You are in a store, holding a movie in your hand. You’re planning to buy it, because you looked at the Internet Movie Database (IMDB) earlier, and saw it has a 7.5 out of 10 average rating from 200 people. But you haven’t read any full-length reviews. A stranger sees you standing there and comes over to you.
‘I’ve watched that,’ he says. ‘It was awful. I didn’t like it at all.’ And he walks out of the store.
Would you still buy the movie?
Maybe you would, maybe you wouldn’t. But studies have shown that most people would think pretty seriously about it before making a decision. In other words, the impact of that one face-to-face discussion far outweighs the 200 opinions from online ratings aggregates.
But what has really changed? Before, there were 200 ratings with an average of 7.5. Now let’s say the stranger in the store gives the movie a rating of 2. Now there are 201 opinions, with an average of 7.48. Two-hundredths of a point really shouldn’t matter. You don’t know any more about the movie itself. The stranger’s comments were very generic. So why does it make you think twice?
We are social creatures. As such, we are programmed to respond to individuals in a very different way than we do to abstract data. Our brain doesn’t see this situation as 201 people reviewing something. It just sees two pieces of information: IMDB liked it. A stranger didn’t like it.
Psychological studies have shown that we respond the least to numbers, more to written reviews, and most to face-to-face encounters.
In one study, students were split into four groups, each given different information about courses, and asked to choose which they would take. One group was given just the course titles. A second group also got a numeric rating. The third group was given written reviews of the classes, although they were phrased in quite a generic way. And the fourth group had someone sit with them and verbally give them the same written review.
The numeric ratings had a slight impact on course selection, and the written review more. But the face-to-face encounter had the biggest impact by far – even though neither the written nor verbal presentations gave any basis for decision-making. The study also found that more weight was given to negative reviews than to positive ones. One bad apple can indeed spoil the bunch.
You should be aware of this when you read a review – especially when you read one from someone you don’t know. Of course, the more information there is in a review, the more you can get out of it. And the more reviews you read from a particular reviewer, the more you understand what they like and don’t like, and how that matches up with your own preferences.
This is why more frequent reviewers can be more valuable than one-shot wonders, even if you don’t always share their opinions. So be aware that your brain is hardwired to give more weight to someone saying, ‘I give this movie a 2, I didn’t like it at all,’ than just seeing a rating of 2 with no comment. Yet, in reality, they both have the same information. And 100 people giving a 7 has even more information, especially compared to the stranger making generic negative comments.
By understanding our inherent bias towards certain types of information, we can all make more informed decisions.
On a slight side note, but still within the realm of inherent bias in decision-making, I’d like to play a game with you. I’m going to give you two options.
• Option A: You get $3000 guaranteed.
• Option B: Roll a die. On a 1 to 5, you get $4000. On a 6 you get nothing.
Which would you pick?
Strictly mathematically, option B has a higher expectation value. Your average gain is $3200, and in option A it is $3000. But most people, about 80 per cent, choose option A, the sure thing.
Here’s another game. I’m going to give you two new options.
• A: You lose $3000 guaranteed.
• B: Roll a die. On a 1 to 5 you lose $4000. On a 6, you lose nothing.
How about this one?
Well, in this case, a whopping 92 per cent of people take the second choice – even though it actually increases their expected loss. In both examples, the vast majority of people will choose the worst option mathematically speaking.
What’s going on here? Is it just that people are bad at math?
This experiment was performed by two psychologists, Daniel Kahneman and Amos Tversky. They did a variety of experiments that repeatedly showed that people do not operate strictly by rational judgements. Based on their experiments, they developed a theory to explain this behaviour called ‘prospect theory’, which has developed into a fertile area of research, and offers a rich theoretical foundation for economics, finance, insurance, psychology and other areas. Kahneman received the Nobel Prize in 2002 for the development of prospect theory. Tversky would have shared the prize, but sadly he had passed away a few years earlier. ‘
The amazing thing is that neither Kahneman nor Tversky was trained in economics. They were both cognitive psychologists and wrote many highly readable books about decision-making. I particularly recommend Thinking, Fast and Slow.
Now, in a nutshell, prospect theory says a few things. First, people have different risk attitudes towards gains and losses: people are less likely to take risks to increase their gains, but they are more likely to take risks to avoid losses. This is clearly seen in the examples above, where most people wanted the sure $3000, but would take a gamble – a bad gamble, in this case – to avoid losing $3000.
Second, the way the problem is presented is critical. This is called ‘framing’. You can present the same problem to people to make it seem like they are losing instead of gaining. So, you can manipulate what they decide.
For example, here’s a slight variation on the original problem.
Participants were asked to imagine that the US was preparing for the outbreak of a disease that was expected to kill 600 people. The first group was asked to select between two different courses of action, with the following outcomes:
• A: 200 people will be saved.
• B: There is a 1/3 chance that 600 people will be saved, and a 2/3 chance that no people will be saved.
Over two-thirds (72 per cent) of people preferred option A, to definitely save 200 people.
A second group was presented with these outcomes:
• C: 400 people will die.
• D: There is a 1/3 chance that nobody will die, and a 2/3 chance that 600 people will die.
Now, in this framing, 78 per cent preferred program D, with the remaining 22 per cent opting for the program where 400 people die. However, A and C are exactly the same, as are B and D. The only difference is that A and B are expressed positively, in terms of saving people, versus in negative terms of people dying.
One game that explores this is Deal or No Deal.
