Modelling Reality Through Games

In the (underrated) musical Chess by Björn Ulvaeus, Benny Andersson and Tim Rice, the cast explains how the game of Chess came to be:

Not much is known of early days of chess beyond a fairly vague report

That fifteen hundred years ago two princes fought

Tough brothers for a Hindu throne

The mother cried, for no one really likes their offspring fighting to the death

She begged to stop the slaughter with her every breath

But sure enough one brother died

Sad beyond belief, she told her winning son

“You have caused such grief, I can’t forgive this evil thing you’ve done!”

He tried to explain how things had really been

But he tried in vain

No words of his could mollify the queen

And so he asked the wisest men he knew

The way to lessen her distress

They told him he’d be pretty certain to impress

By using model soldiers on a checkered board

To show it was his brother’s fault —

They thus invented chess

This is a rather elaborate fiction, at least as far as I’m aware. There were no such duelling princes, and the idea that a mother would be consoled by a bunch of ivory pieces on a grid is laughable. What is true, and what is relevant to our discussion here, is that the game of chess did most likely originate in India, and it was likely used to some degree or another in the study of military strategy.

Chaturanga, the game that is considered to have evolved into chess, used six distinct types of pieces, as does modern chess: the Rajah (king), the Mantri (counsellor), Ratha (chariot), Gaja (elephant, Ashra (horse) and Padati (footsoldier). Their movement on the game board approximates their movement on the battlefield:

  • The Ratha moves like a rook in chess, in straight horizontal or vertical lines, mimicking a charging chariot.
 Rook icon created by LAFS from Noun Project (CC-BY).

Rook icon created by LAFS from Noun Project (CC-BY).

  • The Ashra moves like the modern chess knight, a slower heavy cavalry unit that is more adept at close quarters combat than the lighter chariot.
 Knight icon created by LAFS from Noun Project (CC-BY).

Knight icon created by LAFS from Noun Project (CC-BY).

  • The Gaja’s moves are less well-defined. The great fount of knowledge that is Wikipedia describes three variants:
 Elephant icon created by Icon Fair from Noun Project (CC-BY).

Elephant icon created by Icon Fair from Noun Project (CC-BY).

Conceptually, any of these movements would fit the role of a war elephant perfectly: slow movement that is adept at wreaking havoc up and down enemy lines. (The modern chess bishop, with diagonal movement limited only by obstructions in its path, is a Renaissance European invention.)

  • The Padati, the lowly footsoldier, can only move forward one step at a time, just like on the battlefield. They cannot retreat. I’m not sure why they capture diagonally — if there was a specific reason for it, it has long since been lost to the sands of time. To make up for the Padati’s handicapped movement, there are lots of them — eight, to be exact — and they start on the board in a horizontal line.
 Pawn icon created by LAFS from Noun Project (CC-BY).

Pawn icon created by LAFS from Noun Project (CC-BY).

What’s striking about this configuration is that this line of pawns mimics infantry movement in unexpected ways. Pawns are individually weak but in aggregate are used to form a strong defensive line. The middle pawns often advance to form a salient, claiming control of territory in the middle of the board, but can sometimes become isolated from the rest of the line. Two lines of pawns often face off in what is effectively a deadlock, unless a player can make a diagonal capture (mimicking flanking) or bring in one of the “heavy” units to bear on the line of pawns.

Of course, this comparison has its limits. Infantry does often form the frontline, but rarely does infantry enter the battlefield first. That role goes to reconnaissance units and light cavalry. Since chess is a perfect information game, there is no need for reconnaissance, but this limits its utility as a tool for exploring real-world military strategy. Additionally, in combat, long-range units engage before close-range units, but chess’s capture system models only close-range combat.

All models are inaccurate, some models are useful

Of course, none of this should reflect negatively on chess. Whatever its origins, it has long ceased to be a model for military strategy. What interests me is the abstraction of chess. In trying to create a high-level model of military strategy, many aspects of warfare had to be abstracted away. First of all, the terrain: a chess board does not model the advantages and disadvantages provided by different types of terrain. Secondly, the game does not account for asymmetry of manpower, matériel, or other force multipliers such as training or technology: both sides start with the same number and type of pieces. Thirdly, the game does not account for any asymmetry of information: you always know exactly where your opponent’s pieces are at any given time. This isolates the importance of battlefield tactics.

For a game, this is ideal, because this means that the differentiator between two players is the quality of their tactical and strategic play, and nothing else. As a model of battle, however, this limits the utility of chess.

