What Can We Learn from Games?

ImageThis holiday season I enjoyed giving, receiving, and playing several new card and board games with friends and family. These included classics such as cribbage, strategy games like Dominion and Power Grid, and the whimsical Munchkin.

Can video and board games teach us more than just strategy? What if games could teach us not to be better thinkers, but just to be… better? A while ago we discussed how monopoly was originally designed as a learning experience to promote cooperation. Lately I have learned of two other such games in a growing genre and wanted to share them here.

The first is Depression Quest by Zoe Quinn (via Jeff Atwood):

Depression Quest is an interactive fiction game where you play as someone living with depression. You are given a series of everyday life events and have to attempt to manage your illness, relationships, job, and possible treatment. This game aims to show other sufferers of depression that they are not alone in their feelings, and to illustrate to people who may not understand the illness the depths of what it can do to people.

The second is Train by Brenda Romero (via Marcus Montano) described here with spoilers:

In the game, the players read typewritten instructions. The game board is a set of train tracks with box cars, sitting on top of a window pane with broken glass. There are little yellow pegs that represent people, and the player’s job is to efficiently load those people onto the trains. A typewriter sits on one side of the board.

The game takes anywhere from a minute to two hours to play, depending on when the players make a very important discovery. At some point, they turn over a card that has a destination for the train. It says Auschwitz. At that point, for anyone who knows their history, it dawns on the player that they have been loading Jews onto box cars so they can be shipped to a World War II concentration camp and be killed in the gas showers or burned in the ovens.

The key emotion that Romero said she wanted the player to feel was “complicity.”

“People blindly follow rules,” she said. “Will they blindly follow rules that come out of a Nazi typewriter?”

I have tried creating my own board games in the past, and this gives me renewed interest and a higher standard. What is the most thought-provoking moment you have experienced playing games?

What’s the Best Way to Learn? Just-In-Time versus Just-In-Case

18c-classroom

Illustration of an 18th-century classroom

You will never be dumber than you are right now. You will also never have more time than you do right now. Thus, you have a relative abundance of time and a relative dearth of knowledge. How do we strike a balance between these resources to optimally leverage them for learning?

These questions came up as I listened to two episodes of the Ruby Rogues podcast. In episode 70 David brings up just-in-time versus just-in-case learning. David’s ideas were prompted by Katrina Owen, who has a list of learning resources here. The other thought-provoking episode (responsible for the above paragraph) was number 87 in which the rogues discusses Sandi Metz’s new book, Practical Object-Oriented Design in Ruby. (I had the pleasure of meeting Sandi last night at a local Ruby meetup, after a draft of this post was written.) Here’s Chuck riffing off of a quote from the book:

“Practical design does not anticipate what will happen to your application. It merely accepts that something will and that in the present, you cannot know what. It doesn’t guess the future. It preserves your options for accommodating the future.” And so, what that says to me is you don’t always have enough information. You may never have enough information. You will never have less information than you have now. So make the design decisions that you feel like you have to and defer the rest, until you don’t have to anymore. And so it was basically, “Here are some rules. But use your best judgment because you’re going to get more information that’s going to inform you better later.” And so, that kind of opens things up. Here are the rules but if you have the information that says that you have to break them, then break them.

The just-in-time and just-in-case distinctions are useful in answering the question I posed at the beginning. But before I give concrete examples I think it is important to introduce another dimension to our learning classification: formal and informal. Being the good social scientists that we are, we can now formulate a two-by-two table.

LearningStylesJust-in-case learning is done well ahead of the time that it is needed for practical purposes. Children learn English (or whatever their native language) without thought for or anticipation of the letters, emails, and blog posts they will write in years to come. In a formal setting this can lead to the use of toy problems to make the skill seem practical. Students in an algebra class may have trouble seeing ‘the point’ of those skills until much later–and even then they may not fully recognize where that learning originated.

Just-in-time learning occurs at or very near the point of need. I could ask for travel directions to your house when we first meet, but that would be useless until you actually invite me (not to mention presumptuous). It is better to learn something like that when I can use it right away, since it has little value in the abstract. Programming–for me at least–has been much more of a just-in-time skill. I have taken one formal course in the topic and am currently enrolled in another. But the great benefit of these courses is that you get to put your skills to work immediately.

