Tiago Forte's Blog, page 48
May 20, 2018
Just-In-Time PM #4: Intermediate Packets
In Part III, I argued that having a personal knowledge base is the linchpin of success in a creative economy.
A knowledge base allows you to reuse past work, draw from past experiences, share your knowledge in concrete form, and eventually, build products and services out of that knowledge.
This requires strategically structuring your work in the first place, as a series of what I call intermediate packets.
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Just-In-Time PM IV: Intermediate Packets
In Part III, I argued that having a personal knowledge base is the linchpin of success in a creative economy.
A knowledge base allows you to reuse past work, draw from past experiences, share your knowledge in concrete form, and eventually, build products and services out of that knowledge.
This requires strategically structuring your work in the first place, as a series of what I call intermediate packets.
To read this story, become a Praxis member.
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May 19, 2018
Just-In-Time PM #3: Flow Cycles
In Part II, I described the sublime and powerful experience of flow, which could be considered the “holy grail” of productivity.
I argued that there is theoretically no minimum amount of time necessary to get into flow, contrary to popular belief. But in reality, as always, it’s a bit more complicated. Let’s look at what this looks like in a typical working session of a couple hours.
The way work is currently organized and performed, it takes a tremendous investment of resources to get into flow.
First, you have to set up your environment: making your coffee, getting your workspace ready, clearing away desktop clutter, opening the programs you’ll need, and arranging the windows on your computer screen, for example.
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Just-In-Time PM III: Flow Cycles
In Part II, I described the sublime and powerful experience of flow, which could be considered the “holy grail” of productivity.
I argued that there is theoretically no minimum amount of time necessary to get into flow, contrary to popular belief. But in reality, as always, it’s a bit more complicated. Let’s look at what this looks like in a typical working session of a couple hours.
The way work is currently organized and performed, it takes a tremendous investment of resources to get into flow.
First, you have to set up your environment: making your coffee, getting your workspace ready, clearing away desktop clutter, opening the programs you’ll need, and arranging the windows on your computer screen, for example.
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Just-In-Time PM II: The Fundamentals of Flow
In Part I, I introduced Return-on-Attention (ROA) as a way to evaluate how we invest our most precious resource – our attention.
ROA is derived from the traditional metric of ROI (Return-on-Investment), but there is a key difference. The “units” of ROI are currency, which is always uniform and interchangeable. Units of attention, on the other hand, are NOT created equal.
One minute of attention in a deep, tranquil state of concentration is potentially 100 times more valuable than the same minute waiting in line at the cashier. One hour of close collaboration with a thought partner potentially produces 100 times more value than an hour of small talk.
In other words, the state of mind you are in at any given moment powerfully shapes the quality of the attention you have at your disposal. This includes your moods and emotions, energy and stress levels, attitudes or mindset, and other internal factors.
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May 14, 2018
Case Study: Creating an Online Course, with Lauren Valdez
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May 7, 2018
Just-In-Time Project Management: A Digital-First Framework for Modern Projects
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April 30, 2018
Anatomy of a $20k Webinar
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April 27, 2018
A Pattern Recognition Theory of Mind
In 2006, inventor Ray Kurzweil released the book The Singularity Is Near, with a bold prediction that by the year 2049 we’d enter a “technological singularity.”
Around that time, he argued, the pace of improvement in technology would become a runaway phenomenon that would transform all aspects of human civilization. The word “singularity” became a Silicon Valley buzzword, striking fear, hope, and (often) derision into the hearts of millions.
In 2012 Kurzweil released How to Create a Mind, focusing on perhaps the most controversial of his many predictions: that we will create artificial, human-equivalent intelligence by the year 2029, capable of passing the Turing test.
The book argues that the structure and functioning of the human brain is actually quite simple, a basic unit of cognition repeated millions of times. Therefore, creating an artificial brain will not require simulating the human brain at every level of detail. It will only require reverse engineering this basic repeating unit.
I won’t comment on the feasibility of this project, but I want to draw on many of the same sources to offer a more conservative hypothesis: that many of the capabilities of the human mind can be extended and amplified now, using standard, off-the-shelf hardware and software.
