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15 Cognitive Biases That You Need To Be Aware Of When Developing Games

by Eugene Matveev on 03/05/18 10:00:00 am

The following blog post, unless otherwise noted, was written by a member of Gamasutra’s community.
The thoughts and opinions expressed are those of the writer and not Gamasutra or its parent company.

 

We all tend to make decisions based on emotions, unclosed Gestalts, previous experience. Thus, you unconsciously expose your data to the cognitive biases.

Cognitive biases is a scientific concept that means systematic deviations in behavior, perception and thinking caused by the subjective prejudices and stereotypes, social, moral and emotional reasons, failures in the processing and analysis of information.

I'd like to emphasize those cognitive biases which may lead to the erroneous decisions in the field of developing games.

1. Confirmation bias 

Confirmation biases stand for a tendency to search for or interpret information in a way to confirm the available pre-conception. Perhaps it is the most common for game analysts. When conducting analysis, the analyst generates some ideas based on the results of this analysis in advance, i.e. bets on the result, and then interprets the results of the analysis on the basis of these ideas, sometimes adjusting the results to the answer.

E.g.: weekly retention of your game after the launch was unexpectedly low, for example, 5%. Shortly before the start, you offered to change the icon but no one agreed. As a result, you analyze the reasons for low retention, and begin to see that people are not interested in your product at once, from the first event, from the tutorial. You go to the department of development and set the task to change the icon. And in fact, the reasons may be totally different: from the unsuccessful design of the entire application or inappropriate setting — to the unclear tutorial or just poor balance of the game.

A particular case of the confirmation bias can be called anchoring, an adoption of numerical solutions by the person causing irrational bias of responses towards the number that already was on the mind before making a decision. 

For example, you just watched the movie 47 Ronin and it really got under your skin. So much that the number 47 just etched in your brain. Among the numbers of the huge spreadsheet you see only those records in which the numerical entry is “47”, not paying attention to other metrics. Or you can start offering to add to the game AK-47 and the character of the Hitman game series (Agent 47).

2. Curse of knowledge

When better-informed people find it extremely difficult to think about problems from the perspective of lesser-informed people, come the curse of knowledge. Game developers tend to overestimate how much other people, including their precious users, are versed in their product.

It’s so obvious that here it is necessary to press the green button and then the tank will jump! No, it’s not obvious. It's only n your head. You already have an understanding, the appropriate structures and templates, you look at your product everyday and every night you see it in your dreams but the user sees everything for the first time. What should be done for the user to understand the product as good as you do? That’s right! To explain everything to him.

There are several ways to understand whether the users perceive your game as you want them to:

  • Play-tests. A great invention of mankind which is necessary in order to get rid of the curse of knowledge. Hear what your users are saying, listen to their feedback, and you will understand your product better.
  • Analysis of user profiles. You may see the sequence of actions of each user without conducting expensive individual play-tests.Modern analytical systems allow you to see the product through the eyes of the certain real people.

A screenshot from the devtodev analytics platform

Another example of the curse of knowledge can be called communication between the departments. For example, the analysts speak their own language suggesting that everyone understands them, while most of the team do not know what ARPU and LTV are, and they certainly never heard of the models of ARMA and ARIMA.

E.g.: once I had to present a mathematical model of predicting to the city officials. I spoke with passion about how well this regression model works, about how long we were developing and testing it. And then I was asked, “We’re a progressive city, why do you have a regression model?”

3. Illusion of transparency

People overestimate others’ ability to understand them. Question is solely about communication :  are you sure that you were understood correctly? Are you sure that you have understood everything correctly? Maybe it makes sense to talk it over once again?

Let’s assume you’re a producer and you’ve just invented a cool new game. You have gathered a meeting and said “We should make a game about Super Sonic the Hedgehog! Well, you all sure played it. Let’s do exactly the same!”

And you leave in full confidence that the game designer have understood you. Now please go to the website and answer the question, which of those games did you have in mind?

4. Retrospective bias

Talking about choice-supportive bias - a tendency to remember your choices to be more right than they were when they were made, and hindsight effect - the tendency to see past events as being predictable at the time those events happened. Both biases could be described by the phrase “I-knew-it-all-along”.

A striking example: a flurry of statements from the representatives of the gaming industry who began to say that they invented this game long time ago immediately after the successful release of the Pokemon GO.

In order to avoid such biases, I recommend to conduct a detailed analysis of all the changes in your game, preferably from its very start. You’ll know how each change affected the project, which metrics it has changed, how it worked: it's revenue and audience. Basing subsequent decisions on the analysis of the previous results, you will be able to more accurately select the most effective hypothesis of several options.

5. Contribution effect

There is one more similar cognitive bias —  a contribution effect - a tendency to overestimate the value of the object in creation of which you were involved. To avoid it, again, be guided by the statistics, keep a clear log of all changes and it’s detailed analysis.

6. Texas sharpshooter fallacy

It's a selection or adjustment of the hypothesis after the data is collected which makes it impossible to test the hypothesis fairly. I think you all know the example when the shooter shoots at the barn, and then, in the place where the most holes are, draws the target. When talking about this cognitive bias, Nostradamus is mentioned very often as his prophecies are adjusted to the occurred events.