Games as models of reality

With the rise and dominance of video games, the potential of games as models has gotten a lot more interesting. In particular, simulation, base-building and strategy games are often designed to be semi-realistic models of something that exists — or could exist — in the real world: SimTower is a model of elevator traffic, Cities: Skylines is a model of urban traffic, and SimPark (a sorely underrated game) is a model of the ecology of a park habitat. Some strategy games are models of hypothetical or counterfactual situations: Frostpunk is about surviving a volcanic winter in the year 1886, Surviving Mars is a model of a future colonisation of Mars, and Jurassic World Evolution is a model of a series of dinosaur zoos.

What’s interesting about these types of model-building is that they tend to be based on complex systems.

You may have heard of the Cynefin framework, which divides problem-solving situations into four types: simple, complicated, complex and chaotic. The chaotic condition doesn’t interest us here, as it’s typically not modellable. The best breakdown of the remaining three conditions I’ve seen comes from a paper on healthcare reform by Sholom Glouberman and Brenda Zimmerman:

 From Glouberman and Zimmerman (2002)

From Glouberman and Zimmerman (2002)

The Cynefin framework is designed to explain problem-solving circumstances or conditions, but that’s just the flip side of working on a system. A rocket is a complicated system and building one is a complicated problem. A child is a complex system and raising one is a complex probleem. A habitat’s ecology is a complex system and managing one is a complex problem. Urban traffic is a complex system and managing it is a complex problem. International diplomacy is a complex system and navigating it is a complex problem.

Games with the highest replay value are often built on complex systems, with many interrelated variables that are not strictly solvable through maths alone. This gives the player complex problems to solve that are never quite the same on each playthrough.

(An aside: if a computer is a logic machine built on maths and maths alone, it’s worth asking if any computer games are truly complex, as opposed to merely complicated: given an infinite amount of time and computing resources, couldn’t an optimal solution to any game be found? Well, yes — that is what speedrunners try to do. The most complex games have an element of randomness to them, but computers can’t really generate “true” randomness, which is also why random number generator (RNG) manipulation is even a thing. So, if you want to a total pedant: such computer games are, strictly speaking, systems that are so complicated they are, for all practical purposes, complex systems.)

Emergent patterns in complex systems: Cities: Skylines

A distinguishing feature of complex systems of any kind is that they will produce emergent patterns. Consider the pawn on a chessboard. The rules of pawn movement are simple: One square forward at a time. Two squares forward, if you wish, on any given pawn’s first move. Capture one square diagonally. And yet, within the game of chess, this simple movement gives rise to a whole range of theories and strategies revolving around pawn structure.

What some of the best games do is to create a model with similarly simple rules that create emergent patterns. Look at a city-simulation game like Cities: Skylines. You lay out roads, zone residential, commercial and industrial districts, place amenities such as schools and hospitals, and implement a public transport network. The game simulates up to one million individual citizens’ movements: people wake up, go to work, go to the shops, go home. At work, they take deliveries or make them. Students go to school and, in the mid-afternoon, hang out in town or go to their friends’ homes. Tourists go from airport to hotel to tourist attraction.

(click on any of the following images to enlarge)

A city I built: the main city in the north-east, with two separate “centres”, a large industrial zone in the west, and the tourist district in the south-east.

City rhythms emerge out of up to one million individual modelled movements. Here, citizen Ashleigh Dixon is going to Club de la Crème, and she’s on foot for this part of her journey.

At Ashleigh’s workplace, Aero Designs, a company van (with a donut on top) is leaving the industrial zone to deliver goods to Busy Corner Shop.

Outside the Busy Corner Shop, a public bus is running its route. This one is almost full.

This tourist has gotten hold of a bicycle, and he’s cycling to the Expo.

A tourist is walking to the Stadium.

Another tourist walking to the Stadium. There must be an event happening there.

There’s no way to see if there’s an event at the stadium, but you can see that it draws both residents and tourists.

A truck brings forestry products to the cargo train station near the industrial park for export…

… while another trucks moves forestry imports from the cargo train terminal to The Lumber Mill.

Imports can also travel the entire distance by truck. (If your city has coal or oil power plants but no coal or oil, they will have to be imported — and if the truck can’t get there in time, the plant stops running.)

Of course, exports can travel out of the city entirely by truck, too.