Extended cognition will be the bridge from human to artificial intelligence, and construction of that bridge is well underway.
In this article I summarize some of Kurzweil’s arguments, and draw lessons for our understanding of Personal Knowledge Management today.
Think of your favorite song. Try to start singing it from a completely random starting point. You’ll find it’s difficult. You either want to start at a natural break point like a verse or chorus, or you have to play it in your head from the beginning in order to “find” a random spot.
This implies that our memories are organized in discrete segments. If you try to start mid-segment, you’ll struggle for a bit until your sequential memory kicks in.
[image error]
Do you know your social security number by heart? Now try reciting the numbers backward. You’ll find it’s very difficult or impossible to do without either writing them down, or at least visualizing them in your head.
This implies that your memories are sequential, like symbols on a ticker tape. They are designed to be read in a certain direction and in order.
[image error]
Now think about a simple habit like brushing your teeth. If you look closely, it consists of many small steps: pick up the toothbrush, squeeze some toothpaste onto the bristles, turn on the faucet, wet the bristles, and so on. If you look even closer, each of these small actions contains many even smaller steps, down to individual muscles flexing and neurons firing. All of this activity you perform effortlessly, each sequence triggered by a decision.
This implies that your memories are nested. Every action and thought is made up of smaller actions and thoughts.[image error]
Now put these three characteristics together. Which structure best describes nested segments organized in sequential order?
A hierarchy.
The basic structure and functioning of the human brain is hierarchical. This may not seem intuitive at first. It sounds like how a computer works.
But consider how we use language. Our brains are able to collect a pattern – of ideas, images, emotions, experiences, facts, people – and encompass all of it with a word label, like “Indonesia.” Then we get that label, and use it as an element in another pattern, which we give a name, like “Southeast Asia.” Then we get that pattern, and put it in yet a higher-order pattern, like “Earth.”
Language evolved to take advantage of the hierarchical structure of our brains. Every concept is made up of smaller concepts, all the way down to the most fundamental ideas. We call this array of recursively linked concepts our “conceptual hierarchy.”
[image error]Note: the conceptual hierarchy is not physically structured this way, since the neocortex is only one cortical column thick. It is hierarchical in its connections.
But why would our brains have evolved to be hierarchical before language?
Because reality itself is hierarchical: trees contains branches; branches contain leaves; leaves contain cells; cells contain organelles. Buildings contain floors; floors contain rooms; rooms contain doorways, windows, walls, and floors. Every object in the universe has parts, and those parts are made up of even smaller parts.
Massively parallel pattern recognition
So if human cognition is hierarchical, what are these hierarchies made up of?
Patterns.
The human brain has evolved to recognize patterns, perhaps more than any other single function. Our brain is weak at processing logic, remembering facts, and making calculations, but pattern recognition is its deep core capability.
Deep Blue, the computer that defeated the chess champion Garry Kasparov in 1997, was capable of analyzing 200 million board positions every second. Kasparov was asked how many positions he could analyze each second. His answer was “less than one.”
So how was this even a remotely close match?
Because Kasparov’s 30 billion neurons, while relatively slow, are able to work in parallel.
He is able to look at a chess board and compare what he sees with all the (estimated) 100,000 positions he has mastered at the same time. Each of these positions is a pattern of pieces on a board, and they are all available as potential matches within seconds.
This is how Kasparov’s brain can go head to head against a computer that “thinks” 10 million times faster than him (and also is millions of times more precise): his processing is slow, but massively parallel.
This doesn’t just happen in the brains of world chess champions.
Consider the last time you played tennis (or another sport)². As light from the bouncing tennis ball hit your eyes, photoreceptors turned that light into electrical signals that were passed along to many different kinds of neurons in the retina.
By the time two or three synaptic connections have been made, information about the location, direction, and speed of the ball has been extracted and is being streamed in parallel to the brain. It’s like sending a fleet of cars down an 8-lane freeway, instead of a bullet train down a single track – some cars can depart as soon as they’re ready, without having to wait for the others.
