[hyperpape]

John Gruber has been arguing that the only meaningful way to do ratings is a simple thumbs up/thumbs down. I don't necessarily agree, but I see the appeal.

I usually don't want ratings, I want the Wirecutter treatment. Sometimes, I know/care enough to really research the topic, in which case star reviews are relatively unhelpful. The rest of the time, I just want someone trustworthy to say "buy this if you want to pay a lot, buy this if you want something cheap, but this third thing is no good at any price".

[dntrkv]

I've been saying this for years, thumbs up/down is the only system that makes sense to me.

Foursquare uses it and I've found their scores to be way more useful than Yelp's.

The biggest problem with star ratings is that it's so arbitrary. What is the difference between 3 and 3.5? What is a 1 vs a 2? 3/5 is 60%, that's almost failing when you think about it on a grading scale, if I scored something as a 3/5 I would never use that product or service again, yet, many of the best restaurants are rated 3/5 on Yelp.

Unless the user has some scoring system in place for different qualities of the product or service, there is no way you can get anything resembling an accurate score.

I would never trust a user to accurately assess a score given 10 different options (.5-5) but I would be way more likely to trust a user to say either "I like this product" or "I do not like this product."

But yes, the Wirecutter approach works great, but it just doesn't scale.

[crazygringo]

Counterpoint: I almost solely rely on the stars histogram in Yelp (available only on the website, not the app), completely ignoring whatever Yelp's calculated "average" is.

If a place has more 5-star ratings than 4-star ratings, it's generally amazing. If it has more 4-star ratings than 5-star ratings, it's generally fine but not something particularly special.

Just thumbs up/down would eliminate what is, to me, the single most useful aspect of Yelp.

It doesn't matter that star ratings are arbitrary -- when you average enough of them out, a clear signal overrides the noise. You can distrust any given user, while still trusting the aggregate.

(Curiously enough, I don't find any equivalent value on Amazon. On Yelp, you're really evaluating an overall experience along a whole set of dimensions, so there's a lot more to discriminate on. On Amazon, it does seem to be more of a binary evaluation -- does the product work reliably or not?)

[BoiledCabbage]

I used to think the same thing until I realized the most accurate and consistent ratings I use on a regular basis is rotten tomatoes. And they're based on strict thumbs up/ down.

It ensures votes hold equal weight and that "extreme polar" voters don't skew things. It also avoids the opposite problem of "everything is neutral" vote unless horrible/incredible.

RT also handles high brow and low brow well. You get less voting of "eh I didn't love it, but it's sophisticated so I'll give it an extra star."

I'm sold on simple up/down.

[stinkytaco]

Rotten Tomatoes is good and predicting a movie I (or others) like, but not really at "ranking". Zootopia, one of their top movies of 2016 and a 98% rating, is a good movie, but one I'm unlikely to pursue again. The Godfather (with a 99%) rating, is a movie I will pick up on Blu Ray and revisit many times. It's far more than 1% better than Zootopia.

So RT is good at predicting "should I watch this movie I haven't watched before", but bad at predicting more sophisticated habits or preferences. I wouldn't buy the Blu Ray off a RT prediction, but I would rent.

So it becomes a question of what are you trying to accomplish? For some issues up/down is a good way to solve a problem, for others it isn't.

[icebraining]

Rotten Tomatoes actually has both ratings, meaning they recognize the limitation you're referring to. In the other, Zootopia has 8.1/10 and The Godfather has 9.2/10, showing that difference in quality.

[Houshalter]

Also you just aren't the demographic for zootopia. If you have kids then it probably is worth buying and they will watch it many times. There are so many genres of films, it's best to compare within a single genre and not between.

[BoiledCabbage]

> Rotten Tomatoes is good and predicting a movie I (or others) like, but not really at "ranking". Zootopia, one of their top movies of 2016 and a 98% rating, is a good movie, but one I'm unlikely to pursue again

It feels like you're mixing together two different arguments. Rotten Tomatoes is good at predicting whether someone will like a movie. What is "ranking"? That is a very undefined concept. Ranking of what? It's clearly not ranking of likelyhood of a person liking a movie because rotten tomatoes already does that.

Later you mention likelihood of repeat watchings of a movie. Rotten Tomatoes thumbs up or down based on whether someone liked a movie, as a result it produces a metric on likelihood of someone liking of movie. Instead if rotten tomatoes immediately after watching a movie, asking "Did you like this movie?", asked "Would you watch this movie again?" then it would produce an indicator of re-watchability.