This commuter blimp is empty in the middle of the day…

… while this one, plying the route from the city to the tourist district, is full.

This tourist is leaving the city. Once he reaches the airport, he will despawn (I think).

Municipal facilities also generate their own traffic. Here, a fully-loaded garbage truck makes its way to the incineration plant.

Municipal services also employ people, adding to the realism of the simulation and the patterns that emerge from it.

By modelling individual movements, the game creates an emergent picture of traffic patterns in the city. People move en masse from residential to commercial and industrial districts, and then they go home en masse. Industrial districts generate traffic to commercial districts, as well as to and from other cities in the form of exports and imports. The sum of individual movements produces consistent patterns, and the joy of the game is to figure out how to make the adjustments that modify or constrain that emergent pattern.

In the case of Cities: Skylines, the key to managing your city’s traffic turns out to be isolating your industrial districts. Residential and commercial districts can mix, but industrial districts must be separate and have their own routes into and out of the city. (The fact that this is exactly how actual cities tend to be laid out is not a coincidence.)

Another Example: SimTower

An older and clunkier example of simple rules producing emergent patterns, perhaps, is SimTower. In SimTower, you’re tasked with building a tower that turns a profit and keeps all its tenants happy. Low rent makes people happy. Long elevator wait times make them unhappy. Having to travel far to get to a food or retail outlet makes them unhappy. People will use escalators where available, but never more than seven; people will use stairs where available, but never more than four. People will only change modes of transport once.

What these rules create is an emergent pattern of play that encourages players to keep their office tenants separate from the residential and hospitality tenants in order to manage traffic. Here’s an excerpt of a post from r/SimTower titled Optimal Tower Design:

Office workers should never be using regular elevators to get anywhere. Office worker population density is too high. They must be forced to take the stairs (disable elevator access to their floors!)

David Wolever reached a similar conclusion. Here’s a passing comment from his post detailing how he beat the game:

… You will need to make sure that the lazy sims working in the offices don’t use these elevators.

These two players have very different styles of play, but they converged on a principle of play that is not immediately suggested by the rules. Furthermore, this happens to be the solution that real towers employ to manage traffic: separating the modes of transport used by office tenants from other tenants.

 Optimal tower design: elevators do not run to offices.

Optimal tower design: elevators do not run to offices.

You might argue that no real-life tower would prevent office workers from using the elevators. This is obviously true, and points to limitations in even well-designed complex models: any model is an abstraction, and will abstract away important information to some degree or another. In this case, SimTower’s model assumes that all units of a floor are accessible from any other unit of the same floor. There is no way to create segregated office blocks or areas with a shared retail area. (Project Highrise, a more recent tower simulation game, allows this.) This leads to players artificially separating them by forcing office workers to use the stairs while residents get to use the elevators.

The educational value of games

If game models are flawed, what value do they provide? What can they really tell us about the thing they are modelling?

That’s what I want to look at next, with a thorough examination of Civilization VI and the gameplay that emerges from it.

Speedrunning and the Scientific Method

In a previous post, I wrote about speedrunning and I asked myself why I found it so enthralling.

Well, I should clarify that. I’ve watched a couple of Pokémon speedrunners grinding runs, which was supremely uninteresting. I watched pokeguy84 swear and reset repeatedly, and dropped in on an Exarion stream right after his main Pokémon died to a critical hit and the very promising run died with it. The only sound on the stream was the Pokémon battle music playing over and over. A viewer typed in Twitch chat, “He’s not coming back, is he?” Exarion did come back, and he did attempt a few more runs, but none of them were as promising as the dead run had been.

Huh, I thought. So this is speedrunning — long stretches of grinding boredom, punctuated by a few flashes of elusive euphoria.

When I wrote my last speedrunning post, there was no real insight to it. Obviously there was a link to be made between speedrunning and competitive sport, but to my mind, they differ in one key respect: there is a very clear reason to play sport besides the simple desire to be elite at something, or even to simply improve at something. Being able to run farther or lift heavier loads or respond faster to external stimuli has clear advantages outside of the arena of sport. I can’t think of any specific skills in speedrunning, whether mental or motor, that are in any way comparable. (Of course, I don’t speedrun, so perhaps there are real mental advantages that speedrunners develop that I can’t see from here.)

I sound like I think speedrunning is a waste of time, which is not the case. It’s worth repeating that my question is not: “Why do people speedrun?” My question is, “Why do I find good speedruns compelling?”

