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Solving for X in an Idea Fog
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1/ At a recent client meeting, I heard the phrase solve for X used a lot. It was a pleasant change from plan for X. As regular readers know, I have a bit of an aversion to planning and goals.
2/ The difference between the two phrases is huge. Plan is a managerial word. Its working material is solved problems. Solve is an engineering word. Its working material is untamed reality.
3/ Planning assumes that known means need to be arranged towards an end, where a "means" is a solved problem: a bit of reality understood as a known cause-effect map rather than as territory.
4/ Planning also assumes that ends are known, and the challenge mainly lies in choosing among available ends, by weighing "costs and benefits" and working out the "details."
5/ The assumption is that this process can be confined to a certain range of "operations" abstraction levels between “strategy” and “tactics": regimes meant for head-in-the-clouds bullshitters and lowly flunkies respectively.
6/ As some unimaginative military types like to say, amateurs worry about strategy and tactics, professionals talk about operations. This is not actually a flattering image of professionals.
7/ Professionals in this narrow sense are the sort who always deliver on time, as promised. They can do that because they never wander into regimes that require challenging “solves.”
8/ Solving domains, unlike planning domains, do not tolerate such "professionalism". If you are not comfortable being an "amateur" 2/3 of the time, you cannot solve. You can only plan.
9/ Planning can work with a purely instrumental view of the environment where everything has a clear purpose that it fulfills well, and can be understood in functionally fixed ways.
10/ As a result, planning in an everyday sense is mainly exercises in administrative logistics that pretend to offer predictable, time-bound outcomes because they avoid tough solves.
11/ This is why planners love middle-management bullshit like goals that satisfy SMART criteria: if you can literally set such goals, you're probably not solving problems of any significance.
12/ At a purely mathematical level, even “solving” a “planning” problem, something clueless types think they are competent to do because they are “organized process people", is non-trivial.
13/ "The literature on planning and scheduling in artificial intelligence generally takes it on faith that any interesting problem is at least NP-hard" as this widely cited paper says.
14/ Translated to layspeak (try this if the term NP is unfamiliar), this means unless you find a creative local exploit, most such problems cannot be solved in meaningfully time-bound ways. It'll get done when it gets done.
15/ Worse: often you cannot even solve such problems approximately in "SMART" ways: the approximate versions of many NP-hard problems are themselves NP-hard.
16/ And that's just uncertainty in time. There's plenty of other kinds of uncertainty in meaningful problems. And we haven't even touched on ambiguity yet.
17/ This means all the professionals who pride themselves on their on-time delivery and reliability are likely not solving real planning and scheduling problems.
18/ At best they are brute-forcing bad solves through some mix of bullying and indifference to what's actually delivered. Fulfilling the letter, but violating the spirit of expectations set.
19/ At worst, they are creating an illusion of "execution" that does nothing ("going through the motions", "phoning it in"), through bad-faith theatrical displays of productivity and busyness.
20/ "Solving," unlike "planning" couples purposeful behavior and iterative outcome selection via a reality loop. What you solve for depends on what you can do, and vice-versa. What you deliver is a discovery, not a promise.
21/ Solving does not promise more certainty and clarity in outputs that can actually be delivered given the uncertainty and ambiguity in inputs, and current "solve rate."
22/ How does this work? Our habits and mental associations around the words planning and solving are set early, in educational institutions.
23/ As a kid or college student you probably argued at some point about how in math there's "always a right answer" and in the humanities "there are no right or wrong answers."
24/ Possibly, you picked one side or the other, and found some way to adopt a superior stance towards the other side. These attitudes set in early and hard.
25/ This schism is created by how the subjects are taught. You might solve the algebra problem x^2-4=0 with x=+2, -2 (that's still really just one answer, not two).
26/ You might answer an essay question like "Describe Lady Macbeth's character" with either a conventional Cliff-notes type answer, or a risky, contrarian reading of the play.
27/ The two kinds of "solutions" are actually not that different. They just represent different mixes of play, skill, insight and orienteering.
28/ Real life, fortunately, is way more interesting than school or college mid-terms. "Solve for < 2C global warming" is a challenge with both math-like and literature-like aspects.
29/ You get to choose what to solve for (solve for clean energy? solve for no-fossil fuels? solve for carbon sequestration? solve for refugee relocation?)
30/ You cannot afford to be a functionally fixed thinker. Are hybrid cars better than all-electric? Depends. Maybe hybrids delay the solve by creating a false sense of progress.