The way our brains work is through massively parallel pattern recognition. And the organ that has evolved to perform this activity is the neocortex.
The neocortex
The neocortex is an elaborately folded sheath of tissue covering the whole top and front of the brain, making up nearly 80% of its weight. It is responsible for sensory perception, recognition of everything from visual objects to abstract concepts, controlling movement, reasoning from spatial orientation, reason and logic, language – basically, everything we regard as “thinking.”
[image error]The neocortex in red
For our purposes, the most important thing to understand about the neocortex is that it has an extremely uniform structure.
This was first hypothesized by American neuroscientist Vernon Mountcastle in 1978. You would think a region responsible for much of the color and subtlety of human experience would be chaotic, irregular, and unpredictable. Instead, we’ve found the cortical column, a basic structure that is repeated throughout the neocortex. Each of the approximately 500,000 cortical columns is about two millimeters high and a half millimeter wide, and contains about 60,000 neurons (for a total of about 30 billion neurons in the neocortex).
[image error]Illustration of a cortical column
Mountcastle also believed there must be smaller sub-units, but that couldn’t be confirmed until years later. These “mini-columns” are so tightly interwoven it is impossible to distinguish them, but they constitute the fundamental component of the neocortex. Thus, they constitute the fundamental component of human thought.
Pattern Recognizers (PRs)
We’ll call these cortical mini-columns Pattern Recognizers, or PRs for short. Each PR contains approximately 100 neurons, and there are on the order of 300 million of them in the entire neocortex.
The basic structure of a PR has three parts: the input, the name, and the output.
[image error]
The first part is the input – dendrites coming from other PRs that signal the presence of lower-level patterns (generally, dendrites and axons are both nerve fibers, but dendrites receive neuron signals and carry them toward the neuron, while axons transmit nerve signals to other neurons³).
Reading a book and seeing certain shapes – like two conjoined diagonal lines combined with a crossbar – will trigger the inputs to a higher-level pattern, like the letter “A.” The shapes are the “inputs” to a PR dedicated to recognizing the letter “A.”
[image error]
The second part of a PR is its “name.” That is, the specific pattern it is designed to detect. Although some PRs are coded to recognize language, these patterns are not limited to letters and words. They could be shapes, colors, feelings, sensations – basically anything we are capable of thinking, learning, predicting, recognizing, or acting on.
In the example above, “A” is the name of the PR designated to recognize the letter A.
The third part is the output – axons emerging from the PR that signal the presence of its designated pattern.
When the inputs to a PR cross a certain threshold, it fires. That is, it emits a nerve impulse to the higher-level PRs it connects to. This is essentially the “A” PR shouting “Hey guys! I just saw the letter “A”!” When the PR for “Apple” hears such signals for a, p, p again, l, and e, it fires itself, shouting “Hey guys! I just saw “Apple!” And so on up the hierarchy.
[image error]
PRs for lower-level concepts (like the letter “A”), when fired, become the inputs for higher-level concepts, like the word “Apple.” Once a PR for “Apple” fires, it may form part of a still higher-level concept, like the sentence “An Apple a day keeps the doctor away.”
Eventually (after fractions of a second), all these inputs and outputs bubble up and emerge into consciousness, assembled into such abstract concepts as attractiveness, irony, happiness, frustration, and love.
The power of hierarchical thinking combined with massively parallel processing is that lower-level patterns do not need to be endlessly repeated at every subsequent level. The PRs for “Apple” or any other word that contains the letter “A” don’t need to fully describe the “A” pattern. They can all just “link” to the PRs that recognize that letter, much like a webpage can have hyperlinks to and from many other webpages.
We create the world as we discover the world
What’s important to know about this conceptual hierarchy is that signals flow downward as well as upward.
Higher-order PRs are actively adjusting the firing thresholds for lower-level PRs they’re connected to. If you’re reading left to right and see the letters A-P-P-L, the “Apple” PR will predict that it’s likely the next letter will be “e.” It will send a signal down to the “e” PR essentially saying “Please be aware there is a high likelihood that you will see your “e” pattern very soon, so be on the lookout for it” (neurons are very polite).
