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Unusual ways to teach economics. I’m currently playing Kitty Powers’ Matchmaker, a silly but fun little game in which you run a dating agency and try to get your clients on successful dates and, eventually, into a successful relationship.

Now one way of playing this would be to just prioritize the benefit of each client, trying to get them in maximally satisfying relationships as fast as possible. But while I sometimes do that, often I do things differently.

In one case, I had a client who’d been on two bad dates already, and was threatening to march out and give my company a bad reputation if she’d have one more bad date. I didn’t have any good matches lined up for her. I could have just kicked her out, but that wouldn’t have given my company any money. So instead I put her on a date with someone who seemed incompatible, but just had her lie about all the incompatibilities and say what the other person wanted to hear. That way, they’d end up together, and I’d get my money and be rid of the troublesome client. Of course I knew that they’d break up later and that would hurt my reputation a bit, but I figured that it would still be better for the company than kicking her out now.

(In my defense, I have only done this once, and I felt kinda bad about it.)

This situation is known in economics as the principal-agent problem: a situation where someone (the “principal”) hires someone else (the “agent”) to do something on the principal’s behalf, but the self-interests of the principal and the agent differ. So for example, you may try to get a real estate agent to sell your house and give them a cut of the profit. It would be in your interest if the agent sold it for as high a price as possible, but the agent may actually benefit more if they spend less time on each individual sale and instead sell a lot of houses more cheaply, but in a shorter time. This was confirmed in a study in which it was found that real estate agents tended to sell other people’s houses considerably faster and cheaper than they sold their own houses.

Or, you might go to a matchmaking agency to get into the relationship of your dreams, but your matchmaker also has an interest in getting your money and benefiting the company.

Here’s another thing that I do in the game that some might consider questionable. When a client comes in, they will tell me their personality traits, e.g. introvert vs. extrovert. It’s best to pair them off with someone who has the same personality traits. But when the game shows me a list of people I can try to match my client with, by default I don’t know the personality traits of those people. Instead, I have to have some client date those people and discover their personality traits, and then I too will learn them.

Now suppose that a new client comes in, and I know of someone I could have them date who’d be perfectly compatible. I also have a bunch of other possibilities, whose personality traits I don’t know. Do I send my client on the best possible date right away? Of course not! Instead, I’ll send them on a few dates with the unknowns, so that I can discover the personality traits of the unknowns, and only after a few bad dates will I pair my client with the best match. This way, I’ll know the personality traits of as many people as possible, and will always be able to know of a compatible match for my next client.

Is this ethical? You could argue either way. Yes: I’m still sending my client to a good relationship eventually, and although it might give my client a few bad dates in the beginning, that helps other clients eventually get a good date. No: I have an obligation to prioritize the interest of my current client at all times, and it’s not in their interest to have a bad time. The first argument has a bit of a consequentialist vibe, and the second one has a bit of a deontologist vibe. If you were teaching an introductory ethics course and wanted to give your students a different example than the usual ones, maybe you could have them play the game and then ask them this question.

Comedy dating sims: useful for teaching both economics and ethics.

Originally published at Kaj Sotala. You can comment here or there.

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I feel like my progress with my academy game has been frustratingly slow. Lots of natural language, little code. Over Christmas leave I finally put together a simple Bayes net and belief propagation implementation, but when I was about to move on to start actually implementing the game side of things, I realized that some of the things that I had planned didn’t quite work after all. Then I got stuck for a while, trying to decide what would work in the first place. But then I fortunately started having new ideas, and finally came up with a core gameplay mechanic that would seem promising.

For example, suppose that the player wants to persuade an NPC to dance with her. Below is a representation of what the player character knows of the situation, somewhat similar to how it might be shown in the game.

Some of you will recognize this as resembling an influence diagram (but note that it doesn’t follow all the conventions of one, so if you do know influence diagrams, don’t try to read it literally as one). The rectangles, which would ordinarily be called decision nodes, are two possible dialogue options that the player has. She could try to take a flattering approach and say “I heard you’re a good dancer” in an attempt to appeal to the other’s vanity, or she could rather try to focus on his kindness and nurturing instinct by admitting that she herself has never danced before.

The ovals – uncertainty nodes - represent things that influence the way that the NPC will react to the different dialogue options. A character who’s confident of their dancing skills is going to react to flattery differently than someone who’s insecure about his skills.  The ovals form a standard Bayesian network (or several of them), meaning that the player can have differing amounts of knowledge about the different uncertainty nodes. Maybe she knows that the NPC has high self-esteem, and that this makes it more likely that he’s confident about his dancing skills, but she doesn’t know how good he actually is at dancing, which also influences his confidence about his skills.

Finally, the diamonds – usually called value nodes, but I call these reaction nodes - represent the different ways that the NPC might react to the various dialogue options. Depending on the values of the uncertainty nodes, flattery may please him, intimidate him, or make him react in an indifferent manner. In order to get the NPC to dance with her, the player has to elicit some number of positive reactions from the NPC, and avoid causing any bad reactions. A simple counter sums positive reactions minus negative reactions that have been caused while discussing the topic of dance: if it exceeds some threshold, he agrees, and if it falls below another threshold, he’ll refuse to even consider the thought anymore. The thresholds are partially determined by how big of a favor the player is asking for, partially by how much the NPC likes/trusts the player character.

Suppose the player doesn’t know much about this NPC, but she also doesn’t want to risk things by just saying things that might or might not upset him. Now, in addition to picking one of the dialogue options, she can also experiment with the network to see which variables would influence his decision the most. Suppose that she is considering the “appeal to kindness” route, and has determined that he really needs to be kind in order for this strategy to work. Now she can select the kindness node, and bring up a different set of dialogue options which are aimed at revealing how kind he is. But here too she needs to be careful, because another counter also tracks the NPC’s overall interest in the conversation. If she gets too many indifferent or negative reactions while talking about his kindness, he may just end the conversation and wander off, before she has ever had a chance to get back to the topic of dance. So there’s also an element of risk there.

Alternatively, if the player doesn’t think that getting to dance is that important after all, she can just try her luck with one of the original dialogue options. Maybe those will reveal something about the NPC that’s useful for something more important. After the conversation is over, the NPC’s basic liking for the player character may be adjusted depending on the total positive and negative reactions that she got out of him.

It’s also easy to add additional different complications or advanced options: e.g. an option to pressure the NPC into something, which if successful contributes points for the goal counter, but reduces their liking for you afterwards.

So that’s the basic mechanic. Notice that this basic structure is a very flexible one: instead of talking to an NPC, the player character could have broken into his room in order to look for her stolen necklace, for instance. In that case the decision nodes could represent different places where the necklace might be, the chance nodes might offer chances to look for clues of where he might have hidden the necklace, and the “hit point” counters might represent time before someone shows up, or before the player character loses her nerve and decides to get out. So the same mechanic can be recycled for many different situations.

As for the educational content, the fact that you’ll need to read the Bayes network involved with each variable in order to decide what to do should teach a bunch of useful things, like a basic ability to read the network, evaluate the value of information related to each chance node, and notice and understand d-separation. Not to mention coming to understand how many different situations can all be seen through the same probabilistic framework. And it ought to all relate to the game content in a meaningful manner.

(I also have lots of ideas for other core mechanics. I also should really get the first version of this game done eventually. So I’m making a note of all those other ideas, but for now I’ll focus on milking this core mechanic for all that it’s worth.)

One interesting question is: should the results of the decision nodes be deterministic or random? In other words, suppose the player knows the values of every node that influences e.g. the outcome of the flattery dialogue option. Does that mean that they know what the outcome of choosing that option will be? Or do they only know the odds of each outcome?

I’m inclined with going for the deterministic route: if you know everything that influences the outcome, then you know the final result. I’m not sure that having a random factor in there would be all that realistic, and more importantly, if you’ve spent your time and resources on discovering the value of every parent node of an option, then you deserve to know exactly what outcome your choice will have, rather than getting screwed over by a 10% chance for an unexpected result. And if you don’t know the value of each parent, then there will effectively already be a random factor involved in terms of what you don’t know.

This should hopefully be my last post before I actually have a playable prototype done. Even if a very short one.

Originally published at Kaj Sotala. You can comment here or there.

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So far I have spoken about the possibility of edugames being good, sketched out the basic idea of an edugame built around Bayesian networks, and outlined some design constraints. Now it’s finally the time to get to the actual meat of the matter – the game mechanics.

Note that everything here is subject to change. I’m aiming to outline things to a sufficient level of detail that I have a reasonable clue of how to start implementing the first prototype. Then, when I do have an early prototype together, I’ll keep experimenting with it and throw away anything that doesn’t seem fun or useful. Still, this post should hopefully give some idea of what the final product will be like.

(This post might not be enough for anyone else to start implementing an actual prototype, since there are a lot of missing pieces still. But I think I have a lot of those missing pieces in my head, it just wouldn’t be useful to try to write all of it down.)

To make things concrete enough to start implementing the game, I need to define a concrete goal for (a part of) the game, some of the concrete ways of achieving that goal, as well as the choices involved in achieving the goal. And of course I need to tie all that together with the educational goals.

Goal. Let’s say that, in the first part of the game, you are trying to get yourself voted into the Student Council as the representative of the first-year students. This requires you to first gain the favor of at least three other first-year students, so that you will be nominated to the position. After that, you need to gain the favor of the majority of the most influential students, so that you will actually be the winning candidate.

From that will follow the second part of the game, where you need to also persuade others in the council to support your agenda (whatever your agenda is), either by persuading them or having them replaced with people who are more supportive of you. But for now I will just focus on the first part.

I’m actually starting to think that I should possibly make this into more of a sandbox game, with no set goals and letting you freely choose your own goals. But I’ll go with this for the first prototype.

How to achieve the goals. The game keeps track of your relationship to the different characters. You achieve the nomination if at least three others characters like you enough. To be voted to the council, a majority of the characters have to both like and trust you more than the other candidates.

This seems like a good time to talk more about relationships, which are a rather crucial element of any social drama. Relationships are one of the main types of resource in the game, the rest of which include your public image, personal skills, and time. I’ll cover the rest shortly.

Relationships. Most games that model your relationships with other characters do so by assigning it a single numerical score, with different actions giving bonuses or penalties to the score. So if you give someone a gift you might get +10 to the relationship and if you insult them you might get -20, and your total relationship is the sum of all these factors.

This is a little boring and feels rather game-ish, so I would like to make it feel a little more like you’re actually interacting with real people. To keep things simple, there will still be an overall “relationship meter” (or actually several, if I can make them distinct and interesting enough), but affecting its value shouldn’t feel like just a mechanical process of giving your friends gifts until they love you.

Borrowing from The Ferrett’s three relationship meters, the ones I’m initially considering are like, trust, and infatuation/love.

Like measures the general extent to which someone, well, considers you likeable, even if they don’t necessarily trust you. A relationship that’s high on like but low on trust is the one that you might have with a nice acquaintance with whom you have intellectual conversations online, or that co-worker who’s friendly enough but who you haven’t really done any major projects with or interacted outside work. A high like makes people more inclined to help you out in ways that don’t involve any risk to them.

Things that affect liking include:

  • Having a public image that includes personality traits that other people like. Some traits are almost universally liked or reviled. Other traits are neutral, but other people tend to like people who they feel are similar to them – or, with some traits, dissimilar.
  • Acting in the interests of others, or contrary to them.
  • People having a crush on you.
  • Other people that you’re friends with.
  • Various random events.