I was still pondering this question when I decided to watch a video by Summoning Salt, about the most infamous level in Super Mario Bros:

Summoning Salt is a real historian of speedrunning, and a really good storyteller, able to carve out the broad arcs of each game’s story while clearly explaining the little details that motivate each breakthrough. I quickly became a fan. I watched his Super Mario Bros video, then his Pokémon Red/Blue video, then his Sonic 2 video, then his Metroid and Super Metroid videos, then his Portal and Half-Life 2 videos, Donkey Kong (which is something special), Mike Tyson, Legend of Zelda

And I started to realise, the real comparison here wasn’t speedrunning and sport, it was speedrunning and science.

The Incremental Nature of Science

As a kid, the stories of scientists that I read about tended to focus on the big breakthrough discoveries. I was especially interested in physics, and the history of early 20th century physics feels like it turns on a few major personalities: Max Born, Max Planck, Marie Curie — wait, how about this:

This was my impression of what physicists did: they made earth-shattering, groundbreaking discoveries that altered the landscape of human knowledge.

Only in college, when I was taking classes in linguistics and found myself loving the discipline, did I start to understand that this vision of science wasn’t the full picture. The paradigm shifters in any field get all the glory, but science is primarily incremental in nature: for every scientist whose name goes in the history books, there are a thousand others working on the little advances that make the big ones possible.

Speedrunning as a scientific endeavour

The process of speedrunning is, in many ways, similar to the process of science. It isn’t simply about playing a video game as fast as possible. Most of us, when playing video games, are content to live in the virtual world that’s placed before us and take it at face value, but speedrunners — they want to push their understanding of that virtual world to the absolute limit.

In an interview on the speedrunning podcast My Insane Pace, Pokémon speedrunner Shenanagans divided the Pokémon speedrunning community into three groups: the glitch hunters, the routers and the runners. I’m writing here as a total outsider, but I doubt these are strict categories: I imagine most Pokémon speedrunners will straddle two of these categories to some degree. The glitch hunters find ways to break the game that are useful to speedrunners. The routers are the theorycrafters who use their knowledge of the game (including any glitches that are allowed) to plan the fastest route through the game, and the runners are the ones who actually execute the routes.

Shenanagans was talking about Pokémon, but it’s clear from Summoning Salt’s videos that this is true of all speedrunning communities. The first runs in any game are usually superbly-executed playthroughs by highly skilled players, similar to what a casual player might do except highly refined and nearly error-free. At this stage, the runners are the dominant force. They bring the target time down, until someone does a run that is so flawless, so optimal, that the possibilities of the route are exhausted.

Routers: The Engineers

At this point, the routers take over. They examine their assumptions about what the fastest path through the game is, and work to cut out any parts of the game that are not essential to finishing the game. Depending on the nature of the game, this work can take many forms. A game that is relatively linear, like Super Mario Bros or Metroid, might present relatively few routing possibilities. A game that is relatively open, like the main series Pokémon games, presents a multitude of possible routes, but very few of them will allow for a competitive speedrun.

Of the games that are commonly speedrun, there are only a few that I have completed myself and can talk with any degree of confidence about: Pokémon Generations 1-3, and Jet Set Radio Future (JSRF). JSRF is a relatively linear game, where the goals needed to unlock the next game levels are clear, and the fastest path through the game presents itself relatively quickly, even to a novice player. From a routing point of view, there are a few things you can do to save time: not all the characters need to be unlocked, some fights and races can be avoided, and an entire level (the sewers) can be skipped. However, because the game itself gives you a fairly limited set of permutations to progress through the game, the fastest route through JSRF is straightforward to learn.

The main series Pokémon games, on the other hand, are much more complex and present much more elaborate routing possibilities. A regular Pokémon player wants to maximise the total amount of experience points that the six Pokémon in their party have, and to catch as many Pokémon as possible along the way. This entails fighting as many battles and walking through as much tall grass as possible. This takes a great deal of time and makes up the bulk of a Pokémon playthrough.

A Pokémon speedrunner, however, is not trying to maximise total experience points. They are trying to do the opposite, to minimise the total needed to complete the game.

For a certain kind of gamer, this kind of question is an intellectual playground. What is the least amount of battling needed to get through the game? Six strong Pokémon take too long to train. One strong Pokémon is better than six weak Pokémon. What should that one Pokémon be? The biggest advantages you can have in battle are stats, moveset and type, so this one strong Pokémon should have high base stats, a powerful moveset, and preferably have few type weaknesses.

