How (far) can rationality be naturalized?

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.     

Précis of Simple heuristics that make us smart

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.     

Environments That Make Us Smart: Ecological Rationality

ABSTRACT— Traditional views of rationality posit general-purpose decision mechanisms based on logic or optimization. The study of ecological rationality focuses on uncovering the "adaptive toolbox" of domain-specific simple heuristics that real, computationally bounded minds employ, and explaining how these heuristics produce accurate decisions by exploiting the structures of information in the environments in which they are applied. Knowing when and how people use particular heuristics can facilitate the shaping of environments to engender better decisions.     

Cognitive niches: an ecological model of strategy selection

How do people select among different strategies to accomplish a given task? Across disciplines, the strategy selection problem represents a major challenge. We propose a quantitative model that predicts how selection emerges through the interplay among strategies, cognitive capacities, and the environment. This interplay carves out for each strategy a cognitive niche, that is, a limited number of situations in which the strategy can be applied, simplifying strategy selection. To illustrate our proposal, we consider selection in the context of 2 theories: the simple heuristics framework and the ACT-R (adaptive control of thought-rational) architecture of cognition. From the heuristics framework, we adopt the thesis that people make decisions by selecting from a repertoire of simple decision strategies that exploit regularities in the environment and draw on cognitive capacities, such as memory and time perception. ACT-R provides a quantitative theory of how these capacities adapt to the environment. In 14 simulations and 10 experiments, we consider the choice between strategies that operate on the accessibility of memories and those that depend on elaborate knowledge about the world. Based on Internet statistics, our model quantitatively predicts people's familiarity with and knowledge of real-world objects, the distributional characteristics of the associated speed of memory retrieval, and the cognitive niches of classic decision strategies, including those of the fluency, recognition, integration, lexicographic, and sequential-sampling heuristics. In doing so, the model specifies when people will be able to apply different strategies and how accurate, fast, and effortless people's decisions will be.     

Modeling experimentally induced strategy shifts

In dynamic decision-making environments, observers must continuously adjust their decision-making strategies. Previous research has focused on internal fluctuations in decision mechanisms, without regard to how these changes are induced by environmental changes. We developed a simple paradigm in which we manipulated task difficulty, thereby inducing changes in decision processes. We applied this paradigm to recognition memory, manipulating task difficulty by changing the similarity of lures to targets. More difficult decision environments caused participants to make more careful decisions, but these changes did not appear immediately. We propose a simple theoretical account for these data, using a dynamic version of signal detection theory fitted to individual subjects. Our model represents a significant departure from existing models because it incorporates subject-controlled parameters that may adjust over time in response to environmental changes.     

Predicting similarity judgments in intertemporal choice with machine learning

Similarity models of intertemporal choice are heuristics that choose based on similarity judgments of the reward amounts and time delays. Yet, we do not know how these judgments are made. Here, we use machine-learning algorithms to assess what factors predict similarity judgments and whether decision trees capture the judgment outcomes and process. We find that combining small and large values into numerical differences and ratios and arranging them in tree-like structures can predict both similarity judgments and response times. Our results suggest that we can use machine learning to not only model decision outcomes but also model how decisions are made. Revealing how people make these important judgments may be useful in developing interventions to help them make better decisions.     

The Path of Least Resistance: Using Easy-to-Access Information

Abstract— Recent work on judgment and decision making has focused on how people preferentially use cues, or pieces of relevant information, that are easy to access when making decisions. In this article, we discuss a framework for understanding the ways that cues become accessible. We begin by identifying two components of cues and show how these components can become accessible during different parts a decision process. We highlight evidence for the use of accessible information and discuss implications for future research on heuristics.     

认知闭合需要、框架效应与决策偏好

在带有模糊性的决策情境中,决策者个人的认知特征会对其判断决策产生重要影响。通过实验的方法,考察了认知闭合需要和特征框架效应对个体决策偏好的影响。93名工商管理硕士（MBA）参与了实验,研究的结果支持了本研究的3个假设,即认知闭合需要与特征框架效应不仅对被试的决策偏好存在显著的影响,而且二者还存在显著的交互作用。具体来说,研究发现,在模糊情境中：高认知闭合需要的被试偏好于立刻做出决策,而低认知闭合需要的被试偏好于暂缓做出决策;接收到正向框架信息的被试偏好于立刻做出决策,而接收到负向框架信息的被试偏好于暂缓做出决策;认知闭合需要与特征框架对被试的决策偏好还存在显著的交互作用。研究结论为根据个体认知闭合需要的水平来选拔决策者、利用框架效应来影响个体的信息加工方式进而提高决策质量提供了理论依据     

Holistic Information Integration in Child Decision Making

Weighted‐additive (WADD) strategies require decision makers to integrate multiple values weighted by their relevance. From what age can children make choices in line with such a WADD‐strategy? We compare multi‐attribute decisions of children (6–7; 8–10; 11–12‐year‐olds) with adults in an open information‐board environment without pre‐decisional information search. In two experiments, we classify decision strategies based on individual choice patterns and find that in all age groups the majority of participants are users of a WADD‐strategy. Simple decision heuristics such as lexicographic strategies were applied rarely by children and not at all by adults. In two additional follow‐up studies, we further investigate the underlying process of WADD‐application by analysing decision latencies in combination with a retrospective think‐aloud study. Results suggest that children did not apply WADD‐strategies in a deliberate fashion in our experiments. Overall, our findings demonstrate that the ability to make good and quick decisions by holistically integrating information is already present in young children. Copyright © 2017 John Wiley & Sons, Ltd.     