Trust measures the extent to which people are willing to rely on you, and the extent to which they think you’re not going to stab them in the back. A high trust may make it easier to repair damage to the other meters, since it makes people more inclined to give you another chance. It also makes people more inclined to help you out in ways that involve a risk to themselves, to vote for you, to confide in you, and to ask you for help.

Things that build up trust include:

  • Telling people information about yourself, indicating that you trust them. If you tell someone a secret you haven’t told anyone else, this will impress them more than if you tell them something that’s common knowledge. (Just don’t jump to revealing all your deepest secrets on the first meeting, or you’ll come across as a weirdo.)
  • Making and keeping promises.
  • Acting in the interests of others, or contrary to them.
  • People having a crush on you.
  • Other people that you’re friends with.
  • Various random events.

Different characters have different kinds of ideals that they are attracted to. If your public image happens to match their ideal, they may develop a crush on you and build up infatuation. The infatuation will grow in strength over time, assuming that your public image continues to match their ideal. If you then also build up their like and trust, the infatuation may turn into love.

The fact that infatuation also increases their like and trust towards you makes it easier to convert their feelings to love, but the feelings may also come crashing down very quickly if you demonstrate untrustworthiness. An infatuated character is more likely to ignore minor flaws, but anything that produces a major negative modifier to any relationship meter may cause them to become completely disillusioned and start wondering what they ever saw in you in the first place. (Love is more stable, and makes it easier to take advantage of your lovers without them leaving you. But you’d never stoop that low, would you?)

A sufficiently high love makes people willing to do almost anything for you.

Besides the things that were already mentioned, love may be affected by:

  • The amount of trust and like that the person has towards you.
  • You committing yourself to a romantic relationship with them.
  • Whether you have any other lovers (some characters are fine with sharing you, others are not).
  • You choosing to genuinely fall in love with them as well – they’ll sense this and receive a considerable boost to their love meter, but you will also end up permanently prevented from ever taking certain negative actions towards them.
  • Various random events.

Every modifier to any of the relationship meters is associated with some source, which you may try to influence. For example, suppose that you promise your friend to do something by a certain time, and then fail to do so. This will produce a negative modifier to their trust rating. You can then try to talk to them and apologize, and possibly offer some compensation for the misdeed. If you play your cards right, you may be able to erase the penalty, or even turn it into a bonus.

Your public image. I have already mentioned your public image a few times, when I mentioned that your perceived personality traits influence the extent to which others like you, as well as the chance of someone developing a crush on you.

Basically, there’s a set of different personality traits that any character may or may not have. Some, like being kind or being cruel, are mutually exclusive. In the beginning, you don’t know anyone’s personality traits, nor does anyone know yours. If you act kindly, you will develop an image as a kind person, and if you act in a cruel way you’ll develop a reputation as a cruel person. And of course, not everything depends directly on your actions – your rivals may try to spread negative rumors about you.

I haven’t yet determined how exactly knowledge about your actions spreads. I don’t want all of the player’s actions to magically become common knowledge the moment the action is made, but neither do I want to keep track of every separate piece of information. And I do want to offer the player the option to try to keep some of their doings secret. My current compromise would be to keep track of who knows what for as long as the amount of people who knew a particular piece of information remained under a certain number. So if the limit was 6, then the game would keep track of any piece of information that was known to at most six people: once the seventh person found out, it would be assumed to have become common knowledge.

People who like you are less inclined to pass on negative rumors about you, as well as more inclined to pass on positive ones. You can also try to spread negative rumors about your rivals, yourself – but this risks developing a reputation as a lying gossip.

Personal skills. The best way of acting in any particular situation depends on what you know of the people involved. If you cultivate a certain kind of image, who will end up liking you more as a result of it, and who will end up liking you less? Is the concession you are offering to your offended friend sufficient to make them forgive you? How much should you trust your new lover or friend? Who might be spreading those nasty rumors about you?

There are several ways by which you could find this out. First, empathy skills can be learned by study: if you have a high relevant empathy skill, you can make a good guess of what someone is like, or how they might react in some situation, just based on your skill. Learning the skills takes considerable time that could be spent on other things, however.

Also, many personality traits correlate with each other, and various actions correlate with different personality traits. It’s almost as if their connections formed a… wait for it… Bayesian network! But in the beginning of the game, you only have a very rough idea of what the structure of the network is like, or what the relevant conditional probabilities are. So you have to figure this out yourself.

Ideally – and I’m not yet sure of how well I’ll get this to work – the game will give you a tool which you can use to build up your own model of what the underlying network structure might be like: a Bayesian network that you can try to play around with. In the beginning, nearly all of the nodes in the model will be unconnected with each other, though some of the most obvious nodes start out connected. For instance, you’ll know that cruel people are more likely to insult others than kind people are, though your estimate of the exact conditional probability is likely to be off.

If you suspect that two nodes in the underlying graph might be linked, you can try joining them together in your model and test how well your model now fits your observations. You can adjust both the conditional probability tables and linkages in order to create a model that most closely represents what you have seen, with the game automatically offering suggestions for the probabilities based on your experiences so far. (Just be careful to avoid overfitting.)

In addition, you can study various knowledge skills. Knowing psychology, for instance, may reveal some of the links in the network and their associated probabilities to you with certainty, which will automatically update your model.

Time. Everything you do takes time, and you can only be in a single place at once. Your lovers and friends will expect you to hang out with them regularly, studying skills takes time, and your plans may be completely interrupted by the friend of yours who has a mental breakdown and needs you there to comfort them RIGHT NOW. (But if you have a sufficiently good reason why you can’t make it, maybe they’ll understand. Perhaps.)

What choices do you need to make? The above discussion should have suggested some choices that you might run across in the game, such as:

  • Who do you make friends with?
  • Do you try to build a small group of strong friendships, or a large group of weak friendships?
  • Who do you trust with information that could hurt you?
  • How much time do you spend on learning the various skills?
  • How much time do you spend on chasing down the source of various rumors?
  • Do you want to spread any nasty rumors yourself?
  • Do you get romantically involved with someone?
  • If so, who will it be? One lover or many?
  • Do you risk doing things that would damage your reputation if people found out?
  • Do you generally side with the people who are your closest allies, or the ones who have actually been wronged?

And others. As you might notice, the choice you’ll want to make in most of these depends on what you’ve figured out about others… which should help us fulfill our goal of making the player genuinely interested in how to figure these things out, via the formalisms that the game employs and attempts to teach.

Free feel to suggest more the comments!

Originally published at Kaj Sotala. You can comment here or there.

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My work on my Master’s thesis and the Bayesian academy game was temporarily interrupted when I had to focus on finishing the work I had piled up for another course. Now I’m slowly trying to get back into the flow, so here’s a post on some of the things that I’ll be trying to keep in mind while creating the game, and which should help shape its design. This post is still somewhat abstract: more concrete material should hopefully follow in the next post.

I’ve also started on the actual programming, putting together what should hopefully be a flexible Bayes net framework. (I looked at some existing libraries, but it seemed easier to just put together my own implementation.) Mostly model level stuff so far, though, so not much to show yet. I’ll probably put a copy up on Github or somewhere eventually, once it stops looking terribly embarrassing.

Constraints

“Design is the successive application of constraints until only a unique product is left.” — Donald Norman, The Design of Everyday Things

Having some constraints is nice. They help narrow down the space of possible designs and give an idea for what you need to do. So let’s establish some constraints for our game design.

Has to teach useful things about Bayesian networks / probabilistic reasoning. Kinda obvious, but still worth stating explicitly. At the same time, the game has to be fun even if you had no intrinsic interest in Bayesian networks. Balancing these two gets tough, since you can attract gamers by various story elements and interesting game mechanics, but then these elements might easily become ones that do nothing to teach the actual subject matter. My general approach for solving this is to build the mechanics so that they are all tied to various pieces of hidden information, with Bayes nets being your tool for uncovering that hidden information. More on that later.

Every choice should be interesting. Even if you manage to figure out exactly who knows what, that shouldn’t dictate the right option to pick, just as in XCOM, correctly figuring out the probability of killing your opponent given a certain tactic isn’t enough to dictate the choice of the best tactic. Rather, correctly using the skills that the game tries to teach you should be something that better informs you of the benefits and drawbacks of the different choices you make. If there’s only one obvious option to pick, that’s not interesting.

Of course, eventually somebody will figure out some optimal strategy for playing the game which dictates an ideal decision for various situations, but that’s fine. If we design the game right, figuring out the perfect strategy should take a while.

Must not be ruined by “save-scumming”. In my previous article, I gave an example of a choice involving Bayesian networks: you are given several options for how to act, with the best option depending on exactly which character knew what. Now, what does your average video game player do when they need to make a choice based on incomplete information, and they find out the truth state of affairs soon afterwards? They reload an earlier save and choose differently, that’s what.

Constantly reloading an earlier save in order to pick a better decision isn’t much fun, and really ruins the point of the whole game. But if that’s the optimal way to play, people will feel the temptation to do it, even if they know that it will ruin their fun. I would like to give people the freedom to play the game the way they like the most, but I’m worried that in this case, too much freedom would make the experience unfun.

Other games that rely on randomness try to combat save-scumming by saving the random seed, so reloading an earlier save doesn’t change the outcome of any die rolls. We could try doing the opposite: re-randomizing any hidden states once the player reloads the game. But this could turn out to be tricky. After all, if we have a large network of characters whose states we keep track of and who influence each other, setting up the game in such a way that their states can be re-randomized at will seems rather challenging. So I’m inclined to go for the roguelike approach, with only a single, constantly updating save slot per game, with no ability to go back to earlier saves. That gives us a new constraint:

Each individual game should be short. A constantly updating save means that if you lose the game, you have to start all over. This is fine with a game like Faster Than Light, where a single pass through the game only takes a couple of hours. It would be less fine in an huge epic game that took 50 hours to beat. Based on my own gut feel, I’m guessing that FTL’s couple of hours is quite close to the sweet spot – long enough that you get depth to a single game, short enough that your reaction to failure will be to start a new game rather than to quit the whole thing in disgust. So I will be aiming for a game that can be finished in, say, three hours.

This constraint is probably also a good one since my natural inclination would be to make a huge epic sprawling game. Better to go for something easier to handle at first – a limited-duration game is also easier to extensively playtest and debug. I can always expand this after finishing my thesis, making the game so far the first chapter or whatever.

One could also allow reloading, but restricting where you are allowed to save. Recently I have been playing Desktop Dungeons, where your kingdom grows as you go out on quests that are about 10 minutes long each. You can’t save your progress while on a quest, but you can save it between quests, and the temptation to try just one more quest makes for an addictive experience in the same way that FTL’s short length also makes for an addictive experience. But I’m not sure of whether my current design allows for any natural “units” that could be made into no-save regions in the same way as the quests in Desktop Dungeons can.

Another consequence of having constant saves is that

Failures should be interesting.

