social control and are also moral. Ultimately, McCarthy’s idea that AI is and can be built upon a paradigm of reasoning and logic so as to simulate a perfectly rational abstraction of human thinking is shown to be motivated by his view that AI systems should be controlled, as servants. This understanding of human thinking is questionable, however, given current research on human irrationality, as well as on the argumentative role of reasoning. And yet, the process of development toward the unachievable goal of controllable, rational, logical AI systems, has handily come to serve as a blind for increasing techno-corporate control of society, with techno-corporate interests displacing blame for devaluations and harms caused by their push to develop the technology to AI systems which they can conveniently ‘lose control of.’. (shrink)

Citing an epistemic or cognitive asymmetry between experts and the public, it is easy to view the relation between scientists and citizens as primarily based on trust, rather than on the content of expert argumentation. In criticism of this claim, four theses are defended: Empirical studies suggest that content matters, while trust boasts persuasiveness. In social policy controversies, genuine expert-solutions are normally not available; if trust is important here, then a clear role for cognitive asymmetry is wanting. Social policy controversies (...) pivot on values, so that biases and ideologies may explain participant behavior. Few experts communicate perfectly; rather than cognitive ones, one might cite social differences. (shrink)

I discuss under what conditions the objection that an expert’s argument is biased by her self-interest can be a meaningful and sound argumentative move. I suggest replacing the idea of bias qua self-interest by that of a conflict of interests, exploit the distinction between an expert context and a public context, and hold that the objection can be meaningful. Yet, the evaluation is overall negative, because the motivational role of self-interest for human behavior remains unclear. Moreover, if recent social-psychological results (...) from the “heuristics and biases” program are accepted, it is plausible to assume that humans also satisfice (rather than optimize/maximize) when identifying and then acting in their self-interest. My thesis is: insofar as the objection is sound with a particular audience, it is not needed; and insofar as the objection is needed, it is unsound. (shrink)

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

All of us make judgments of probability, and we rely on them for our decision-making. This paper argues that such judgments are trustworthy only to the extent that one has good reasons to think that they are produced by maximally inclusive, well calibrated cognitive processes. A cognitive process is maximally inclusive when it takes into account all the evidence which one regards as relevant, and it is well calibrated when anything it would assign, say, an 80% probability to would be (...) true 80% of the time. We further have good reasons to think these judgments are trustworthy when, inter alia, they are produced by processes that have good track records of calibration. Call this inclusive calibrationism—or just “calibrationism” for short. In arguing for calibrationism, I also appeal to various empirical results, including research into probabilistic reasoning funded by the US intelligence community. Together, these ideas and results have implications for some important philosophical problems: the problem of the priors, the problem of unique events and the use of intuition in probabilistic reasoning. These theses and results also imply that our judgments are often less trustworthy than we might hope for potentially many domains, including law, medicine and others—barring good track records, that is. (shrink)

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)

The Internet has been identified in human enhancement scholarship as a powerful cognitive enhancement technology. It offers instant access to almost any type of information, along with the ability to share that information with others. The aim of this paper is to critically assess the enhancement potential of the Internet. We argue that unconditional access to information does not lead to cognitive enhancement. The Internet is not a simple, uniform technology, either in its composition, or in its use. We will (...) look into why the Internet as an informational resource currently fails to enhance cognition. We analyze some of the phenomena that emerge from vast, continual fluxes of information–information overload, misinformation and persuasive design—and show how they could negatively impact users’ cognition. Methods for mitigating these negative impacts are then advanced: individual empowerment, better collaborative systems for sorting and categorizing information, and the use of artificial intelligence assistants that could guide users through the informational space of today’s Internet. (shrink)

When deciding on an online purchase, consumers often face a plethora of information. Yet, individuals consumers differ greatly in the amount of information they are willing and able to acquire and process before making purchasing decisions. Extensively processing all available information does not necessarily promote good decisions. Instead, the empirical evidence suggests that reviewing too much information or too many choice alternatives can impair decision quality. Using simulated contract conclusion scenarios, we identify distinctive types of information processing styles and find (...) that certain search and selection strategies predict the quality of the final choice. Participants chose a cellular service contract in a web-based environment that closely resembled actual online settings in the country of study. Using information processing data obtained with tracking software, we identify three consumer segments differing along two dimensions – the extent dimension, referring to the overall effort invested in information processing, and the focus dimension, referring to the degree to which someone focuses on the best available options. The three subgroups of respondents can be characterized as follows: consumers with a low-effort and low-focus information processing strategy ; consumers with a moderate-effort and high-focus information processing strategy ; and consumers with high-effort and low-focus information processing strategy. The three groups differed not only in their information processing but also in the quality of their decisions. In line with the assumption of ecological rationality, most successful search strategies were not exhaustive, but instead involved the focused selection and processing of a medium amount of information. Implications for effective consumer information are provided. (shrink)

