The Ambiguity Effect bias

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Why do so many companies keep making the “wrong” decisions time and again?

And what impact does this have on innovation projects?

It may be linked to a cognitive bias called the Ambiguity Effect bias.

The ambiguity effect is a cognitive bias that describes how we tend to avoid options that we consider to be ambiguous or to be missing information.

Humans dislike uncertainty and are therefore more inclined to select an option for which the probability of achieving a certain outcome is known. Even if a more ambiguous option could bring a much higher benefit.

This is because humans are hardwired to feel loss more strongly than gain, and therefore will try what they can to reduce risk and increase security.

The concept was first developed in experiments by Dr Daniel Ellsberg, reported in research studies from 1961.

He recounts an experiment where participants were asked to choose one of 30 coloured balls hidden in a bucket with the chance of winning a cash prize.

The balls are either red, black or white. Ten of the balls are red, and the remaining 20 are either black or white, with all combinations of black and white being equally likely. In option X, drawing a red ball wins a person $100, and in option Y, drawing a black ball wins them $100. The probability of picking a winning ball is the same for both options X and Y. In option X, the probability of selecting a winning ball is 1 in 3 (10 red balls out of 30 total balls). In option Y, despite the fact that the number of black balls is uncertain, the probability of selecting a winning ball is also 1 in 3. This is because the number of black balls is equally distributed among all possibilities between 0 and 20. The difference between the two options is that in option X, the probability of a favourable outcome is known, but in option Y, the probability of a favourable outcome is unknown (“ambiguous”).

In spite of the equal probability of a favourable outcome, people have a greater tendency to select a ball under option X (known red balls), where the probability of selecting a winning ball is perceived to be more certain. The uncertainty as to the number of black balls means that option Y tends to be viewed less favourably. Despite the fact that there could possibly be twice as many black balls as red balls, people tend not to want to take the opposing risk that there may be fewer than 10 black balls. The “ambiguity” behind option Y means that people tend to favour option X, even when the probability is the same.

This effect is seen in countless examples of people preferring to choose “known” options even though they could in fact be inferior than ambiguous options, such as people choosing secure but low savings rates instead of investing in stocks and funds (even though statistics show these provide a better return), or selecting a higher fixed mortgage rate than a lower rate which could vary with the market.

The impact of the Ambiguity Effect in innovation

Innovation projects are especially likely to suffer from decision makers not understanding their own Ambiguity Effect bias, since innovation can never provide all of the desired evidence in advance.

When comparing two options, where one option is for a solution where the variables are known (because it has been done before), or a more innovative solution (where the variables are more ambiguous), it is human nature for the decision maker to feel more comfortable choosing the less ambiguous solution.

This is backed up by other research showing that decision makers do not know that they prefer less creative ideas compared to more creative ideas.

So how can companies help to reduce the impact of this bias?

One of the best ways is to reduce the number of times when decisions need to be made in the first place.

By putting in place something like a budget pool, resources can be allocated to more small projects more freely, compared to having to weigh up all the potential impacts too far in advance for information to be known.

Then, an innovation pipeline management framework can reduce the uncertainty around decisions during regular intervals.

All of these processes can help to reduce the impact of the ambiguity effect and other unconscious biases in your decision making, and therefore allow innovation projects to progress faster.