Up/down doesn't matter - it's the question that's being asked.

note the caveat RT obviously doesn't actually ask critics these questions, they read and judge their reviews and interpret them as answering those questions.

[ehhnetfliz]

In my experience, my favorite movies I find via glowing reviews. Rotten tomato completely obscures this view: if all the reviewers kind of like it, it'll get 100%, whereas polarizing films always suffer. I'll take "kids" over "star wars" any day for a better movie. Why? I'm gonna see star wars because i want to, not because I expect a meaningful aesthetic. But Rotten Tomatoes takes the opposite tact, pushing me towards crowd favorites rather than what i might rate highly.

Really this comes down to how terrible one dimensional comparisons are: it only measure popularity, which is a terrible filter for quality.

[alexilliamson]

I used to religiously research movies on RT - with a lot of success in my mind. With the user rating, the critic rating, and the "top" critic rating, you can infer a surprising amount about who is going to like any given film, and you learn over time where you fall on the critic/top critic/audience graph.

Recently, however, it seems like more (imo undeserving) movies that are "just ok" - like decent, but nothing special, romantic comedies and big blockbusters - are scoring above 90%. I might be being curmudgeonly about it, but I've nearly stopped checking it because it feels like there's no information there. My theory is that this started happening once Roger Ebert died... without such a leader in the field, no one is willing to say they didn't like a film unless it's obviously very bad.

[albedoa]

I pay a lot of attention to histograms when there are many high-rated options for the same Amazon product type. A histogram that curves sharply in its number of 5-star reviews to almost nothing on the other end is the product you want (ignoring fake reviews for the sake of this conversation).

Amassing a bunch of 4- and 5-star ratings is easy, but leaving nothing for even the most habitual of complainers to complain about? That's an monumental achievement.

[ghaff]

For things like books, I also find that reading the middling reviews often gives the best S/N ratio. It weeds out the fanboys and weeds out those who were clearly not the audience for the book (or just have some ax to grind). You're more likely to get the "I really love this author in general but I didn't care for this book because 1.) 2.) 3.)."

[VanillaCafe]

Agreed. For products in Amazon above a certain star threshold (say, 3+), I evaluate given the shape of the review histogram, particularly minimizing the size of the bump down at 1-star and 2-star.

[stinkytaco]

If the provider is in a position to provide a prediction, then the rating system is useful. For example, on Netflix I used the Hated It, Didn't Like It, Liked It, Really Liked It and Loved It system. When they predicted a star rating, it was pretty close. When they said we predict you'll give this a three star rating(which is probably well below the "average") that was generally a movie I liked.

[ghaff]

Which, in practice, tends to devolve to what's effectively a four-star rating of some sort: Want two hours of my life back, OK/meh, Good, Excellent

A humorous take: https://xkcd.com/1098/

[stinkytaco]

But my point is that for me, it didn't. Netflix's system was good enough to take into account that people have different systems. Thus when Netflix says "we predict you'll give this 3 stars", that means it was a movie I would like. That might mean you gave it 4 stars or 2 stars or whatever, even though you liked it as much as me. They made my system the only one that matters, as long as I was consistent. Reviews in aggregate are pretty much meaningless, but a good system weighs that problem in.

[beefsack]

Perhaps the issue isn't the granularity of a single dimensional rating scale, but the lack of expressive options when in reality your feeling about something is complex and multifaceted.

I've been really interested in the idea of emotive reviews as an alternative to single dimensional scores. The best idea I have at the moment is something akin to emoji reactions like you see on GitHub issues, finding a way to encode some feelings relevant to product reviews in a mechanism like that seems really intriguing to me.

[evincarofautumn]

I envision a panel of emoticons akin to the Facebook reaction set, but where the user can select as many as they want to quickly convey different combinations of their reactions:

    (thumbs up)      I liked this
    (heart)          I loved this
    (thumbs down)    I didn’t like this
    (smiling face)   This made me happy or satisfied
    (frowning face)  This made me sad or disappointed
    (surprised face) This made me surprised or impressed
    (angry face)     This made me angry or frustrated

Of course, it gets complicated. Did Sam U. Zerr give that product an (angry face) because they used it and didn’t like it, or because they’re offended that you would recommend it, or what?