In this US TV game show, the contestant is presented with 26 briefcases, each of which has a hidden amount of money, ranging from $1 to $1,000,000. The contestant picks one to keep, and then selects cases to be opened, removing them from the game. So, the remaining pool of dollar amounts gradually shrinks.
At certain points in the game, the ‘banker’ makes the player an offer for their case, which they can either accept, ending the game, or reject in order to play on, opening more cases.
This game is basically a psychological exploration of prospect theory. A fair offer from the banker would be the average of the remaining dollar amounts. But the banker never offers this. They always offer significantly less. So, a strictly mathematically inclined person should never take the offer.
But then, a mathematical answer is not always a correct answer. Consider a simple game where you roll a die, doubling your money on a 2 to 5, and losing everything on a 6. Mathematically, you should continue to play indefinitely. Realistically, though, we know you have to stop at some point.
So, people do take the banker’s deal. And the amount of money they are willing to leave on the table, to be able to walk away with a sure thing, helps economists fine-tune prospect theory, and learn more about people’s risk tolerance.
Keep this in mind when negotiating in a game, or trying to anticipate what people what will do. Most people will take a sure gain, but take a risk to avoid a loss.
Review or Analysis?
Tim’s series on Final Fantasy 7 is an example of analysis, specifically on the game’s localisation.
When we talk about a board game ‘critic’, as with many things, the terminology can be a little fuzzy. Usually we are referring to somebody who critically reviews games, similar to a movie reviewer, or a film critic.
The second type of critic performs critical analysis – looking at themes, historical context and comparable works. Literary criticism is often of this ilk. So, we’ve got two types of criticism: review and analysis. These serve two completely different purposes, which leads to a lot of confusion.
Reviews are there to help you decide whether to buy a game, or see a film, or read a book. Analysis is there to help you think more deeply about the topic and see connections or features that weren’t readily apparent at first glance. It can also help you understand the period in which the game was developed, or how the game reflects the designer, or how it fits into their larger body of work.
So, reviews by their very nature are time-sensitive. It is no accident that film reviews are typically published the day the film is released in theatres. Similarly, in the game world, it sometimes feels like there is a race between reviewers to get that first review up. It does seem that the earliest reviews garner the most attention, regardless of their ultimate utility. And it is rare that a review that comes out for a five-year-old product – whether it is a game or a film or a book – will attract that much attention.
Analysis is the exact opposite. Analysis requires time and perspective. Analysis requires that the critic be versed in many examples of whatever genre they are working in, be it poetry, literature, or film. Analysis requires expertise, and expertise requires time.
A game review does not require a game expert who has played hundreds or thousands of different games. Anyone can write a review that says what the game is about, how it plays, what works, what doesn’t, and why they loved or hated it. A review is a snapshot of one person’s reaction at a particular moment in time.
Sometimes reviewers attempt to incorporate analysis. The New York Times Book Review, for example, is famous for reviews that attempt to both review the book and place it in a larger literary context. But though I enjoy reading those reviews, I often find, at the end of a review, that I’m not sure whether I should buy the book or not. I’m just not given enough tools by the author, or enough personal bias, to make a judgement.
But there’s a twist for games that doesn’t exist for films, books, or even art. There is an expectation that board games will be played multiple times. There are certainly games that reveal all their depth on one playthrough, but others, especially classics like Go, Chess or Bridge, only reveal their depth and subtlety over time, and demand multiple plays.
So, are game reviewers obligated to play a game multiple times before rendering judgement?
This topic flared up in early 2012 over the game A Few Acres of Snow, where some felt that hidden depths or intricacies in the game were not being explored by early reviewers.
I disagree. I think there are certainly examples where those playing multiple times will have a deeper appreciation of the game, and that will come through in more in-depth reviews. And I think that the thoughtful reader of reviews will take that into consideration. Review is not analysis, however.
Analysis demands multiple plays. The ability to develop deep thoughts about a game – to put it into a context – requires familiarity with and exploration of the designer’s intent. And regardless of the simplicity of the game, you can’t get that with a single playthrough. If someone is doing an in-depth critical film analysis of 2001: A Space Odyssey, or a dissection of Hamlet, I fully expect that person to have gone through the material more than one or two times. And I expect no less from game analysis.
So, as I read an article about a game, I try to put the article into the review or analysis bucket – and, depending on how it’s classified, I approach it differently and have different expectations. From one, I am looking for quick reads on what a game is about and what works and what doesn’t. For the other, I am looking for deeper analysis, multiple plays, and more thought. It is very rare for anyone to combine these two forms, for obvious reasons.
In the board game world, there are many reviewers, some better than others, but a real lack of analysis. And I think that this is the crux of the bemoaning of the state of board game criticism. But I think it is unfair to lump these two together – review and analysis are really two different animals with two different expectations. If a review of A Few Acres of Snow doesn’t discuss the presence of a killer strategy, that’s fine by me. But a piece of critical analysis on the game would be sorely lacking if the killer strategy was omitted.
The above chapter is an excerpt from GameTek, a book about the big questions of life through games by Geoffrey Engelstein. Engelstein is an adjunct professor of Board Game Design at the NYU Game Center and an award-winning table-top game designer.
He has degrees in Physics and Electrical Engineering from the Massachusetts Institute of Technology, and is currently the president of Mars International, a design engineering firm.
Since 2007 he has been a contributor to the leading table-top game podcast Dice Tower, presenting ‘GameTek’, a series on the math, science, and psychology of games. He also hosts Ludology, another weekly podcast on board games.