This paper combines Alfred Shultz and Herbert Simon's theories of action in order to understand the grey area between dynamic and completely unstructured decision making better. As a result I have put together a specific scheme of how choice elements are represented from an agent's personal experience, so as to create a bridge between the phenomenological and cognitive-procedural approaches of decision making. I first look at the key points of their original models relating Alfred Schutz's “provinces of meaning” and Herbert (...) Simon's “satisficing” mechanism. I then consider the particular concept of intentionality and reasoning by analogy for different choice settings. Finally I have suggested a perspective based on creative behaviour and sense-making for ill-structured conditions. (shrink)

Our programmatic article on Homo heuristicus (Gigerenzer & Brighton, 2009) included a methodological section specifying three minimum criteria for testing heuristics: competitive tests, individual-level tests, and tests of adaptive selection of heuristics. Using Richter and Späth’s (2006) study on the recognition heuristic, we illustrated how violations of these criteria can lead to unsupported conclusions. In their comment, Hilbig and Richter conduct a reanalysis, but again without competitive testing. They neither test nor specify the compensatory model of inference they argue for. (...) Instead, they test whether participants use the recognition heuristic in an unrealistic 100% (or 96%) of cases, report that only some people exhibit this level of consistency, and conclude that most people would follow a compensatory strategy. We know of no model of judgment that predicts 96% correctly. The curious methodological practice of adopting an unrealistic measure of success to argue against a competing model, and to interpret such a finding as a triumph for a preferred but unspecified model, can only hinder progress. Marewski, Gaissmaier, Schooler, Goldstein, and Gigerenzer (2010), in contrast, specified five compensatory models, compared them with the recognition heuristic, and found that the recognition heuristic predicted inferences most accurately. (shrink)

Recently proposed models of risky choice imply systematic violations of transitivity of preference. This study explored whether people show the predicted intransitivity of the two models proposed to account for the certainty effect in Allais paradoxes. In order to distinguish “true” violations from those produced by “error,” a model was fit in which each choice can have a different error rate and each person can have a different pattern of preferences that need not be transitive. Error rate for a choice (...) is estimated from preference reversals between repeated presentations of the same choice. Results showed that few people repeated intransitive patterns. We can retain the hypothesis that all participants were transitive. (shrink)

Confronted with the possibility of severe environmental harms, such as catastrophic climate change, some researchers have suggested that we should abandon the principle at the heart of standard decision theory—the injunction to maximize expected utility—and embrace a different one: the Precautionary Principle. Arguably, the most sophisticated philosophical treatment of the Precautionary Principle is due to Steel. Steel interprets PP as a qualitative decision rule and appears to conclude that a quantitative decision-theoretic statement of PP is both impossible and unnecessary. In (...) this article, we propose a decision-theoretic formulation of PP in terms of lexical utilities. We show that this lexical model is largely faithful to Steel’s approach, but also that it corrects three problems with Steel’s account and clarifies the relationship between PP and standard decision theory. Using a range of examples, we illustrate how the lexical model can be used to explore a variety of issues related to precautionary reasoning. (shrink)

Gerd Gigerenzer and Thomas Sturm have recently proposed a modest form of what they describe as a normative, ecological and limited naturalism. The basic move in their argument is to infer that certain heuristics we tend to use should be used in the right ecological setting. To address this argument, we first consider the case of a concrete heuristic called Take the Best (TTB). There are at least two variants of the heuristic which we study by making explicit the choice (...) functions they induce, extending these variants of TTB beyond binary choice. We argue that the naturalistic argument can be applied to only one of the two variants of the heuristic; we also argue that the argument for the extension requires paying attention to other “rational” virtues of heuristics aside from efficacy, speed, and frugality. This notwithstanding, we show that there is a way of extending the right variant of TTB to obtain a very well behaved heuristic that could be used to offer a stronger case for the naturalistic argument (in the sense that if this heuristic is used, it is also a heuristic that we should use). The second part of the article considers attempts to extending the naturalistic argument from algorithms dealing with inference to heuristics dealing with choice. Our focus is the so-called Priority Heuristic, which we extend from risk to uncertainty. In this setting, the naturalist argument seems more difficult to formulate, if it remains feasible at all. Normativity seems in this case extrinsic to the heuristic, whose main virtue seems to be its ability to describe actual patterns of choice. But it seems that a new version of the naturalistic argument used with partial success in the case of inference is unavailable to solve the normative problem of whether we should exhibit the patterns of choice that we actually display. (shrink)

