On the shelf in front of you are two jars of strawberry jam; the label on one of them carries the name of a well-known and popular brand of food, on the other jar appears an unfamiliar name. At the same price, which one do you choose to buy? The majority of consumers are likely to go for the well-known brand. It is a fast and easy-to-use heuristic — choose the product of the familiar brand name as it predicts the product is more likely to be of high quality. This fast-and-frugal decision rule is most useful when a consumer is not familiar with any of the jams from personal experience.

However, well-known brand names can affect preferences even when consumers do have experience with both products, and the products are in fact identical. This is a key theme surrounding blind tests of taste. For example, subjects in an experiment were given peanut butter to taste from three jars. The same peanut butter was put in all jars, yet one jar carried a familiar brand name whereas the other two were displayed with “no-name” labels. After tasting, 75% chose the branded jar for tasting better than the peanut butter in the two other jars (chosen by 17% and 8% of subjects). The power of recognized brand names is even more striking when mixing the labels between higher quality and lower quality peanut butter products: 73% chose the jar carrying a name-brand even though it contained the lower quality product (experiments conducted by Hoyer and Brown, 1990, cited by Gigerenzer, 2007). Familiar brand names may create a bias in their favour (“brand names taste better” as put by Gigerenzer). Researchers aim to remove the bias when testing how the taste of food products is perceived, but this potential bias should not be ignored when predicting consumer choice. Nonetheless, at time of choice, relying on the recognized name can aid consumers make correct choices more often under uncertainty at relatively little effort.

Take another example of the practicality of heuristics from a different field (criminology): the problem at hand is to find where a serial criminal might be located based on information on sites of his or her suspected crimes, a problem known as “geographic profiling”. The common approach is to apply sophisticated statistical models that calculate the probabilities of locations that are spatially distributed. In comparison, researchers Snook, Taylor and Bennell (2004, cited by Gigerenzer and Gaissmaier, 2011) tested a more economic “circle heuristic”: the criminal would be located at the centre of a circle drawn through the two most distant sites of crime; this heuristic performed better than ten alternative statistical profiling strategies in predicting the locations of criminals. Gigerenzer and Gaissmaier give this heuristic as an example for one-reason decision-making: the strategy employs a single and rather simple informative cue as its guide yet can perform better than more complex information-rich strategies.

Gerd Gigerenzer, a professor of psychology and Director of the Centre for Adaptive Behaviour and Cognition at the Max Planck Institute for Human Development in Berlin, commends a more positive approach towards heuristics and their role in decision-making than the critical view that has been accepted in psychology since the 1970s. A seminal paper published by Gigerenzer together with Wolfgang Gaissmaier (2011) is illuminating and instructive. In their article the authors work towards establishing a comprehensive theory of heuristics, and especially describe methods that need to be applied to properly define their strategies, specify the information they utilise, and assess their performance.

• A heuristic is defined concisely by Gigerenzer and Gaismmaier as “a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods” (p. 454).  The function of a heuristic can fundamentally be specified by three building blocks: search rules, stop rules, and decision rules.

Gigerenzer disagrees with the paradigm in psychology on decision-making, led by Kahneman and Tversky and their colleagues. that associates heuristics with human error and biases. The paradigm is concerned with mistakes people make in processing information and drawing inferences (e.g., because of memory and cognitive limitations). Heuristics are used as a way of simplifying complex decision problems but are marred by inaccuracies and biases that result in sub-optimized  decisions or irrational decision-making. Gigerenzer and Gaissmaier assert that heuristics are not “irrational” and show that they do not necessarily “cost” the decision-maker in incorrect or less accurate outcomes.

Gigerenzer has had further reservations about the interpretation of “bounded rationality”, conceived by Herbert Simon, as the source of such biases or fallacies in human decision-making; Gigerenzer and Selten (2001) argue that bounded rationality is not identified with the class of error and fallacies in judgement and decision-making often demonstrated empirically in experiments in psychology and behavioural economics. In their view, bounded rationality is not about the discrepancy between human reasoning and laws of probability and optimization, looking instead for alternative norms to direct human decisions and studying the actual behaviour of “minds and institutions”.