That narrows the 150 (or more) possibilities down to a much smaller pool of candidates, but the work of really optimising the route has only just begun at this point. Which battles are unavoidable? What Pokémon are encountered in these battles? What stats do these Pokémon have? What moves? Working from this information, what is the minimum amount of damage needed at each stage of the game to get through these battles, and the minimum amount of defense, special defense and health needed to survive? In a way, the game becomes an engineering problem, and all routers are trying to do is to find the most time-efficient solution.

Glitch Hunters: The Field Researchers and Lab Scientists

It is impossible to discuss routing in many games without discussing the glitch hunters. Glitch hunters look for ways to break the game, often as a way to try to understand how the game is built. Think about a child playing with wooden blocks who builds a structure and then removes blocks, one by one, until it collapses: this is not too different from what glitch hunters do. They look at a game as a carefully-constructed structure, and try to find which cornerstones and support pillars hold up the game, and how the structure holds up or falls apart when some of these elements are modified or removed.

(There is some discussion about what constitutes a glitch and what doesn’t, but I’m not going to get into that here. For our purposes, I’m going to group glitches, exploits and level skips together, because the differences are not important in this post.)

I could try to give you an example of how glitches help speedrunners to understand the game better, but it’s easier to let the experts do it. If you’ve ever played Pokémon Red/Blue/Yellow, I urge you to watch Shenanagan’s masterclass in catching all 151 Pokémon in Pokémon Blue from Summer Games Done Quick 2015:

Many glitches have effects that are undesirable in normal gameplay, sometimes irreversibly breaking the game. However, glitches of all stripes are of interest to speedrunners because they suggest ways that the game can be manipulated, and therefore open up new routing possibilities that non-speedrunners would not even dream of considering. Some speedrunning communities maintain separate glitched and glitchless categories for speedrunners who prefer their games glitchless, but glitches are still useful discoveries in glitchless speedrunning because discoveries about the structure of the game may alter the calculus of routing possibilities in glitchless speedrunning.


Here is where, to my mind at least, the parallels to the scientific method really start to take shape. A glitch is useless unless it is replicable.

In this respect, a glitch is no different from any other software bug. If you encounter a bug in a piece of software, the developer needs to know what you were doing right before the bug happened, because that’s the key to figuring out why it happens and how to debug the code. The critical difference between a software bug and a gaming glitch is how it is viewed. Developers understand how their software works, and bugs reveal oversights in the logic of the software. Glitch hunters do not have that privileged view of the software, and glitches reveal insights into the game logic.

Developers are watchmakers who craft a complex piece of machinery, while glitch hunters are more akin to field researchers and lab scientists, rigorously documenting new discoveries and tweaking experimental variables.

If a glitch cannot be replicated, it might as well be an accident caused by the sneezing of the universe. If it can be replicated, however, routers can add it to their arsenal of game knowledge and look at how this glitch or exploit can help gain time on existing routes.

Tool-Assisted Speedrunning: The Theoreticians

In some speedrunning communities, there is an additional group of speedrunners often critical to helping lower records: the tool-assisted speedrunners. A tool-assisted speedrun (TAS) is a run done in an emulator by a player who has meticulously planned out the game inputs (i.e. button presses) frame by frame, so that routes requiring multiple frame-perfect inputs that might be impossible for human reflexes can be run. In other words, the route is run by a computer.

TASes do not qualify for records, but I noticed that they often feature prominently in Summoning Salt’s world record progression videos. If a route is theoretically possible but very difficult to execute, a TAS is often presented as a proof of concept. Sometimes TAS routes are run, then shelved as being too difficult for a human to run, then revisited when the potential of easier routes is exhausted, as in the case of Super Mario Bros Level 4-2 (this link goes to the relevant TAS section, but you may want to start from the beginning for context). Other times, a difficult route is run by TAS to set a mark for human players to work towards, as in the case of Mario Kart’s Choco Mountain’s Weathertenko (this link also goes to the relevant TAS section, but you may want to start from the beginning for context).

The obvious scientific analogue here is the theoretician, the scientists who work with models and predictions, and whose work helps to advance understanding of what is possible but is not immediately applicable.

TASes are not useful for all speedrunning games. They are useful for testing extremely challenging routes in games with almost no randomness in the gameplay, like Super Mario Bros. In a game like Pokémon with a considerable amount of randomness, however, there are too many moving parts for TASes to be useful. In such speedrunning communities, the routers are the ones who take on the job of presenting these proofs of concept.