Always make failure entertaining. In fact, failure should be more entertaining than success. Success is its own reward. If you succeed at solving a puzzle, you feel good, you barely need any confirmation from the game. Just a simple ding will be satisfying. But if you’re struggling, if the game sort of is in on the joke with you and laughs about it, and gives you a funny animation, it’s actually sort of saying, yeah we want — it’s okay to be here. You can have fun while you’re here. You’ll get there eventually — you’ll get to the other end eventually, but while you’re here you can enjoy yourself; you can relax. And that’s really something I learned sort of from doing the game, but that’s really become an ongoing principal of ours in design is to make the failure — keep failure interesting.Scot Osterwald

Even a game being short doesn’t help if a couple of bad decisions mean that you’re stuck and would be better off restarting rather than playing to the end. FTL solves this by quickly killing you off when you’re starting to do badly: this is interesting, since you can almost always trace your failure back to some specific decision you made, and can restart with the intention not to make that mistake again. I’m not sure how well this would work in this game, though I have been thinking about splitting it into a number of substages, each with a limit on the number of actions that you are allowed to do. (First stage: become elected as the Student Council representative of the first year students by winning them over before the elections. Etc.) Failing to achieve the objective before the time limit would lead to a game over, thus killing you quickly once you started doing too badly to recover. Possibly each stage could also be made into a single no-save unit, allowing saves between them.

Another option would be to take the Princess Maker approach: in these games, you are trying to raise your daughter to become a princess, but even if you fail in that goal, there are a variety of other interesting endings based on the choices that you made in the game. A third option would be to ensure that even a sub-optimal choice opens some new paths through the game – but it could be difficult to ensure that you never ended up in a hopelessly unwinnable state.

Still not entirely sure of the exact form of this constraint and the previous one: will just have to try things out and see what seems to be the most fun.

The next update should either be about some concrete game mechanics (finally!) and the ways that relationships are handled in this game, or about the way that the information is represented to the player.

Originally published at Kaj Sotala. You can comment here or there.

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In my previous article, I argued that educational games could be good if they implemented their educational content in a meaningful way. This means making the player actually use the educational material to predict the possible consequences of different choices within the game, in such a manner that the choices will have both short- and long-term consequences. More specifically, I talked about my MSc thesis project, which would attempt to use these ideas to construct a learning game about Bayesian networks.

However, I said little about how exactly one would do this, or how I was intending to do it. This article will take a preliminary stab at answering that question – though of course, game design on paper only goes as far, and I will soon have to stop theorizing and start implementing prototypes to see if they’re any fun. So the final design might be something completely unlike what I’m outlining here. But advance planning should be valuable nonetheless, and perhaps this will allow others to chime in and offer suggestions or criticism.

So, let’s get started. In order to figure out the best way of meaningfully integrating something into a game, we need to know what it is and what we can do with it. What are Bayesian networks?

Bayesian networks in a nutshell

Formally, a Bayesian network a directed acyclic graph describing a joint probability distribution function over n random variables. But that definition isn’t very helpful in answering our question, so let’s try again with less jargon: a Bayesian network is a way for reasoning about situations where you would like to know a thing X, which you can’t observe directly, but you can instead observe a thing Y, which is somehow connected to X.

For example, suppose that you want to know whether or not Alice is a good person, and you believe that being a good person means caring about others. You can’t read her thoughts, so you can’t directly determine whether or not she cares about others. But you can observe the way she talks about other people, and the way that she acts towards them, and whether she keeps up with those behaviors even when it’s inconvenient for her. Combining that information will give you a pretty good estimate of whether or not she does genuinely care about others.

A Bayesian network expresses this intuitive idea in terms of probabilities: if Alice does care about people, then there’s some probability that she will exhibit these behaviors, and some probability that she does not. Likewise, if she doesn’t care about them, there’s still some other probability that she will exhibit these behaviors – she might be selfish, but still want to appear as caring, so that others would like her more. If you have an idea of what these different probabilities are like, then you can observe her actions and ask the reverse question: given these actions, does Alice care about other people (or, what is the probability of her caring)?

Towards gamifying Bayesian networks

Now how does one turn this into a game?

Possibly the simplest possible game that we can use as an example is that of Rock-Paper-Scissors. You’re playing somebody you don’t know, it’s the second round, and on the first round you both played Rock. From this fact, you need to guess what he intends to play next, and use that information to pick a move that will beat him. The observable behavior is the last round’s move, and the thing that you’re trying to predict is your opponent’s next move. (The pedants out there will correctly point out that the actual network you’d want to build for predicting RPS moves would look quite a bit different and more complicated than this one, but let’s disregard that for now.) Similarly, many games involve trying to guess what your opponent is likely to do next – based on the state of the game, your previous actions, and what you know of your opponent – and then choosing a move that most effectively counters theirs. This is particularly obvious with games such as Poker or Diplomacy.

In the previous article, we had a very simple probabilistic network involving Alice, Bob, and Charlie. From the fact that Charlie knew something, we concluded something about the probability of either Alice or Bob knowing something about it; and from the added fact that Bob knew something about it, we could refine our probability of Alice knowing something about it.

Suppose that the piece of knowledge in question was some dark secret of yours that you didn’t want anyone else to know. By revealing that secret, anyone could hurt you.

Now let’s give you some options for dealing with the situation. First, you could preemptively reveal your dark secret to people. That would hurt most people’s opinion about you, but you could put the best possible spin on it, so you wouldn’t be as badly hurt as you would if somebody else revealed it.

Second, you could try to obtain blackmail material on the people who you thought that knew, in order to stop them from revealing your secret. But obtaining that material could be risky, might make them resent you in case they had never intended to reveal the secret in the first place, or encourage them to dig up your secret if they didn’t actually know about it already. Or if you didn’t figure out everyone who knew, you might end up blackmailing only some of them, leaving you helpless against the ones you missed.

Third, you might elect to just ignore the whole situation, and hope that nobody who found out had a grudge towards you. This wouldn’t have the costs involved with the previous two alternatives, but you would risk becoming the target of blackmail or of your secret being revealed. Furthermore, from now on you would have to be extra careful about not annoying the people who you thought knew.

Fourth, you could try to improve your relationship with the people you thought knew, in the hopes that this would improve the odds of them remaining quiet. This could be a wasted effort if they were already friendly with you or hadn’t actually heard about the secret, but on the other hand, their friendship could still be useful in some other situation. Those possible future benefits might cause you to pick this option even if you weren’t entirely sure it was necessary.

Now to choose between those options. To predict the consequences of your choice, and thus the best choice, you would want to know 1) who exactly knew about your secret and 2) what they currently thought of you. As for 1, the example in our previous article was about just that – figuring out the probability of somebody knowing your secret, given some information about who else knew. As for 2, you can’t directly observe someone’s opinion of you, but you can observe the kinds of people they seem to hang out with, the way they act towards you, and so on… making this, too, a perfect example of something that you could use probabilistic reasoning to figure out.

You may also notice that your choices here have both short- and long-term consequences. Choosing to ignore the situation means that you’ll wish to be extra careful about pissing off the people who you think you know, for example. That buys you the option of focusing on something more urgent now, at the cost of narrowing your options later on. One could also imagine different overall strategies: you could try to always be as honest and open as possible about all your secrets, so that nobody could ever blackmail you. Or you could try to obtain blackmail material on everyone and keep everybody terrified of ever pissing you off, and so on. This is starting to sound like a game! (Also like your stereotypical high school, which is nice, since that was our chosen setting.)

Still, so far we have only described an isolated choice, and some consequences that might follow from it. That’s still a long way from having specified a game. After all, we haven’t answered questions like: what exactly are the player’s goals? What are the things that they could be doing, besides blackmailing or befriending these people? The player clearly wants other people to like them – so what goal does it serve to be liked? What can they do with that?

We still need to specify the “big picture” of the game in more detail. More about that in the next article.

Interlude: figuring out the learning objectives

Ideally, the designer of an edugame should have some specific learning objectives in mind, build a game around those, and then use their knowledge about the learning objectives to come up with tests that measure whether the students have learned those things. I, too, would ideally state some such learning goals and then use them to guide the way I designed the “big picture” of the game.

Now my plans for this game have one problem (if you can call it that): the more I think of it, the more it seems like many natural ways of structuring the overall game would teach various ways of applying the basic concepts of the math in question and seeing its implications. For example, it could teach the player the importance of considering various alternative interpretations about an event, instead of jumping to the most obvious-seeming conclusion, or it might effectively demonstrate the way that “echo chambers” of similar-minded people who mostly only talk with each other are likely to reach distorted conclusions. And while those are obviously valuable lessons, they are not necessarily very compatible with my initial plan of “take some existing exam on Bayes networks and figure out whether the players have learned anything by having them do the exam”. The lessons that I described primarily teach critical thinking skills that take advantage of math skills, rather than primarily teaching math skills. And critical thinking skills are notoriously hard to measure.

Also, a game which teaches those lessons does not necessarily need to teach very many different concepts relating to Bayes nets – rather, it might give a thorough understanding of a small number of basic concepts and a few somewhat more advanced ones. A typical math course, in contrast, attempts to cover a much larger set of concepts.

Of course, this isn’t necessarily a bad thing either – a firm grounding in the basic concepts may get people interested in learning the more advanced ones on their own. For example, David Shaffer’s Escher’s World was a workshop in which players became computer-aided designers working with various geometric shapes. While the game did not directly teach much that might come up on a math test, it allowed the students to see why various geometric concepts might be interesting and useful to know. As a result, the students’ grades improved both in their mathematics and art classes, as the game had made the content of those classes meaningful in a new way. The content of math classes no longer felt like just abstract nonsense, but rather something whose relevance to interesting concepts felt obvious.

Again, our previous article mentioned that choices in a game become meaningful if they have both short- and long-term consequences. Ideally, we would want to make the actions of the players meaningful even beyond the game – show them that the mathematics of probabilistic reasoning are interesting because it’s very much the kind of reasoning that we use all the time, in our daily lives.

So I will leave the exact specification of the learning objectives until later – but I do know that one important design objective will be to make the game enjoyable and compelling enough that people will be motivated to play it voluntarily, even on their spare time. And so that, ideally, they would find in it meaning that went beyond it being just a game.

For the next article, I’ll try to come up with a big-picture description of the game that seems fun – and if you people think that it does, I’ll finally get to work on the prototyping and see how much of that I can actually implement in an enjoyable way.

Next post in series: Bayesian academy game – constraints.

Originally published at Kaj Sotala. You can comment here or there.

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As a part of my Master’s thesis in Computer Science, I am designing a game which seeks to teach its players a subfield of math known as Bayesian networks, hopefully in a fun and enjoyable way. This post explains some of the basic design and educational philosophy behind the game, and will hopefully also convince you that educational games don’t have to suck.

I will start by discussing a simple-but-rather-abstract math problem and look at some ways by which people have tried to make math problems more interesting. Then I will consider some of the reasons why the most-commonly used ways of making them interesting are failures, look at the things that make the problems in entertainment games interesting and the problems in most edutainment games uninteresting, and finally talk about how to actually make a good educational game. I’ll also talk a bit about how I’ll try to make the math concerning Bayesian networks relevant and interesting in my game, while a later post will elaborate more on the design of the game.

So as an example of the kinds of things that I’d like my game to teach, here’s an early graph from the Coursera course on Probabilistic Graphical Models. For somewhat mathy people, it doesn’t represent anything complicated: there’s a deterministic OR gate Y that takes as input two binary random variables, X1 and X2. For non-mathy people, that sentence was probably just some incomprehensible gibberish. (If you’re one of those people, don’t worry, just keep reading.)

I’m not going to go through the whole example here, but the idea is to explain why observing the state of X1 might sometimes give you information about X2. (If the following makes your eyes glaze over, again don’t worry – you can just skip ahead to the next paragraph.) Briefly, if you know that Y is true, then either X1 or X2 or both must be true, and in two out of three of those possible cases, X2 is true. But if you find out that X1 is true, then that eliminates the case where X1 was false and X2 was true, so the probability of X2 being true goes down to .5 from .66. In the course, the explanation of this simple case is then used to build up an understanding of more complicated probabilistic networks and how observing one variable may give you information about other variables.