Humans regularly pursue activities characterized by dramatic success or failure outcomes where, critically, the chances of success depend on the time invested working toward it. How should people allocate time between such make‐or‐break challenges and safe alternatives, where rewards are more predictable (e.g., linear) functions of performance? We present a formal framework for studying time allocation between these two types of activities, and we explore optimal behavior in both one‐shot and dynamic versions of the problem. In the one‐shot version, we (...) illustrate striking discontinuities in the optimal time allocation policy as we gradually change the parameters of the decision‐making problem. In the dynamic version, we formulate the optimal strategy—defined by a giving‐up threshold—which adaptively dictates when people should stop pursuing the make‐or‐break goal. We then show that this strategy is computationally inaccessible for humans, and we explore boundedly rational alternatives. We compare the performance of the optimal model against (a) a myopic giving‐up threshold that is easier to compute, and even simpler heuristic strategies that either (b) only decide whether or not to start pursuing the goal and never give up or (c) consider giving up at a fixed number of control points. Comparing strategies across environments, we investigate the cost and behavioral implications of sidestepping the computational burden of full rationality. (shrink)

When people must either save a greater number of people from a smaller harm or a smaller number from a greater harm, do their choices reflect a reasonable moral outlook? We pursue this question with the help of an experiment. In our experiment, two-fifths of subjects employ a similarity heuristic. When alternatives appear dissimilar in terms of the number saved but similar in terms of the magnitude of harm prevented, this heuristic mandates saving the greater number. In our experiment, this (...) leads to choices that are inconsistent with all standard theories of justice. We argue that this demonstrates the untrustworthiness of distributive judgments in cases that elicit similarity-based choice. (shrink)

For a long time, the dominant approach to studying decision making under risk has been to use psychoeconomic functions to account for how behavior deviates from the normative prescriptions of expected value maximization. While this neo-Bernoullian tradition has advanced the field in various ways—such as identifying seminal phenomena of risky choice —it contains a major shortcoming: Psychoeconomic curves are mute with regard to the cognitive mechanisms underlying risky choice. This neglect of the mechanisms both limits the explanatory value of neo-Bernoullian (...) models and fails to provide guidance for designing effective interventions to improve decision making. Here we showcase a recent “attentional turn” in research on risk choice that elaborates how deviations from normative prescriptions can result from imbalances in attention allocation and that thus promises to overcome the challenges of the neo-Bernoullian tradition. We argue that a comprehensive understanding of preference formation in risky choice must provide an account on a mechanistic level, and we delineate directions in which existing theories that rely on attentional processes may be extended to achieve this objective. (shrink)

The strength of emotions in moral judgment and decision-making under risk The focus of this paper is the role of emotions in judgments and choices associated with moral issues. Study 1 shows that depending on the strength of emotions when making a moral decision, people become sensitive to the severity and the probability of harm that their decisions can bring to others. A possible interpretation is that depending on the strength of emotions, people in their moral judgments choose to be (...) either utilitarian or deontologist. In Study 2, following the priority heuristic model, we found that in situations in which the violation of moral norms does not evoke strong negative emotions, people are sensitive to quantitative risk parameters, and the decision-making process requires a relatively longer time. In moral situations in which a violation of the moral norm evokes strong emotions, decision-making is based on arguments other than quantitative risk parameters, and the process takes a shorter time. (shrink)