The Structure of World Record Progressions

Thomas Kuhn is credited with introducing the idea of the paradigm shift in his 1962 book, The Structure of Scientific Revolutions. He argues that “normal science” is punctuated by periods of “revolutionary science”, when existing models of science prove to be inadequate, and a period of tumult follows as new models are constructed to accommodate paradigm-breaking discoveries. These new paradigms demand the re-evaluation of old data, and eventually one will become the basis for the next period of normal science.

Kuhn’s presentation of the idea was controversial, but the concept is nonetheless useful in relation to speedrunning. If we think of a route as a paradigm, then the rest of the analogy falls in place easily.

In the early days of a game’s speedrunning history, skilled players converge on one optimal strategy to speedrun the game, and successive runs refine this route/paradigm until its potential is exhausted. Then, routers start examining assumptions about the best way to run the game, theorycrafting new routes/paradigms and incorporating lesser-known tricks and more difficult manoeuvres. In doing so, they work closely with glitch hunters and tool-assisted speedrunners to figure out how the game is built and what the theoretical limits are. They look at known glitches and explore ways to incorporate them into new and better routes, re-evaluating all their assumptions about established routes along the way.

Then, a breakthrough: either a new glitch is found, or an old glitch is “rediscovered” and successfully incorporated into an improved route/paradigm that might lower the theoretical speed record once more, so that the runners can have another go. The cycle begins again.

Obviously, this is a very abstract and overly simplified picture. The real process of lowering a speedrun record is much more nuanced, and all of the processes I describe in this post — glitch-hunting, tool-assisted speedrunning, routing, and the actual speedrunning — occur simultaneously, not sequentially. If you consider the scientific analogues — engineers, field and experimental researchers, theoreticians — it’s the same thing: progress in each domain happens simultaneously, not sequentially. Theoreticians don’t stop working while they wait for their colleagues in the lab to publish. Nonetheless, I think this division of labour is a useful lens through which to look at speedrunning.

The speedrunners: what about them?

So far, this analogy hasn’t accounted for the runners themselves. The runners are the ones who actually perform the speedruns, so what would their scientific analogues be? Truth be told, I don’t know. The analogy breaks down here, because so much of science is predicated on progressing knowledge through the accumulation of controlled, repeatable experiments, and speedrunning records are, by definition, exceptional. The methods to perform the speedruns may be rigorously documented, but few people on the planet will ever be able to repeat the methods to the required precision, and even then they may not match or beat the speedrun records of any given category. This is antithetical to the idea of replicability.

This is where speedrunning diverges from science and converges with sport. In fact, speedrunning uses the language of speed-based sports: pace, split, personal best, world record.

This is the face of speedrunning, the only dimension that most of us ever interact with twice a year when Games Done Quick rolls around and millions of gamers watch the world’s best speedrunners performing jawdropping feats of gaming. It’s probably why the comparison of speedrunning with sport seemed so natural to me at first.

Every GDQ run includes a commentator’s couch, where other speedrunners explain what the runner is doing and, very very briefly, the underlying mechanics that allow each section of the game to be optimised to such a degree. In a way, the couch commentators represent the real experience of speedrunning — the experimentation, the refinement of each route, and the collective knowledge of the community.

It would be easy to regard speedrunners as the leading edge of the speedrunning community, the ones who push past the limits and establish new frontiers, like the star scientists at the Solvay Conference. Perhaps it is more fitting, though, to think of the star speedrunners at GDQ as the keystone of an arch. Each block, each voussoir, is an incremental step towards the keystone. The keystone is merely the final piece of an architectural puzzle that rests upon all the blocks below it and gives the arch its shape. The speedruns are the raison d’être of the speedrunning community, but the joy of speedrunning lies in the process of discovery and inquiry that makes the speedruns possible.