If you’re only using icons to make recommendations to an individual user based on their own history, maybe you don’t need to infer the actual meanings; you can add all sorts of icons without any particular meaning and just make recommendations by correlation:

    (thinking face)  I’m considering this / I’m confused by or dubious of this
    (gear)           This was useful / this made me think
    (fire)           This album was great / this sauce was spicy
    (heart eyes)     I really want this / this is adorable
    ...

E.g. a recommendation for me might be “(thumbs up)(gear)(heart eyes)” because some product or content is similar, by some hidden metrics, to other things that I’ve reacted to in those ways.

Just brainstorming here. There are obviously many possible approaches in this space.

[astrobe_]

Put differently, a set of binary choices: amusing, interesting, sad, ... It's a bit difficult to come up with a good set to rate any thing, but I can see it working for specific topics, like movies or games.

Or, one could just let users tag the subject and the interface would display the "weights" of the tags.

[ghaff]

That's part of the problem here. Appropriately rating different types of things differ in various ways.

A simple utilitarian object? It mostly works or it doesn't.

A movie? Just to start with, there's the rating of the movie itself vs. the rating for this particular DVD. And then there are the dimensions on which the movie itself could be rated.

Or you just throw your hands up in the air and either do a thumbs up/down or a 5 star rating system on the grounds that it's better than nothing.

[xaedes]

How about vision based emotion recognition of viewers with cameras in the televisions and monitors? Sure sounds creepy and behaving different when observed etc. But I believe people will forget they are "observed" so the effect dimishs after a time. Than we would have a quite honest emotional feedback for movies. Even for specific scenes, for advertisment, etc

[throwaway613834]

To be honest, the fact that 60% is a failing grade is a failure of the grading system, not a fact to take for granted. We've basically lost the entire dynamic range of 0-60% for no good reason.

[andrewflnr]

I would actually say it's often not strict enough. In what serious field is it acceptable to only know, say, 70% of the material? Do you want to drive on a bridge designed by an engineer who only got 70% on their exams? It depends on how the test is structured, really, but unless it was one of those tests designed to bring smart people to their knees, I'd rather not.

[adrianratnapala]

We probably cross bridges designed by engineers who only got 70% on their exams all the time. That was pretty satisfactory score when I was in Uni.

[antod]

Yeah - Exam performance from a decade or two ago is quite irrelevant for evaluating senior design engineers.

I wouldn't trust an engineering graduate who scored 100% on all their exams to design a bridge at all. Where as someone with 10+yrs relevant experience but who got 60-70% in their exams would be preferable to me.

Mastery of the math isn't that relevant due to all the design standards you have to understand and comply with anyway, while all the little pragmatic solutions to real world constraints (incl how the builders work and what they need to be effective) learnt from experience and mentoring from your senior peers are far more important.

[logfromblammo]

So you're saying that the measurement of student mastery has no noise floor?

[waterhouse]

It depends on how things are graded. On a multiple-choice test with four choices per question, someone with no knowledge who guesses randomly will get ~25%. On a true-false test, someone with no knowledge gets ~50%. On a project graded by a human, or a worksheet whose answers are real numbers, someone with no knowledge and a hard-eyed grader might well get 0%. Different classes will have different proportions of these things that contribute to the overall grade (at least, I haven't heard of any requirement that classes have the same proportions of such). The simple approach of summing total points achieved over each graded item, divided by total points possible, is straightforward to calculate, but I think there's no mathematical justification for choosing one percentage-based grading scale and applying it uniformly to all classes.

[hanbura]

>On a multiple-choice test with four choices per question, someone with no knowledge who guesses randomly will get ~25%.

That would be terrible test design. At my (German) university, most Multiple Choice tests give one point for a correct answer, minus half a point for a wrong answer. That way you expect negative points from people who think they know everything but are no better than random guessing, zero points from somebody who knows nothing, some points from someone who can always narrow it down to two choices.

I guess my point is that you can arbitrarily raise the floor with a bad grading scheme, but there's no inherent reason to do that.

[ThrustVectoring]

Yup. You can't assume that what you think a 3/5 means is the same as what someone else thinks a 3/5 means. You can basically assume that for thumbs up/down. And really, the question you want answered is "how likely am I to like this", and thumbs-up % of the overall population is a decent proxy for that for good reason.

[ehhnetfliz]

What does a thumbs up mean, though? In a netflix context, am i recommending it to others? Trying to train the recommendation for my own taste? Making sure i rewatch it if I don't remember watching it the first time? What do I do if i like a movie but it's objectively terrible? All of the above questions weigh heavily, and the end result is I just avoid binary voting systems (including voting on hn) and it becomes feature bloat with little use.