Let’s say you create a revenue forecasting model and test it on the same data that was used for teaching. The results of such testing would be excellent, although the model may well prove to be unworkable.

7. Survivorship bias

When there is lots of data on one group (“survivors”), and none on the other group (“dead ones”) , and the researchers are trying to find common features among the “survivors” forgetting that not the less important information is hidden among the “dead ones".

Most often this fallacy is illustrated by an excellent example:

Rumors about the mind and kindness of dolphins are based on the stories of the tired swimmers that were pushed to the coast but we are unable to hear the stories of those who were pushed in the other direction.

If talking about the analytics, it is incorrect to draw conclusions only based on the user data which is already active in your project. It is also necessary to take into account those who left the project. It is possible that they also could give you enough information for the development of the project. Here is a good example of how you can optimize the user activation on the basis of a comparison of the “survivors” and the “dead ones”.

8. Selective perception 

It occurs when not all the data is taken but only some part of it is selected beforehand. Of course, such an analysis would be irrelevant, though it would look good enough. The developers just would not talk about the data they did not take into their consideration.

Let’s say you release a beta version of the project and collect the feedback but pay attention only to the positive reviews because those who wrote negative reviews do not understand anything and are just trolls. The result is that much of the feedback passes by, and the product does not change the way it should have been.

9. Persistence

Surprisingly, persistence  is also a cognitive bias and it lies in continuing to work on what has already lost its value.

Let’s say you are still waiting for the product to take off only by the means of the viral traffic. The months are passing by and it still does not take off, and you’re still waiting.

10. Information bias

It's a tendency to seek information even when it cannot affect action.

Often in the developer team someone can be entrusted both quantitative and qualitative research. If there is plenty of time, they may Google the information needed. It may happen that in solving some issue, that team member may purely instinctively take a few days for research and RnD. But before you go on a journey through the depths of the Internet for a few days, ask yourself this question: are you definitely sure you can’t begin to address the problem now? Are you sure to find the information that will help you in the following analysis and pay these days? If it’s a definite “yes”, then certainly go for exploring.

11. “Well traveled road” effect

When the decisions are made in favor of the more familiar and studied technology, even if it may not be effective.

Let’s say, you were involved only in the web projects before, and then decided to enter the mobile market. And you immediately begin to imagine how much new you will have to explore. So you insist on sticking to the web and not meddling in this terra incognita.

12. Zero-risk bias

Very similar cognitive bias is zero-risk bias — a preference for reducing a small risk to zero over a greater reduction in a larger risk. People are afraid of medical complications more than of the disease itself. How to deal with this bias? If there is a choice, then collect the data, be as much unbiased as possible, and do not be guided by the personal preferences.

13. Bandwagon effect

On the other side, there is bandwagon effect — a tendency to do things because many other people do the same. For each of us there are people whom we look up to. And it may often be the case that we begin to imitate them, to use the same tools and techniques. Or, once learned about the new method, you start to see it everywhere (it is known as the Baader-Meinhof Phenomenon) and now seek to use it anywhere, even if it is not quite reasonable.

That’s my case. I learned about the factor analysis and the method of principal component analysis, I was impressed with them, especially with the pictures of the transition from one space to another:

image source

Asking around friends and analysts revealed that it was long used by them, though I was the one to know nothing about it before. And I began to use this method everywhere, even if it was not required. The method of principal component analysis is needed to reduce the dimension (e.g. you have 100 variables, and you need to leave only 5 of them), but I used it even when I had only three variables on hand, and no reduction in the dimension was required.

14. Discounts revaluation

The next cognitive bias makes sense to use for your own benefit. Discounts revaluation — its name speaks for itself. People often fall victim to marketers and buy what was they do not need but with the discount.

How to take advantage of that? Just try to make a small discount for your goods, virtual or real — it does not matter. It may often be the case that a short-term small discount raises the revenue greatly. You can read more about the pricing on virtual goods and discounts in our free ebook here.

In my practice, I have seen a very high price elasticity of demand, particularly in the virtual goods. Sometimes a small (let’s say 10%) discount could give a strong (let’s say, 50%) increase in demand. You can experiment with the goods to offer, with the size of the discounts, and I’m sure that you find an optimal strategy of sales, maximizing revenue and minimizing risks.

15. Clustering illusion and illusory correlation

Finally, a couple of CB that are peculiar only for game analysts:

  • clustering illusion — a tendency of seeing phantom patterns
  • illusory correlation —  an inaccurately perceiving the relationship between two unrelated events.

Can not be said that the correlation doesn’t imply causation, and if between two objects there is a correlation, it does not mean that a change to one will inevitably lead to a change in another.

And that’s not all cognitive biases that exist! I very much want you to know what kind of biases there are, that they are fairly ubiquitous, and it is possible that you identify some of them in your work, and this is an excellent first step. In general, I recommend to periodically review these lists and wonder whether you have any biases mentioned in them. It’s like going to the dentist every six months.

The main thing is not to be overzealous. After all, the continuous search of CBs — it is also a kind of cognitive bias. Developing games, it's better to rely on the statistical significance, and not on the templates and emotions!


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