The point of maximum pondering
Entrepreneurs like to think of themselves as quick thinking, like sports stars. But business decisions can be taken too hastily as well as too slowly
“There is a kind of romance attached to being hectic,” says Rolf Dobelli, the author of several bestsellers on clever decision-making. He is no mere dispassionate onlooker. In the late 90s he co-founded getAbstract, a firm offering summaries of business books that became a market leader.
“As a founder and manager, you want to make a lot of decisions. Because that’s how you like to see yourself: as a potent decision-maker.” Many so-called deciders like to compare themselves to sports stars. The more action around them, the more of a sporting hero they feel. Dobelli sees that as a case of distorted perception.
According to research by the prominent business psychologist Daniel Kahneman, author of Thinking, Fast and Slow, this means that in business decisions there is always time for rational appraisals – despite all the sporting metaphors that abound in management literature. Managers are not like footballers; they don’t have to trust their gut feeling and instantly decide whether to go for a long ball or a short pass. The main thing is to avoid an artificially hectic pace. Kahneman’s follow-up question is: how slow can “slow thinking” be? Or putting it another way: when do we decide too late?
In the search for an answer Dobelli has highlighted what he calls “the point of maximum pondering” – when further delay does not provide any more insights. “The silly thing is that as a rule this point is reached far sooner than we suspect when we have once shifted into the mode of rational decision-making.” Behavioural psychology teaches us that during decision-making other factors gradually win out over our tendency to be hectic: aversion to risk, for example, which Dobelli sees as particularly prevalent in large organisations. Brooding over something is safer, cheaper and more comfortable than taking a decision, acting on it and having to face the consequences.
I must gather more information to have a better basis for my choice, reasons the decision-maker. “The problem is, you actually never know if you have enough information,” says Thorsten Pachur, a researcher at the Max Planck Institute for Human Development’s Centre for Adaptive Rationality in Berlin. The so-called “exploration-exploitation dilemma” indicates the transition from exploration to a hoped-for profitable implementation of the decision. It is in the nature of dilemmas that they cannot be resolved. You can never be sure the early bird will catch the worm or whether it will be the second mouse that gets the cheese.
Studies have long demonstrated that people with a high IQ are better at recognising whether a gut reaction is required for a decision or a long search for and assessment of information. Intelligence clearly helps people to assess whether it is worth waiting for additional relevant information, known as predictors. A frequently quoted study on different decision-making strategies in industry comes to the conclusion that the more dynamic the sector, the more successful rapid decision-making strategies are likely to be with the aid of so-called heuristics ‑ simplified rules that help to decide intuitively.
In the data-driven digital sector, intuition contributes more to the success of a business than in energy conglomerates, where market changes are slow and decisions have long-term effects that cannot be speedily reversed. For Pachur, it seems surprising that in stable industries managers would actually have enough time to “correlate their heuristics with their knowledge and know-how”, meaning time to adjust their intuitions to their experience.
But it is precisely at this point that a phenomenon comes into play that seduces experts into making overhasty decisions even though they have plenty of experience. Pachur investigated the Swiss customs service, getting customs officers and non-professionals to oversee a border and asking them things such as how they decided whether to search somebody. The researchers were surprised. The professional officers focused on a few features for their decision and consequently fished out suspects quickly from the stream of travellers. In contrast, the amateurs ran through complex decision models in their heads and took much longer. Unfortunately, the experiment could not test whether the professionals caught more smugglers. However, it has been shown that doctors build up their maximum practical knowledge relatively quickly and that more experience does not necessarily then lead to them making better decisions. It has also been established that in general people tend to make rash decisions when they are in a competitive situation in which they do not know how their rival will act.
It is precisely this behaviour that Jens Noll observes among managers in companies that face competition both internally and in the market. For the consultants at Fehr Advice in Zurich – founded by the Austrian behavioural economist Ernst Fehr and his brother Gerhard – correct timing means one thing above all: a test of patience.
Noll sees patience as the ability to go without something today in order to have more tomorrow. People are generally not very patient, but even when they are, they underestimate the effects of other people’s impatience. According to Noll, in business decision-making this means that patient people are rushed into impetuous decisions by the impatient. Business psychology has identified one of the main causes of this as the wish for an immediate response that is ideally also a reward. Naturally, this can only happen after a decision has been made.
Fail fast, fail early
For Noll and his colleagues, this is key to solving the dilemma of exploration and implementation. “Most major decisions in companies can be prepared by smaller ones in the run-up to them.” The big American online companies are a case in point: True to the start-up motto “fail fast, fail early”, little experiments produce precisely the kind of information that allows vital corporate decisions to be taken with less risk.
At Google, it has been standard practice since the firm was founded that the slightest changes to the website, even the colours of tabs to click on, have to be tested on users first. Different options are relayed to various groups, often in so-called A/B or two-sample hypothesis testing. The most popular colour or function or the most clicked-on service gets the final vote. This approach is not just suitable for making gradual improvements by small steps; strategic decisions too are increasingly being taken with the help of experiments. Before Amazon decided to plunge into the risky business of fresh food deliveries, it tested the service on a small scale at its headquarters in Seattle.
European firms are now doing the same, such as Daimler introducing and developing its mobility services. The principle is the same as with the US role models. Before launching a ridesharing service with small buses on a big scale, the Daimler subsidiary Moovel first tested the idea with a few thousand selected customers in Stuttgart, Karlsruhe and Berlin. Such an experiment not only supplied data on how to improve the ridesharing algorithms, but it also gave valid information on how much a ride should cost so it can make a profit while ensuring customers are ready to pay for it.
Scientists and consultants agree that early, systematic and intensive experiments with prototypes and customers boost the speed of innovation and cut the risk of developing new products or services for which there is no demand. The founders of the German online fashion traders Zalando have proved that you do not need to be a huge concern to develop clever tests. Before launching into selling shoes and then clothes on a grand scale and with a full range, they tested the vision they had imported from the US with a mini online shop selling flip-flops.
Small-scale experiments can meet managers’ need for action, rapid feedback and reward. So the romance attached to being hectic is still preserved without the risk of making major blunders due to impatience. At best, systematic and smart experiments increase both the speed of decisions and their quality. But experiments can also show it is still too early for a decision. So in doubtful cases enterprises can leave others to make expensive wrong decisions and score points as smart followers – just as Amazon was not the first online trader, Google was not the first search engine and Facebook not the first social network.
But that not only requires many experiments that systematically keep sights fixed on big targets but also lots of resources. Dobelli, Paruch and Noll stress independently of each other that one can only avoid being hectic in decision-making if there is not a constant fight to survive. Those who want to make money fast on the stock exchange have to carry out lots of transactions and cannot speculate on achieving steady growth with a broad portfolio. And a big high-tech concern can make several high-risk parallel decisions in the hope that one of them will turn up trumps. If everything remains the same, the business will still survive – and can unhurriedly begin new experiments that will not trigger long periods of pondering.