A decision under ‘complete uncertainty’ is one where the decision maker knows the set of possible outcomes for each decision, but cannot assign probabilities to those outcomes. This way, the problem of ranking decisions is reduced to a problem of ranking sets of outcomes. All rankings that have emerged in the literature in this domain imply transitivity. In the current study, transitivity is subjected to an empirical evaluation in two experiments, where subjects are asked to choose between sets of monetary (...) consequences. After analysis with a Bayes Factor, very few violations of transitivity were found. It can be concluded that transitivity seems a plausible condition for the ranking of sets of monetary consequences. (shrink)

Moral dilemmas have long been debated in moral philosophy without reaching a definitive consensus. The majority of value pluralists attribute their origin to the incommensurability of moral values, i.e. the statement that, since moral values are many and different in nature, they may conflict and cannot be compared. Neuroscientific studies on the neural common currency show that the comparison between allegedly incompatible alternatives is a practical possibility, namely it is the basis of the way in which the agent evaluates choice (...) options. Indeed, both in economic and moral decision-making, the value of options is represented and directly compared in the ventromedial prefrontal cortex. Therefore, we contend that moral dilemmas do not originate from value incommensurability and, on the basis of the neuroscientific discoveries on the neural currency, we derive the implications for the philosophical debate on moral dilemmas. We also provide a possible connection between the experience of moral dilemmas and their neural representation: one of the causes of the individual’s indecision is the neural tie, i.e. the condition in which two options have the same value at neural level, and her regret could be due to the motivational force of the rejected option that is still signalled by affective processes in the brain. We apply this interpretation and the common currency hypothesis to vocational decisions and propose that, although from the agent’s perspective the options are qualitatively different, they may be nevertheless equivalent at neural level. This can be seen as a reason for downgrading the importance commonly attributed to the risk of making the “wrong choice”. (shrink)

The relative input of payoffs and probabilities into risk judgment The study was designed to investigate the relative input of payoffs and probabilities into risk judgment on the basis of the analysis of information search pattern. The modified version of MouselabWeb software was used as an investigative tool. The amount, the kind and the order of information accessed by subjects to evaluate risk was collected from ordinary respondents and respondents trained in mathematics and statistics. In the latter group were 75 (...) students and young researchers working at National Aeronautics and Space Administration. The ordinary subjects were 67 Polish students of social sciences. As expected, the NASA group considered more information than ordinary students and searched for more information about probabilities. However, the ratio of information about payoffs to probabilities was close to 1 in both groups. Moreover, average risk rates were similar in both groups. It was also observed that risk rates were positively related with the amount of information about probabilities considered by subjects. (shrink)

The first goal of this study was to validate the Rational-Experiential Inventory (REI) and the Cognitive Reflection Test (CRT) through checking their relation to the transitivity axiom. The second goal was to test the relation between decision strategies and cognitive style as well as the relation between decision strategies and the transitivity of preferences. The following characteristics of strategies were investigated: requirements for trade-offs, maximization vs. satisficing and option-wise vs. attribute-wise information processing. Respondents were given choices between two multi-attribute options. (...) The options were designed so that the choice indicated which strategy was applied. Both the REI-R and the CRT were found to be good predictors of the transitivity of preferences. Respondents who applied compensatory strategies and the maximization criterion scored highly on the REI-R and in the CRT, whereas those who applied the satisficing rule scored highly on the REI-R but not in the CRT. Attribute-wise information processing was related to low scores in both measurements. Option-wise information processing led to a high transitivity of preferences. (shrink)

One of the paradigms in judgment and decision-making involves decision-makers sample information before making a final consequential choice. In the sampling paradigm, certain computational models have been proposed where a set of single or distribution parameters is calibrated to the choice proportions of a group of participants. However, currently little is known on how aggregate and hierarchical models would account for choices made by individual participants in the sampling paradigm. In this paper, we test the ability of aggregate and hierarchical (...) models to explain choices made by individual participants. Several models, Ensemble, Cumulative Prospect Theory, Best Estimation and Simulation Techniques, Natural-Mean Heuristic, and Instance-Based Learning, had their parameters calibrated to individual choices in a large dataset involving the sampling paradigm. Later, these models were generalized to two large datasets in the sampling paradigm. Results revealed that the aggregate models accounted for individual choices better than hierarchical models upon generalization to problems that were like those encountered during calibration. Furthermore, the CPT model, which relies on differential valuing of gains and losses, respectively, performed better than other models during calibration and generalization on datasets with similar set of problems. The IBL model, relying on recency and frequency of sampled information, and the NMH model, relying on frequency of sampled information, performed better than other models during generalization to a challenging dataset. Sequential analyses of results from different models showed how these models accounted for transitions from the last sample to final choice in human data. We highlight the implications of using aggregate and hierarchical models in explaining individual choices from experience. (shrink)