The “e” PR will then lower its threshold (increasing its sensitivity) so it’s more likely to recognize its letter.
The neocortex is not just recognizing the world. It is always attempting to predict what will happen next, moment by moment. If it expects something strongly enough, the recognition threshold may be so low that it fires even when the full pattern is not present.
This is the neurological basis for how our narratives become our reality. You can find evidence for anything if your neocortex is looking for it hard enough. You literally see what you expect to see, and hear what you expect to hear.
These downward signals can also be negative or inhibitory. If you are holding a higher-level pattern called “my wife is in Europe,” PRs dedicated to recognizing your wife will be suppressed. That is, their recognition thresholds will be raised. They can still fire if, for example, you see your wife in the checkout line at the grocery store. But it will take more evidence, and you’ll do a double take.
This is the neurological basis for blindspots. If you don’t expect to see opportunities, upsides, or possibilities, you will become less likely to recognize even the ones that do show up. Thus your narrative is reinforced, making it even harder to see them.
The word “recognition” is actually a stretch.
What the mind is doing when it “recognizes” an image is not matching it against a database of static images. There is no such database in the brain. Instead, it is reconstructing that image on the fly, drawing on many conceptual levels, mixing and matching thousands of patterns at many levels of abstraction to see which ones fit the electric signals coming in through the retina.
According to this model, recursively stepping through hierarchical lists of patterns constitutes the language of human thought.
Patterns triggered in the neocortex trigger other patterns. Partially complete patterns send signals down the conceptual hierarchy, fitting new lenses to the data. Completed patterns send signals up, fitting new data to the lenses. Some patterns refer to themselves recursively, giving us the ability to think about our thinking or to “go meta.” An element of a pattern can be a decision point for another pattern, creating conditional relationships. Many patterns are highly redundant, with PRs dedicated to linguistic, visual, auditory, and tactile versions of the same object, which is what allows us to recognize apples in many different contexts.
The basic unit of human cognition
This Pattern Recognition Theory of Mind for how the neocortex works offers a radical possibility: that the basic unit of cognition is not the neuron, but the cortical mini-column (i.e. pattern recognizer). In other words, the idea that “neurons that fire together, wire together,” which emphasizes the plasticity of individual neurons and is known as the Hebbian Theory, may be incorrect.
Swiss neuroscientist Henry Markram, in his investigation of mammalian neocortices, went looking for “Hebbesian assemblies” (neurons that had wired together). What he found instead were “elusive assemblies [whose] connectivity and synaptic weights are highly predictable and constrained.” He speculated that “[these assemblies] serve as innate, Lego-like building blocks of knowledge for perception and that the acquisition of memories involves the combination of these building blocks into complex constructs.”
In other words, learning is not a matter of individual neurons wiring together in endlessly complex, unique configurations. Instead, the basic architecture of cortical columns makes up an orderly, grid-like lattice, like city streets.
[image error] MRI imagery of the lattice-like grid of neuronal pathways in the neocortex, from a National Institutes of Health study (4)
The brain starts out with a huge number of these “connections in waiting.” When two PRs want to connect to store a pattern relationship, they don’t need to extend a dendrite across whole brain regions. They just hook up to the nearest axons, like new apartment buildings hooking up to the municipal water system.
In this model, learning is not a matter of reconfiguring or building physical structures (which would be difficult and energy-intensive). It is a matter of connectivity between highly uniform pattern recognizers. What changes as we learn and experience things is the connectivity between these modules. The plasticity of our brains comes not from the fact that we can easily construct new nerve fibers or neurons, but that changing these connections is almost plug-and-play.
We are told continuously that the brain is hopelessly complex. But it could also be hopefully simple, a basic unit of cognition repeated millions of times.
