{"id":4943,"date":"2018-12-11T16:36:05","date_gmt":"2018-12-11T16:36:05","guid":{"rendered":"http:\/\/www.behaviouraleconomic.co.uk\/?p=4943"},"modified":"2022-02-17T06:20:30","modified_gmt":"2022-02-17T06:20:30","slug":"supporting-decision-making-under-uncertainty-nudging-boosting-or-both","status":"publish","type":"post","link":"https:\/\/www.behaviouraleconomic.co.uk\/supporting-decision-making-under-uncertainty-nudging-boosting-or-both\/","title":{"rendered":"Supporting Decision-Making under Uncertainty: Nudging, Boosting or Both?"},"content":{"rendered":"
By Martina Raue<\/em><\/p>\n <\/p>\n Make a thumbs up, extend your arm all the way, close one eye, and see if you can hide the animal with your thumb<\/em>. This rule of thumb is suggested by the organization Leave No Trace to help children to judge the safe distance from a wild animal. Rules of thumb that make complex decisions a little easier have been termed heuristics<\/a>\u00a0<\/em>in the scientific literature. Making decisions can be overwhelming when there is too much information or too little time. We can consciously process only a limited amount of information at a time, a characteristic which Herbert Simon termed\u00a0bounded rationality<\/em><\/a>. As a result, people simplify decisions by using heuristics. Heuristics are simple decision rules that allow judgments of acceptable accuracy without integrating all the information available. They play an important role in daily judgments and decision-making processes. For example, we can safely cross a street without accurately analyzing the speed of approaching cars. Children learn quickly how to do this long before taking their first physics class. However, the use of heuristics may also lead us astray and leave us prone to biases<\/a>. In fact, a debate is ongoing over whether heuristics primarily serve to help us or harm us.<\/p>\n The first scholars to systematically study heuristics were psychologists Amos Tversky and Daniel Kahneman in their heuristics and biases <\/em>program. Tversky and Kahneman wondered how people come up quickly with intuitive answers to complex questions. In numerous studies, they demonstrated that a basic mechanism of most heuristics is the substitution of an easy question for a more complex one. For example, if asked about the likelihood of a terrorist attack, some people may answer based on how easily they can think of a recent terrorist attack, which is known as the availability heuristic<\/a>. If asked about the likelihood that a given person is a potential criminal, some people may answer based on subjective associations with the person\u2019s looks or nationality, which is known as the representativeness heuristic<\/a>. The heuristics and biases program has inspired researchers in psychology and economics by showing how people simplify decisions, but especially because it demonstrated how human judgment can be biased and result in systematic errors. These insights resulted in behavioral interventions known as nudges<\/em><\/a>, which respond to biases and structure choices in a way that makes it easier for people to make better decisions. The use of nudges has been promoted as a non-regulatory and cost-efficient policy instrument. For example, automatic<\/a> enrollment in a savings plan is a nudge that responds to inertia<\/a> and present bias<\/a>\u00a0– which often lead to procrastinating retirement planning.<\/p>\n Another group of researchers has emphasized the advantages of using heuristics in situations of uncertainty rather than the potential biases resulting from their use. Gerd Gigerenzer and his colleagues investigate fast and frugal<\/em><\/a> heuristics, which are simple rules that do not necessarily sacrifice accuracy. They describe decisions as ecologically rational when they reduce effort and increase accuracy by matching the mind\u2019s capacities with the current environment. Fast and frugal heuristics can sometimes outperform complex algorithms in real-world situations. A typical example is the gaze heuristic, which describes how a person or a dog can ably catch a ball or a frisbee without analyzing all the factors that affect the object\u2019s trajectory. Similar to the advocates of nudges, fast and frugal researchers call for designing environments in ways that trigger successful heuristic strategies. However, they suggest environments that support<\/em> informed decision-making without steering people in a certain direction, as nudges do. As an alternative to nudges, Till Gr\u00fcne-Yanoff and Ralph Hertwig introduced boosts<\/em>, which are based on fast and frugal heuristics and aim at expanding (boosting) people\u2019s decision-making competences by supporting them to apply their existing skills and tools more effectively. A boost of statistical understanding to make informed medical decisions may include the presentation of statistical information in frequencies rather than probabilities. Boosts in the form of decision-trees<\/a> have been quite successful in medicine, for example, to support informed decision-making of both patients and providers.<\/p>\n Heuristics are often portrayed as a deviation from optimal reasoning, yet can also be viewed as the optimal human strategy to reduce complexity in a given situation. A public discussion between Gigerenzer and Kahneman and Tversky in the 1990s was the beginning of this debate, which is still ongoing. In the 2016 Behavioral Economics Guide<\/a>, Gerd Gigerenzer congratulates Daniel Kahneman and Amos Tversky for promoting heuristics in psychology, before lamenting their linking of heuristics to systematic errors and biases, which has led to the wrong assumption, in his opinion, that humans are irrational, lack accuracy, or are simply not very smart. Daniel Kahneman, however, has repeatedly stated<\/a> that he and Amos Tversky never intended to show that human choices are irrational, but rather that human beings are not well-described as purely rational agents. Ultimately, both groups of researchers agree that heuristics generally work well, and that in many cases a good-enough or satisfying outcome is sufficient and an optimal outcome not feasible. It is unfortunate that the two approaches to heuristics are still seen and act as opposing parties.<\/p>\nHeuristics and Biases<\/h3>\n
Fast and Frugal Heuristics<\/h3>\n
The Debate<\/h3>\n
Nudges vs. Boosts<\/h3>\n