Many compelling examples have recently been provided in which people can achieve impressive epistemic success, e.g. draw highly accurate inferences, by using simple heuristics and very little information. This is possible by taking advantage of the features of the environment. The examples suggest an easy and appealing naturalization of rationality: on the one hand, people clearly can apply simple heuristics, and on the other hand, they intuitively ought do so when this brings them high accuracy at little cost.. The ‘ought-can’ (...) principle is satisfied, and rationality is meaningfully normative. We show, however, that this naturalization program is endangered by a computational wrinkle in the adaptation process taken to be responsible for this heuristics-based (‘ecological’) rationality: for the adaptation process to guarantee even minimal rationality, it requires astronomical computational resources, making the problem intractable. We consider various plausible auxiliary assumptions in attempt to remove this obstacle, and show that they do not succeed; intractability is a robust property of adaptation. We discuss the implications of our findings for the project of naturalizing rationality. (shrink)

Many economic theorists hold that social institutions can lead otherwise irrational agents to approximate the predictions of traditional rational choice theory. But there is little consensus on how institutions do so. I defend an economic internalist account of the institution-actor relationship by explaining economic rationality as a feature of individuals whose decision-making is aided by institutional structures. This approach, known as the subjective transaction costs theory, represents apparently irrational behavior as a rational response to high subjective transaction costs of thinking (...) and deciding. The theory has two attractive features. First, it reconciles rational choice theory with the increasing body of evidence cataloguing putative errors in economic decision-making. Second, it vindicates the explanatory power of individual choice against externalist challenges; the subjective transaction costs theory keeps economic rationality in the head. (shrink)

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)

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)

Recent work in judgment and decisionmaking has stressed that institutions, like individuals, often rely on decisionmaking heuristics. But most of the institutional decisionmaking heuristics studied to date are highly firm- and industry-specific. This contrasts to the individual case, in which many heuristics are general-purpose rules suitable for a wide range of decision problems. Are there also general-purpose heuristics for institutional decisionmaking? In this paper, I argue that a number of methods recently developed for decisionmaking under deep uncertainty have a good (...) claim to be understood as general-purpose decisionmaking heuristics suitable for a broad range of institutional decision problems. (shrink)

A common view in philosophy is that the way human beings reason is not only gradually better, but that our way of reasoning is fundamentally distinctive. Findings in the psychology of reasoning challenge the traditional view according to which human beings reason in accordance with the laws of logic and probability theory, but rather suggest that human reasoning consists in the application of domain specific rules of thumb similar to those that we ascribe to some intelligent non-human animals as well. (...) However, this view on human reasoning is unable to explain human accomplishments like technological innovations or scientific progress. David Papineau offers a theory of human theoretical rationality that is consistent with the psychological view on human reasoning but that can also explain how humans sometimes are able to transcend the limitations of their biologically quick and dirty modes of thought and thereby reach a high level of accuracy. Papineau claims that the abilities that constitute theoretical rationality are unique to the human species and thus, that human reasoning is fundamentally distinctive after all. In this paper I am going to discuss to what extent these abilities in fact are unique to our species and whether this theoretical rationality can be called an anthropological difference. (shrink)

I survey four categories of factors that might give a digital mind, such as an upload or an artificial general intelligence, an advantage over humans. Hardware advantages include greater serial speeds and greater parallel speeds. Self-improvement advantages include improvement of algorithms, design of new mental modules, and modification of motivational system. Co-operative advantages include copyability, perfect co-operation, improved communication, and transfer of skills. Human handicaps include computational limitations and faulty heuristics, human-centric biases, and socially motivated cognition. The shape of hardware (...) growth curves, as well as the ease of modifying minds, are found to have a major impact on how quickly a digital mind may take advantage of these factors. (shrink)