Dharmendra Modha, manager of Cognitive Computing for IBM Research, writes that “neuroanatomists have not found a hopelessly tangled, arbitrarily connected network, completely idiosyncratic to the brain of each individual, but instead a great deal of repeating structure within an individual brain and a great deal of homology across species…. The astonishing natural reconfigurability gives hope that…much of the observed variation in cortical structure across areas represents a refinement of a canonical circuit; it is indeed this canonical circuit we wish to reverse engineer.”
We are the sum of our connectivity.
Takeaways
The model described above has many implications for our understanding of cognition, learning, knowledge, and even consciousness. But I want to focus on the implications for our endeavor of building a “second brain.”
We can build a second brain
The repetitive simplicity of cortical columns and PRs gives us hope that this won’t be an impossible task. It is actually simpler to build a whole brain at a high level of abstraction, than trying to model every chemical and atomic interaction. Just like we’ve built an artificial pancreas by duplicating its functionality, not simulating every tiny islet cell. The world wide web is an early example of extending a single capability, communication, to a massive scale. Imagine if we did the same with all the other activities of our brains.
The truth is, we already have multiple brains. As I’ve written about previously, we have always extended our cognition into our tools and our environment. Even purely biologically, there is plentiful evidence that brain regions operate semi-independently and can substitute for each other. People born without certain brain regions and even missing an entire hemisphere can lead perfectly normal lives. The natural plasticity already found within our biological brain should make the possibility of additional extension more likely.
Our minds have a turning radius (or transaction cost)
The phenomenon of action potentials slowly climbing their way up a conceptual hierarchy explains a lot we’ve observed about how the brain works.
The Zeigarnik Effect describes how, when people move on from an incomplete task, the details of that task remain as a sort of “cognitive residue” for some time afterwards. You may have noticed, when stepping away from an intensely focused activity, it takes awhile for your mind to “let go” of the problem. There is a limit to how fast you can switch tasks, because changing context requires “desaturating” the neurons before resaturating them with other thoughts.
Traversing each level takes between a few hundredths to a few tenths of a second. Thus a moderately high-level pattern such as a human face can take as long as an entire second to recognize.
These cognitive transaction costs can be understood as a “turning radius.” The more patterns you have loaded up into multiple levels of your hierarchy, the faster you can make progress, as you recognize patterns everywhere and everything seems to connect to everything else.
But with that momentum you sacrifice agility, as “loading up” a different context is a biological process with physical constraints. It’s like trying to turn onto a side street while going 90 miles per hour. Even computers have such a turning radius, by the way: when you restart your computer or reset your RAM, you are “flushing” the memory of the information it’s been working on.
What’s exciting about this is it implies a floor for the smallest size packet of work it makes sense for a human to work on. Smaller batch sizes are powerful, but does this mean we should be working in 30-second chunks? No. The bottom limit of our work sessions is defined by our mental transaction costs, i.e. by how quickly we can activate, deactivate, and reactivate our pattern recognizers.
Desired outcomes are lenses
This model could also explain the neurological basis of the law of attraction, the belief that “by focusing on positive or negative thoughts people can bring positive or negative experiences into their life.” Setting an intention or formulating a desired outcome is not just wishful thinking. It may actually be our conscious mind triggering a cascade of signals that either inhibit or strengthen connections at every lower level, even the unconscious ones. This is sometimes called the reticular activating system.
There is effectively infinite information we could take in through our senses, and therefore an infinite number of interpretations of what it means. Like a GPS system that leads us through any terrain to get us where we want to go, the conceptual hierarchy of our mind is designed to surface what we decide is important and valuable. Whether that includes opportunities and possibilities or problems and challenges is up to us.
Randomness is a feature
Paradoxically, the discovery that our brain is highly ordered and regular actually makes randomness even more important. Cortical mini-columns have been found to be so tightly interwoven, that they “leak” action potentials into each other. This is a feature, allowing us to think thoughts outside the strictly logical (like in art, music, dance, etc.).
Similarly, the purpose of P.A.R.A. is to create sandboxes where the contents of notes can leak into each other. The purpose of RandomNote is to allow even notes in different notebooks to encounter each other. The purpose of Progressive Summarization is to allow ideas and phrases to stay in their raw form for as long as possible, where they remain available for “mixing” into a wider variety of “idea recipes.”