For mathy types the full explanation is probably relatively easy to follow, at least if you put in a little bit of thought. But for someone who is unfamiliar with math – or worse, scared of it -, it might not be. So the question is, how to convert that explanation to a form that is somewhat easier to understand?

The traditional school math approach would be to convert the abstract explanation into a concrete “real-life” case. Let’s say that the variables are people. X1 becomes Alice, X2 becomes Bob, and Y becomes Charlie. A variable being true means that the person is question has heard about some piece of information – say, that Lord Deathfist the Terrible is on a rampage again. If one takes the lines to mean “Alice tells Charlie stuff and Bob tells Charlie stuff (but Alice and Bob don’t talk with each other)”, the “OR gate” thing becomes relatively easy to understand. It means simply that Charlie knows about the rampage if either Alice or Bob, or both, know about it and have told Charlie.

Now we could try to explain it in common-sense terms like this: “Suppose that Charlie knows about Lord Deathfist. That means that either Alice, or Bob, or both, know about it, and have told him. Now out of those three possibilities, Alice knows about it in two possible cases (the one where Alice knows, and the one where Alice and Bob both know) and there’s one possible case where she does not know (the scenario where only Bob knows), so the chance of Alice knowing this is ⅔. But if we are also told that Bob knows it, that only allows for the possibilities where 1) Bob knows and 2) both Alice and Bob know, so that’s 1 possibility out of 2 for Alice knowing it, so the chance of Alice knowing goes down to ½ where it used to be ⅔.”

This is… slightly better. Maybe. We still have several problems. For one, it’s still easy to lose track of what exactly the possible scenarios are, though we might be able to solve that particular problem by adding animated illustrations and stuff.

But still, the explanation takes some effort to follow, and you still need to be motivated to do so. And if we merely dress up this abstract math problem with some imaginary context, that still doesn’t make it particularly interesting. Who the heck are these people, and why should anyone care about what they know? If we are not already familiar with them, “Alice” and “Bob” aren’t much better than X1 or X2 – they are still equally meaningless.

We could try to fix that by picking names we were already familiar with – like Y was Luke Skywalker, and X1 and X2 were Han Solo and Princess Leia, and Luke would know about the Empire’s new secret plan if either Han or Leia had also found out about it, and we wanted to know the chance of all of them already knowing this important piece of information.

But we’d still be quite aware of the fact that the whole Star Wars gimmick was just coating for something we weren’t really interested in. Not to mention that the whole problem is more than a little artificial – if Leia tells Luke, why wouldn’t Luke just tell Han? And even if we understood the explanation, we couldn’t do anything interesting with it. Like, knowing the logic wouldn’t allow us to blow up the Death Star, or anything.

So some games try to provide that kind of significance for the task: work through an arithmetic problem, and you get to see the Death Star blown up as a reward. But while this might make it somewhat more motivating to play, we’d rather play an action game where we could spend all of our time shooting at the Death Star and not waste any time doing arithmetic problems. Additionally, the action game would also allow us to shoot at other things, like TIE Fighters, and that would be more fun.

Another way of putting this would be that we don’t actually find the math task itself meaningful. It’s artificial and disconnected from the things that we are actually interested in.

Let’s take a moment to contrast this to the way that one uses math in commercial entertainment games. If I’m playing XCOM: Enemy Unknown, for instance, I might see that my enemy has five hit points, while my grenade does three points of damage. Calculating the difference, I see that throwing the grenade would leave my enemy with two hit points left, enough to shoot back on his turn. Fortunately I have another guy nearby, and he hasn’t used his grenade either – but I also know that there are at least six more enemies left on the battlefield. Do I really want to use both of my remaining grenades, just to take out one enemy? Maybe I should just try shooting him… both of my guys have a 50% chance to hit him with their guns, and they’d do an average of three points of damage on a hit, so that’s an expected three points of damage if both take the shot, or – calculating it differently – a 25% chance of killing the alien dead… which aren’t very good odds, so maybe the other guy should throw the grenade and the other shoot, and since grenades magically never miss in this game, I’d then have a 50% chance of killing the alien.

So as I play XCOM, I keep running arithmetic calculations through my head. But unlike in the “solve five arithmetic problems, then you get to see the Death Star blowing up” example, these calculations aren’t just glued-on. In fact, while playing, I never actually think that I am solving a set of arithmetic and probability problems in order to be rewarded with the sight of the enemies dying and my soldiers surviving. I think that I’m out killing aliens and doing my best to keep my guys alive. (How many of you had realized that XCOM is an educational game that, among other things, drills you on arithmetic problems? Well, it is!)

This can be a bad thing in some senses – it means that I’m engaging in “stealth learning”, learning a skill without realizing it. Not realizing it means that I can’t consciously reflect and introspect on my learning, and I may have difficulties transferring the skill to other domains, since my unawareness of what I’m doing makes it harder to notice if I happen to run across a problem that employs the same principles but looks superficially different. But it does also mean that the calculations are very much meaningful, and that I don’t view them as an unnecessary annoyance that I’d rather skip and move on to the good parts.

The game scholars Katie Salen and Eric Zimmerman write:

Another component of meaningful play requires that the relationship between action and outcome is integrated into the larger context of the game. This means that an action a player takes not only has immediate significance in the game, but also affects the play experience at a later point in the game. Chess is a deep and meaningful game because the delicate opening moves directly result in the complex trajectories of the middle game-and the middle game grows into the spare and powerful encounters of the end game. Any action taken at one moment will affect possible actions at later moments.

The calculations in XCOM are meaningful because they let me predict the immediate consequences of my choices. Those immediate consequences will influence the outcome of the rest of the current battle, and the end result of the battle will influence my options when I return to the strategic layer of the game, where my choices will influence how well I will do in future battles…

In contrast, the arithmetic exercises in a simple edutainment game aren’t very meaningful: maybe they let you see the Death Star blowing up, but you don’t care about the end result of the calculations themselves, because they don’t inform any choices that you need to make. Of course, there can still be other ways by which the arithmetic “game” becomes meaningful – maybe you get scored based on how quickly you solve the problems, and then you end up wanting to maximize your score, either in competition with yourself or others. Meaning can also emerge from the way that the game fits into a broader social context, as the competition example shows. But of course, that still doesn’t make most edutainment games very fun.

So if we wish people to actually be motivated to solve problems relating to Bayesian networks, we need to embed them in a context that makes them meaningful. In principle, we could just make them into multistage puzzles: DragonBox is fantastic in the way that it turns algebraic equations into puzzles, where you need to make the right choices in the early steps of the problem in order to solve it in the most efficient manner. But while that is good for teaching abstract mathematics, it doesn’t teach much about how to apply the math. And at least I personally find games with a story to be more compelling than pure puzzle games – and also more fun to design.

So I’ll want to design a game in which our original question of “does Bob also know about this” becomes meaningful, because that knowledge will inform our choices, and because there will be long-term consequences that are either beneficial or detrimental, depending on whether or not we correctly predicted the probability of Bob knowing something.

My preliminary design for such a game is set in an academy that’s inspired both by Harry Potter’s Hogwarts (to be more specific, the Hogwarts in the fanfic Harry Potter and the Methods of Rationality) and Revolutionary Girl Utena’s Ohtori Academy. Studying both physical combat and magic, the students of the academy are a scheming lot, ruled over by an iron-fisted student council made up of seven members… And figuring out things like exactly which student is cheating on their partner and who else knows about it, may turn out to be crucial for a first-year student seeking to place herself and her chosen allies in control of the council. If only she can find out which students are trustworthy enough to become her allies… misreading the evidence about someone’s nature may come to cost her dearly later.

In my next post, I will elaborate more on the preliminary design of the game, and of the ways in which it will teach its players the mathematics of Bayesian networks.

Originally published at Kaj Sotala. You can comment here or there.

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The world is becoming ever-more addictive and distracting, showering us with short-term rewards. But we can still take control of those mechanisms in order to do good in the world, and make ourselves into better people.

Taking a walk in a beautiful forest, I come up with the opening for a brilliant essay or short story. Burning with the inspiration to write it down, I hurry back home and open up my computer. As I wait for my word processor to boot up, I instinctively check whether I have any new e-mails or Facebook notifications. It turns out that I do, so I stop for a moment to read them… and answer them… and look at the link that somebody shared…

Half an hour later, I wrench myself away from these distractions and return to my writing task. But both my inspiration and motivation are gone, and I scarcely even remember what I was intending to write. I stare at the blank screen for a moment, make a haphazard attempt of writing something, and then switch back to Facebook. I re-share the funny link that kept me entertained earlier, find some more things to post, and spend the rest of the evening checking back to see if there have been any new “likes” on those items. When not, I go back to my e-mails to see whether anybody has had anything to say to me. I go to bed feeling twitchy and deflated, feeling like I’ve wasted the whole day – not only have I done nothing useful, I did not even get to relax.

A few days pass, and a new computer game is released. I figure I’ll give it a try… and I end up playing it for the whole day. The next day, I feel compelled to continue playing, until I’ve finally beaten it. If I am lucky, the game continues to be fun throughout. If I am unlucky, as often happens, it loses its charm at some point, and playing becomes tedious; but something forces me to keep playing nonetheless, even when I am starting to find it actively unpleasant. When I’m finally free, several days have gone by, and I again feel like I’ve wasted them.

***

We are being surrounded by addictive influences from all directions, and increasing amounts of money and science are used for changing our behavior.

Marketers use sophisticated statistical profiling to identify new parents as early as possible, because the birth of a child changes entrenched habits and allows a supermarket chain to tempt the parents into becoming loyal customers. Of course, marketers and companies have been always been trying to influence our habits, and often been quite successful. Fast food is no doubt popular because it is quick and convenient, but it may also be addictive.

Still, as anyone who has gotten stuck checking their Facebook status or mail inbox knows, there are more serious dangers online. A person isn’t in a constant risk of visiting a supermarket or fast food place – going there takes too much effort. But you never know when there might be a new notification, interesting status update or worthwhile e-mail waiting for you, and it takes very little effort to check. Whenever you do check for new items and find something interesting, your brain registers a small amount of reward. Thus, the action that led to the reward – the act of checking your inbox, say – gets reinforced and more likely to be repeated when you’re in a similar context. The more you are online, the more situations there will be that your brain registers as a similar context… and pushes you to check the inbox once again.

At worst, you will be compulsively checking your status and mail, to the point where it prevents you from getting anything else done. Sure, you could go do something more productive, but then the reward would be a long time away… while the Internet always provides a potential opportunity for an instant reward.

Games are also addictive and easy to access. People refer to EverQuest as “EverCrack”, not always entirely jokingly. A bit of Googling brings up news articles about World of Warcraft addicts, and I know at least one person who broke up with her boyfriend after he became too obsessed with the game. Several authors have analyzed the various psychological and outright deceitful tricks that games like Candy Crush Saga pull on their players to keep them hooked. And as Charles Stross comments, even the news media does it:

When a particular incident like today’s bombing of the Boston marathon kicks off a news cycle, a common pattern asserts itself. First, there’s photographic evidence and rumour. Then there’s some initial information—immediate numbers of dead and injured, scary photographs. But the amount of new information coming out tapers off rapidly after the first hour or two, and gives way to rumour and speculation. There probably won’t be any meaningful updates for a couple of days: but the TV channels and newpapers have to fill the dead air somehow, to keep the eyeballs they’ve attracted on the advertisements, so they cobble together anything they can grab—usually talking heads speculating without benefit of actual information. Such speculation in turn increases anxiety levels and causes depression, bringing the onlookers back for more.

