Frugal Heuristics Research Articles

A review of conceptual and empirical discussions about fast and frugal heuristics

A review of conceptual and empirical discussions about fast and frugal heuristics

Fast, frugal, and fit : Simple heuristics for paired comparison

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.

Reasons, Robots and the Extended Mind

A suitable project for the new Millenium is to radically reconfigure our image of human rationality. Such a project is already underway, within the Cognitive Sciences, under the umbrellas of work in Situated Cognition, Distributed and De‐centralized Cognition, Real‐world Robotics and Artificial Life. Such approaches, however, are often criticized for giving certain aspects of rationality too wide a berth. They focus their attention on such superficially poor cousins as ‘adaptive behaviour’, ‘ecologically sound perception‐action routines’, ‘fast and frugal heuristics’ and ‘fast, fluent real‐time real‐world action control’. Is this robbery or revelation? Has ‘embodied, embedded’ cognitive science simply lost sight of the very phenomena it was meant to explain? Or are we finally seeing rationality aright, as fully continuous with various forms of simpler, ecologically situated, adaptive response? I distinguish two ways of developing the ‘embodied, embedded’ approach. The first, which does indeed threaten to lose sight of the key targets, is fully committed to a doctrine of biological cognitive incrementalism according to which full‐scale human rationality is reached, rather directly, by some series of tweaks to basic biological modes of adaptive response. The second depicts human capacities for advanced reason as at best the indirect products of such a process. Such capacities, it is argued, depend heavily upon the effects of a special kind of hybridization in which human brains enter into an increasingly potent cascade of genuinely symbiotic relationships with knowledge‐rich artifacts and technologies. This latter approach, I finally suggest, does better justice to our peculiar profile, which combines deep biological continuity with an equally deep cognitive discontinuity.

Making good decisions with minimal information: Simultaneous and sequential choice

The adaptive pressures facing humans and other animals to make decisions quickly can be met both by increasing internal information-processing speed and by minimizing the amount of information to be used. Here we focus on the latter effect and ask how, and how well, agents can make good decisions with a minimal amount of information, using two specific tasks as examples. When a choice must be made between simultaneously-available options, minimal information in the form of binary recognition (whether or not each item is recognized) can be used in the recognition heuristic to choose effectively. When options are encountered sequentially one at a time, minimal information as to whether or not each option is the best encountered so far is sufficient to guide agents using a simple search-cutoff rule to high performance along several choice criteria. Both of these examples have important economic as well as biological applications, and show the power of simple fast and frugal heuristics to produce good decisions with little information.

Fast and frugal heuristics: What about unfriendly environments?

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.

Rationality, logic, and fast and frugal heuristics

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.

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.

The evolution of rational demons

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.

Against an uncritical sense of adaptiveness

The “adaptive toolbox” model of the mind is much too uncritical, even as a model of bounded rationality. There is no place for a “meta-rationality” that questions the shape of the decision-making environments themselves. Thus, using the ABC Group's “fast and frugal heuristics,” one could justify all sorts of conformist behavior as rational. Telling in this regard is their appeal to the philosophical distinction between coherence and correspondence theories of truth.

How good are fast and frugal inference heuristics in case of limited knowledge?

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.

Keeping it simple, socially

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

Simple heuristics: From one infinite regress to another?

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.

The Ecological Rationality of Mechanisms Evolved to make up Minds

Selective pressures favoring rapid decisions would have led to the evolution of simple decision-making mechanisms that could take the form of heuristics and rules that use as little available information as possible. Such decision heuristics can only be ecologically rational—yielding accurate inferences in particular problem domains—if they exploit the way that information is structured in the environment. The author presents a variety of fast and frugal heuristics that are ecologically rational and shows how they can be organized in the mind's adaptive toolbox of decision-making strategies.

Regulatory Cooperation and Competition - The Search for Virtue

This article argues that proponents of international regulatory cooperation need to confront the welfare losses that might occur due to missed opportunities for cooperative regulatory competition. Cooperative regulatory competition takes places when jurisdictions agree to honor formal and manipulable choice-of-law rules, such as contractual choice, the place-of-incorporation rule, and the territoriality principle, that enable persons engaged in international business to pick regulatory systems that will govern their transactions. I review current opportunities for this kind of competition with respect to securities regulation, bankruptcy, intellectual property, and antitrust. I then discuss the substantive case for maintaining or expanding these opportunities in light of potential benefits from races to the top. I base my argument partly on new research by cognitive psychologists on fast and frugal heuristics. This research undermines arguments against respecting individual choices that have dominated much of the discussion about regulation in the legal academic literature over the last fifteen years.

Simplicity and Robustness of Fast and Frugal Heuristics

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.

Action as a Fast and Frugal Heuristic

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.

How do Simple Rules `Fit to Reality' in a Complex World?

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.