The role of our biological brain will be as an “expert manager”
What will our first, biological brain do once we have all these technological systems in place? It will become an “expert manager.” This term comes from experiments in which a software program is put in charge of selecting and managing a collection of other, more specialized software programs.
Watson, the IBM computer that famously beat the top Jeopardy champions, worked exactly this way. Its Unstructured Information Management Architecture (UIMA) deploys hundreds of different subsystems. The UIMA knows the strengths and weaknesses of each problem-solving subsystem, and mixes and matches them together to solve the problem it’s presented with. It can incorporate answers from subsystems even when they don’t know the final solution, by using them to narrow down the problem space. The UIMA can also calculate the confidence of its answer, just like a human brain.
This is a model for how our biological brains could manage cognitive extension. Interestingly, professional knowledge tends to be more organized, structured, and less ambiguous than common sense knowledge. I think we’ll increasingly see a “division of cognition” where we outsource subject matter knowledge to external systems, but keep our own common sense and general knowledge in the captain’s chair.
These expert systems will serve us like army staffs serving a general: thinking up and pre-planning numerous courses of action, and presenting them to us as possibilities for approval. In science fiction stories, these alternative futures are spun up into simulated universes running thousands of times faster than real time, which then report back to us their outcomes.
The mind as computer
The Pattern Recognition Theory of Mind asks the question, “What if the human brain was a computer?” and then takes its conclusions to the furthest extremes. It is not “true” or “correct,” any more than historical analogies comparing the brain to a steam engine.
But it is potentially useful. Paradoxically, a conceptual hierarchy made up of massively parallel pattern recognizers would explain a lot about our subjective experience. The feeling that something is “on the tip of the tongue” could be pattern recognizers firing below the level they become conscious. The certainty of “I know it when I see it” could be combinations of PRs firing without a corresponding, higher-order word label. Our intuition acquires new depths when it isn’t limited to conscious patterns.
Innovations in deep learning famously came from examinations of the human brain. Maybe now it’s time for the human brain to learn from computers.
Note: I’ve published this as a free article because I would really like feedback on it, especially from neurologists and other brain experts. I know the theory is not the “right” one, but it has huge implications for what I’m working on and I’d like to know what exactly are its limits. I’m not looking for hot takes like this popular Aeon article, which specifically calls out Kurzweil’s book without the author having read it (in my estimation, based upon his critiques). You can email feedback to tiago@fortelabs.co.
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1: How to Create a Mind, by Ray Kurzweil (my notes here)
2: http://nautil.us/issue/59/connections/why-is-the-human-brain-so-efficient
3: http://www.differencebetween.net/science/difference-between-axons-and-dendrites/
April 25, 2018
Visualizing the Theory of Constraints with Mini Metro
Alex the Plant Manager has returned home from a hectic day at the factory. Hoping to unwind, he decides to try a new game. Alex has loved trains since childhood, so he is delighted when he discovers Mini Metro.
[image error]
The clean, minimalist game promises to be a nice reprieve from his overwhelming day.
Alex loads up Mini-London and connects the first few stations. Trains shuttle between circle, triangle, and square stations with a relaxing electric hum. Each time a passenger reaches her destination, a subtle ping adds layers to the subtle background music.
More stations are added, and Alex extends his lines to service new passengers. At first it’s no problem, but soon the relaxing atmosphere starts to disappear. The music that earlier was so soothing begins to speed up. Impatient passengers ping with an angry tone instead of their earlier pleasantries. His eyes dart across the screen. More and more stations are overcrowding.
[image error]
Relaxation time is over. Now Alex is in full reactive mode, putting out fires all over the map. He drags locomotives and carriages between lines, hoping to assuage the crush of passengers. All Alex can do is pray he can stay in the game long enough to get capacity upgrades.
[image error]
The passengers of Mini-London are fed up with the poor service. It’s game over.
If only Alex knew about the Theory of Constraints! He could start by reading Theory of Constraints 101: Table of Contents.