Additional thoughts

  • Obviously there isn’t a true one-to-one analogy with science here, especially since many speedrunners play multiple roles. This is just a useful way to frame the process of lowering speedrunning records.
  • Kuhn’s paradigm shift model is often contrasted with incremental science. In this post, I have blended the two, just to avoid a long and unnecessary philosophy of science discussion.
  • Another area that’s worth a comparison with speedrunning is speed climbing. I know very little about climbing, having never climbed myself (two half-days of climbing at Outward Bound doesn’t count). What prompted this idea was an article by Kelly Cordes in Outside magazine about the risks of speed climbing. Like other speed-based sports that occur on established routes, speed climbing involves route-based records, a continual search for time saves, and the need for near-perfection on record climbs. What’s unusual about speed climbing — and what it shares with speedrunning — is the use of techniques never used by traditional climbers. Recreational runners, swimmers, cyclists, rowers, skiiers, etc. all use the same techniques as the fastest professionals, just not as fast. Speed climbers and speedrunners, on the other hand, engage in their hobbies in ways that traditional climbers and gamers would never consider doing, or even actively object to.

Process Update #1: Civ VI, Lance Armstrong and Speedrunning

Meh! I’ve done it again. I've gotten so caught up in trying to produce a refined, finished product, that I haven't posted anything in nearly two weeks. So, as a reminder to myself that this blog is about process and not result, I have decided to write about the things that I am working on writing about, as unnecessarily meta as it might seem.

Civilization VI Epic Discussion

For a really long time now, I've thought that the Civilization video game series makes a really good learning and teaching tool for students to learn about how societies work. It's a really good simulation, based on very sound first principles, that leads to a lot of realistic emergent gameplay.

This is a series that I have been working on for – well, a very long time now. I've tried many different ways, and failed many different times, to find a format and a structure for discussing Civilization that is both clear and comprehensive. After many different attempts at trying to create this series, I think I've finally figured out how to do it.

There is so much to discuss here that I am going to need quite a long time to finish it all, but here's a list of some of the themes that I'm working on:

  • model thinking and the interaction of complex systems
  • emergent behaviour versus programmed behaviour
  • strategic thinking and decision making in Civ
  • tactical thinking, political pragmatism and cost-benefit analysis
  • the origins and limits of civilization
  • why is the monument one of the first builds?
  • the interchangeability of religion
  • what assumptions does the Civ game make?
  • war: extension of politics by other means
  • peace: what is it good for?
  • great people: accelerating or deflecting the course of history?
  • the value of culture and the difficulty of modelling culture
  • victory conditions, or: what is the role of the state?
  • diplomacy, the nation-state model and the balance of power
  • historical thinking and parallel universes

How Lance Armstrong Changed Cycling

This Deadspin article has been making the rounds recently. It's easily the best summary of what Lance Armstrong did during his time as a professional cyclist, and comes from the deepest well of knowledge about the sport of cycling out of all the coverage that I've seen so far. I highly recommend it.

One argument in the Deadspin article stuck out to me: the reason that the FBI and USADA were eventually able to gather all this evidence against Armstrong may very well be precisely that he intimidated everybody into submission for so long. That worked against him when Floyd Landis and Tyler Hamilton, two of his former teammates with the least personal loyalty to Armstrong and the most to gain by exposing him, turned against him and turned the FBI's attention onto Armstrong.

From that point, Armstrong's former teammates – most of whom he had alienated personally, if not professionally – testified one by one by one. With the force of the federal government to mitigate their fear of Armstrong, there was less and less incentive to keep the omertà, the code of silence in professional cycling. When even George Hincapie, possibly the only Armstrong teammate to feel any genuine fraternal affection for him, testified – it was over. It was Armstrong's "et tu, Brute?" moment.

To understand how this dynamic built up in the first place, it's instructive to look at the very structure of professional cycling, the sport's financial incentives, the way a cycling race plays out on the road, the professional race calendar, cycling's doping history, and Armstrong's own particular personality. All of these separate factors played into the peculiar Greek tragedy that is Armstrong's transcendent rise and devastating fall.

The Incremental Nature of Speedrunning

In my last post about speedrunning, I was struggling to fit speedrunning into the framework of sport. Since then I've realised that there may be a better lens through which to view speedrunning: science.

Sure, speedrunners like to go fast. But a big part of what drives speedrunners is understanding the structure and logic of the virtual world they find themselves in, and looking for ways to use that logic to their advantage. There's an emphasis on the replicability of glitches, and the refinement of particular routes to their theoretical maximum potential. When the potential of a speedrun route is exhausted, the hunt begins anew for a new glitch, a new trick, a new route that offers more possibilities, which is a process that mimics Karl Popper's paradigm shifting model of science.

I've still got more ideas, but they'll have to wait. These three topics alone will take a while to write about. I'm hoping that these process posts will help to help me on track, by providing an outlet for me to express some intermediate ideas and arguments.

That's all for today!