There’s also “All-in”, aka “Go for broke”, which picks high utility OR high disutility, with a distribution of probability that is less extreme than in the case of a lottery ticket (though not necessarily fifty-fifty chances). For instance “with all the hype and all the expectations I have formed, Watchmen-the-movie is either going to be a joyride or a horrible disappointment.”
Assuming I understand what you’re aiming at… These four don’t quite seem to answer the question itself, but rather how you evaluate the possible answers to the question.
This seems to leave open the real issue, which is how you enumerate possible answers to the question.
To take a concrete example, suppose I am fed up with my job, so fed up that I’d take “something, anything, other than that”. That’s not literally true—it just feels that way. I’m not going to inquire at the nearest McDonald’s, for instance.
In this particular case, which should count as a “problem” by your previous definition, I don’t believe I would carve up the search space first in terms of approaches such as the four you offer in this post, i.e. asking what would be a big gain, or how do I guarantee no huge loss, etc.. My very first question would be something like “what are the things I would be trading off against one another ?”
My first pass at this, by the availability heuristic, might yield things that are salient properties of the current job (salary, location, etc.). Obviously because that’s the most available thing of all, my first pass will include the reason I’m unhappy about the current job: that might be annoying coworkers, a horrible boss, etc.
One of the key skills in problem solving is to also include the less obvious attributes that (possibly) have an even greater weight in my utility function. So my second pass would be “what exactly am I trying to achieve here ?” This may start to yield non-obvious insights, such as why I need a job in the first place, and what acceptable substitutes may be.
I might even say that it’s better to explore as much of the problem’s causal underpinnings as a first pass.
As a budding design engineer, one of the things that has been hammered into me is first to understand the problem in its wider context. Oftentimes just identifying a PROBLEM as opposed to a TASK is not enough: you need to understand the system that enabled the problem to exist. What aspect of the system is directly detrimental? Why is it detrimental? What features of the system influence that detrimental aspect? Why do those features exist in the first place? Can their core function be satisfied through a different principle of operation, or by restructuring the functions and flows of the system, or even by redefining your requirements?
Only once you understand the system holistically and identify functional requirements, causal structure, and your available tools can you really begin to accurately evaluate your options.
“All in”, after some thought, looks like a “lottery ticket” special case—without raising the stakes, you can’t get at the preferred best-case, so you raise the stakes to enable that outcome.
You’ve also confirmed my suspicion that I wrote these in the wrong order; I probably should have done the next one before this one.
In what way is “all in” a special case of “lottery ticket” ? Or to put it another way, how are you classifying everything that you’d see as a possible approach ?
In “lottery ticket” I am guaranteed a tolerable loss, for a tiny chance of a huge gain. When going “all in” what I forsake is any outcome close to zero (“tolerable loss” or “piddling gain”). I am guaranteed an outcome of large magnitude, but the probabilities are much closer to even. Either those are different beasts, or I’m totally confused as to what you’re trying to achieve with your classification, and your reply above doesn’t help me at all in the latter case. (I could be patient and wait for the next post in the series, however it sounds as if my confusion would be an issue of exposition with the current post.)
While in the actual purchase of a literal lottery ticket, you guarantee a loss to enable a huge gain, the criterion to be a “lottery ticket” case in the Alicorn-loves-cutesy-titles sense is just that the motivation is to make the huge gain possible. Sometimes, you can do this without guaranteeing a loss of any size—all it requires is that you move to open up the possibility of a large gain. Raising the stakes does exactly that: before you raise the stakes, the large gain isn’t possible. After you do so, the large gain is possible, although not guaranteed. Presumably, you’d never raise stakes if that never made it possible to win big—you wouldn’t raise the stakes on a bet you were certain to lose!
I’ll wait for your next post then, and see how your classification fits in with that.
While I was thinking about your post initially, I envisioned a 2d graph, with “probability” on one axis and “(dis)utility” in another. I was toying with formalizations of your concepts as linked blobs of area at various locations on that graph, and my visualizations (of all-in vs lottery) were quite different. So, if I raise that particular point again, it probably will be in terms of that picture.
