Why are heuristic strategies important for decision-making in complex environments?

Published on: Jun 28, 2021

All individuals and organizations have to make important decisions. It is usually assumed that people behave rationally. However, taking into account the flood of information that decision makers face in reality, it can be doubted whether strictly rational behavior is still possible. In everyday life it becomes more and more difficult to make informed decisions and a new understanding of human behavior is needed, taking into account that individuals can only rely on limited information. A much-discussed approach are heuristic strategies, as they are supposed to explain decision making when only limited information or time capacities are available. Thus, the question arises of the importance of heuristic decision making in an increasingly complex environment. This post will look at what is meant by heuristic strategies, why they are becoming increasingly important, and how they can be used successfully in the context of individual decision making.


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Science assumes that people behave completely rationally in the context of individual decision-making. However, this would only be possible if all relevant information were available and could be taken into account accordingly. In practice, this condition is almost never met. Individuals resort to logic, statistics and heuristics when making decisions. While logic and statistics are associated with rational behavior, heuristics are often seen as error-prone and even irrational. Whether this distinction is actually meaningful is at least debatable. In the following, we will look at what is meant by heuristic strategies and how they are used in decision making. For this purpose, a delimitation of the term is necessary at this point. Gigerenzer and Gaissmaier (2011) define heuristics in their widely cited work as follows:

A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods.¹

It is difficult to clearly separate heuristic and non-heuristic strategies because the amount of information considered or explicitly not considered is variable. Not considering information leads to a reduction in complexity and can simplify decision making because fewer options need to be weighed against each other. Thus, heuristic strategies usually also result in a reduction in the amount of time required for decision making. Classically, the time savings due to less effort in searching and evaluating relevant information is associated with poorer accuracy of the goal-oriented decision. Such a trade-off can be justified, for example, by the fact that not all decisions are important enough to require an enormously high effort, but also by the fact that humans are subject to cognitive limitations. In practice, however, it can also be stated that – under suitable conditions – the quality of heuristic decisions is not necessarily worse or that they sometimes even achieve better results than more complex decision-making methods that take into account a larger proportion of the available information. Moreover, whether and to what extent the use of heuristics is successful depends on the structure of the environment in which the decisions are made. The preconditions set by the environment have a significant influence on which heuristic strategies are promising and which quality in terms of results these strategies have in comparison to more complex decision-making methods.²

The extensive use of heuristic strategies for decision making in everyday life is also empirically proven. Decision-makers resort to a variety of simplified decision patterns, such as brand recognition or the selection of consumer goods on the basis of individual attributes or functions of the product. The use of such strategies can also be justified by the fact that there are situations in which no optimal decision exists. In addition, heuristic strategies have a significant advantage over complex models: They do not take into account any or only hardly any past data. Complex decision models are generally very good at describing already known information mathematically but are imprecise in predicting future events. Heuristics are less prone to this because they are not designed to reflect past information. Thus, the quality of heuristic strategies with respect to predicting future events or decisions may be higher than that of complex models. Thus, in the context of individual decision making, under certain conditions, less is (sometimes) more

Decision-makers have more and more information at their disposal that they can take into account when making decisions. It hardly makes a difference whether private purchasing decisions or corporate decisions are involved. It is conceivable that this flood of information makes it impossible, even with the use of enormous computing power, to take all relevant data into account and make an optimal decision. If rules of thumb can be used that lead to a near-optimal outcome, then their use may not only be more efficient, but even effective. The resources saved, such as the (work) time not needed, could then be used to add value elsewhere. While in a business context, analytical thinking and decisions may be more applicable, private consumption decisions are often subject to individual preferences, which do not necessarily have to be rational. Another reason to use heuristic strategies could be that, especially for simple everyday decisions, the enormous effort needed to make an optimal decision is not justified. The more serious a decision or its consequences are, the more important it would be for the decision to be as close to the optimum as possible.

However, in addition to the undisputed benefits of heuristic strategies in decision making, it is also conceivable that such strategies are deliberately exploited. If, for example, organizations can assume that consumers make a large part of their consumption decisions only on the basis of recognition of certain brands, then they could use their advertising budget in such a way that recognition on the part of potential consumers is maximized. In some circumstances, organizations could acquire customers in this way even though their own products or services are of inferior quality and do not represent the optimal purchase decision for the consumer. Although quick gut decisions and flexible decision structures can be beneficial, decision makers should always be aware that heuristic strategies are merely a simplification of decision making and can be prone to error. However, the same is true for complex decision-making methods – especially when future scenarios are modeled using past data and decisions are made in an uncertain environment.

The current flood of information means that in many cases it is no longer possible, or at least not economically viable, to make completely rational decisions. The effort that would have to be expended to take all relevant information into account in the decision-making process exceeds the resulting yield in the vast majority of cases. Therefore, heuristic strategies are used more and more frequently, in which a part of the (theoretically) available information is deliberately ignored. Even though it has been argued for a long time that heuristic decisions are often error-prone or even irrational, it has turned out that this is only partly true. In fact, heuristics sometimes lead to better decisions than complex models that take into account much of the available data. Especially in an uncertain environment, both private and professional decision makers can benefit from heuristic strategies if they base their decisions on a few meaningful parameters.

¹ Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62, page 454.

² Gigerenzer, G., & Gaissmaier, W. (2011). Heuristic decision making. Annual review of psychology, 62, 451-482.

³ Kurz-Milcke, E., & Gigerenzer, G. (2007). Heuristic decision making. Marketing: Journal of Research and Management, 3(1), 48-56.

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