On similar grounds, Gigerenzer and Gaissmaier (2011) take a critical stand towards the trade-off between accuracy of different decision rules or strategies and the effort in employing them, formally defined and extensively studied by Payne, Bettman, and Johnson. In the important framework of adaptive behaviour of decision makers, the researchers share concepts on rules and strategies as decision tools that people select from their “toolbox” (mostly subconsciously)  to fit the characteristics of each decision problem. Gigerenzer and Gaissmaier question, however, the implication that people use heuristics as imperfect tools to save time and effort but inherently sacrifice in less accurate or sub-optimal outcomes. The critique seems to target the type and variety of rules considered in analysing the accuracy-effort trade-off. They are more favourably oriented towards the alternative view of ecological rationality which converges more closely with bounded rationality, that is, how well a behaviour (e.g., using a heuristic) is adapted to fit the structure of the environment.

Gigerenzer champions the principle that “less-can-be-more”. It challenges the common rational and logical belief that methods and models using more information (as predictors) inherently lead to more accurate predictions. In environments with much uncertainty, when using more information, and applying it in complex calculations, it does not necessarily lead the decision maker to better results. Research on formalised  heuristics and alternative statistical models has often revealled “less-is-more” effects: “There is an inverse-U-shaped relation between level of accuracy and amount of information, computation, or time” (Gigerenzer and Gaissmaier, 2011, p. 453). It means that at some point adding more of those resources stops improving our performance and furthermore harms it. Hence, there are cases where a decision based on one reason (such as the “circle heuristic”) can be a sufficient and even a more advantageous method to achieving our goals.

Let us return briefly to the example on top of consumer choice of familiar brands. In the case that one of two alternatives (e.g., strawberry jams) is recognized and the other is not, a Recognition heuristic can be applied. The formal heuristic states: infer that the recognized alternative has the higher value with respect to the criterion (cf. Gigerenzer and Gaissmaier, 2011). The criterion, as suggested earlier, may be overall quality or taste. The efficacy of the heuristic has been demonstrated in quiz-like experiments. In one of such experiments, for example, participants were given pairs of cities in Switzerland and were asked for each pair which city had the larger population — the recognition heuristic succeeded in inferring the larger city in 89% of pairs (the rule is less accurate in inferring distance from the country’s centre, just 54%). However, one can benefit from the recognition heuristic only when he or she is ignorant in part, that is, one is familiar with just some of the objects and not others (i.e., when asking non-Swiss residents about Swiss cities).

• Apparently, a study on tennis plays in Wimbledon 2004 has shown that amateur players, for whom the recognition heuristic is more applicable than for tennis professionals, were more successful in choosing the winner of plays in the tournament (72%) than the professionals or tennis experts (66%-69%).

When one is able to recognize both alternative objects in a pair, he or she can apply a Fluency heuristic which instructs to infer that the alternative recognized faster has the higher value with respect to the criterion. When we need to trust our own recall ability in generating alternatives, a third heuristic is specified by Gigerenzer and Gaissmaier, Take-the-First heuristic: choose the first alternative that comes to mind.

In his book on intuition and gut feelings published a few years back, Gigerenzer (2007) explains and demonstrates in an eloquent manner and a friendly style why and how heuristics work to our benefit. It is less formal and scientific-laden than academic articles yet is thus accessible to a wider audience of readers. The theory on intuition, unconscious cognition and heuristics is delivered in a way that is comprehensible and interesting, supported by many examples such as illustrations of the rules and in related research findings.

The heuristics identified by Gigerenzer and his colleagues are not meant to be newly invented — they may actually seem familiar to many people in one form or another. The significance of the contribution is in collating those heuristics from different fields of life, classifying and formally describing them according to systemic criteria, putting them into context of a theory of heuristics. This step is also important for analysing and evaluating their performance against more complex “rational” models.

Ron Ventura, Ph.D. (Marketing)

References:

Gerd Gigerenzer, 2007, Gut Feelings: The Intelligence of the Unconscious, London: Penguin Books

Gerd Gigerenzer and Wolfgang Gaissmaier, 2011, Heuristic Decision Making, Annual Review of Psychology, Vol. 62, pp. 451-482.

Gerd Gigerenzer and Reinhard Selten, 2001, Rethinking Rationality, in Bounded Rationality: The Adaptive Toolbox, G. Gigerenzer and R. Selten (editors), Cambridge, MA; London, UK: MIT Press