There’s also “All-in”, aka “Go for broke”, which picks high utility OR high disutility, with a distribution of probability that is less extreme than in the case of a lottery ticket (though not necessarily fifty-fifty chances). For instance “with all the hype and all the expectations I have formed, Watchmen-the-movie is either going to be a joyride or a horrible disappointment.”
Assuming I understand what you’re aiming at… These four don’t quite seem to answer the question itself, but rather how you evaluate the possible answers to the question.
This seems to leave open the real issue, which is how you enumerate possible answers to the question.
To take a concrete example, suppose I am fed up with my job, so fed up that I’d take “something, anything, other than that”. That’s not literally true—it just feels that way. I’m not going to inquire at the nearest McDonald’s, for instance.
In this particular case, which should count as a “problem” by your previous definition, I don’t believe I would carve up the search space first in terms of approaches such as the four you offer in this post, i.e. asking what would be a big gain, or how do I guarantee no huge loss, etc.. My very first question would be something like “what are the things I would be trading off against one another ?”
My first pass at this, by the availability heuristic, might yield things that are salient properties of the current job (salary, location, etc.). Obviously because that’s the most available thing of all, my first pass will include the reason I’m unhappy about the current job: that might be annoying coworkers, a horrible boss, etc.
One of the key skills in problem solving is to also include the less obvious attributes that (possibly) have an even greater weight in my utility function. So my second pass would be “what exactly am I trying to achieve here ?” This may start to yield non-obvious insights, such as why I need a job in the first place, and what acceptable substitutes may be.
I might even say that it’s better to explore as much of the problem’s causal underpinnings as a first pass.
As a budding design engineer, one of the things that has been hammered into me is first to understand the problem in its wider context. Oftentimes just identifying a PROBLEM as opposed to a TASK is not enough: you need to understand the system that enabled the problem to exist. What aspect of the system is directly detrimental? Why is it detrimental? What features of the system influence that detrimental aspect? Why do those features exist in the first place? Can their core function be satisfied through a different principle of operation, or by restructuring the functions and flows of the system, or even by redefining your requirements?
Only once you understand the system holistically and identify functional requirements, causal structure, and your available tools can you really begin to accurately evaluate your options.
“All in”, after some thought, looks like a “lottery ticket” special case—without raising the stakes, you can’t get at the preferred best-case, so you raise the stakes to enable that outcome.
You’ve also confirmed my suspicion that I wrote these in the wrong order; I probably should have done the next one before this one.
You’re welcome. :)
In what way is “all in” a special case of “lottery ticket” ? Or to put it another way, how are you classifying everything that you’d see as a possible approach ?
In “lottery ticket” I am guaranteed a tolerable loss, for a tiny chance of a huge gain. When going “all in” what I forsake is any outcome close to zero (“tolerable loss” or “piddling gain”). I am guaranteed an outcome of large magnitude, but the probabilities are much closer to even. Either those are different beasts, or I’m totally confused as to what you’re trying to achieve with your classification, and your reply above doesn’t help me at all in the latter case. (I could be patient and wait for the next post in the series, however it sounds as if my confusion would be an issue of exposition with the current post.)
While in the actual purchase of a literal lottery ticket, you guarantee a loss to enable a huge gain, the criterion to be a “lottery ticket” case in the Alicorn-loves-cutesy-titles sense is just that the motivation is to make the huge gain possible. Sometimes, you can do this without guaranteeing a loss of any size—all it requires is that you move to open up the possibility of a large gain. Raising the stakes does exactly that: before you raise the stakes, the large gain isn’t possible. After you do so, the large gain is possible, although not guaranteed. Presumably, you’d never raise stakes if that never made it possible to win big—you wouldn’t raise the stakes on a bet you were certain to lose!
I get it now, thanks.
I’ll wait for your next post then, and see how your classification fits in with that.
While I was thinking about your post initially, I envisioned a 2d graph, with “probability” on one axis and “(dis)utility” in another. I was toying with formalizations of your concepts as linked blobs of area at various locations on that graph, and my visualizations (of all-in vs lottery) were quite different. So, if I raise that particular point again, it probably will be in terms of that picture.
Putting a lot of work into a career like acting where there’s a low chance of a very high reward strikes me as an “all in” strategy.