When the brain decides: a familiarity-based approach to the recognition heuristic as evidenced by event-related brain potentials

Humans can make fast and highly efficient decisions by using simple heuristics that are assumed to exploit basic cognitive functions. In the study reported here, we used event-related potentials (ERPs) to disclose the psychological mechanisms underlying one of the most frugal decision rules, namely, the recognition heuristic. According to this heuristic, whenever two objects have to be ranked by a specific criterion and only one object is recognized, the recognized object is ranked higher than the unrecognized object. Using a standard recognition-heuristic paradigm, we predicted participants' decisions by analyzing an ERP correlate of familiarity-based recognition occurring 300 to 450 ms after stimulus onset. The measure remained a significant predictor even when later ERP correlates were taken into account. These findings are evidence for the thesis that simple heuristics exploit basic cognitive processes. Specifically, the findings show that familiarity--that is, recognition in the absence of recollection--contributes to decisions made on the basis of such heuristics.     

Verbalization of Decision Strategies in Multiple‐Cue Probabilistic Inference

In multiple‐cue probabilistic inference, people choose between alternatives based on several cues, each of which is differentially associated with an alternative's overall value. Various strategies have been proposed for probabilistic inference (e.g., weighted additive, tally, and take‐the‐best). These strategies differ in how many cue values they require to enact and in how they weight each cue. Do decision makers actually use any of these strategies? Ways to investigate this question include analyzing people's choices and the cues that they reveal. However, different strategies often predict the same decisions, and search behavior says nothing about whether or how people use the information that they acquire. In this research, we attempt to elucidate which strategies participants use in a multiple‐cue probabilistic inference task by examining verbal protocols, a high‐density source of process data. The promise of verbal data is in their utility for testing detailed information processing models. To that end, we apply protocol analysis in conjunction with computational simulations. We find converging evidence across outcome measures, search measures, and verbal reports that most participants use simplifying heuristics, namely take‐the‐best. Copyright © 2015 John Wiley & Sons, Ltd.     

Resource-Allocation Behavior in Complex but Commonplace Tasks

We all face resource-allocation tasks, yet little is known about how people make resource-allocation decisions. Three earlier papers (Langholtz, Gettys, & Foote, 1993, 1994, 1995) established that people can intuitively solve technical two-dimensional resource-allocation problems, where allocations are made on a continuous scale, reaching solutions that approach the optimum solution determined with Linear Programming (LP). In this research we expand our understanding of how people make resource-allocation decisions by examining how people perform complex but commonplace problems using a discrete scale where optimal solutions are determined with Integer Programming. We also contrast performance under conditions where progressively unequal scheduling is required to reach the optimum solution. The data show people can achieve solutions that provide most of the payoffs found with LP, even when they are faced with a complicated three-dimensional problem, but there is a tendency toward equal scheduling under all conditions.     

Integrating Criminological and Decision Research Theory: Implications for Understanding and Addressing Crime in Marginalized Communities

Minority groups are significantly overrepresented in crime. Theories of racial differences in crime developed using two separate and distinct approaches that highlight either increased exposure to criminogenic factors at the individual level or greater risk of crime due to disadvantaged neighborhood conditions. Neighborhood theories describe how structural disadvantage disrupts neighborhood social processes and produces oppositional street cultures. In the article, we advance theorizing on race and crime by linking the neighborhood experience to individual-level decision making via new conceptualizations of culture. Rather than a “values as goals” view of culture, culture may include a “tool kit” of ways to solve problems and this cultural toolkit may, in turn, influence how an individual makes decisions. Specifically, culturally learned toolkits may increase flaws in the decision process (e.g., fast and intuitive rather than deliberate decision processes, the use of decision heuristics) to produce more crime, which would explain the association between race and crime. We integrate this conceptualization of culture and these flaws in the decision-making process into rational choice theory at the individual level and describe how they may be exacerbated in disadvantaged neighborhood contexts. Implications for understanding race and crime and directions for future research are discussed.     

