A heuristic is a rule of thumb. In psychology, heuristics are relatively simple rules for making judgments. A fast heuristic is easy to use and allows one to make judgments quickly. A frugal heuristic relies on a small fraction of the available evidence in making judgments. Typically, fast and frugal heuristics (FFHs) have, or are claimed to have, a further property: They are very reliable, yielding judgments that are about as accurate in the long run as (...) ideal non-fast, non-frugal rules. This paper introduces some well-known examples of FFHs, raises some objections to the FFH program, and looks at the implications of those parts of the FFH program about which we can have some reasonable degree of confidence. (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)

Simple heuristics that make us smart offers an impressive compilation of work that demonstrates fast and frugal (one-reason) heuristics can be simple, adaptive, and accurate. However, many decision environments differ from those explored in the book. We conducted a Monte Carlo simulation that shows one-reason strategies are accurate in “friendly” environments, but less accurate in “unfriendly” environments characterized by negative cue intercorrelations, that is, tradeoffs.

Decision making is usually viewed as involving a period of thought, while the decision maker assesses options, their likely consequences, and his or her preferences, and selects the preferred option. The process ends in a terminating action. In this view errors of thought will inevitably show up as errors of action; costs of thinking are to be balanced against costs of decision errors. Fast and frugal heuristics research has shown that, in some environments, modest thought can lead to (...) excellent action. In this paper we extend this work to situations in which action is taken after little or no thought. We show that these `highly active' or `decision cycles' processes can lead to excellent results at the cost of almost no thought. The paper examines the settings in which this effectiveness is possible, and lists a number of environmental features that are required for decision cycles to work well. Several research directions for analytical, laboratory, and field-based research are identified. (shrink)

Research on “improper” linear models has shown that predetermined weighting schemes for the linear model, such as equally weighting all predictors, can be surprisingly accurate on cross-validation. We review recent advances that can characterize the optimal choice of an improper linear model. We extend this research to the understanding of fast and frugal heuristics, particularly to the ecologically rational goal of understanding in which task environments given heuristics are optimal. We demonstrate how to test this model using (...) the Recognition Heuristic and Take the Best heuristic, show how the model reconciles with the ecological rationality program, and discuss how our prescriptive, computational approach could be approximated by simpler mental rules that might be more descriptive. Echoing the arguments of van Rooij et al., we stress the virtue of having a computationally tractable model of strategy selection, even if one proposes that cognizers use a simpler heuristic process to approximate it. (shrink)

The purpose of this paper is to examine the normative interpretation of the fast-and-frugal research program and in particular to contrast it with the normative reading of rational choice theory and behavioral economics. The ecological rationality of fast-and-frugal heuristics is admittedly a form of normative naturalism – it derives what agents “ought” to do from that which “is” ecologically rational – and the paper will examine how this differs from the normative rationality associated with rational choice theory. (...) I will also attempt to assess the relative adequacy of normative ecological rationality. (shrink)

Both the fast and frugal heuristics and the naturalistic decision making research programmes have identified important areas of inquiry previously neglected in the traditional study of human judgment and decision making, and have greatly contributed to the understanding of people's real-world decision making under environmental constraints. The two programmes share similar theoretical arguments regarding the rationality, optimality, and role of experience in decision making. Their commonalities have made them appealing to each other, and efforts have been made, by (...) their leading academics, to promote synergy and integration. However, there has been little progress towards this during the last decade. This paper seeks to address this gap by seeking to better understand their commonalities and differences. To do so, literature relating to the two programmes is reviewed. The findings of the review indicated that an integration of the two could enhance FFHs' field research in applied settings, facilitate its investigation on boundary conditions of people's decision strategy selection, enable NDM to embrace emerging research opportunities in the age of big data, as well as permit each programme to enlighten the research topics and to validate the research findings of the other. (shrink)