People often face preferential decisions under risk. To further our understanding of the cognitive processes underlying these preferential choices, two prominent cognitive models, decision field theory (DFT; Busemeyer & Townsend, 1993) and the proportional difference model (PD; González‐Vallejo, 2002), were rigorously tested against each other. In two consecutive experiments, the participants repeatedly had to choose between monetary gambles. The first experiment provided the reference to estimate the models’ free parameters. From these estimations, new gamble pairs were generated for the second (...) experiment such that the two models made maximally divergent predictions. In the first experiment, both models explained the data equally well. However, in the second generalization experiment, the participants’ choices were much closer to the predictions of DFT. The results indicate that the stochastic process assumed by DFT, in which evidence in favor of or against each option accumulates over time, described people’s choice behavior better than the trade‐offs between proportional differences assumed by PD. (shrink)

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP and QP theory share the (...) fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality. (shrink)

I examine some behavioural and heuristic models of individual decision-making and argue that the diverse psychological mechanisms these models posit are too demanding to be implemented, either consciously or unconsciously, by actual decision makers. Accordingly, and contrary to what their advocates typically claim, behavioural and heuristic models are best understood as ‘as-if’ models. I then sketch a version of scientific antirealism that justifies the practice of as-if modelling in decision theory but goes beyond traditional instrumentalism. Finally, I relate my account (...) of decision models to the recent controversy about mentalism versus behaviourism, reject both positions, and offer an alternative view. (shrink)

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architectures.

There have been calls for some time for a new approach to public health in the United Kingdom and beyond. This is consequent on the recognition and acceptance that health problems often have a complex and multi-faceted aetiology. At the same time, policies which utilise insights from research in behavioural economics and psychology have gained prominence on the political agenda. The relationship between the social determinants of health and behavioural science in health policy has not hitherto been explored. Given the (...) on-going presence of strategies based on findings from behavioural science in policy-making on the political agenda, an examination of this is warranted. This paper begins by looking at the place of the SDoH within public health, before outlining, in brief, the recent drive towards utilising behavioural science to formulate law and public policy. We then examine the relationship between this and the SDoH. We argue that behavioural public health policy is, to a certain extent, blind to the social and other determinants of health. In section three, we examine ways in which such policies may perpetuate and/or exacerbate health inequities and social injustices. We argue that problems in this respect may be compounded by assumptions and practices which are built into some behavioural science methodologies. We also argue that incremental individual gains may not be enough. As such, population-level measures are sometimes necessary. In section four we defend this contention, arguing that an equitable and justifiable public health requires such measures. (shrink)

Human decision making shows systematic simplifications and deviations from the tenets of rationality (‘heuristics’) that may lead to suboptimal decisional outcomes (‘cognitive biases’). There are currently three prevailing theoretical perspectives on the origin of heuristics and cognitive biases: a cognitive-psychological, an ecological and an evolutionary perspective. However, these perspectives are mainly descriptive and none of them provides an overall explanatory framework for the underlying mechanisms of cognitive biases. To enhance our understanding of cognitive heuristics and biases we propose a neural (...) network framework for cognitive biases, which explains why our brain systematically tends to default to heuristic (‘Type 1’) decision making. We argue that many cognitive biases arise from intrinsic brain mechanisms that are fundamental for the working of biological neural networks. In order to substantiate our viewpoint, we discern and explain four basic neural network principles: (1) Association, (2) Compatibility (3) Retainment, and (4) Focus. These principles are inherent to (all) neural networks which were originally optimized to perform concrete biological, perceptual, and motor functions. They form the basis for our inclinations to associate and combine (unrelated) information, to prioritize information that is compatible with our present state (such as knowledge, opinions and expectations), to retain given information that sometimes could better be ignored, and to focus on dominant information while ignoring relevant information that is not directly activated. The supposed mechanisms are complementary and not mutually exclusive. For different cognitive biases they may all contribute in varying degrees to distortion of information. The present viewpoint not only complements the earlier three viewpoints, but also provides a unifying and binding framework for many cognitive bias phenomena. (shrink)