Previous studies have shown that people often use heuristics in making inferences and that subjective memory experiences, such as recognition or familiarity of objects, can be valid cues for inferences. So far, many researchers have used the binary choice task in which two objects are presented as alternatives (e.g., “Which city has the larger population, city A or city B?”). However, objects can be presented not only as alternatives but also in a question (e.g., “Which country is city X in, (...) country A or country B?”). In such a situation, people can make inferences based on the relationship between the object in the question and each object given as an alternative. In the present study, we call this type of task a “relationships‐comparison task.” We modeled the three inference strategies that people could apply to solve it (familiarity‐matching [FM; a new heuristic we propose in this study], familiarity heuristic [FH], and knowledge‐based inference [KI]) to examine people's inference processes. Through Studies 1, 2, and 3, we found that (a) people tended to rely on heuristics, and that FM (inferences based on similarity in familiarity between objects) well explained participants' inference patterns; (b) FM could work as an ecologically rational strategy for the relationships–comparison task since it could effectively reflect environmental structures, and that the use of FM could be highly replicable and robust; and (c) people could sometimes use a decision strategy like FM, even in their daily lives (consumer behaviors). The nature of the relationships–comparison task and human cognitive processes is discussed. (shrink)

According to the paradigm of adaptive rationality, successful inference and prediction methods tend to be local and frugal. As a complement to work within this paradigm, we investigate the problem of selecting an optimal combination of prediction methods from a given toolbox of such local methods, in the context of changing environments. These selection methods are called meta-inductive strategies, if they are based on the success-records of the toolbox-methods. No absolutely optimal MI strategy exists—a fact that we call the “revenge (...) of ecological rationality”. Nevertheless one can show that a certain MI strategy exists, called “AW”, which is universally long-run optimal, with provably small short-run losses, in comparison to any set of prediction methods that it can use as input. We call this property universal access-optimality. Local and short-run improvements over AW are possible, but only at the cost of forfeiting universal access-optimality. The last part of the paper includes an empirical study of MI strategies in application to an 8-year-long data set from the Monash University Footy Tipping Competition. (shrink)

So‐called nudge policies utilize insights from behavioral science to achieve policy outcomes. Nudge policies try to improve people's decisions by changing the ways options are presented to them, rather than changing the options themselves or incentivizing or coercing people. Nudging has been met with great enthusiasm but also fierce criticism. This paper provides an overview of the debate on the ethics of nudging to date. After outlining arguments in favor of nudging, we first discuss different objections that all revolve around (...) the worry that nudging vitiates personal autonomy. We split up this worry into different dimensions of autonomy, such as freedom of choice, volitional autonomy, rational agency, and freedom as nondomination. We next discuss worries that nudging is manipulative, violates human dignity, and prevents more important structural reform. Throughout, we will present responses that proponents of nudging can muster. On the whole, we conclude that the objections fail to establish that the nudge program as a whole should be rejected. At the same time, they give us important guidance when moving towards an ethical assessment of nudges on a case‐by‐case basis. Towards the end, we provide some possible ways forward in debates around the ethics of nudging. (shrink)

Many philosophers appeal to intuitions to support some philosophical views. However, there is reason to be concerned about this practice as scientific evidence has documented systematic bias in philosophically relevant intuitions as a function of seemingly irrelevant features (e.g., personality). One popular defense used to insulate philosophers from these concerns holds that philosophical expertise eliminates the influence of these extraneous factors. Here, we test this assumption. We present data suggesting that verifiable philosophical expertise in the free will debate-as measured by (...) a reliable and validated test of expert knowledge-does not eliminate the influence of one important extraneous feature (i.e., the heritable personality trait extraversion) on judgments concerning freedom and moral responsibility. These results suggest that, in at least some important cases, the expertise defense fails. Implications for the practice of philosophy, experimental philosophy, and applied ethics are discussed. (shrink)

One of the most discussed issues in psychology—presently and in the past—is how to define and measure the extent to which human cognition is rational. The rationality of human cognition is often evaluated in terms of normative standards based on a priori intuitions. Yet this approach has been challenged by two recent developments in psychology that we review in this article: ecological rationality and descriptivism. Going beyond these contributions, we consider it a good moment for psychologists and philosophers to join (...) forces and work toward a new foundation for the definition of rational cognition. We take a first step in this direction by proposing that the rationality of both cognitive and normative systems can be measured in terms of their cognitive success. Cognitive success can be defined and gauged in terms of two factors: ecological validity (the system's validity in conditions in which it is applicable) and the system's applicability (the scope of conditions under which it can be applied). As we show, prominent systems of reasoning—deductive reasoning, Bayesian reasoning, uncertain conditionals, and prediction and choice—perform rather differently on these two factors. Furthermore, we demonstrate that conceptualizing rationality according to its cognitive success offers a new perspective on the time-honored relationship between the descriptive (“is”) and the normative (“ought”) in psychology and philosophy. (shrink)

Data which constitute satisfactory evidence in other contexts are sometimes not treated as valid evidence in the context of scientic conrmation. I give a justicatory explanation of this fact, appealing to the incentives, biases, and social situatedness of scientists.