Intractability and optimality are two sides of one coin: Optimal models are often intractable, that is, they tend to be excessively complex, or NP-hard. We explain the meaning of NP-hardness in detail and discuss how modem computer science circumvents intractability by introducing heuristics and shortcuts to optimality, often replacing optimality by means of sufficient sub-optimality. Since the principles of decision theory dictate balancing the cost of computation against gain in accuracy, statistical inference is currently being reshaped by a vigorous (...) new trend: the science of simplicity. Simple models, as we show for specific cases, are not just tractable, they also tend to be robust. Robustness is the ability of a model to extract relevant information from data, disregarding noise.Recently, Gigerenzer, Todd and the ABC Research Group (1999) have put forward a collection of fast and frugal heuristics as simple, boundedly rational inference strategies used by the unaided mind in real world inference problems. This collection of heuristics has suggestively been called the adaptive toolbox. In this paper we will focus on a comparison task in order to illustrate the simplicity and robustness of some of the heuristics in the adaptive toolbox in contrast to the intractability and the fragility of optimal solutions. We will concentrate on three important classes of models for comparison-based inference and, in each of these classes, search for that to be used as benchmarks to evaluate the performance of fast and frugal heuristics: lexicographic trees, linear modes and Bayesian networks. Lexicographic trees are interesting because they are particularly simple models that have been used by humans throughout the centuries. Linear models have been traditionally used by cognitive psychologists as models for human inference, while Bayesian networks have only recently been introduced in statistics and computer science. Yet it is the Bayesian networks that are the best possible benchmarks for evaluating the fast and frugal heuristics, as we will show in this paper. (shrink)

Gigerenzer and his co-workers make some bold and striking claims about the relation between the fast and frugal heuristics discussed in their book and the traditional norms of rationality provided by deductive logic and probability theory. We are told, for example, that fast and frugal heuristics such as “Take the Best” replace “the multiple coherence criteria stemming from the laws of logic and probability with multiple correspondence criteria relating to real-world decision performance.” This commentary explores just (...) how we should interpret this proposed replacement of logic and probability theory by fast and frugal heuristics. (shrink)

It is difficult to overestimate Paul Meehl's influence on judgment and decision-making research. His 'disturbing little book' Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence is known as an attack on human judgment and a call for replacing clinicians with actuarial methods. More than 40 years later, fast and frugal heuristics - proposed as models of human judgment - were formalized, tested, and found to be surprisingly accurate, often more so than the actuarial (...) models that Meehl advocated. We ask three questions: Do the findings of the two programs contradict each other? More generally, how are the programs conceptually connected? Is there anything they can learn from each other? After demonstrating that there need not be a contradiction, we show that both programs converge in their concern to develop domain-specific models of judgment and nonlinear process models that arise from the bounded nature of judgment. We then elaborate the differences between the programs and discuss how these differences can be viewed as mutually instructive: First, we show that the fast and frugal heuristic models can help bridge the clinical - actuarial divide, that is, they can be developed into actuarial methods that are both accurate and easy to implement by the unaided clinical judge. We then argue that Meehl's insistence on improving judgment makes clear the importance of examining the degree to which heuristics are used in the clinical domain and how acceptable they would be as actuarial tools. (shrink)

It is difficult to overestimate Paul Meehl’s influence on judgment and decision-making research. His ‘disturbing little book’ (Meehl, 1986, p. 370) Clinical versus Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (1954) is known as an attack on human judgment and a call for replacing clinicians with actuarial methods. More than 40 years later, fast and frugal heuristics—proposed as models of human judgment—were formalized, tested, and found to be surprisingly accurate, often more so than the (...) actuarial models that Meehl advocated. We ask three questions: Do the findings of the two programs contradict each other? More generally, how are the programs conceptually connected? Is there anything they can learn from each other? After demonstrating that there need not be a contradiction, we show that both programs converge in their concern to develop (a) domain-specific models of judgment and (b) nonlinear process models that arise from the bounded nature of judgment. We then elaborate the differences between the programs and discuss how these differences can be viewed as mutually instructive: First, we show that the fast and frugal.. (shrink)

The notion of “bounded rationality” was introduced by Simon as an appropriate framework for explaining how agents reason and make decisions in accordance with their computational limitations and the characteristics of the environments in which they exist (seen metaphorically as two complementary scissor blades).We elaborate on how bounded rationality is usually conceived in psychology and on its relationship with logic. We focus on the relationship between heuristics and some non-monotonic logical systems. These two categories of cognitive tools share fundamental (...) features. As a step further, we show that in some cases heuristics themselves can be formalized from this logic perspective. We have therefore two main aims: on the one hand, to demonstrate the relationship between the bounded rationality programme and logic, understood in a broad sense; on the other hand, to provide logical tools of analysis of already known heuristics. This may lead to results such as the characterization of fast and frugal binary trees in terms of their associated logic program here provided. (shrink)