Strangely, it gets even harder with the thumbs down—there are vanishingly few things i actively wish didn't exist. Why downvote at all?

[ThrustVectoring]

If I see an approve/disapprove button, I try to click it if it's for something I've chosen to consume (watch, buy, visit, etc). If it's a decision I'm glad I made, I thumb it up. If it's a decision I regret making, I thumb it down. People and systems will read that input for one of two ways: either optimizing stuff for my preferences, or using that data to make choices further in line with my preferences. Either way, the world is marginally more like I like it.

[ehhnetfliz]

Right, but what about consumers that want the rating to be meaningful? Assumably netflix has a history of videos you've seen entirely; they don't need your rating to know you consumed it.

Personally I just stop watching the moment I feel regret—the thumbs down button has no role in how I consume.

Two star ratings, though—that is meaningful, at least to me.

[dennisgorelik]

You may stop watching a movie on Netflix because you do not like it. You may also stop watching a movie on Netflix because you already saw it multiple times and only wanted to rewatch few minutes snippet from it.

Without your thumbs up/down feedback it is hard for Netflix to figure out what is your opinion about the movie.

[Houshalter]

Just normalize ratings. If the average rating is in the 50th percentile of all ratings on the site, convert the rating to 50%. That way it carries the maximum possible information. If someone rates something 60% that just means it's better than 60% of similar products.

School grading systems serve a completely different purpose and are a terrible comparison.

[kfriede]

What about a thumbs up, thumbs down, and a neutral? In the case of restaurants, there are plenty of places I've eaten where I wouldn't give them a thumb's up "best place ever", but also not deserving of a thumb's down "terrible."

[dredmorbius]

This really depends on how thick or thin the data are.

If any given option only gets a small handful of votes, then you might see a strong bias (favourable or otherwise) where neutral would be appropriate.

In Likert scale design (where favourability options >2), there's a strong debate over even or odd choices -- should someone be able to give a "meh" rating, or do you want to force a positive or negative, if even slight.

Hence, 3, 4, 5, 6, and 7 point (typically) scales.

[jonknee]

Even Netflix finally moved over to up/down and they were famous for squeezing every drop out of their previous star based reviews [1]. In theory stars work better, but the issue seems to be everyone has a different ranking system.

For example, Uber seems to think anything but a 5/5 is a failure. I know this so I skew to accommodate, but in my personal ranking system I've only had a couple 5 star rides (someone really going above and beyond).

Up/down with an optional qualifier afterwards (e.g. "why were you unhappy?" after a thumbs down) seems to remove a lot of confusion.

[1] https://en.wikipedia.org/wiki/Netflix_Prize

[yoz-y]

Maybe the problem is with stars and wording. Currently most of systems are worded (e.g. amazon) in a way that 3 stars is the base and people would add stars if their expectations were exceeded and remove them if they were not met. However at all places that I have seen it is like you say 5 stars is for expectations being met and it only goes downhill from that.

I think that a wording and iconography in 4 steps could be useful. -2 = something really bad happened, -1 = below expectations, 0 = happy customer, 1 = exceeded expectations. Forcing people to write a detail on any rating other than 0 would make most ratings 0. Angry people usually like to write comments anyways.

[ghaff]

I'm not sure an expectations-based rating system is the norm though.

To use an example I gave elsewhere. I order a cable from Amazon. It works. Therefore it met my expectations of a working cable. Yet, I think most people would interpret a 3-star rating as my being lukewarm on my purchase. I'm not. But what the heck do I expect a cable to do other than being a fair price and to work?

With respect to movies. Some movies get really built up and I go in expecting great things (e.g. Fury Road). I come out thinking they were just OK. So maybe 3 stars. But definitely not -1 or 2 stars. My personal expectations aren't necessarily a good baseline.

[yoz-y]

For a single data point they are useless. But I thing a rating in aggregate could be informative. Of course, the rating is self defeating as too many people who were positively surprised will raise the expectations.

For movies I would love to have reviews in style: Somebody writes up their expectations before going to the movie, and then rates the movie according to that. I find most "press and critics" reviews useless as if somebody is not a fan of story-less action movies, then why the hell is he reviewing them.