El objetivo de este trabajo es proponer un concepto de agente crítico que dialogue con una práctica democrática deliberativa, considerando qué significa el acto de dar razones. Para tal efecto, en este trabajo se discute, primero, qué significaría ser crítico o tender hacia la criticidad tanto autorreferente como hacia terceros. Esta sección está apoyada principalmente con ideas provenientes de la teoría de la argumentación y de la lógica informal. En segundo término, se aborda el concepto de democracia deliberativa a la (...) luz de la sinergia conceptual que habría entre la participación -potencial- de un agente crítico en tal contexto sociopolítico. En esta sección se pasa revista a algunas de las ideas centrales de la teoría de la democracia deliberativa. Y en la tercera y última parte, se aborda el problema de dar y recibir razones discutiendo la propuesta semántica inferencialista de Brandom (2002, 2005) para observar de qué manera este acercamiento puede aportar a una teoría de la deliberación y a un concepto -y educación- de agente crítico. En las conclusiones se sintetizan las ideas principales y, además, se hace un contrapunto entre la noción de agencia, concepto que es importante en mi propuesta, y la noción de persona, concepto de especial valor en la teoría de la democracia deliberativa. (shrink)

A postcompletion error is a type of procedural error that occurs after the main goal of a task has been accomplished. There is a strong theoretical foundation accounting for postcompletion errors (Altmann & Trafton, 2002; Byrne & Bovair, 1997). This theoretical foundation has been leveraged to develop a logistic regression model of postcompletion errors based on reaction time and eye movement measures (Ratwani, McCurry, & Trafton, 2008). This study further develops and extends this predictive model by (a) validating the model (...) and the general set of predictors on a new task to test the robustness of the model, and (b) determining which specific theoretical components are most important to postcompletion error prediction. (shrink)

In this conceptual analysis contribution to the special issue on radical embodied cognition, we discuss how embodied cognition can exist with and without representations. We explore this concept through the lens of judgment and decision making in sports (JDMS). Embodied cognition has featured in many investigations of human behavior, but no single approach has emerged. Indeed, the very definitions of the concepts “embodiment” and “cognition” lack consensus, and consequently the degree of “radicalism” is not universally defined, either. In this paper (...) we address JDMS not from a rigid theoretical perspective, but from two embodied cognition approaches: one that assumes there is mediation between the athlete and the environment through mental representation, and another that assumes direct contact between the athlete and the environment and thus no need for mental representation. Importantly, our aim was not to arrive at a theoretical consensus or set up a competition between approaches, but rather to provide a legitimate scientific discussion about how to explain empirical results in JDMS from contrasting perspectives within embodied cognition. For this, we first outline the definitions and constructs of embodied cognition in JDMS. Second, we detail the theory underlying the mental representation and direct contact approaches. Third, we comment on two published research papers on JDMS, one selected by each of us: (1) Correia et al. (2012) and (2) Pizzera (2012). Fourth, following the interpretation of the empirical findings of these papers, we present a discussion on the commonalities and divergences of these two perspectives and the consequences of using one or the other approach in the study of JDMS. (shrink)

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

This paper discusses Stanovich's appeal to individual differences in reasoning and decision-making to undermine the “adaptive rationality” project put forth by Gigerenzer and his co-workers. I discuss two different arguments based on Stanovich's research. First, heterogeneity in the use of heuristics seems to be at odds with the adaptationist background of the project. Second, the existence of correlations between cognitive ability and susceptibility to cognitive bias suggests that the “standard picture of rationality” (Stein, 1996, 4) is normatively adequate. I argue (...) that, as matters stand, none of the arguments can be seen as fully compelling. Nevertheless, my discussion is not only critical of Stanovich's research, as I also show that (and how) his research can push forward the so-called “rationality debate” by encouraging greater theoretical and experimental work. (shrink)