31/ Assumptions shift all the time, "$200/barrel oil will drive supply and demand to solve the problem naturally". Oops we invented fracking and oil is now under $50/barrel.
32/ Sometimes you wander really far off solving a seemingly unrelated problem. What engineers call yak-shaving. This can be a good thing.
33/ Why? Remember those clever mazes you solved as a kid where the right path wandered far , and the apparent straight line path dead-ended somewhere far from the exit?
34/ Or you end up with low self-awareness and mistake your lack of courage/skill for the conviction that the color of the bike-shed matters in the design of the nuclear powerplant.
35/ Balaji Srinivasan coined the term idea maze for the context in which problem solving happens. I've come to prefer my updated term, an idea fog.
36/ Mazes are a great metaphor, and recall to mind the right kind of childhood problem-solving experiences to apply in adult life.
37/ They do suggest a designed playing field created by a planner though. I prefer "fog" because it suggests unplanned uncertainty, ambiguity, and creative path finding.
38/ It's a smooth rather than a striated metaphor for path-finding. One that suggests you can punch through walls and clamber through ventilation ducts.
39/ A planner views every domain as though it were a city with low map-territory divergence and no nasty lurking surprises.
40/ A solver views all domains, including cities, like poorly mapped wildernesses rich with possibilities and new knowledge.
41/ There are trail maps, but they tell you only a little of what you might need to know. Your hike can take unexpected turns, involve direction resets, use what you have in new ways, tap unexpected skills.
42/ You'll notice that I've been using the word solve without necessarily associating it with the word problem. I've also avoided the noun solution.
43/ It's not grammatical, but think of solve as an intransitive verb that doesn't need a "problem" object. It also doesn't need a defined outcome/output: a "solution."
44/ Solving is about puzzling your way through life, applying a certain kind of creative, reality-anchored action orientation to life, where you drive change as you live.
45/ Constantly driving positive change by tapping an expanding mastery of the elegant workings of reality: that's solving. Y
45/ You don't need no stinkin' problems or solutions to be a solver. Just a drive to change what you see in interesting ways.
46/ I just found some hot chocolate in the kitchen, so I'm now solving for dry AND warm. Also, the sun is out now, and I'm wondering if I can solve for "dry faster" somehow.
47/ Happy Labor Day weekend. Hope you manage to solve for fun :)
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1/ At a recent client meeting, I heard the phrase solve for X used a lot. It was a pleasant change from plan for X. As regular readers know, I have a bit of an aversion to planning and goals.
2/ The difference between the two phrases is huge. Plan is a managerial word. Its working material is solved problems. Solve is an engineering word. Its working material is untamed reality.
3/ Planning assumes that known means need to be arranged towards an end, where a "means" is a solved problem: a bit of reality understood as a known cause-effect map rather than as territory.
4/ Planning also assumes that ends are known, and the challenge mainly lies in choosing among available ends, by weighing "costs and benefits" and working out the "details."
5/ The assumption is that this process can be confined to a certain range of "operations" abstraction levels between “strategy” and “tactics": regimes meant for head-in-the-clouds bullshitters and lowly flunkies respectively.
6/ As some unimaginative military types like to say, amateurs worry about strategy and tactics, professionals talk about operations. This is not actually a flattering image of professionals.
7/ Professionals in this narrow sense are the sort who always deliver on time, as promised. They can do that because they never wander into regimes that require challenging “solves.”
8/ Solving domains, unlike planning domains, do not tolerate such "professionalism". If you are not comfortable being an "amateur" 2/3 of the time, you cannot solve. You can only plan.
9/ Planning can work with a purely instrumental view of the environment where everything has a clear purpose that it fulfills well, and can be understood in functionally fixed ways.
10/ As a result, planning in an everyday sense is mainly exercises in administrative logistics that pretend to offer predictable, time-bound outcomes because they avoid tough solves.
11/ This is why planners love middle-management bullshit like goals that satisfy SMART criteria: if you can literally set such goals, you're probably not solving problems of any significance.
12/ At a purely mathematical level, even “solving” a “planning and scheduling” problem, something point-haired bosses think they are competent to do because they are “organized” administrative types, is non-trivial.
13/ "The literature on planning and scheduling in artificial intelligence generally takes it on faith that any interesting problem is at least NP-hard" as this widely cited paper says.
14/ Translated to layspeak (try this if the term NP is unfamiliar), this means unless you find a creative local exploit, most such problems cannot be solved in meaningfully time-bound ways. It'll get done when it gets done.