The Theory of Constraints
In The Goal , Eliyahu Goldratt explores the Theory of Constraints (TOC).
TOC posits: in any system, there is at least one constraint holding back throughput. Attempting to improve throughput anywhere besides the constraint makes the problem worse.
The Goal is a great book, but the best way to learn the concepts is to use them in the real world.
While on the surface it’s just a game, Mini Metro is actually a great sandbox for practicing Goldratt’s ideas. Why?
Built in visual identifiers of flow help you quickly identify bottlenecks.
Overcrowding is explicitly punished, an equivalent to having too much inventory.
Because it’s a game, we can practice more quickly and with less risk than a real-world situation.
Just like real life the system continuously changes. This forces you into an iterative approach.
Using The Five Focusing Steps to Get a New High Score
The Goal contains an iterative method for resolving constraints called The Five Focusing Steps.
We can use the Five Focusing Steps as a decent playing strategy for Mini Metro, and in turn, use Mini Metro as a decent way to practice the Five Focusing Steps.
The Steps are:
Identify the constraint
Optimize the constraint
Subordinate to the constraint
Elevate the constraint
Repeat
For a deeper exploration of these ideas, check out Theory of Constraints 106: The Five Focusing Steps.
1. Identify:
We want to figure out where the constraint is.
In Mini Metro, there’s usually a line that’s constantly at risk of overcrowding. Lines containing rarer stations like squares, crosses, or diamonds will collect passengers quickly.
[image error]Passenger buildup at the Triangle station indicates our constraint is probably the Red Line
If you aren’t sure where the constraint is, make a best guess. Remember that the whole process is iterative.
(see: TOC 107)
2. Optimize:
Here we focus our efforts on the constraint. We do everything we can to optimize the constraint before adding capacity. Additional trains (capacity) are rare and expensive.
An optimized Mini Metro line has all of its trains full of passengers as often as possible. Here are a few ways to achieve that:
Shorten the line to allow the locomotive to cycle through stations faster.
Can the ordering of stations, or direction of trains be optimized to promote efficiency?
If most of the traffic is headed in one direction, use a loop to make sure the trains are carrying passengers more of the time.
(see: TOC 108)
3. Subordinate to:
Now we look at the non-constraints and subordinate their decisions to the constraint. Optimize globally, not locally.
“what subordination looks like is all but one of the resources having idle time”
It’s okay to have a spare locomotive or a spare line ‘in the yard’ and not on the board. It’s okay for non-constraint lines to have empty trains, as long as the constraint line continually has passengers waiting.
Leaving some slack in your ‘production line’ is often the best course of action. When things get hectic you’ve got the spare capacity to handle things with ease.
This is obvious in a transit game. It’s less obvious when your employees are ‘slacking off’. It’s less obvious when you’ve been reading too many productivity blogs and think you can work 12-hour days forever.
(see: TOC 109)
4. Elevate:
Now is the time to add extra locomotives and carriages. This is intuitively the step we jump to first, but carriages are a very limited resource. You may find the previous steps resolved the issue.
“because adding any capacity is extremely expensive in terms of time and money, we do it as a last resort, instead of a first resort.”
(see: TOC 110)
5. Repeat:
As your city grows, the constraint moves! If you identified the wrong constraint, you’ll now have a better idea of where to look for the real one. Repeat the whole process to keep improving flow.
We’ve played a few games of Mini Metro, learned about the Theory of Constraints, and achieved a new high score!
Spending time ‘tinkering’ and connecting ideas is an important part of improving your own productivity throughput. Video games can be a huge time sink, but here we’ve stumbled on a great game that also helped us learn about the Theory of Constraints.
I’m sure there’s more to learn from Mini Metro. Can we learn something about Networking? About Central vs. Iterative planning? What else?
James Stuber is a Design Engineer living in Seattle. He writes about connecting ideas across domains at jamesstuber.com
Further Reading:
The Goal by Eliyahu Goldratt
Theory Of Constraints 101
Original Tweetstorm: Learning from Mini Metro
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