[enobrev]

This is how I see it as well. It would also likely get rid of comments / ratings like "I would give this 0 stars if I could". I think the numbering you mention is just as essential (especially 0).

The way I generally rate things falls in line with this idea:

5: Excellent

4: Pretty Good

3: Average; Unsurprising. Not Impressed, Not Disappointed

2: Kinda Sucks

1: Run Away

This definitely doesn't seem to be how everyone else is using the 5-star scale.

[ghaff]

This was (is? I haven't really used them much in a while) an issue with eBay's reputational system as well. Anything other than a Positive and "A++++++++++++ seller" was interpreted to mean they shipped you a box full of bricks rather than they took a week to ship things.

[dredmorbius]

Normalising a given rater's stars, or asssigning costs to higher / lower ratings, is another option. Essentially you have a "ratings budget" you can spend, up or down, on your assessments.

Stack Exchange has this to an extent, where negative ratings cost the rater points -- you have to really want to assign a negative.

[stinkytaco]

Everyone does have a different ranking system, but Netflix was good at predicting what I would give a movie, so my ranking system was the only one that mattered. Their special sauce in the background made it pretty accurate.

The problem with an up/down for me is that it doesn't capture an ambivalent reaction. That means transactions will tend toward the mode, imo. You will do enough to get a thumbs up, that's all.

[kalleboo]

I often wish it was an up/down/love

[dennisgorelik]

5-star ratings probably carry more information than binary thumbs up/down, but every 5-star vote is more complex to collect, so Netflix was probably getting less 5-star votes than they are getting up/down votes.

Overall number of votes matters too.

[dredmorbius]

Close, but still, IMO, wrong.

The ultimate question is is this going to be useful to me, and the answer to that is ... somewhat complicated.

Informative, timely, accurate, significant (which may be none of the above), funny (may be appropriate or inappropriate, based on context and/or volume).

Some information is often (though not always) better than no information. Bad information is almost always worse.

(Aside: troubleshooting a systems issue yesterday I had the problem of someone trying to offer answers to questions where "I don't know" was far more useful than "I think ...". Unfortunately, I was getting the "I think ..." response, though not phrased as such.)

What you describe, the wirecutter treatment, is the case of an expert opinion. Here there remain issues -- particular of the biased expert. But if I could give a hierarchy of opinions from least to most useful:

-2. Biased. -1. Ignorant. 0. None. 1. Lay. 2. Novice. 3. Experienced. 4. Expert. 5. Authority in field and unbiased.

Note that the problem of judging expertise itself recapitulates much of the same problem.

Qualification and reputation of the raters themselves is a critical element missing from virtually all ratings systems.

[hammock]

Offering a five stars to the rater can cause them to treat it as a thumbs up/thumbs down (as your parent comment alludes to when he references "game theory" and giving a one-star), with one star being the most powerful thumbs down, the obvious choice.

The other alternative is for users to actually SORT and RANK all products in that category that they have reviewed. Not a tenable solution.

Side comment, the Yelp histograms ARE useful... but that is more of a side effect/emergent from a bad rating scheme than anything else. Because people are using the stars not ideally, the histogram gives you insight into that. So it's not a bad solution, but a better solution would be something other than the stars.

[ehhnetfliz]

Ehh, netflix switched to that. It's even less useful now: there's no way to indicate you really like a show vs it's not terrible; this means your taste approximately correlates with abailable content, not content you prefer.

The real win would be empowering the user to choose their own rating style. I don't see this happening because it's much harder to push content at users this way.

[mrexroad]

To be fair, Netflix is less interested in that you liked the thing itself, and more interested in the attributes of the films you liked or watched to the end. Note, this is based on an article from 2014 [1], but a good read nonetheless.

[1] https://www.theatlantic.com/technology/archive/2014/01/how-n...

[ehhnetfliz]

Right, but they don't ask me what I like about it. Judging by their recommendations they certainly aren't discerning it well.

Netflix is interesting because their content is pretty bad (compared to say IMDB they have very few movies); recommendations are incredibly useful to pretend its library is much larger than it actually is.

I wonder why they didn't go the amazon route of actual reviews + semantic analysis.

Mostly I just hate cross referencing netflix with reviews to figure out if it's worth my time.

[zolloie]

That's the funny thing to me, that people are using Netflix as an example. To me, Netflix ratings are just about the most useless ratings of all the ratings I'm aware of, maybe even more so than Amazon's ratings.