To answer the question we started with, I think that we need to place more value on just-in-time learning and less on just-in-case learning. As the quote from Sandi’s book points out, we live in a world of uncertainty. There are some skills that you simply cannot learn at a just-in-time pace (math being the main one that comes to mind). But for the plethora of other cases that our modern world and its tools make available, learning at the point of need is satisfactory and perhaps even superior. That is why we need to develop more avenues for just-in-time learning. Programmers have this in spades with sites like StackOverflow, but many other skill areas do not. Sites like Coursera also have a chance to provide a middle road between the categories in the table above. The ability to iterate quickly and pick up new skills on the fly will be increasingly valuable in the years to come.

Reading in Graduate School

Caveat: this is a skill that I am working to develop over the next few years, not one that I have mastered.

Reading in graduate school is different from that required for undergraduate coursework. This is true not only of the sheer quantity (it has been likened to drinking from a firehose) but also the types of readings assigned. As Thomas Kuhn has noted, most of the readings assigned to undergraduates are in textbook form. The advantage to this approach is that the reading is comprehensive, or at least provides most of the requisite information for the course.

But there is also a key disadvantage: the textbook is given as ‘received wisdom’ from sages of ages past without any indication that those findings were not uncontroversial at the time, or indeed even presently. This is like a movie: we see the final product, but we don’t know which scenes ended up on the cutting room floor (or at least are being saved for the DVD), which changes were made to the script, and so on.* These differences are apparent sometimes in movies that are adapted from books, but often they are invisible to the major audience. (Have you heard many favorable comparisons between movie adaptations and the original book? I haven’t.)  The movie analogy shows that while the final product is often perfectly fine in its own right, it is usually lacking the substance or nuance of the original.

This difference between watching the movie and reading the script is similar to the change from undergrad to graduate course readings.** Rather than having a nice, clean package of information in the form of a textbook, you spend much more time reading journal articles and short papers. Often you will read opposing viewpoints on the same issue/question, either in the same week or over the course of this semester. This type of reading has the impact, on me at least, of showing that science*** is a fluid process. It is not a collection of right answers, it is a resource of ideas that seem to fit with certain facts when they are viewed in a certain way.

Jeff Ely put it very well recently:

My tests don’t contain any information in them that isn’t in the raw data.  My tests are just a super sophisticated way to summarize the data.  If I just showed you the tables it would be too much information.  So really, my tests do nothing more than save you the work of doing the tests yourself.

But I pick the tests.  You might have picked different tests.  And even if you like my tests you might disagree with the conclusion I draw from them.  I say “because of these tests you should conclude that H is very likely false.”  But that’s a conclusion that follows not just from the data, but also from my prior which you may not share.

What if instead of giving you the raw data and instead of giving you my test results I did something like the following.  I give you a piece of software which allows you to enter your prior and then it tells you what, based on the data and your prior, your posterior should be?  Note that such a function completely summarizes what is in the data.  And it avoids the most common knee-jerk criticism of Bayesian statistics, namely that it depends on an arbitrary choice of prior.  You tell me what your prior is, I will tell you (what the data says is) your posterior.

Pause and notice that this function is exactly what applied statistics aims to be, and think about why, in practice, it doesn’t seem to be moving in this direction.

First of all, as simple as it sounds, it would be impossible to compute this function in all practical situations.  But still, an approach to statistics based on such an objective, and subject to the technical constraints would look very different than what is done in practice.

A big part of the explanation is that statistics is a rhetorical practice.  The goal is not just to convey information but rather to change minds.  In an imaginary perfect world there is no distinction between these goals.   If I have data that proves H is false I can just distribute that data, everyone will analyze it in their own favorite way, everyone will come to the same conclusion, and that will be enough.

Like reading the script of a movie and seeing how ideas change, graduate school offers the chance to peek behind the curtain of the scientific process. We can discover many things, some of them profound and some of them fundamental. But hopefully through it all we can remember something that we should not have forgotten in the first place: we are only human.

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*Another way that this is sometimes apparent is in closed captioning. When a movie’s subtitles don’t match up precisely with what’s being said on screen it is often because the CC is based on a version of the script rather than someone actual viewing the movie and captioning it.

** If you have some interest in reading movie scripts, see here here and here.

*** By “science” here I mean simply the organized, falsifiable pursuit of human knowledge.