***

Why have things become so addictive now?

In part, it is because of general technological progress. Paul Graham discusses this point in his essay The Acceleration of Addictiveness: technological progress causes things to become more efficient. Solar cells, computers, medicine… everything gets both better and cheaper to produce, but the same process also makes it possible to create drugs, games, and services that are ever more addictive than before. Technological progress makes everything better at its intended task – whether or not everyone would consider that task a good one.

There are also some more specific reasons which are worth mentioning.

I already mentioned low switching costs. If you have a smartphone or tablet with a fast Internet connection, you can pretty much always engage in the addictive activity. You don’t need to expend much effort to do so – in fact, it soon starts requiring more effort not to engage in the activity, as it becomes a thoroughly automated habit.

Next, addictions become intertwined with useful things. I complain a lot about Facebook making me less productive, and ruining entire days of work. So why do I keep using it? Because despite all its problems, I find it really valuable in meeting new people, keeping in touch with acquaintances, inviting people to different events, finding valuable and insightful articles, and exposing my thoughts to feedback from many different people. I have occasionally taken complete breaks from Facebook, and while I have felt more productive and content during times, I have also felt more socially isolated.

Similarly, even though many people find e-mail addictive, few if any can really stop using it. Way too much communication happens via e-mail.

Dropping a psychological addiction would be much easier if we could just quit the source of the addiction entirely. But for many current-day addictions, it is impossible to “just say no”. We have to keep coming back – and then we end up spending much more time with those things than we intended. While the things in question are useful when used in moderation, overuse causes them to become harmful.

There are also economies of scale involved. If you make a silly game that costs .99 cents to play, once a week, and get a million people to play it, you are already making close to four million each month. Online, it’s easy to increase your ability to serve more people, especially if you provide minimal to no customer support – just add more servers. Zynga, the company behind FarmVille, made $1.28 billion in revenue in 2012. That’s a downright crazy sum for a company making simple online games.

So you can make a lot of money making things more addictive… which means that everyone will be eagerly looking at the companies who are successful, and copying everything that those companies did right. Meanwhile, those companies themselves have lots of money to spend on further research aimed at making their games ever more addictive.

Which brings us to tight feedback loops. A company that is selling a physical product has a harder time making improvements to it, since that requires manufacturing new physical copies and then trying to gauge the effect of the changes. An online games company, on the other hand, can tweak a couple of settings in a product, see how this influences the behavior of a randomly selected 5% of their user base, and then adopt the settings which produce the highest returns. The behavior of each user can be tracked, with extra rewards being offered to customers who are in a danger of quitting the service. The most addictive ways of hooking people can be found very quickly.

Feedback loops also show up in the broader economy. The companies which are the most successful in addicting their customers prosper and grow. Companies which fail to do this will either go entirely out of business, or at least fail to grow any further.

This gets us an addiction economy: an economy in which the most successful companies are those which are the best at addicting us, and where their success feeds into itself, making the world ever-more addictive.

***

What is wrong with this? If people are voluntarily engaging in these activities, is that not a good thing?

Well, if people genuinely enjoy those activities, then maybe so. And sometimes that is what happens. But often, my experience at least is quite different: I just end up feeling drained and miserable – either afterwards, or even during the activity itself. When I do manage to avoid the addictions, I often feel happier and get more done.

Most people want to feel like they’re doing something meaningful with their lives. And many people do not feel like these kinds of addictions cause them to do anything meaningful. Quite the opposite.

***

But this is not another doom and gloom essay about how technology will destroy us. (If you are into that kind of thing, I do have another essay for you.) For even though these things may be addictive and reduce our productivity, they can also increase it.

I’ve written essays that have started off as Facebook updates and then been expanded as people critiqued them and forced me to express a more nuanced position, until I could just copy-paste pieces of my original update and of my later comments into a whole essay. Where traditional essay-writing may be a lonely activity requiring a lot of focus and motivation, having a dialogue with people is easy, and good tools allows us to merge the two processes.

Or let’s look at the potential of addictive games for education, a topic I have a special interest in. Why do I have problems gathering the motivation to do many traditional math exercises, whereas games that might require just as much intellectual effort addict me without problems?

At least one of the differences is that with traditional exercises, I often need to do quite a bit of work to understand the problem and the desired method well enough that I can get even started on it. Even after I do get started, solving the exercise is often a laborious process. Research on procrastination suggests that we are much more likely to procrastinate on tasks that have remote rewards and which feel like we might not be able to solve them.

In contrast, a game will start with easy problems and provide a smooth and gradual path to the hard ones, keeping you suitably challenged all the time. The classic game structure starts off with a simple level that even a beginner can beat, and then proceeds to ever-more complicated and challenging levels, while regularly giving the player access to new kinds of powers once they’ve mastered the old ones. At the same time, it constantly keeps the difficulty exactly right, so that even as the player gets better in the game, it always feels just challenging enough to be interesting but not so challenging as to be frustrating. (By tradition, there should be a reference to flow theory here, though the challenges may occasionally also be tough enough to make one drop out of flow.)

The math courses that I’ve enjoyed the most, and also felt like I’ve learned the most from, have been ones that employed a similar methodology. They broke the problems down into small pieces and made them easy to approach, and then gradually ramped up the difficulty. This is not just anecdote, either – math courses where the exercises have been similarly broken down into smaller subproblems have seen better results. Hautala et al. (2012) provide the example of a large linear algebra exercise that has been split into four parts, and comment:

Now the set of tasks guides the students through the solving process. This is important especially when the topic is introduced for the first time. If the students encounter difficulties, they are able to ask detailed questions instead of just saying “I don’t understand a thing.”

As an aside, this is part of the reason why I don’t like the term ”gamification” very much – because games are still generally considered something trivial and frivolous, and gamification makes it sound like one is adding pointless and frivolous things on top of education in order to trick students to study. But although there are exceptions, one could say that a game is good if it also employs good educational techniques to constantly keep the player challenged and engaged… and once you take that perspective, “gamification of education” really is just the same thing as good education.

So part of the reason why we keep getting sidetracked by various addictions is that the other things that we should or could be doing in life don’t feel as rewarding. And that is because those other traditional activities haven’t been designed to be properly motivational, and they have always been flawed in this way!

Previously, the best ways of motivating people haven’t been as clear, and there also hasn’t been as much pressure to make our traditional activities more interesting, because there weren’t that many addictive alternatives. The addictive alternatives that did exist were things like drugs and sex, which weren’t as strongly intertwined with useful things and which one could either “just say no” to, or split them off from the periods when one was supposed to be working. So we could just grit our teeth and force ourselves to do things, even if they weren’t much fun. Today, that is becoming harder and harder, so we will become forced to also make our work and studies just as fun. And all the reasons for why things have been becoming more addictive also let us make things like education and work more addictive.

Of course, there are tradeoffs between things such as educational value and entertainment. A game that can just focus on being as entertaining as possible might always be more fun than a game that also has to be educational. An educational game has to teach things that are true, an entertainment game can just make up whatever seems most fun. And in general, the things that are being optimized for pure addictiveness might be able to do that more effectively than the things that are also trying to be useful.

But although it would be great if the best educational games were as fun as the best entertainment games, they don’t necessarily need to be. If they are just fun enough that we can enjoy them without constantly getting the feeling that we’d rather be doing something else, that could be enough. There have been plenty of silly little games that were nowhere as fun as the best entertainment games, but still enjoyable enough that I kept playing them once I’d gotten started. And if I knew that a game was actually teaching me useful content, that would give me an extra incentive to keep playing.

The same principle should generalize to things beyond just games: as long as we make the activities that we engage in rewarding enough, we have an easier time resisting our addictions. And while the pure addictions can provide us with plenty of short-term rewards, ultimately it is the long-term rewards which feel the most fulfilling. If we can make ourselves addicted to those, we can break the grip of the useless addictions.

So. Let us take all the psychological tricks and intricate reward systems and other things that various groups are developing in order to addict us. And then let’s turn our attention to the things that we currently have to force ourselves to do, and make those just as compelling and intrinsically enjoyable, so that we can achieve the things that we really want to achieve.

***

As I have presented it, my argument suffers from one major problem.

“Let’s use these techniques to make everything more compelling” works fine as long as we are dealing with well-understood problems that can be broken down into small chunks with a clear and logical progression. But not every problem is like that. Somebody who is tackling a truly novel problem often feels at a complete loss, having no idea of whether they are even on the right track. Someone who is facing a tough problem that is known to be solvable, but which nobody has yet turned into an addictive one, might feel similarly. If we motivate people to work by giving them frequent external rewards, does that train them to become even more impatient and eager to quit in cases where no such rewards are forthcoming?

Apparently, Western cultures are already doing badly with this. According to this NPR piece, American first-graders who were given an impossible math problem to work on tended to give up within less than 30 seconds. Japanese students, on the other hand, spent a whole hour trying to solve it, stopping only when the researchers told them to. We Westerners have already been trained to desire instant gratification, and it might not be a good idea to turn society even more in that direction.

I am not at all sure that we have a choice, however. It is all well and good to say that we should stop being so focused on instant gratification and train ourselves to work on problems for longer before giving up. But how are we going to do it in a society that keeps becoming ever more addictive? Most people have big projects and noble goals that they say they intend to accomplish, one day – and then they never do, because there are easier rewards available. “We should train ourselves to have a longer tolerance for uncertainty and delayed rewards” is exactly the kind of goal that provides an uncertain reward sometime late in the future… and is thus likely to be put aside in favor of easier goals.

Unless we find a way to make that goal, too, more rewarding. I actually think that the techniques for making things more compelling could even be used for helping us do things which aren’t as immediately compelling. We just have to figure out how, and I’ve personally been experimenting with some such tricks.

Previously, each new Facebook notification created in me an urge to click it. Now I have been training myself to instead take pleasure in seeing the number of unread notifications go up. Accumulated 27 notifications over three days? Yay! Good job at not having clicked on those! Maybe I could get that number up to 28 if I waited for a bit longer!

When I started meditating, I used an IRC channel to train myself to meditate for longer at a time: after each meditation session, I reported the duration of my session to that channel. It was pleasing to gradually make the number grow higher.

Similarly, we can train ourselves to get rewards from the process rather than the result. Those Japanese kids were so persistent partially because they had been taught that hard work is a virtue, so not giving up was already a reward by itself. We could learn to reward ourselves for working without interruptions for some set amount of time, regardless of how much got done. Arguably, the reason why many people find the Pomodoro Technique so effective might be because it does exactly this.

Ultimately, the rise of addictiveness is because we are learning to more reliably modify our behavior. That knowledge can be used for bad ends, but it can also be used for good ends – even for increasing our resistance to becoming addicted.

***

I experiment with various techniques for improving my own effectiveness and breaking the hold of harmful addictions. I make public commitments, such as declaring on Facebook that I will not be making any Facebook posts for a week. I employ structured procrastination. When I notice that I’m having trouble working from home, I try to find a job where I’m actually surrounded by other people. I ease on my perfectionism and my desire to write big essays in one go, and encourage myself to write down my ideas even if I only have energy or material for a couple of paragraphs.

I look for ways of motivating myself by briefly playing non-addictive games that are related to what I want to do. I tell myself that shipping is what differentiates a generalist from a dabbler. I employ different instant motivation techniques. I practice meditation, both to increase my tolerance to delay, and to develop a better awareness of my mind to help me notice motivating feelings better.