There's also things to consider, like time, that becomes relevant. Dichotomous ratings are known to be inferior statistically speaking, but they are faster, so there's a convenience angle. Tradeoffs.

These discussions always get frustrating to me because there's so much armchair ad hoc stuff that goes on when there's a huge scientific literature on this already.

People also don't seem to be aware of the assumptions they're making. About ratings being skewed, for example: for a lot of products, people probably do kind of want to know basically "is this meeting my needs?" and then everything is just a decrement away from that. Laundry detergent, for example, is something where I want it to clean my clothes well without damaging them. Why should that be normally distributed?

Also, there's a difference between ratings and how they're used. My guess is that 1-3 star rating variance is meaningful from an experiential point of view, but not from a purchasing point of view. That is, if you had the choice of a 3-star product or a 1-star product, I think people would prefer the 3-star product. When we say "1-3 stars don't matter" we don't actually mean that, we mean that they don't matter because it's below our threshold of what we'd be willing to spend money on.

[ghaff]

Wirecutter is particularly good for a lot of reasons. I'd actually argue that the "Wirecutter treatment" is mostly less applicable to Wirecutter than other areas given that many of the items they review are relatively pricey and sophisticated.

But, to your overall point, there are a lot of things that I just want a hopefully mostly unbiased expert to tell me what to buy and I'm just fine with that. When I buy a garden hose nozzle, I'm just fine with whatever one of Wirecutter's sister sites tells me to buy. I don't need or want to do a lot of research into the finer points of garden hose nozzle design.

[makecheck]

Up/down is sufficient to capture the vote itself but there is more data in there to consider, like:

1. How long did it take for the person to vote in the first place? (Might change weight; if we're talking cars, nobody really knows if they "like" it 4 minutes after purchasing and it means something different if the rating appears 3 months later.)

2. Has the vote changed between up/down? Has this happened twice?

3. Has the person voted for other things in similar categories? Might make sense for phones, over a period of years. Doesn't make sense for a person to buy and rate 20 different chairs in a week. Use it to give credibility.

[zolloie]

I do research in this area and have many reactions to a lot of topics being brought up. I read this piece when it first was written and didn't think to look at the posting on HN until now.

The problem with dichotomous ratings (binary, thumbs up-down) is that they lose a lot of meaningful information without eliminating the problems you're referencing.

That is, the same problems apply to dichotomous ratings, in that people still have tendencies to use the rating scale differently. Some tend to give thumbs up a lot, others down, and people interpret what's good or bad differently. People who are ambivalent split the difference differently.

On top of that, you lose the valid variance in moderate ranges, and actually amplify a lot of these differences in use of the response scale, by forcing dichotomous decisions, because now you've elevated these response style differences to the same level of the "meaningful part" of the response. E.g., maybe one person tends to rate things more negatively than another person, rating 4 and 5 respectively. But when you dichotomize, now that becomes 1 and 2.

The question is whether or not, on balance, the variance associated with irrelevant response scale use is greater than the meaningful variance, and generally speaking studies show the meaningful variance is bigger. In general, you see a small but significant improvement in rating quality going from 2 to 3, and from 3 to 4, and then you get diminishing returns after 4-6 options.

Also, people really don't like being forced to take ambivalence and choose up or down, so in the very least having a middle option is better (unless you want to lose ratings).

It's fairly straightforward to adjust for rating style differences if you have a bunch of ratings of an individual on a bunch of things whose rating properties are fairly well-known. Amazon could do this if they wanted to, and Rotten Tomatoes I think might do something like this already.

RT, in fact, is kind of a bad example, because their situation is so different from typical product ratings, in that you have a small sample of experts who are rating a lot of things. They also are aggregating things that themselves are not standardized-- their use of the tomatometer in part stems from them having to aggregate a wild variety of things, as if everyone on Amazon used a different rating scale, or no rating scale at all. Note too that there's then a "filtering" process involved by RT. Finally I also feel obliged to note they do have ratings and not just the tomatometer, which I've started paying attention to after realizing that things like Citizen Kane show up as having the same tomatometer score as Get Out--a fine movie but not the same.

The game theory angle is interesting to think about. It's something I don't deal with usually because in the situation I'm used to, the raters don't have access to other rater's ratings. That's one solution, but impractical. A sort of meta-rating is one solution--a lot like Amazon's "helpfulness" ratings. It's imperfect but probably does well in adjusting for game theory-type phenomena, like retaliatory rating, etc.