This article provides an overview of recent results on lexicographic, linear, and Bayesian models for paired comparison from a cognitive psychology perspective. Within each class, we distinguish subclasses according to the computational complexity required for parameter setting. We identify the optimal model in each class, where optimality is defined with respect to performance when fitting known data. Although not optimal when fitting data, simple models can be astonishingly accurate when generalizing to new data. A simple heuristic belonging to the class (...) of lexicographic models is Take The Best (Gigerenzer & Goldstein (1996) Psychol. Rev. 102: 684). It is more robust than other lexicographic strategies which use complex procedures to establish a cue hierarchy. In fact, it is robust due to its simplicity, not despite it. Similarly, Take The Best looks up only a fraction of the information that linear and Bayesian models require; yet it achieves performance comparable to that of models which integrate information. Due to its simplicity, frugality, and accuracy, Take The Best is a plausible candidate for a psychological model in the tradition of bounded rationality. We review empirical evidence showing the descriptive validity of fast and frugal heuristics. (shrink)

A research program is announced, and initial, exciting progress described. Many inference problems, poorly modeled by some traditional approaches, are surprisingly well handled by kinds of simple-minded Bayesian approximations. Fuller Bayesian approaches are typically more accurate but rarely are they either fast or frugal. Open issues include codifying when to use which heuristic and to give detailed evolutionary explanations.

Simple heuristics that make us smart presents a valuable and valid interpretation of how we make fast decisions particularly in situations of ignorance and uncertainty. What is missing is how this intersects with thinking under even greater uncertainty or ignorance, such as novice problem solving, and with the development of expert cognition.

Simple Heuristics That Make Us Smart invites readers to embark on a new journey into a land of rationality that differs from the familiar territory of cognitive science and economics. Traditional views of rationality tend to see decision makers as possessing superhuman powers of reason, limitless knowledge, and all of eternity in which to ponder choices. To understand decisions in the real world, we need a different, more psychologically plausible notion of rationality, and this book provides it. It is (...) about fast and frugal heuristics--simple rules for making decisions when time is pressing and deep thought an unaffordable luxury. These heuristics can enable both living organisms and artificial systems to make smart choices, classifications, and predictions by employing bounded rationality. But when and how can such fast and frugal heuristics work? Can judgments based simply on one good reason be as accurate as those based on many reasons? Could less knowledge even lead to systematically better predictions than more knowledge? Simple Heuristics explores these questions, developing computational models of heuristics and testing them through experiments and analyses. It shows how fast and frugal heuristics can produce adaptive decisions in situations as varied as choosing a mate, dividing resources among offspring, predicting high school drop out rates, and playing the stock market. As an interdisciplinary work that is both useful and engaging, this book will appeal to a wide audience. It is ideal for researchers in cognitive psychology, evolutionary psychology, and cognitive science, as well as in economics and artificial intelligence. It will also inspire anyone interested in simply making good decisions. (shrink)

Gigerenzer and his collaborators have shown that the Take the Best heuristic (TTB) approximates optimal decision behavior for many inference problems. We studied the effect of incomplete cue knowledge on the quality of this approximation. Bayesian algorithms clearly outperformed TTB in case of partial cue knowledge, especially when the validity of the recognition cue is assumed to be low.

Some theorists, ranging from W. James to contemporary psychologists, have argued that forgetting is the key to proper functioning of memory. The authors elaborate on the notion of beneficial forgetting by proposing that loss of information aids inference heuristics that exploit mnemonic information. To this end, the authors bring together 2 research programs that take an ecological approach to studying cognition. Specifically, they implement fast and frugal heuristics within the ACT-R cognitive architecture. Simulations of the recognition heuristic, (...) which relies on systematic failures of recognition to infer which of 2 objects scores higher on a criterion value, demonstrate that forgetting can boost accuracy by increasing the chances that only 1 object is recognized. Simulations of the fluency heuristic, which arrives at the same inference on the basis of the speed with which objects are recognized, indicate that forgetting aids the discrimination between the objects' recognition speeds. (shrink)

