I am assuming the first point is about this post and the second two are about the planning primer?
The first two are about this article and the third is about the planning fallacy primer. I mentioned hypothesis generation because you talked about ‘pair debugging’ and asking people to state the obvious solutions to a problem as ways to increase the number of hypotheses that are generated, and it pattern matched to what I’d read about hypothesis generation.
There are definitely several papers on memory bias affecting decisions, although I’m unsure if we’re talking about the same thing here. What I want to say is something like “improperly recalling how long things took in the past is a problem that can bias predictions we make” and this phenomena has been studied several times.
I’m definitely talking about this as opposed to the other thing. MINERVA-DM is a good example of this class of hypothesis in the realm of likelihood judgment. Hilbert (2012) is an information-theoretic approach to memory bias in likelihood judgment.
I’m just saying that it looks like there’s a lot of fruit to be picked in memory theory and not many people are talking about it.
Okay, gotcha. Thanks for the clarification on the points.
I admit I don’t quite understand what MINERVA-DM is...I glanced at the paper briefly and it appears to be a...theoretical framework for making decisions which is shown to exhibit similar biases to human thought? (With cells and rows and ones?)
I’m definitely not strong in this domain; any chance you could summarize?
I admit I don’t quite understand what MINERVA-DM is...I glanced at the paper briefly and it appears to be a...theoretical framework for making decisions which is shown to exhibit similar biases to human thought? (With cells and rows and ones?)
I can’t describe it too much better than that. The framework is meant to be descriptive as opposed to normative.
A complete description of MINERVA-DM would involve some simple math, but I can try to describe it in words. The rows of numbers you saw are vectors. We take a vector that represents an observation, called a probe, along with all vectors in episodic memory, which are called traces, and by evaluating the similarity of the probe to each trace and averaging these similarities, we obtain a number that represents a global familiarity signal. By assuming that people use this familiarity signal as the basis of their likelihood judgments, we can simulate some of the results found in the field of likelihood judgment.
I suspect that with a bit of work, one could even use MINERVA-DM to simulate retrospective and prospective judgments of task duration, and thus, planning fallacy.
The first two are about this article and the third is about the planning fallacy primer. I mentioned hypothesis generation because you talked about ‘pair debugging’ and asking people to state the obvious solutions to a problem as ways to increase the number of hypotheses that are generated, and it pattern matched to what I’d read about hypothesis generation.
I’m definitely talking about this as opposed to the other thing. MINERVA-DM is a good example of this class of hypothesis in the realm of likelihood judgment. Hilbert (2012) is an information-theoretic approach to memory bias in likelihood judgment.
I’m just saying that it looks like there’s a lot of fruit to be picked in memory theory and not many people are talking about it.
Okay, gotcha. Thanks for the clarification on the points.
I admit I don’t quite understand what MINERVA-DM is...I glanced at the paper briefly and it appears to be a...theoretical framework for making decisions which is shown to exhibit similar biases to human thought? (With cells and rows and ones?)
I’m definitely not strong in this domain; any chance you could summarize?
I can’t describe it too much better than that. The framework is meant to be descriptive as opposed to normative.
A complete description of MINERVA-DM would involve some simple math, but I can try to describe it in words. The rows of numbers you saw are vectors. We take a vector that represents an observation, called a probe, along with all vectors in episodic memory, which are called traces, and by evaluating the similarity of the probe to each trace and averaging these similarities, we obtain a number that represents a global familiarity signal. By assuming that people use this familiarity signal as the basis of their likelihood judgments, we can simulate some of the results found in the field of likelihood judgment.
I suspect that with a bit of work, one could even use MINERVA-DM to simulate retrospective and prospective judgments of task duration, and thus, planning fallacy.
Huh, okay, cool. Thanks for the additional info!