Satisficing means selecting the first option that is good enough, i.e. that meets or exceeds a certain threshold of acceptability. In contrast, maximizing means the tendency to search for so long until the best possible option is found.
I see no mention of costs in these definitions.
Let’s try a basic and, dare I say it, rational way of trying to achieve some outcome: you look for a better alternative until your estimate of costs for further search exceeds your estimate of the gains you would get from finding a superior option.
That’s not satisficing because I don’t take the first option alternative that is good enough. That’s also not maximizing as I am not committed to searching for the global optimum.
Let’s try a basic and, dare I say it, rational way of trying to achieve some outcome: you look for a better alternative until your estimate of costs for further search exceeds your estimate of the gains you would get from finding a superior option.
Agree. Thus in footnote 3 I wrote:
[3] Rational maximizers take the value of information and opportunity costs into account.
That’s not satisficing because I don’t take the first option alternative that is good enough. That’s also not maximizing as I am not committed to searching for the global optimum.
I agree: It’s neither pure satisficing nor pure maximizing. Generally speaking, in the real world it’s probably very hard to find (non-contrived) instances of pure satisficing or pure maximizing. In reality, people fall on a continuum from pure satisficers to pure maximizers (I did acknowledge this in footnotes 1 and 2, but I probably should have been clearer).
But I think it makes sense to assert that certain people exhibit more satisficer-characteristics and others exhibit more maximizer-characteristics. For example, imagine that Anna travels to 127 different countries and goes to over 2500 different cafes to find the best chocolate cookie. Anna could be meaningfully described as a “cookie-maximizer”, even if she gave up after 10 years of cookie-searching although she wasn’t able to find the best chocolate cookie on planet Earth. :)
Somewhat relatedly, someone might be a maximizer in a certain domain, but a satisficer in another domain. I’m for example a satisficer when it comes to food and interior decoration, but (more of) a maximizer in other domains.
in the real world it’s probably very hard to find (non-contrived) instances of pure satisficing or pure maximizing.
That’s not true—for example, in cases where the search costs for the full space are trivial, pure maximizing is very common.
In reality, people fall on a continuum from pure satisficers to pure maximizers
My objection is stronger. The behavior of optimizing for (gain—cost) does NOT lie on the continuum between satisficing and maximizing as defined in your post, primarily because they have no concept of the cost of search.
Anna could be meaningfully described as a “cookie-maximizer”
Then define “maximizing” in a way that will let you call Anna a maximizer.
That’s not true—for example, in cases where the search costs for the full space are trivial, pure maximizing is very common.
Ok, sure. I probably should have written that pure maximizing or satisficing is hard to find in important, complex and non-contrived instances. I had in mind such domains as career, ethics, romance, and so on. I think it’s hard to find a pure maximizer or satisficer here.
My objection is stronger. The behavior of optimizing for (gain—cost) does NOT lie on the continuum between satisficing and maximizing as defined in your post, primarily because they have no concept of the cost of search.
Sorry, I fear that I don’t completely understand your point. Do you agree that there are individual differences in people, such that some people tend to search longer for a better solution and other people are more easily satisfied with their circumstances – be it their career, their love life or the world in general?
Maybe I should have tried an operationalized definition: Maximizers are people who get high scores on this maximization scale (page 1182) and satisficers are people who get low scores.
Sorry, I fear that I don’t completely understand your point. Do you agree that there are individual differences in people, such that some people tend to search longer for a better solution and other people are more easily satisfied with their circumstances
Yes, I agree that there are individual differences in people. But your post is, at its core, not about people, it’s about decision strategies or algorithms. You defined them in a particular way. I am, essentially, saying that your definitions have some issues.
But note that if you “operationalize” your definitions, you switch what is being defined—from algorithms to humans, and these are very very different things.
That’s not satisficing because I don’t take the first option alternative that is good enough.
Talking about computational complexity, I would probably put this one in the category of “satisficing,” since this is just a generalization of “good enough” from “objective value” to “convergence criterion.” It could be an optimality gap, the derivative of the objective function of found solutions, time since a new best solution was found, some complicated guess from solution space generalization, or so on.
(Another way to think about this is “acceptability” doesn’t have to apply to just the solution; it can also apply to the search process.)
I would probably put this one in the category of “satisficing,” since this is just a generalization of “good enough” from “objective value” to “convergence criterion.”
You can just as easily put it into the category of “maximizing” since you’re maximizing your expected return (gain—costs).
You can just as easily put it into the category of “maximizing” since you’re maximizing your expected return (gain—costs).
In other frameworks, yes: a biologist or economist would think it natural to talk about real-world maximization (read: improvement) where costs are very relevant to profit.