Some of these work better. Some of them work worse. But I’m slowly making progress. This essay gets finished by the “write a few paragraphs at a time” technique; I find the motivation to finally provide some people with feedback on their philosophy paper by playing Socrates Jones: Pro Philosopher and arguing with John Stuart Mill in-game. I also write this essay partially to motivate myself to continue working on the topic of educational games.

It is a struggle, but there is yet hope.

—-

Edited to add: Technology is Heroin is a great essay that makes many similar points, though it’s more focused on the doom and gloom side.

Originally published at Kaj Sotala. You can comment here or there.

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The Serious Games Market blog showcases a number of interesting edugames, and I thought that I should try some. One of the posts linked to an interesting-sounding site called iCivics.org, which has a number of educational games that are designed to teach kids about the way the US government works.

Some of the games were relatively good. Others were dreadful enough that even with a designed playtime of half an hour or even less, I couldn’t bring myself to play them to the end. One in particular reminded me of old point-and-click adventure games in the worst possible ways: not allowing the kinds of actions that would have felt the most logical, ultimately leaving “try using everything on everything” as the only way to proceed. Few were anywhere near as pleasing to play as a good entertainment game. But still, there were some entertaining ones, and several that would be interesting to analyze.

A map of Washington DC, with different buildings in diferent locations.

I have to keep running around this map to get laws executed. The distance between places matters.

The first game that I tried was Executive Decision, in which you take the role of the President of the United States. In practice, this involves running around a minimap of Washington DC, where letters keep popping up in your mailbox at the White House. Mostly, the letters contain new laws that Congress has sent for you to sign. If they’re good laws, you should sign them and then take them to the relevant government building to be implemented. If they contain bad parts, you should flag those parts as bad and veto the bill, in which case they will soon come back without the bad parts. Occasionally you’ll be asked to fly to a meeting in a foreign country, to go over to the Pentagon to choose the most appropriate branch of the military to deal with a conflict, or to go to the Congress to hold a speech to bolster support for your pet issue.

All of this requires running around the map, and running around the map takes up time, which you have a limited amount of. So Executive Decision is basically a resource-management game where you need to plan your moves as efficiently as possible, in order to maximize the amount of actions you can take. This kept me moderately entertained as I played it, and it was short enough that I didn’t have the time to get bored, though I wouldn’t call it a crowning moment of fun.

Now, what was the game intended to teach, and what does it actually teach?

Fortunately for us, iCivics gives us teacher aids to be used in conjunction with the games. There’s an “Executive Command Post-game Powerpoint” with a a number of questions which one is supposed to know the answers to after having played the game:

  1. What does the Chief of Staff do? (He aids you in your duties as President)
  2. What is the purpose of the State of the Union address? (To identify key issues to focus on (set the agenda))
  3. Why must the President go back and speak to the Congress again? (To raise support for the issues on the agenda)
  4. What do you do if you approve of a bill and want it to become law? (Sign it)
  5. If you disagree with a bill you should… (…veto it)
  6. Can you sign only part of a bill into law? (No, you must sign the whole bill or veto the whole bill)
  7. What is it called when you deliver a law to someone else to carry out? (Delegating it)
  8. When war breaks out, what must you do as President? (Command the armed forces)
  9. When the President acts as our representative to other countries, it is called… (…diplomacy)
  10. Bonus question: What is the name of the President’s plane? (Air Force One)

The game probably does an adequate job of teaching most of those. I’d expect the items 3-6 and 8 to be remembered the best, since they are things that you spend the most time doing. The others require somewhat more attention – you might forget that the guy giving you advice on what to do was called the Chief of Staff, or that the part at the very start of the game where you choose your pet issue was the State of the Union speech.

But the game also teaches a number of other things, which were probably not intended by designers. To quote Vaniver on Less Wrong:

People should be expected to learn the game, not the reality, and that will especially be the case when the game diverges from reality to make it more fun/interesting/memorable. If you decide that the most interesting way to get people to play an interactive version of Charles Darwin collecting specimens is to make him be a trainer that battles those specimens, then it’s likely they will remember best the battles, because those are the most interesting part.

One of the research projects I got to see up close was an educational game about the Chesapeake; if I remember correctly, children got to play as a fish that swum around and ate other fish (and all were species that actually lived in the Chesapeake). If you ate enough other fish, you changed species upwards; if you got eaten, you changed species downwards. In the testing they did afterwards, they discovered that many of the children had incorporated that into their model of how the Chesapeake worked; if a trout eats enough, it becomes a shark.

Here are some unintended lessons from the game:

  1. The President needs to physically visit different government departments in order to delegate the task actually of implementing various laws. While doing this, it is important for the President to plan his route in a way that lets him visit many departments in a very brief time.
  2. It’s better to be an Education President than a Security President, because the Department of Education is physically closer to the White House than the Department of Homeland Security is, so it takes less time to run between the White House and the Department Education.
  3. Some of the laws that the President gets to sign are obviously stupid. The President may choose to veto these, in which case he’ll soon get to sign a new version of the law without the stupid parts. Bad laws are always obviously bad, and Congress never overrides the veto of a bad law. (I don’t know what would have happened if I’d tried vetoing a good law, though the Teacher’s Guide says that Congress is likely to override you if they try to veto your declaration of war.)
  4. Good laws have noble-sounding goals, and the President never needs to worry about unintended consequences. Nor is the monetary cost of a law an issue. Even though some laws are titled “deficit reduction” laws, the only difference is in their name, and in the fact that they belong to the Treasury’s jurisdiction.
  5. Congress will randomly invite the President to hold a speech for them, and the President can win over their support for his pet issue by holding a speech filled with platitudes each time, until they start sending him nothing but bills related to that issue.
  6. Having a foreign country declare war on the US is inconvenient, because then you have to keep running over to the Pentagon to tell your generals how to deal with the constant acts of aggression, when you’d rather be promoting your pet issue.
  7. The President can end wars by waiting to be invited to meetings in other countries and then flying over to those meetings sufficiently many times.

You may think that I’m being facetious here. But these really are things that one learns when playing the game, because you need to learn them to play the game well. For the same reason, they’re things which are quite likely to stick to the player’s mind the most. Of course, the player also remembers the context of the game, and may be able to use other knowledge to figure out that which parts are only specific to this game and untrue in real life. So hopefully nobody learns many untrue things from Executive Command. Also, many of those points, such as the possibility of Congress overriding a Presidential veto, are addressed in the other games.

But the point is that the game mechanics are a large part of what the player’s focus and attention are on. If the mechanics are divorced from the actual educational content of the game, that means that part of the game’s educational potential is wasted, since part of what the players learn while playing the game is useless. On the other hand, if mastering the game mechanics is the same thing as learning the educational content, then a much larger part of what the game teaches is the thing that you actually want to teach. As a loose analogy to physics, you could talk about the efficiency of an edugame: how much of the “learning energy” that goes into a game is converted into “useful learning” and how much gets wasted? In other words, of all the things that a player learns while playing the game, how much is actually the kind of learning that we want them to learn, and how much does the player need to discard as an artificial quirk of the mechanics? As in physics, we can probably never get a 100% efficiency, but we can try to get a pretty good ratio. Of course, if you are happy with your players only learning simple things like “the President can veto bills he doesn’t like”, then you might be content with accepting even a large amount of wasted learning.

A good example of a game where even a moderately high amount of “wasted learning” is probably fine was Do I Have a Right? Together with Branches of Power (which I’ll cover in a moment), this game was one of my favorites. It has you running your own law firm, and in many respects it’s similar to various real management games such as Theme Park. In the beginning you only employ one lawyer, but as you proceed in the game, you can hire more, level up your hires, buy them better equipment, purchase various extra furniture to your office to make customers happier, and place newspaper advertisements to attract more customers. The game is divided to a number of days, and after each day, your achievements are chronicled in a newspaper with a style of humor that reminds me of the writing in the various SimCity games.  If not for the fact that it’s quite short, and that it won’t take very long to acquire all the upgrades, this could have been a real entertainment game.

Do I Have a Right?

The learning component comes from various customers walking into your office and explaining their case. For example, one customer said the following: “I was found guilty of littering and paid an $80 fine. Now they want to put me on trial and fine me again for the same littering. Do I have the right to stop this trial?” You are provided a list of various civil rights as outlined in the US Constitution, and consulting it, you see that the Fifth Amendment prohibits double jeopardy. If you have a lawyer who specializes in that right, you lead the customer to that lawyer and have the case taken care of. If you don’t, you can tell the customer to come back later. Sometimes customers also think that they have rights which the Constitution doesn’t actually give them, in which case you can turn them away.

This is a quite nice way of incorporating a learning element into the gameplay in a way that feels natural and uncontrived, and is actually effective at teaching the player to recognize what the different rights are and which of them are relevant for various cases. This is much more fun than rote memorization would be, and doesn’t even feel like you’re working to learn. Also, you need to level up your lawyers and hire more of them in order to have every possible Constitutional right covered by your company, which gives you another in-game reason to spend a lot of attention looking at the various rights.

One thing that I also found clever was that you can earn a small number of extra points by clicking on the important ideas in the client’s story – in this case, the game considered “put me on trial and fine me again” an important idea, and awarded five points for clicking on that part of the text. Clicking on unimportant parts produced pictures of unhappy faces, and might have cost some points. Since identifying the right in question involves recognizing the key elements of the story and disregarding the irrelevant ones (such as this being about littering in particular), and a young player might initially be at a loss about what the relevant parts are, it’s a nice touch to put in an extra feedback mechanism that provides immediate assistance on that in particular.

Another game which I enjoyed and thought was interesting was Branches of Power, in which you are trying to push different agendas into laws that are accepted by each of the three branches of government, in the following order: the executive branch promotes an idea, the legislative branch makes the idea into a bill, the executive branch signs it into law, and the judicial branch resolves any court cases that challenge it. There are ten different issues that you can try to promote, and if you can make each of them into a law that survives judicial review, you win.

The interesting thing is that you can control the actions of each of the branches – but you can only control one of them at a time, and the rest keep acting independently in the meanwhile. So while you’re running the legislative branch and crafting bills in a way that will pass both Congress and Senate, the President is running around deciding whether to support your bills, and the Supreme Court is running around deciding the legality of your bills. If you want to be sure that the President will actually sign your bills, you can either engineer them in such a way that it’s in his best interests to sign them, or you can jump to take control of him and make sure that he does sign them. (At least I think that’s how it works, since I never actually crafted a bill which wouldn’t have been in the President’s interests to sign.) And of course, if you do need to take control of the president, that means that in the meanwhile, the legislative branch might craft the bills in ways that you wouldn’t want them to.

In addition to being a pretty novel and interesting mechanic, which I don’t remember having seen anywhere else, this is also quite educational. Not only does it teach people about the different and partially opposing incentives that the various parts of government have, it also helps convey a more generally useful lesson: that people are more likely to do what you want them to do if you manufacture situations where their interests align with yours. That’s a very general rule about politics and human interaction overall… subtly taught in a simple Flash game in a manner which, again, does not even make the learner realize that they’re being taught!

So overall, I was pretty impressed with several of these games, and felt that even some of the ones that were perhaps less successful (like Executive Decision) still had many useful lessons for edugame design in general. They’re still not very great games in terms of entertainment value, but they did give hints of how one could make an edugame that had great entertainment value. I still haven’t played all the games at iCivics, so I may do another post on them if I find more interesting ones.

Originally published at Kaj Sotala. You can comment here or there.

