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Uncertainty matters. Here's why.
Posted Jun 16, 2016
Many of the psychological phenomena that exemplify life in the 21st century can be found in the world of social media. One case in point is the emergence of FOMO, which stands for the fear of missing out, aroused by social media posts. Other great examples are built into social media’s choice architecture. The RSVP response options for Facebook events is one of them. Why? Because it allows not only for a ‘Going’ and ‘Can’t go’ selection, but also ‘Maybe’ – an option that would not exactly have been good etiquette for either hosts or guests a few decades ago. The ability to keep your options open is a prime example of the recent advance of flexibility in all areas of society – from personalized product design to the freedom provided by mobile communication.
The flipside (or a side effect) of flexibility that we all have to live with is uncertainty. In psychology the concept of uncertainty can be found on several different levels. Uncertainty avoidance, as defined by Geert Hofstede, is a society's level of tolerance for ambiguity and uncertainty. Cultures high on uncertainty avoidance tend to be more rigid and intolerant of ideas that deviate from certain principles. In personality psychology, individuals with a high need for certainty, preference for familiarity, etc., are considered to have a high intolerance of ambiguity.
Psychologists, such as Daniel Kahneman, have pointed to the fundamental advantage provided by the trait of optimism: People who are optimists may be more likely to suffer from overconfidence, but on the whole they benefit from better mental and physical health. It could be argued that uncertainty tolerance has become one of the most important siblings of optimism in the 21st century as far as psychological benefits are concerned. People with high uncertainty tolerance are generally more adaptable, which is a major advantage for life in a fast-changing world that is awash with information, full of choices and host to many unpredictable events.
Uncertain conditions are also an important element of psychology and behavioral economics, particularly the study of heuristics – cognitive shortcuts or rules of thumb that simplify decision making. For example, instead of carefully weighing the costs and benefits of two cars, we may choose the one that we consider more attractive. But the relationship between tolerance of ambiguity or uncertainty and the propensity to use heuristics is not necessarily straightforward. On the one hand, people low in this tolerance should be more motivated to achieve clarity by applying extra thought to a problem instead of taking heuristic shortcuts. On the other hand, heuristics are also a way to reduce uncertainty and may be a pragmatic way of making a decision for people with low tolerance for uncertainty. There is value in simplicity.
The fact that greater effort does not always lead to greater accuracy is core theme covered in the introduction to the latest Behavioral Economics Guide (2016), written by the psychologist Gerd Gigerenzer. In uncertain worlds, where we are not always sure about the outcome of our choices, simple heuristics can sometimes make more accurate predictions than complex strategies. For instance, when amateur tennis players were asked to indicate all the names they recognized from Wimbledon matches – the so-called ‘recognition heuristic’ – this strategy turned out to be as accurate or even better than using ATP rankings or Wimbledon experts' seeding to predict tournament winners.
Gigerenzer does not always agree with the assumptions of behavioral economics and offers some food for thought:
1. Take heuristics seriously.
In behavioral economics, heuristics are generally considered to be a source of bias, because they rely on intuition and using less information rather than reflection and using more information. Gigerenzer cautions against the view that complexity is always better. There are conditions under which a heuristic approach may be superior. For example, research has shown that whether a previous customer (of an airline, an apparel retailer, or an online CD retailer) will make a purchase in the future can be predicted better by simply taking into account whether the customer recently made a purchase (a hiatus rule) than by using complex prediction algorithms. Heuristics are particularly useful to decision-makers when the available information is limited or they don’t have the cognitive resources to process information carefully (e.g., under time pressure or stress).
2. Take uncertainty seriously.
There’s a distinction between risk and uncertainty if we assume that risky situations are those in which we know all alternatives, consequences, and their probabilities. When risks are calculable, complex probability models are a better strategy for decision-making than heuristics. Under conditions of uncertainty, on the other hand, not everything is known for sure. This applies to most important decisions we make. In these situations, less is sometimes more and heuristics are a better strategy. According to Gigerenzer, behavioral economics does not adequately distinguish between decisions under risk versus uncertainty.
3. Beware of the bias bias.
According to Gigerenzer, the bias bias is “the tendency to diagnose biases in others without seriously examining whether a problem actually exists.” An excessive focus on deviations from rational norms prevents us from seeing the benefits of simplicity and attempting to better understand what intelligent decision-making actually is in a given situation. From a statistical perspective, ignoring some information – which occurs when heuristics are applied – may reduce the error that arises from the variance or noise when data collected from one sample (e.g. in the past) is used to make predictions for another sample (e.g. in the future).
There is an important question – both philosophical and scientific – that arises from Gerd Gigerenzer’s view of decision-making. Does living in an increasingly fast-paced, uncertain world with an abundance of information and choices mean that we need more complex models to make sense of it all? Or is this the equivalent of fighting fire with fire when what we really need is to go back to basics – working with a view of psychology that reflects simple but fundamentally human decision-making?
Gigerenzer, G. (2016). Taking heuristics seriously. In A. Samson (Ed.), The Behavioral Economics Guide 2016 (pp. V-XII) Retrieved from https://www.behavioraleconomics.com/the-behavioral-economics-guide-2016/.