In the framework of computational complexity, maximization problems are characterized by searching over a set of potential solutions and generating a proof that a particular solution is the best of those solutions. In the worst case where there is no structure on the set, that’s an O(N) operation (start at the first element in the set, compare the element in memory to element i+1 and put the bigger one in memory) on a set that’s typically exponential in problem size. So the generalization of expected return to maximization in this view is proving that a heuristic approach is the best possible heuristic approach to problems of a particular class—which is not a good concrete example of satisficing!
maximization problems are characterized by searching over a set of potential solutions and generating a proof that a particular solution is the best of those solutions
You’re just saying that in this framework the function-to-be-optimized does not contain search/optimization costs. I think for most real-life optimization problems it’s a shortcoming :-)
I don’t believe the field of computational complexity makes reference to search/opportunity costs. As to whether this is a shortcoming, well, I’ll leave that to the professional mathematicians to decide.
If you’re trying to maximize, say, profit, then the time spent searching for a solution to any particular problem definitely has a cost associated to it. The real world has deadlines and impatient customers. There’s nothing preventing the cost of a search from being a parameter in your optimization problem, and many good reasons to include it.
I see no mention of costs in these definitions.
Let’s try a basic and, dare I say it, rational way of trying to achieve some outcome: you look for a better alternative until your estimate of costs for further search exceeds your estimate of the gains you would get from finding a superior option.
That’s not satisficing because I don’t take the first option alternative that is good enough. That’s also not maximizing as I am not committed to searching for the global optimum.
Agree. Thus in footnote 3 I wrote:
Continuation of this comment
It can be maximizing something that has a term for the (opportunity and outright) cost of thinking more.
Continuing my previous comment
I agree: It’s neither pure satisficing nor pure maximizing. Generally speaking, in the real world it’s probably very hard to find (non-contrived) instances of pure satisficing or pure maximizing. In reality, people fall on a continuum from pure satisficers to pure maximizers (I did acknowledge this in footnotes 1 and 2, but I probably should have been clearer).
But I think it makes sense to assert that certain people exhibit more satisficer-characteristics and others exhibit more maximizer-characteristics. For example, imagine that Anna travels to 127 different countries and goes to over 2500 different cafes to find the best chocolate cookie. Anna could be meaningfully described as a “cookie-maximizer”, even if she gave up after 10 years of cookie-searching although she wasn’t able to find the best chocolate cookie on planet Earth. :)
Somewhat relatedly, someone might be a maximizer in a certain domain, but a satisficer in another domain. I’m for example a satisficer when it comes to food and interior decoration, but (more of) a maximizer in other domains.
That’s not true—for example, in cases where the search costs for the full space are trivial, pure maximizing is very common.
My objection is stronger. The behavior of optimizing for (gain—cost) does NOT lie on the continuum between satisficing and maximizing as defined in your post, primarily because they have no concept of the cost of search.
Then define “maximizing” in a way that will let you call Anna a maximizer.
Ok, sure. I probably should have written that pure maximizing or satisficing is hard to find in important, complex and non-contrived instances. I had in mind such domains as career, ethics, romance, and so on. I think it’s hard to find a pure maximizer or satisficer here.
Sorry, I fear that I don’t completely understand your point. Do you agree that there are individual differences in people, such that some people tend to search longer for a better solution and other people are more easily satisfied with their circumstances – be it their career, their love life or the world in general?
Maybe I should have tried an operationalized definition: Maximizers are people who get high scores on this maximization scale (page 1182) and satisficers are people who get low scores.
Yes, I agree that there are individual differences in people. But your post is, at its core, not about people, it’s about decision strategies or algorithms. You defined them in a particular way. I am, essentially, saying that your definitions have some issues.
But note that if you “operationalize” your definitions, you switch what is being defined—from algorithms to humans, and these are very very different things.
Talking about computational complexity, I would probably put this one in the category of “satisficing,” since this is just a generalization of “good enough” from “objective value” to “convergence criterion.” It could be an optimality gap, the derivative of the objective function of found solutions, time since a new best solution was found, some complicated guess from solution space generalization, or so on.
(Another way to think about this is “acceptability” doesn’t have to apply to just the solution; it can also apply to the search process.)
You can just as easily put it into the category of “maximizing” since you’re maximizing your expected return (gain—costs).
In other frameworks, yes: a biologist or economist would think it natural to talk about real-world maximization (read: improvement) where costs are very relevant to profit.
In the framework of computational complexity, maximization problems are characterized by searching over a set of potential solutions and generating a proof that a particular solution is the best of those solutions. In the worst case where there is no structure on the set, that’s an O(N) operation (start at the first element in the set, compare the element in memory to element i+1 and put the bigger one in memory) on a set that’s typically exponential in problem size. So the generalization of expected return to maximization in this view is proving that a heuristic approach is the best possible heuristic approach to problems of a particular class—which is not a good concrete example of satisficing!
You’re just saying that in this framework the function-to-be-optimized does not contain search/optimization costs. I think for most real-life optimization problems it’s a shortcoming :-)
I don’t believe the field of computational complexity makes reference to search/opportunity costs. As to whether this is a shortcoming, well, I’ll leave that to the professional mathematicians to decide.
If you’re trying to maximize, say, profit, then the time spent searching for a solution to any particular problem definitely has a cost associated to it. The real world has deadlines and impatient customers. There’s nothing preventing the cost of a search from being a parameter in your optimization problem, and many good reasons to include it.