When we make decisions, we don’t constantly have all alternatives available to pick from at the same time. Instead they frequently come one after another, when it comes to example when we search for a house or a flight ticket. So we have to pick something without knowing if a better alternative might have occurred later on. A study has shown that our requirements drop more and more in the course of decision-making.
Let’s Take a Closer Look
Be it reserving flight tickets, purchasing an automobile or finding a new apartment or condo, we constantly come up against the exact same question: Should I strike while the iron’s hot, or wait until a much better deal comes along? Individuals often discover it tough to make decisions when choices are presented not simultaneously however one after another. This ends up being even more difficult when time is minimal and a deal that you turn down now might no longer be available later.
“We have to make decisions like this countless times every day, from the small ones like looking for a parking space to the big ones like buying a house or even choosing a partner,” says Christiane Baumann, a doctoral candidate in the Department of Psychology of the University of Zurich. “However, until now, the way we behave in such situations has never been thoroughly examined.” Under the leadership of cognitive psychologist Bettina von Helversen (previously UZH, now University of Bremen) and in collaboration with Professor Sam Gershman (Harvard University), Baumann carried out numerous experiments to investigate this issue. Using the results, she then developed a simple mathematical model for the strategy that people use when they make decisions.
Is there an optimum process?
It is easy, utilizing a computer, to find the best-possible procedure for making decisions of this type. “But the human brain is not capable of carrying out the complex calculations that are required, so humans use a rather simplified strategy,” says Baumann.
Baumann simulated buying situations with as much as 200 participants in each test in order to learn what techniques individuals use. In one test, the individuals were informed to attempt to get a flight ticket as inexpensively as possible– they were provided 10 offers one after the other in which the rate changed; meanwhile the fictional departure date was getting nearer and nearer. In another test, people had to get the best possible offer on products such as groceries or kitchen appliances, with the varying rates drawn from an online store.
Expectations driven down
The evaluation of the experiments confirmed that the test individuals did not use the ideal, yet complicated, technique calculated by the computer system. Instead, Baumann discovered that they utilize a “linear threshold design”: “The price that I am prepared to pay increases every day by the same amount. That is, the further along I am in the process, the higher the price I will accept,” explains Baumann.
This principle can be used not only to acquiring choices, but likewise scenarios such as option of a company or a life partner: “At the beginning perhaps my standards are high. But over time they may lower so that in the end I may settle for someone I would have rejected in the beginning.”
A design to promote the human method
Baumann analyzed the speculative data and established a mathematical model that describes human habits in various circumstances. “That assists us to better comprehend decision-making,” says Baumann. The model also enables us to predict the situations in which we tend to purchase an item too early– or when we delay too long and then have to take whatever is left in the end.
Baumann thinks these findings might assist people make difficult decisions in future: “In the current digital world the amount of information available for decision-making can be overwhelming. Our work provides a starting point for a better understanding of when people succeed or fail in such tasks. That could enable us to structure decision-making problems, for example in online shopping, in such a way that people are supported in navigating the flood of data.”