How can anyone be rational in a world where knowledge is limited, time is pressing, and deep thought is often an unattainable luxury? Traditional models of unbounded rationality and optimization in cognitive science, economics, and animal behavior have tended to view decision-makers as possessing supernatural powers of reason, limitless knowledge, and endless time. But understanding decisions in the real world requires a more psychologically plausible notion of bounded rationality. In Simple heuristics that make us smart (Gigerenzer et al. 1999), (...) we explore fast and frugal heuristics – simple rules in the mind's adaptive toolbox for making decisions with realistic mental resources. These heuristics can enable both living organisms and artificial systems to make smart choices quickly and with a minimum of information by exploiting the way that information is structured in particular environments. In this précis, we show how simple building blocks that control information search, stop search, and make decisions can be put together to form classes of heuristics, including: ignorance-based and one-reason decision making for choice, elimination models for categorization, and satisficing heuristics for sequential search. These simple heuristics perform comparably to more complex algorithms, particularly when generalizing to new data – that is, simplicity leads to robustness. We present evidence regarding when people use simple heuristics and describe the challenges to be addressed by this research program. Key Words: adaptive toolbox; bounded rationality; decision making; elimination models; environment structure; heuristics; ignorance-based reasoning; limited information search; robustness; satisficing; simplicity. (shrink)

Gigerenzer, Todd, and the ABC Research Group argue that optimisation under constraints leads to an infinite regress due to decisions about how much information to consider when deciding. In certain cases, however, their fast and frugal heuristics lead instead to an endless series of decisions about how best to decide.

Simple heuristics are clearly powerful tools for making near optimal decisions, but evidence for their use in specific situations is weak. Gigerenzer et al. (1999) suggest a range of heuristics, but fail to address the question of which environmental or task cues might prompt the use of any specific heuristic. This failure compromises the falsifiability of the fast and frugal approach.

One challenge that has to be addressed by the fast and frugal heuristics program is how people manage to select, from the abundance of cues that exist in the environment, those to rely on when making decisions. We hypothesize that causal knowledge helps people target particular cues and estimate their validities. This hypothesis was tested in three experiments. Results show that when causal information about some cues was available, participants preferred to search for these cues first and to (...) base their decisions on them. When allowed to learn cue validities in addition to causal information, participants also became more frugal, made more accurate decisions, and were more precise in estimating cue validities than was a control group that did not receive causal information. These results can be attributed to the causal relation between the cues and the criterion, rather than to greater saliency of the causal cues. Overall, our results support the hypothesis that causal knowledge aids in the learning of cue validities and is treated as a meta-cue for identifying highly valid cues. (shrink)

Mark Kelman’s recent book, The Heuristics Debate , has two main goals. First, it seeks to reconstruct the controversy in decision science between Kahneman et al.’s heuristics-and-biases approach and Gigerenzer et al.’s fast-and-frugal heuristics approach. Second, it tries to discuss its implications for jurisprudence and policy-making. This study focuses on the first task only. The study attempts to show that, although HD has several important merits, its interpretation of the controversy misses some crucial aspects. Specifically, HD (...) fails to appreciate that the debate is fundamentally about what a “rational” judgment is in the first place. Moreover, because of this, HD also fails to acknowledge the interplay between normative and methodological considerations. With regard to this aspect, HD’s treatment of the controversy fits into a long tradition. This study takes the opportunity to rectify the error. (shrink)

Simple heuristics and regression models make different assumptions about behaviour. Both the environment and judgment can be described as fast and frugal. We do not know whether humans are successful when being fast and frugal. We must assess both global accuracy and the costs of Type I and II errors. These may be “smart heuristics that make researchers look simple.”.

Heuristics make decisions not only fast and frugally, but often nearly as well as “full” rationality or even better. Using such heuristics should therefore meet health care standards under liability law. But an independent court often has little chance to verify the necessary information. And judgments based on heuristics might appear to have little legitimacy, given the widespread belief in formal rationality.