We investigate whether risky choice framing, i.e., the preference of a sure over an equivalent risky option when choosing among gains, and the reverse when choosing among losses, depends on redundancy and density of information available in a task. Redundancy, the saliency of missing information, and density, the description of options in one or multiple chunks, was manipulated in a matrix setup presented in MouselabWeb. On the choice level we found a framing effect only in setups with non-redundant information. On (...) the process level outcomes attracted more acquisitions than probabilities, irrespective of redundancy. A dissociation between acquisition behavior and choice calls for a critical discussion of the limits of process-tracing measures for understanding and predicting choices in decision making tasks. (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)

Research on bounded rationality has two cultures, which I call ‘idealistic’ and ‘pragmatic’. Technically, the cultures differ on whether they build models based on normative axioms or empirical facts, assume that people's goal is to optimize or to satisfice, do not or do model psychological processes, let parameters vary freely or fix them, aim at explanation or prediction and test models from one or both cultures. Each culture tells a story about people's rationality. The story of the idealistic culture is (...) frustrating, with people in principle being able to know what they should do, but in practice systematically failing to do it. This story makes one hide in books for intellectual solace or surrender to the designs of someone smarter. The story of the pragmatic culture is empowering: If people are educated to use the right tool in the right situation, they do well. (shrink)

Some researchers on binary choice inference have argued that people make inferences based on simple heuristics, such as recognition, fluency, or familiarity. Others have argued that people make inferences based on available knowledge. To examine the boundary between heuristic and knowledge usage, we examine binary choice inference processes in terms of attribute substitution in heuristic use (Kahneman & Frederick, 2005). In this framework, it is predicted that people will rely on heuristic or knowledge‐based inference depending on the subjective difficulty of (...) the inference task. We conducted competitive tests of binary choice inference models representing simple heuristics (fluency and familiarity heuristics) and knowledge‐based inference models. We found that a simple heuristic model (especially a familiarity heuristic model) explained inference patterns for subjectively difficult inference tasks, and that a knowledge‐based inference model explained subjectively easy inference tasks. These results were consistent with the predictions of the attribute substitution framework. Issues on usage of simple heuristics and psychological processes are discussed. (shrink)

Most investigations into how people make risky choices have employed a simple drosophila: monetary gambles involving stated outcomes and probabilities. People are asked to make decisions from description . When people decide whether to back up their computer hard drive, cross a busy street, or go out on a date, however, they do not enjoy the convenience of stated outcomes and probabilities. People make such decisions either in the void of ignorance or in the twilight of their own often limited (...) experience of such real-world options. In the latter case, they make decisions from experience . Recent research has consistently documented that decisions from description and decisions from experience can lead to substantially different choices. Key in this description–experience gap is people’s treatment of rare events. In this paper, I briefly review studies that have documented the description–experience gap, offer several explanations for this gap, and discuss to what extent people’s decisions from experience are in conflict with benchmarks of rationality. (shrink)