Adaptive rationality theorists question the manner in which psychologists have typically assessed rational behavior and cognition. According to them, human rationality is adaptive, and the biases reported in the psychological literature are best seen as the result of using normative standards that are too narrow. As it turns out, their challenge is also quite controversial, and several aspects of it have been called into question. Yet, whilst it is often suggested that the lack of cogency comes about due to the (...) implausibility of the alternative normative framework, in this paper I articulate a different strategy to resist the revolutionary rhetoric of AR. As I argue here, even if we accept the normative framework of AR, the challenge from AR is less damaging than usually accepted. In particular, I challenge the claim that biases reported in the literature should be conceived of as violations of axiomatic rationality. I argue that the category of bias refers instead to a range of heterogeneous phenomena and that, since several important families of biases are not just violations of axiomatic rationality, these are not vulnerable to the AR challenge. In fact, I also show that the families I consider here look like plausible cases of irrational behavior from the perspective of AR, and that the outcome of my analysis does not sit well with AR theorists’ claim that people are generally successful at achieving prudential and epistemic goals. (shrink)

Periodically, by a rough estimate twice per decade, a new popular book aspires to shake our common understanding of rationality. Since this concept is not only the backbone of normative analysis in...

Many models of (un)ethical decision making assume that people decide rationally and are in principle able to evaluate their decisions from a moral point of view. However, people might behave unethically without being aware of it. They are ethically blind. Adopting a sensemaking approach, we argue that ethical blindness results from a complex interplay between individual sensemaking activities and context factors.

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)

How can we study bounded rationality? We answer this question by proposing rational task analysis —a systematic approach that prevents experimental researchers from drawing premature conclusions regarding the rationality of agents. RTA is a methodology and perspective that is anchored in the notion of bounded rationality and aids in the unbiased interpretation of results and the design of more conclusive experimental paradigms. RTA focuses on concrete tasks as the primary interface between agents and environments and requires explicating essential task elements, (...) specifying rational norms, and bracketing the range of possible performance, before contrasting various benchmarks with actual performance. After describing RTA’s core components we illustrate its use in three case studies that examine human memory updating, multitasking behavior, and melioration. We discuss RTA’s characteristic elements and limitations by comparing it to related approaches. We conclude that RTA provides a useful tool to render the study of bounded rationality more transparent and less prone to theoretical confusion. (shrink)

Inductive generalization, where people go beyond the data provided, is a basic cognitive capability, and it underpins theoretical accounts of learning, categorization, and decision making. To complete the inductive leap needed for generalization, people must make a key ‘‘sampling’’ assumption about how the available data were generated. Previous models have considered two extreme possibilities, known as strong and weak sampling. In strong sampling, data are assumed to have been deliberately generated as positive examples of a concept, whereas in weak sampling, (...) data are assumed to have been generated without any restrictions. We develop a more general account of sampling that allows for an intermediate mixture of these two extremes, and we test its usefulness. In two experiments, we show that most people complete simple one-dimensional generalization tasks in a way that is consistent with their believing in some mixture of strong and weak sampling, but that there are large individual differences in the relative emphasis different people give to each type of sampling. We also show experimentally that the relative emphasis of the mixture is influenced by the structure of the available information. We discuss the psychological meaning of mixing strong and weak sampling, and possible extensions of our modeling approach to richer problems of inductive generalization. (shrink)

Every emerging technology has its pros and cons; health-conscious users pay more importance to healthy and environment-friendly technologies. Based on the UTAUT2 model, we proposed a comprehensive novel model to study the factors influencing consumers’ decision-making to adopt the technology. Compared to prior studies that focused on linear models to investigate consumers’ technology adoption intentions and use behavior. This study used a Structural Equation Modeling-fuzzy set qualitative comparative analysis approach to account for the complexity of customers’ decision-making processes in adopting (...) new technology. We collected valid responses from 830 consumers, analyzed them, and evaluated them using a deep learning SEM-fsQCA technique to capture symmetric and asymmetric relations between variables. We have extensively incorporated a health-consciousness attitude as a predictor and mediator to understand better the decision-making toward technology adoption, specifically 5G technology. All the factors tested in our model are statistically significant except the economic factors. Health-consciousness attitude and behavioral intention found significant predictors and valid mediators in the process of 5G technology adoption. FsQCA provided six configurations to achieve high 5G adoption. The findings have significant practical ramifications for telecom corporations, advertisers, government officials, and key policymakers. Additionally, the study added substantial theoretical literature to technology adoption, particularly the adoption of 5G technology. (shrink)