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A lot of the hype around educational games centers around “gamification”, and using game techniques to make the boring drilling of facts into something more fun. Which would be a definite improvement, but I don’t think that it’s ambitious enough.

Instead, let’s start by considering the question: what kind of things should education teach, and why?

Typically, school has taught facts. Bad school systems only focus on teaching facts and testing the extent to which they have been memorized, good school systems also make at least some effort to test the ability to apply them. Unfortunately, it is hard to test the ability to apply something, but easy to test whether it has been memorized. But the ability to memorize something says nothing about whether it was understood, so we get laments like the following:

For example, consider college freshmen who have taken their first college-level physics class, passed it with good grades, and can write down Newton’s laws of motion. [...] Lots of studies have shown that many such students, students who can write down Newton’s laws of motion, if asked so simple a question as “How many forces are acting on a coin when it has been thrown up into the air?” (the answer to which can actually be deduced from Newton’s laws) get the answer wrong. Leaving aside friction, they claim that two forces are operating on the coin, gravity and “impetus,” the force the hand has transferred to the coin. Gravity exists as a force and, according to Newton’s laws, is the sole force acting on the coin when it is in the air (aside from air friction). Impetus, in the sense above, however, does not exist, though Aristotle thought it did and people in their everyday lives tend to view force and motion in such terms quite naturally.

So these students have entered the semiotic domain of physics as passive content but not as something in terms of which they can actually see and operate on their world in new ways. There may be nothing essentially wrong with this, since their knowledge of such passive content might help them know, at some level, what physics, an important enterprise in modern life, is “about.” I tend to doubt this, however. Be that as it may, these students cannot produce meanings in physics or understand them in producerlike ways.

They have not learned to experience the world in a new way. They have not learned to experience the world in a way in which the natural inclination to think in terms of the hand transmitting a force to the coin, a force that the coin stores up and uses up (“impetus”), is not part of one’s way of seeing and operating on the world (for a time and place, i.e., when doing modern physics). — James Paul Gee, What Video Games Have to Teach Us about Learning and Literacy, pp. 22-23

The issue that Gee is really highlighting is the fact that although students have learned some words, the mental model of physics that they have is one of folk physics, not scientific physics. A mental model, as I’m using the term, is a mental simulation of some set of laws of cause and effect that exist in the world. If you have a well-formed model of physics, you can ask yourself questions like “how would this object behave under the influence of these forces” or “what forces are acting on this object in this situation”, study your model, and get an answer back.

A mental model doesn’t need to be about a formal and easily-defined domain such as physics: most aren’t. Whenever you hear somebody make a claim that makes you think “that doesn’t sound quite right”, the claim has violated the predictions of your existing models. Models can be very extensive or very limited: a young child might know that on ordinary days of the week, mother will return from work at 5 PM, but have no other idea of what “work” means.

But the important thing about mental models is that they simulate parts of reality. And reality is a dynamic process, where things are constantly changing in ways that we wish to predict. Simulations of reality, in order to be useful, must then be processes as well.

For example, simply knowing that an object in free fall on Earth will accelerate at 9.81 m/s per second isn’t very useful if one only understands it as a string of English words. Physics students need to understand that it is actually a description of a dynamic process, a characterization of the way that a falling object behaves over time. They haven’t really learned the meaning of this before they can use the information to imagine and predict what happens if they drop a rock from their balcony. Although we want our students to learn dynamic models and to understand processes, for the most part we have been forced to communicate those models via static representations (writing, pictures) that require highly non-trivial mental effort to translate into dynamic models.

But we now have computer programs, which can actually function as dynamic representations. A computer program is a process by its very nature, and it can in principle made to represent almost arbitrary other processes. We’re no longer just forced to use a static representation of a dynamic process when we can instead give a student something dynamic to play with. This should hopefully make it much easier to turn the learned content into a dynamic mental model from the start.

This also suggests that we should reconsider the very things that we are teaching in school – today’s curriculum has been shaped by what’s possible or easy to teach and test using only static representations, but computer programs allow for much more dynamic teaching and testing. Instead of telling a student, “you’ll pass if you can write an essay that lists the reasons why the Roman Empire fell”, a teacher could instead say, “you’ll pass if you play this computer game where you’re the ruler of Rome and succeed in preventing its fall”. The essay basically only tests memorization, while the game – if it has been well-designed – tests the ability to actually apply the knowledge, to correctly identify the reasons for the empire’s fall and to then counteract them.

But the reasons for the fall of the Roman Empire, too, are something that’s taken from the current curriculum, which we might want to reconsider entirely. What kinds of models do we want to teach our children, and why? Perhaps what we’re really after is a more general notion of why different societies might collapse, and what kinds of dynamics are in play, using the Roman Empire as a case study that we start out from. Or maybe we decide that this isn’t valuable enough in comparison to the other things that we could be teaching, and we decide to throw away the whole topic.

Why do so many children (and adults!) dislike school? Probably because static representations are often bad at teaching dynamic models, and many teachers might not even realize that that’s what they’re supposed to be teaching. This creates the feeling that school learning is boring, unless the student is already talented at turning the static explanations into dynamic models. Which isn’t to say that writing is all bad: it’s much easier and faster to create, and if the learner can connect the writing to content that’s already in the learner’s head, it can be a very effective way of deepening and broadening one’s understanding. When you already know have a good model of the domain in question, even static materials can be easy to translate into dynamic components that you can add to and integrate with your existing model. The problem only occurs when there isn’t anything that the learner could connect the material to. James Paul Gee compares reading game manuals with reading science texts:

But, in any case, the problem with the texts associated with video game—the instruction booklets, walkthroughs, and strategy guides—is that they do not make a lot of sense unless one has already experienced and lived in the game world for a while. Of course, this lack of lucidity can be made up for if the player has read similar texts before, but at some point these texts originally made sense because the player had an embodied world of experience in terms of which to situate and spell out their meanings.

The same thing is most certainly true of the sorts of texts that show up in learning content areas like science and math in school, especially in the later grades, high school, and college. A biology textbook does not make a lot of sense unless and until one has experienced and lived in the world of biology as practice for a while. And again, this lack of lucidity is mitigated if the student has already read a good many similar texts. However, at some point these texts also originally made sense because the student had an embodied world of experience (in reality or, at least, simulated in his or her mind) in terms of which to situate and spell out their meanings.

When I give talks on video games to teachers, I often show them a manual or strategy guide and ask them how much they understand. Very often they are frustrated. They have no experience in which to situate the words and phrases of the texts. All they get is verbal information, which they understand at some literal level, but which does not really hang together. They cannot visualize this verbal information in any way that makes sense or makes them want to read on. I tell them that that is how their students often feel when confronted with a text or textbook in science or some other academic area if they have had no experiences in terms of which they can situate the meanings of the words and phrases. It’s all “just words,” words the “good” students can repeat on tests and the “bad” ones can’t. – What Video Games Have to Teach Us about Learning and Literacy, pp. 102-103.

To be fair, there are many school teachers who really do focus on teaching a genuine understanding of content, and make a good job of it – the traditional school system isn’t all bad. I had such teachers on all grades, and often they were successful at their pursuits. But they were still required to assign grades, and it is hard to genuinely and fairly test a student’s understanding in some domain if you cannot actually place them in that domain. Ultimately, they too assigned grades based on things like tests and projects, which are fundamentally static measures of understanding and have a hard time measuring dynamic understanding. The need to assign grades, and to measure performance by some fair (and thus, in a pre-computer era, mostly static) method, crucially handicapped efforts aimed at really improving the understanding of the students.

Of course, current edugames aren’t really set up to deliver a new kind of educational experience. Rather, many are designed as aids for teaching the informational content of the existing curriculum – which is rather backwards, when you think of it. We’d really want our students to learn dynamic models but we can’t teach or test those directly, so we teach and test them on static facts instead – and when we finally do get an instructional aid that could teach and test dynamic models, we try to fit it into the mold of teaching facts, because that’s what they’ll be tested on!

It probably isn’t a coincidence that so many edugames are about mathematics, because math is the subject that’s the closest to being tested in a dynamic way, and is thus the most naturally suited for computer instruction.

Another issue that we aren’t yet very good at making games that teach dynamic models. Ian Bogort has coined a term for the teaching of dynamic models: procedural rhetoric. Just as verbal rhetoric is the art of persuading and teaching by using spoken words, while visual rhetoric does the same using pictures, procedural rhetoric persuades and educates by using a dynamic model. Let’s look at his argument in a little more detail.

One way of defining a game is as a collection of rules that define various consequences for the actions that a player takes. Shoot at the alien, the alien loses hit points and gets angry at you. Thus, when somebody plays a game, they are placed in a microcosm where the laws of cause and effect have been defined by the designer of the game, and they need to learn and internalize those laws in order to succeed at the game. In effect, the game designer can be seen as making a statement about the kinds of causal laws that exist, and the player comes to understand that position via their own experience, having discovered and experienced the laws for themselves.

Now the causal laws of many video games are mostly only applicable within the video game itself, and few people think of applying them in any other context. But games could present broader arguments. One of Bogort’s examples is The McDonald’s Videogame, in which

The player controls four separate aspects of the McDonald’s production environment, each of which he has to manage simultaneously: the third-world pasture where cattle are raised as cheaply as possible; the slaughterhouse where cattle are fattened for slaughter; the restaurant where burgers are sold; and the corporate offices where lobbying, public relations, and marketing are managed. In each sector, the player must make difficult business choices, but more importantly he must make difficult moral choices. In the pasture, the player must create enough cattle-grazing land and soy crops to produce the meat required to run the business. But only a limited number of fields are available; to acquire more land, the player must bribe the local governor for rights to convert his people’s crops into corporate ones. More extreme tactics are also available: the player can bulldoze rainforest or dismantle indigenous settlements to clear space for grazing (see figure 1.1). — Ian Bogort, Persuasive Games: The Expressive Power of Videogames, Kindle Locations 721-728.

Presumably, the game designers hope that by playing the game, the player comes to see the laws of cause and effect that push corporations towards unethical behavior by sometimes making it more profitable than ethical behavior. Having personally experienced a situation where those laws operated, the player can apply their experience more generally, and start to be more suspicious about the behavior of not only McDonald’s, but any corporation which is operating under similar laws of cause and effect. Of course, the player may reject the argument and feel that the position that the game designers are advocating is a flawed one – but that is the case with all rhetoric.

The ultimate goal of procedural rhetoric in the service of education is to give the player a genuine understanding of the laws operating in a game, in a way that allows for that understanding to be generalized to similar situations in real life, while also being fun. That’s a very tough challenge, and we don’t really know how to do it yet. On the other hand, there are already games that can be used for a similar purpose despite not being explicitly educational, such as by having students try to evolve a humanity-eradicating plague in Plague Inc. and then talking about the lessons about evolution that this teaches [1 2]. Such an approach is probably the most effective one for now, but it could be much improved if we had games designed expressly for the task.

If we did, we could truly revolutionize schooling. Throw away exams and grades, and just give kids games to play with, and have discussions about the games afterwards. If we wanted to have some measure of how far the students had progressed, just look at how much they had achieved in the game. Of course, massive changes of this kind are going to face a lot of resistance, so for now edugame designers who agree with these goals should be working towards more gradually shifting the system in this direction.

Another important skill, which both Gee and Bogort emphasize, is the ability to study models critically. It’s not enough that we teach students different models of how the world works – they also need to learn to evaluate the merits and plausibility of different models. What simplifying assumptions are being made? How does this model mesh together with others? How can one validate the claims made by a model? And so on.