15/ Worse: often you cannot even solve such problems approximately in "SMART" ways: the approximate versions of many NP-hard problems are themselves NP-hard.
16/ And that's just uncertainty in time. There's plenty of other kinds of uncertainty in meaningful problems. And we haven't even touched on ambiguity yet.
17/ This means all the professionals who pride themselves on their on-time delivery and reliability are likely not solving real planning and scheduling problems.
18/ At best they are brute-forcing bad solves through some mix of bullying and indifference to what's actually delivered, that fulfill the letter, but violate the spirit of the challenge.
19/ At worst, they are creating an illusion of "execution" that does nothing ("going through the motions", "phoning it in"), through bad-faith theatrical displays of productivity and busyness.
20/ "Solving," unlike "planning" couples purposeful behavior and outcome selection via a reality loop. What you solve for depends on what you can do, and vice-versa. What you deliver is a discovery, not a promise.
21/ Solving does not promise more certainty and clarity in outputs that can actually be delivered given the uncertainty and ambiguity in inputs, and rate of intelligent solving progress.
22/ How does this work? Our habits and mental associations around the words planning and solving are set early, in educational institutions.
23/ As a kid or college student you probably argued at some point about how in math there's "always a right answer" and in the humanities "there are no right or wrong answers."
24/ Possibly, you picked one side or the other, and found some way to adopt a superior stance towards the other side. These attitudes set in early and hard.
25/ This schism is created by how the subjects are taught. You might solve the algebra problem x^2-4=0 with x=+2, -2 (that's still really just one answer, not two).
26/ You might answer an essay question like "Describe Lady Macbeth's character" with either a conventional Cliff-notes type answer, or a risky, creative, contrarian reading of the play.
27/ The two kinds of "solutions" are actually not that different. They just represent different mixes of play, skill, insight and objective setting (understood as temporary directions, not rigid goals).
28/ Real life, fortunately, is way more interesting than school or college mid-terms. "Solve for < 2 degrees global warming" is a challenge with both math-like and literature-like aspects.
29/ You get to choose what to solve for (solve for clean energy? solve for no-fossil fuels? solve for carbon sequestration? solve for refugee relocation?)
30/ You cannot afford to be a functionally fixed thinker. Are hybrid cars better than all-electric? Depends. Maybe hybrids delay the solve by creating a false sense of progress.
31/ Assumptions shift all the time, "$200/barrel oil will drive supply and demand to solve the problem naturally". Oops we invented fracking and oil is now under $50/barrel.
32/ Sometimes you wander really far off solving a seemingly unrelated problem. What engineers call yak-shaving. This can be a good thing.
33/ Why? Remember those clever mazes you solved as a kid where the right path wandered far , and the apparent straight line path dead-ended somewhere far from the exit?
34/ Or you end up with low self-awareness and mistake your lack of courage for a conviction that the color of the bike-shed matters in the design of the nuclear powerplant.
35/ Balaji Srinivasan coined the term idea maze for the context in which problem solving happens. I've come to prefer my updated term, an idea fog.
36/ Mazes are a great metaphor, and recall to mind the right kind of childhood problem-solving experiences to apply in adult life.
37/ They do suggest a designing playing field though. I prefer "fog" because it suggests uncertainty and ambiguity, and the possibility of creative path finding where no paths exist.
38/ It's a smooth rather than a striated metaphor for path-finding. One that suggests you can punch through walls and clamber through ventilation ducts.
39/ Where a planner views the world like a set of city streets to navigate with low map-territory divergence and no nasty surprises like extreme grades, a solver views the world like a national park.
40/ There are trail maps, but they tell you only a little of what you might need to know. Your hike can take unexpected turns, and involve setting new objectives like getting to an unmarked view point.
41/ You'll notice that I've been using the word solve without necessarily associating it with the word problem. I've also avoided the word solution.
42/ It's not grammatical, but think of solve as an intransitive verb that doesn't need a "problem object. It also doesn't need a defined outcome/output: a "solution."
43/ Solving is about puzzling your way through life, applying a certain kind of creative, reality-anchored action orientation to life,
44/ Figuring out how to master the workings of reality in elegant ways that give you leverage and power: that's solving. You don't need no stinkin' problems or solutions to be a solver.
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Check out the 20 Breaking Smart Season 1 essays for the deeper context behind this newsletter. If you're interested in bringing the Season 1 workshop to your organization, get in touch. You can follow me on Twitter @vgr
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