Conventional theory assumes that economic agents perform at optimal levels of efficiency by definition and this is achieved when individuals behave in a particular fashion. Moreover, neoclassical production theory masks the process by which optimal output can be achieved. I argue that economic theory should be revised to incorporate some key findings of behavioural economics, while retaining the conventional theory’s normative ideal of optimum output whilst rejecting its normative procedural ideals of how to achieve optimality in production. I argue that (...) neoclassical procedures can be expected to yield sub-optimal levels of output and therefore should not be benchmarks for procedural rationality. I present an alternative more realistic and analytically precise specification of the production function related to the fast and frugal heuristics narrative pioneered by Gigerenzer and Leibenstein’s x-efficient theory. This approach incorporates an understanding of the appropriate procedures, psychological and organization variables, decision-making capabilities and end-goals required to achieve optimality in production and, thereby, grow the wealth of nations, thereby enhancing the material wellbeing of the population at large. This also provides us with the tools to better identify economic inefficiency and the conditions that contribute to it. (shrink)

What does 'if' mean? Timothy Williamson presents a controversial new approach to understanding conditional thinking, which is central to human cognitive life. He argues that in using 'if' we rely on psychological heuristics, fast and frugal methods which can lead us to trust faulty data and prematurely reject simple theories.

[Correction Notice: An erratum for this article was reported in Vol 109 of Psychological Review. Due to circumstances that were beyond the control of the authors, the studies reported in "Models of Ecological Rationality: The Recognition Heuristic," by Daniel G. Goldstein and Gerd Gigerenzer overlap with studies reported in "The Recognition Heuristic: How Ignorance Makes Us Smart," by the same authors and with studies reported in "Inference From Ignorance: The Recognition Heuristic". In addition, Figure 3 in the Psychological Review article (...) was originally published in the book chapter and should have carried a note saying that it was used by permission of Oxford University Press.] One view of heuristics is that they are imperfect versions of optimal statistical procedures considered too complicated for ordinary minds to carry out. In contrast, the authors consider heuristics to be adaptive strategies that evolved in tandem with fundamental psychological mechanisms. The recognition heuristic, arguably the most frugal of all heuristics, makes inferences from patterns of missing knowledge. This heuristic exploits a fundamental adaptation of many organisms: the vast, sensitive, and reliable capacity for recognition. The authors specify the conditions under which the recognition heuristic is successful and when it leads to the counter-intuitive less-is-more effect in which less knowledge is better than more for making accurate inferences. (shrink)

In contrast with the two dominant views in Theory of Mind development, the Perceptual Access Reasoning hypothesis of Fabricius and colleagues is that children don’t understand the mental state of belief until around 6 years of age. Evidence for this includes data that many children ages 4 and 5, who pass the standard 2-location false belief task, nonetheless fail the true belief task, and often fail a 3-location false belief task by choosing the irrelevant option. These findings can be explained (...) by the PAR hypothesis but pose challenges for the two dominant views. I argue against an alternate hypothesis which is proposed by Anika Fiebich in a recent paper. According to Fiebich, PAR is not a distinct transitional stage in children’s theory of mind development, but is a fast and frugal System 1 heuristic which fades once children become fluent in social reasoning. However, I point out a number of problems with Fiebich’s proposal and argue for the superiority of the PAR hypothesis. I also present five reasons to be skeptical about the findings of Perner and Horn which purportedly show that 4- and 5-year-olds can pass the 3-location false belief task when suitably modified. This is a further difficulty for Fiebich’s proposal, since she relies on these findings in her fluency theory. Finally, I sketch a dual systems theory of mind account based upon the PAR hypothesis which is different from Fiebich’s. (shrink)

Gigerenzer and Brighton (2009) have argued for a “Homo heuristicus” view of judgment and decision making, claiming that there is evidence for a majority of individuals using fast and frugal heuristics. In this vein, they criticize previous studies that tested the descriptive adequacy of some of these heuristics. In addition, they provide a reanalysis of experimental data on the recognition heuristic that allegedly supports Gigerenzer and Brighton’s view of pervasive reliance on heuristics. However, their arguments and (...) reanalyses are both conceptually and methodologically problematic. We provide counterarguments and a reanalysis of the data considered by Gigerenzer and Brighton. Results clearly replicate previous findings, which are at odds with the claim that simple heuristics provide a general description of inferences for a majority of decision makers. (shrink)

The theory of fast and frugal heuristics, developed in a new book called Simple Heuristics that make Us Smart (Gigerenzer, Todd, and the ABC Research Group, in press), includes two requirements for rational decision making. One is that decision rules are bounded in their rationality –- that rules are frugal in what they take into account, and therefore fast in their operation. The second is that the rules are ecologically adapted to the environment, which means that (...) they `fit to reality.' The main purpose of this article is to apply these ideas to learning rules–-methods for constructing, selecting, or evaluating competing hypotheses in science, and to the methodology of machine learning, of which connectionist learning is a special case. The bad news is that ecological validity is particularly difficult to implement and difficult to understand. The good news is that it builds an important bridge from normative psychology and machine learning to recent work in the philosophy of science, which considers predictive accuracy to be a primary goal of science. (shrink)