Most models of risky decision making assume that all relevant information is taken into account (e.g., Kahneman & Tversky, 1979; von Neumann & Morgenstern, 1944). However, there are also some models supposing that only part of the information is considered (e.g., Brandstätter, Gigerenzer, & Hertwig, 2006; Gigerenzer & Gaissmaier, 2011). To further investigate the amount of information that is usually used for decision making, and how the use depends on feedback, we conducted a series of three experiments in which participants (...) choose between two lotteries and where no feedback, outcome feedback, and error feedback was provided, respectively. The result show that without feedback participants mostly chose the lottery with the higher winning probability, and largely ignored the potential gains. The same result occurred when the outcome of each decision was fed back. Only after presenting error feedback (i.e., signaling whether a choice was optimal or not), participants considered probabilities as well as gains, resulting in more optimal choices. We propose that outcome feedback was ineffective, because of its probabilistic and ambiguous nature. Participants improve information integration only if provided with a consistent and deterministic signal such as error feedback. (shrink)

Original and cumulative prospect theory differ in the composition rule used to combine the probability weighting function and the value function. We test the predictive power of these composition rules by performing a novel out-of-sample prediction test. We apply estimates of prospect theory’s weighting and value function obtained from two-outcome cash equivalents, a domain where original and cumulative prospect theory coincide, to three-outcome cash equivalents, a domain where the composition rules of the two theories differ. Although both forms of prospect (...) theory predict three-outcome cash equivalents very well, at the aggregate level, we find small but systematic under-prediction of cash equivalents for cumulative prospect theory and small but systematic over-prediction of cash equivalents for original prospect theory. We also observe substantial heterogeneity across subjects and types of gambles, some of which is accounted for by differences in the curvature and elevation of the weighting function across individuals. We also find differences in prediction related to whether the worst outcome is zero or non-zero. (shrink)

The various behavioral disciplines model human behavior in distinct and incompatible ways. Yet, recent theoretical and empirical developments have created the conditions for rendering coherent the areas of overlap of the various behavioral disciplines. The analytical tools deployed in this task incorporate core principles from several behavioral disciplines. The proposed framework recognizes evolutionary theory, covering both genetic and cultural evolution, as the integrating principle of behavioral science. Moreover, if decision theory and game theory are broadened to encompass other-regarding preferences, they (...) become capable of modeling all aspects of decision making, including those normally considered “psychological,” “sociological,” or “anthropological.” The mind as a decision-making organ then becomes the organizing principle of psychology. (Published Online April 27 2007) Key Words: behavioral game theory; behavioral science; evolutionary theory; experimental psychology; gene-culture coevolution; rational actor model; socialization. (shrink)

The paper shows why and how an empirical study of fast-and-frugal heuristics can provide norms of good reasoning, and thus how (and how far) rationality can be naturalized. We explain the heuristics that humans often rely on in solving problems, for example, choosing investment strategies or apartments, placing bets in sports, or making library searches. We then show that heuristics can lead to judgments that are as accurate as or even more accurate than strategies that use more information and computation, (...) including optimization methods. A standard way to defend the use of heuristics is by reference to accuracy-effort trade-offs. We take a different route, emphasizing ecological rationality (the relationship between cognitive heuristics and environment), and argue that in uncertain environments, more information and computation are not always better (the “less-can-be-more” doctrine). The resulting naturalism about rationality is thus normative because it not only describes what heuristics people use, but also in which specific environments one should rely on a heuristic in order to make better inferences. While we desist from claiming that the scope of ecological rationality is unlimited, we think it is of wide practical use. (shrink)

Heuristics are efficient cognitive processes that ignore information. In contrast to the widely held view that less processing reduces accuracy, the study of heuristics shows that less information, computation, and time can in fact improve accuracy. We review the major progress made so far: the discovery of less-is-more effects; the study of the ecological rationality of heuristics, which examines in which environments a given strategy succeeds or fails, and why; an advancement from vague labels to computational models of heuristics; the (...) development of a systematic theory of heuristics that identifies their building blocks and the evolved capacities they exploit, and views the cognitive system as relying on an “adaptive toolbox;” and the development of an empirical methodology that accounts for individual differences, conducts competitive tests, and has provided evidence for people’s adaptive use of heuristics. Homo heuristicus has a biased mind and ignores part of the available information, yet a biased mind can handle uncertainty more efficiently and robustly than an unbiased mind relying on more resource-intensive and general-purpose processing strategies. (shrink)