The influence of information redundancy on probabilistic inferences

Information redundancy affects the accuracy of inference strategies. A simulation study illustrates that under high-information redundancy simple heuristics that rely on only the most important information are as accurate as strategies that integrate all available information, whereas under low redundancy integrating information becomes advantageous. Assuming that people exercise adaptive strategy selection, it is predicted that their inferences will more often be captured by simple heuristics that focus on part of the available information insituations ofhigh-information redundancy, especially when information search is costly. This prediction is confirmed in two experiments. The participants' task was to repeatedly infer which of two alternatives, described by several cues, had a higher criterion value. In the first experiment, simple heuristics predicted the inference process better under high-information redundancy than under low-information redundancy. In the second experiment, this result could be generalized to an inference situation in which participants had no prior opportunity to learn about the strategies' accuracies through outcome feedback. The results demonstrate that people are able to respond adaptively to different decision environments under various learning opportunities.     

研究人类使用启发式之五原则(英文)

快速节俭启发式框架假定人们使用一套简捷的决策策略——启发式——进行推理、选择、评价及其他决策。这些启发式策略能顺应任务情境结构中的规律, 利用人类的基本认知能力。正基于此, 启发式成就了适应性行为。本文拟对启发式框架进行回顾, 并简要陈述引导研究者研究人类适应性工具箱的五条原则。我们强调, 启发式模型应(ⅰ)精确界定(ⅱ)对照检验(ⅲ)与策略选择理论相符(ⅳ)能评估其对新资料的预测力(ⅴ)能既在实验室又在现实世界中得以检验。     

Testing take-the-best in new and changing environments

Take-the-best is a decision-making strategy that chooses between alternatives, by searching the cues representing the alternatives in order of cue validity, and choosing the alternative with the first discriminating cue. Theoretical support for take-the-best comes from the “fast and frugal” approach to modeling cognition, which assumes decision-making strategies need to be fast to cope with a competitive world, and be simple to be robust to uncertainty and environmental change. We contribute to the empirical evaluation of take-the-best in two ways. First, we generate four new environments—involving bridge lengths, hamburger prices, theme park attendances, and US university rankings—supplementing the relatively limited number of naturally cue-based environments previously considered. We find that take-the-best is as accurate as rival decision strategies that use all of the available cues. Secondly, we develop 19 new data sets characterizing the change in cities and their populations in four countries. We find that take-the-best maintains its accuracy and limited search as the environments change, even if cue validities learned in one environment are used to make decisions in another. Once again, we find that take-the-best is as accurate as rival strategies that use all of the cues. We conclude that these new evaluations support the theoretical claims of the accuracy, frugality, and robustness for take-the-best, and that the new data sets provide a valuable resource for the more general study of the relationship between effective decision-making strategies and the environments in which they operate.     

Information search in heuristic decision making

Simple heuristics of the type introduced by Gigerenzer, Todd, and The ABC Research Group ( 1999 ) embody principles for information search, stop and decision making. These heuristics suggest that such processes are simple. In an analysis of general practitioners' (GPs) information search and decision‐making behaviour when prescribing a lipid lowering drug, we examined whether information search was simple, and whether a heuristic that predicts a simple decision‐making process was also accurate at describing information search. We found that GPs' information search behaviour was simple in that it demonstrated characteristics of the matching heuristic (e.g. stopping rule). In addition, although the matching heuristic which correctly predicted on average 75% of GPs' decisions used significantly fewer cues on average than the GPs did in the information search task, it was reasonably accurate in describing order of information search. These findings have implications for the validity of simple heuristics describing both information search and decision making. Copyright © 2009 John Wiley & Sons, Ltd.     

Forward inference in risky choice: Mapping gaze and decision processes

The study of cognitive processes is built on a close mapping between three components: overt gaze behavior, overt choice, and covert processes. To validate this overt–covert mapping in the domain of decision‐making, we collected eye‐movement data during decisions between risky gamble problems. Applying a forward inference paradigm, participants were instructed to use specific decision strategies to solve those gamble problems (maximizing expected values or applying different choice heuristics) during which gaze behavior was recorded. We revealed differences between overt behavior, as indicated by eye movements, and covert decision processes, instructed by the experimenter. However, our results show that the overt–covert mapping is for some eye‐movement measures not as close as expected by current decision theory, and hence question reverse inference as being prone to fallacies due to a violation of its prerequisite, that is, a close overt–covert mapping. We propose a framework to rehabilitate reverse inference.     

Intuitive Political Theory: People's Judgments About How Groups Should Decide

Societies must make collective decisions even when citizens disagree, and they use many different political processes to do so. But how do people choose one way to make a group decision over another? We propose that the human mind contains an intuitive political theory about how to make collective decisions, analogous to people's intuitive theories about language, physics, number, minds, and morality. We outline a simple method for studying people's intuitive political theory using scenarios about group decisions, and we begin to apply this approach in three experiments. Participants read scenarios in which individuals in a group have conflicting information (Experiment 1), conflicting interests (Experiment 2), and conflicting interests between a majority and a vulnerable minority who have more at stake (Experiment 3). Participants judged whether the group should decide by voting, consensus, leadership, or chance. Overall, we find that participants prefer majority‐rule voting over consensus, leadership, and chance when a group has conflicting interests or information. However, participants' support for voting is considerably diminished when the group includes a vulnerable minority. Hence, participants showed an intuitive understanding of Madison's concerns about tyranny of the majority.