Making decisions can be hard, but it can also be facilitated. Simple heuristics are fast and frugal but nevertheless fairly accurate decision rules that people can use to compensate for their limitations in computational capacity, time, and knowledge when they make decisions [Gigerenzer, G., Todd, P. M., & the ABC Research Group (1999). Simple Heuristics That Make Us Smart. New York: Oxford University Press.]. These heuristics are effective to the extent that they can exploit the structure (...) of information in the environment in which they operate. Specifically, they require knowledge about the predictive value of probabilistic cues. However, it is often difficult to keep track of all the available cues in the environment and how they relate to any relevant criterion. This problem becomes even more critical if compound cues are considered. We submit that knowledge about the causal structure of the environment helps decision makers focus on a manageable subset of cues, thus effectively reducing the potential computational complexity inherent in even relatively simple decision-making tasks. We review experimental evidence that tested this hypothesis and report the results of a simulation study. We conclude that causal knowledge can act as a meta-cue for identifying highly valid cues, either individual or compound, and helps in the estimation of their validities. (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)

If fast and frugal heuristics are as good as they seem to be, who needs logic and probability theory? Fast and frugal heuristics depend for their success on reliable structure in the environment. In passive environments, there is relatively little change in structure as a consequence of individual choices. But in social interactions with competing agents, the environment may be structured by agents capable of exploiting logical and probabilistic weaknesses in competitors' heuristics. Aspirations toward the (...) ideal of a demon reasoner may consequently be adaptive for direct competition with such agents. (shrink)

This paper discusses the ecological case for epistemic innocence: does biased cognition have evolutionary benefits, and if so, does that exculpate human reasoners from irrationality? Proponents of ‘ecological rationality’ have challenged the bleak view of human reasoning emerging from research on biases and fallacies. If we approach the human mind as an adaptive toolbox, tailored to the structure of the environment, many alleged biases and fallacies turn out to be artefacts of narrow norms and artificial set-ups. However, we argue that (...) putative demonstrations of ecological rationality involve subtle locus shifts in attributions of rationality, conflating the adaptive rationale of heuristics with our own epistemic credentials. By contrast, other cases also involve an ecological reframing of human reason, but do not involve such problematic locus shifts. We discuss the difference between these cases, bringing clarity to the rationality debate. (shrink)

Fast and frugal heuristics function accurately and swiftly over a wide range of decision making processes. The performance of these algorithms in the social domain would be an object for research. The use of simple algorithms to investigate social decision-making could prove fruitful in studies of nonhuman primates as well as humans.

It is certainly the case that morality governs the interactions that take place between individuals. But what if morality exists because of these interactions? This book, first published in 2007, argues for the claim that much of the behaviour we view as 'moral' exists because acting in that way benefits each of us to the greatest extent possible, given the socially structured nature of society. Drawing upon aspects of evolutionary game theory, the theory of bounded rationality, and computational models of (...) social networks, it shows both how moral behaviour can emerge in socially structured environments, and how it can persist even when it is not typically viewed as 'rational' from a traditional economic perspective. This book also provides a theory of how moral principles and the moral sentiments play an indispensable role in effective choice, acting as 'fast and frugal heuristics' in social decision contexts. (shrink)

In this paper I suggest a reconstruction of the traditional concepts of con-tinent and incontinent action. This reconstruction proceeds along the lines of a standpoint of bounded rationality. My suggestion agrees with some relevant aspects of Davidson’s treatment of this topic. One of these aspects is that incontinent action is typically signalled by the following two subjective experiences: a feeling of surprise towards one’s own action and a difficulty in understanding oneself; another is that incontinence cannot simply be disposed of (...) in terms of some inability of the agent to avoid “succumbing to temptation”; still another is the view that inconti-nent action is common in real human affairs. But my suggestion dis-agrees with other relevant aspects of Davidson’s treatment of inconti-nence too. In particular, it avoids what I take to be its two major draw-backs. These are a view of continent action that falls prey to a com-pletely unrealistic concept of psychological rationality and the idea that incontinence necessarily involves a dimension of essential irrationality. (shrink)