Humans hunt and kill many different species of animals, but whales are our biggest prey. In the North Atlantic, a male long-ﬁ nned pilot whale (Globiceph- ala melaena), a large relative of the dolphins, can grow as large as 6.5 meters and weigh as much as 2.5 tons. As whales go, these are not particularly large, but there are more than 750,000 pilot whales in the North Atlantic, traveling in groups, “pods,” that range from just a few individuals to a (...) thousand or more. Each pod is led by an individual known as the “pilot,” who appears to set the course of travel for the rest of the group. This pilot is both an asset and a weakness to the pod. The average pilot whale will yield about a half ton of meat and blubber, and North Atlantic societies including Ireland, Iceland, and the Shetlands used to manipulate the pilot to drive the entire pod ashore. In the Faroe Islands, a group of 18 grassy rocks due north of Scotland, pilot whale hunts have continued for the last 1200 years, at least. The permanent residents of these islands, the Faroese, previously killed an average of 900 whales each year, yielding about 500 tons of meat and fat that was consumed by local residents. Hunts have declined in recent years. From 2001 to 2005, about 3400 whales were killed, yielding about 890 metric tons of blubber and 990 metric tons of meat. The whale kill, or grindadráp in the Faroese language, begins when a ﬁ shing boat spots a pod close enough to a suitable shore, on a suitably clear day. A single boat, or even a small group of ﬁ shermen, is not sufﬁ cient to trap a.. (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)
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)
What is the nature of moral behavior? According to the study of bounded rationality, it results not from character traits or rational deliberation alone, but from the interplay between mind and environment. In this view, moral behavior is based on pragmatic social heuristics rather than moral rules or maximization principles. These social heuristics are not good or bad per se, but solely in relation to the environments in which they are used. This has methodological implications for the study of morality: (...) Behavior needs to be studied in social groups as well as in isolation, in natural environments as well as in labs. It also has implications for moral policy: Only by accepting the fact that behavior is a function of both mind and environmental structures can realistic prescriptive means of achieving moral goals be developed. (shrink)
Gerd Gigerenzer's influential work examines the rationality of individuals not from the perspective of logic or probability, but from the point of view of adaptation to the real world of human behavior and interaction with the environment. Seen from this perspective, human behavior is more rational than it might otherwise appear. This work is extremely influential and has spawned an entire research program. This volume (which follows on a previous collection, Adaptive Thinking, also published by OUP) collects his most recent (...) articles, looking at how people use "fast and frugal heuristics" to calculate probability and risk and make decisions. It includes a newly writen, substantial introduction, and the articles have been revised and updated where appropriate. This volume should appeal, like the earlier volumes, to a broad mixture of cognitive psychologists, philosophers, economists, and others who study decision making. (shrink)
The terms nested sets, partitive frequencies, inside-outside view, and dual processes add little but confusion to our original analysis (Gigerenzer & Hoffrage 1995; 1999). The idea of nested set was introduced because of an oversight; it simply rephrases two of our equations. Representation in terms of chances, in contrast, is a novel contribution yet consistent with our computational analysis System 1.dual process theory” is: Unless the two processes are defined, this distinction can account post hoc for almost everything. In contrast, (...) an ecological view of cognition helps to explain how insight is elicited from the outside (the external representation of information) and, more generally, how cognitive strategies match with environmental structures. (shrink)
We attack the SSK's rejection of the distinction between discovery and justification (the DJ distinction), famously introduced by Hans Reichenbach and here defended in a "lean" version. Some critics claim that the DJ distinction cannot be drawn precisely, or that it cannot be drawn prior to the actual analysis of scientific knowledge. Others, instead of trying to blur or to reject the distinction, claim that we need an even more fine-grained distinction (e.g. between discovery, invention, prior assessment, test and justification). (...) Adherents of the SSK, however, maintain that the distinction is useless and perhaps nonexistent. We first argue against the assumption that the SSK's objection to the DJ distinction is just the same as Thomas Kuhn's. Second, we point out general weaknesses of the SSK's arguments against the DJ distinction. Finally, we argue that the distinction is useful not only in order to explicate what is meant by an evaluation but even for the empirical explanation of knowledge. We use two case studies from the history of cognitive science to support this point. (shrink)
The Ultimatum Game is commonly interpreted as a two-person bargaining game. The third person who donates and may withdraw the money is not included in the theoretical equations, but treated like a neutral measurement instrument. Yet in a cross-cultural analysis it seems necessary to consider the possibility that the thoughts of a player – strategic, altruistic, selfish, or concerned about reputation – are influenced by both an anonymous second player and the non-anonymous experimenter.
In the study of judgmental errors, surprisingly little thought is spent on what constitutes good and bad judgment. I call this simultaneous focus on errors and lack of analysis of what constitutes an error, the irrationality paradox. I illustrate the paradox by a dozen apparent fallacies; each can be logically deduced from the environmental structure and an unbiased mind.
Most students are trained in using but not in actively choosing a research methodology. I support Hertwig and Ortmann's call for more rationality in the use of methodology. I comment on additional practices that sacrifice experimental control to the experimenter's convenience, and on the strange fact that such laissez-faire attitudes and rigid intolerance actually co-exist in psychological research programs.
Shepard promotes the important view that evolution constructs cognitive mechanisms that work with internalized aspects of the structure of their environment. But what can this internalization mean? We contrast three views: Shepard's mirrors reflecting the world, Brunswik's lens inferring the world, and Simon's scissors exploiting the world. We argue that Simon's scissors metaphor is more appropriate for higher-order cognitive mechanisms and ask how far it can also be applied to perceptual tasks. [Barlow; Kubovy & Epstein; Shepard].
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)
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)