Some of this can be taught by simple means, such as having the students play a model and then ask them to look for differences between it and reality. But there’s also a certain beauty in the discovery that the process by which models are created, evaluated, and argued for is itself a process, and can thus be modeled as a game whose laws and caveats can be learned by playing it. My Bayesian Academy game is one attempt to teach critical evaluation of models by showing some of the ways by which information can be unreliable.

And then, of course, the students will be asked to critically evaluate the model about critically evaluating models. Maybe we’ll even have a game about that.

Originally published at Kaj Sotala. You can comment here or there.

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What Video Games Have to Teach Us about Learning and Literacy. James Paul Gee. Palgrave Macmillan.

(This review is based on the first edition of the book.)

This book was a very nice discussion about video games in light of various academic theories of learning. I particularly liked this point:

“The fact that human learning is a practice effect can create a good deal of difficulty for learning in school. Children cannot learn in a deep way if they have no opportunities to practice what they are learning. They cannot learn deeply only by being told things outside the context of embodied actions. Yet at the same time, children must be motivated to engage in a good deal of practice if they are to master what is to be learned. However, if this practice is boring, they will resist it.

“Good video games involve the player in a compelling world of action and interaction, a world to which the learner has made an identity commitment, in the sense of engaging in the sort of play with identities we have discussed. Thanks to this fact, the player practices a myriad of skills, over and over again, relevant to playing the game, often without realizing that he or she is engaging in such extended practice sessions. For example, the six-year-old we discussed in the last chapter has grouped and regrouped his Pikmin a thousand times. And I have practiced, in the midst of battle, switching Bead Bead to a magic spell and away from her sword in a timely fashion a good many times. The player’s sights are set on his or her aspirations and goals in the virtual world of the game, not on the level of practicing skills outside meaningful, goal-driven contexts.

“Educators often bemoan the fact that video games are compelling and school is not. They say that children must learn to practice skills (“skill and drill”) outside of meaningful contexts and outside their own goals: It’s too bad, but that’s just the way school and, indeed, life is, they claim. Unfortunately, if human learning works best in a certain way, given the sorts of biological creatures we are, then it is not going to work well in another way just because educators, policymakers, and politicians want it to.

“The fact is that there are some children who learn well in skill-and-drill contexts. However, in my experience, these children do find this sort of instruction meaningful and compelling, usually because they trust that it will lead them to accomplish their goals and have success later in life. In turn, they believe this thanks to their trust in various authority figures around them (family and teachers) who have told them this. Other children have no such trust. Nor do I.” (pp. 68-69)

This part struck a particular chord in me since I had just read an opinion piece making exactly such an argument: that not all parts of education can be made to be fun, and that “it’s important to realize early on that mastery often requires persevering through tedious, repetitive tasks and hard-to-grasp subject matter”. I found myself somewhat annoyed with that position, but couldn’t formulate my exact reasons for why.

After reading What Video Games Have to Teach Us about Learning and Literacy, things became much clearer in my head: part of the value of video games is that they can make a subject feel interesting and meaningful on its own. Once a person has encountered a topic in an interesting context, they will be much more likely to find the topic interesting in other contexts as well. Personal example: when we were first taught probabilities in high school, me having read The Hitchhiker’s Guide to the Galaxy made the subject matter feel more interesting, even though our exercises made no mention of the Infinite Improbability Drive.

Yes, children should learn that mastering valuable skills often requires repetitive practice… but if we want them to actually learn, we should also be teaching them how to experience that practice as interesting and meaningful, and as something that is helping them get better in a field they care about. What we should not teach children is the attitude that much of learning is dull, pointless and tedious, detached from anything that would have any real-world significance, and something that you only do because the people in power force you to. Unfortunately, many traditional school systems are very successful at teaching exactly this attitude, and only the kids who have sufficient trust in various authority figures to make the learning feel meaningful manage to avoid it – and even they only succeed partially.

What Video Games Have to Teach Us about Learning and Literacy also talks about the impact of identities on learning, and by associating school with games and school success with success in fun games, we could help learners more easily develop identities as good students, helping make the learning process feel more meaningful – even when they had to tackle tasks that weren’t as inherently fun.

I also liked the discussion of the fact that if a person reads a text that covers a topic the person doesn’t have much experience of, it can be very hard to understand exactly what it is that the text is saying. The words aren’t clearly connected to the concepts that they are discussing. And much of school learning does consist of having the students read elaborate discussions of concepts that they don’t necessarily have much experience of. Even when the students do successfully memorize the rough content of the writing, they are not likely to understand it or be able to apply it very well.

In contrast, somebody playing a video game is actively engaged in the content of the game, free to experiment around with it. Well-designed video games also involve a gradual and natural progression where the players naturally obtain various skills required for playing the game. Once they have beaten the game, it is certain that they have acquired those skills to a far greater extent than if they had just read and memorized the game manual. Games provide for active learning, and the way that a game proceeds from easy initial levels to challenging late-game levels forces a player to constantly acquire additional skills while also practicing the basic skills, in an organic and natural fashion.

The main flaw of the book is that while it provides an excellent discussion of academic theory on learning, its discussion of the way the theory relates to games is at times somewhat superficial. A more detailed analysis of the content of some games in light of the theory would have been nice.

Originally published at Kaj Sotala. You can comment here or there.

xuenay: (Default)

Your honor, the prosecution would like to argue that the way the world is currently organized with regard to education vs. entertainment doesn’t really make any sense.

Exhibit #1: the award-winning strategy game XCOM: Enemy Unknown, a 2012 reboot of an old strategy game franchise. As of this writing, I have logged 94 hours of play on this game, much of that due to getting so addicted that I couldn’t quit even when I wanted to. As a result of playing, I have learned numerous pieces of utterly useless trivia. For example, I know that the easiest enemies in the game (sectoids) have three hit points on difficulties “Easy”, “Normal” and “Classic”, meaning that they can be killed with a single grenade, but they upgrade to having four hit points on “Impossible”. I also know that soldiers who are assigned to the “Sniper” class initially begin with the “Headshot” special ability. At the next level, one may choose between the “Snap Shot” and “Squadsight” special abilities, out of which the “Squadshot” special ability is clearly far superior. And so on.

Exhibit #2: the classical education system. Even when I have a genuine interest in the topic that I’m supposed to be studying, it often involves an active expenditure of willpower to get myself to do so. The human brain is most strongly motivated by frequent and rapid feedback, but traditional education tends to involve rather long feedback cycles. Maybe there are exercises that are due once a week, but it can also be the case that you’re required to spend a considerable time reading a book and listening to lectures before you’ll get a single piece of feedback in the form of your exam grade. Much of the education is delivered in a form that keeps the learner passive: lectures (a terrible way of learning) or books, rather than the kind of interactivity that would really be engaging. When there are exercises, they often feel pointless, boring and unfun.

Thus, games are doing a far better job of teaching things than the education system is. The defense is about to present witnesses who will argue that traditional education is slowly but surely reforming, shifting towards better methods of teaching. The defense will no doubt point out that the prosecutor himself is currently taking a university course based on the problem-based learning paradigm. The prosecutor hastens to grant these points. However, they do not alter his point, which is that such reforms aren’t taking things far enough. All, or at least most, of education could be done via games that were as addictive and enjoyable as traditional games.

Next, the defense will present arguments that educational games are all bad, and that you can’t really make a good one. I request that the honorable judge dismiss this argument as sheer nonsense. We have already shown that enjoyable games can teach quite a lot of things, such as the statistics of various aliens. I would also point out that I began being taught English in school around (I think) the third grade, but I never learned much in school that I wouldn’t already have learned from other sources, computer games being some of the most notable ones. Finally, part of my understanding of history comes from playing games such as Civilization, Colonization, and Europa Universalis. Games are already teaching us countless of things: it’s just that we might want to adjust the things that they are teaching us.

And let us not forget exhibit #3: DragonBox. About a month ago, I witnessed a kid who was around eight years old blaze through ~80 levels of the thing in just a few hours and have a lot of fun doing so, and afterwards she had no trouble solving the equation ax/5=a/b on pen and paper. Also, her older brother was complaining that he wanted to play, too, which was the first time that I’ve ever seen kids argue over who gets to solve first-degree equations. Before this, I also witnessed a four-year old solve about a hundred of such equations playing the game, though with considerably more coaching. This was the game that really opened my eyes for the possibilities of educational games.

But DragonBox, as fantastic it is for teaching the rules of algebra, does nothing to teach the reasons for the rules. It doesn’t impart a deep understanding of why math works the way it does. Because of that, it remains a useful tool for teaching algebra, but only a tool – it doesn’t work as a stand-alone teaching method. You can’t learn math from only playing DragonBox, the way I pretty much learned the basics of English from only playing video games.

What kind of a game would let you learn math only from playing it? Let’s cast away all modesty for a while, and think big instead. Why was math invented in the first place? Part out of intellectual curiosity, part for solving practical problems. Geometry was created to help with things such as planting the crops and building houses. A game which was really good at teaching math might put you in an imaginary world where no abstract math existed yet, and would task you with inventing ways for improving the world. You would invent math from the first foundations, for the same reasons people originally invented it – to solve the concrete problems threatening the kingdom. You’d see your people living in caves or primitive huts, start thinking about how it’d be better if they had some better homes, and then invent geometry for that purpose. Then, based on how well you did, people would start building better houses and you could walk around your kingdom looking for new problems to solve or new improvements to make.

How would that work in practice, given that math is fundamentally an act of creativity? How could there be a game that let players doodle around with math, experimenting with ideas, until they finally discovered the foundations of first geometry, and then the other subfields of mathematics? I don’t really know, but I do think that it could be done. For one, you’d want to equip the game with some sort of a theorem-prover, so that the players could experiment around with putting together various kinds of axioms and lemmas and see whether they produced interesting-looking theorems. Maybe an architect would suggest that it would be useful if you could prove some property about triangles, and then you could play around until you produced a statement that the game deemed to be logically equivalent with the wanted property. If you were running low on ideas, you would be given hints – perhaps in the form of taking a walk around your kingdom, until you saw something in nature that gave you an idea of an intermediate step or useful additional lemma, and the game would then give that to you as an intermediate goal.

Of course, there’s no reason for why this would need to be restricted to just mathematics. Inventing biology, physics, chemistry, medicine, economics, political science, and so on would certainly also be useful for your kingdom. The sciences are the easiest, since they have clear-cut correct answers that can be tested automatically, but one could also think about ways of teaching humanities in this way. History, for one, would be a natural fit, and the students could practice writing skills by composing essays and stories about what happened in the game.

School, then, would become a place where you went to play a fun game and talk about it with your friends and teachers afterwards. (I’m much inspired by the way an Australian teacher had his students by Plague Inc., after which they talked about the game in the light of the theory of evolution.) We could do away with the stressful and unfun exams this way – it’s obvious that we need exams for as long as school is stupid and boring and students won’t study unless they’re tested on the material, but with a game, you’re constantly proving your talents in-game. If we insist on giving kids grades – and I’m not sure that we should – we can do it by scoring their progress in the game.

Your honor, I submit that this kind of an organization would make far more sense. Maybe people would still need to spend some willpower to start playing the educational games rather than the entertainment games – after all, games that are optimized only for fun are likely to win in that department – but they wouldn’t need much willpower. And once they got started, they’d be hooked for a good while.

The prosecution rests. But not for long, because there’s still a lot of work to be done.

Originally published at Kaj Sotala. You can comment here or there.

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