#decisiontheory — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #decisiontheory, aggregated by home.social.
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We Stopped Using the Mathematics That Works
https://gfrm.in/posts/why-decision-theory-lost/index.html
#HackerNews #WeStoppedUsingMathematics #DecisionTheory #LossOfMathematics #HackerNews #Insights #Innovation
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We Stopped Using the Mathematics That Works
https://gfrm.in/posts/why-decision-theory-lost/index.html
#HackerNews #WeStoppedUsingMathematics #DecisionTheory #LossOfMathematics #HackerNews #Insights #Innovation
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We Stopped Using the Mathematics That Works
https://gfrm.in/posts/why-decision-theory-lost/index.html
#HackerNews #WeStoppedUsingMathematics #DecisionTheory #LossOfMathematics #HackerNews #Insights #Innovation
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We Stopped Using the Mathematics That Works
https://gfrm.in/posts/why-decision-theory-lost/index.html
#HackerNews #WeStoppedUsingMathematics #DecisionTheory #LossOfMathematics #HackerNews #Insights #Innovation
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We Stopped Using the Mathematics That Works
https://gfrm.in/posts/why-decision-theory-lost/index.html
#HackerNews #WeStoppedUsingMathematics #DecisionTheory #LossOfMathematics #HackerNews #Insights #Innovation
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Thank you for this fun decision problem!
To start getting a picture I examined the much simpler case of a "two-sided die" or a coin with faces 1 and 2. Same rules otherwise.
Below is a sketch of a truncated decision tree for this case, when the initial roll is "1". Decision nodes are blue squares with decisions as solid blue lines; the decisions are "K" for "keep" and "R" for "roll". Inference/chance nodes are red circles with outcomes as dashed red lines; outcomes are "1" and "2"; it's understood that each has 50% probability.
The expected utility of each decision is in grey at the end of the corresponding line. The fold-back value at each decision branch is in grey above the corresponding decision node.
If the first roll is "2" then it's clearly best to keep it, as the subsequent roll would yield a divisor and a 0$ outcome.
If the first roll is "1", the best decision apparently is to roll once more, and then, if the outcome is "2", keep the result.
Assume that the player stops in any case with "Keep" at the Nth decision, say N = 5. Then the expected utility of the previous "Roll" decision is 9$/2, which is less that the utility of the "Keep" decision, $7. So the previous decision should be "Keep". Folding backwards this situation remains up to the very first decision, for which the expected utility of "Roll" is 3$/2, whereas that for "Keep" is 1$.
It *seems* that the reasoning above would still apply if the player could continue indefinitely, since at each decision the utility of "Keep" is (1+2N)$, whereas that of "Roll" is (1.5 + N)$. But I'm not completely sure about this, there may be some logical gap.
Unfortunately this simplest case is too special, owing to its binary nature, to say something about your score-bound-strategy assumption. It'll be cool to check a 3-sided die :)
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Thank you for this fun decision problem!
To start getting a picture I examined the much simpler case of a "two-sided die" or a coin with faces 1 and 2. Same rules otherwise.
Below is a sketch of a truncated decision tree for this case, when the initial roll is "1". Decision nodes are blue squares with decisions as solid blue lines; the decisions are "K" for "keep" and "R" for "roll". Inference/chance nodes are red circles with outcomes as dashed red lines; outcomes are "1" and "2"; it's understood that each has 50% probability.
The expected utility of each decision is in grey at the end of the corresponding line. The fold-back value at each decision branch is in grey above the corresponding decision node.
If the first roll is "2" then it's clearly best to keep it, as the subsequent roll would yield a divisor and a 0$ outcome.
If the first roll is "1", the best decision apparently is to roll once more, and then, if the outcome is "2", keep the result.
Assume that the player stops in any case with "Keep" at the Nth decision, say N = 5. Then the expected utility of the previous "Roll" decision is 9$/2, which is less that the utility of the "Keep" decision, $7. So the previous decision should be "Keep". Folding backwards this situation remains up to the very first decision, for which the expected utility of "Roll" is 3$/2, whereas that for "Keep" is 1$.
It *seems* that the reasoning above would still apply if the player could continue indefinitely, since at each decision the utility of "Keep" is (1+2N)$, whereas that of "Roll" is (1.5 + N)$. But I'm not completely sure about this, there may be some logical gap.
Unfortunately this simplest case is too special, owing to its binary nature, to say something about your score-bound-strategy assumption. It'll be cool to check a 3-sided die :)
-
Thank you for this fun decision problem!
To start getting a picture I examined the much simpler case of a "two-sided die" or a coin with faces 1 and 2. Same rules otherwise.
Below is a sketch of a truncated decision tree for this case, when the initial roll is "1". Decision nodes are blue squares with decisions as solid blue lines; the decisions are "K" for "keep" and "R" for "roll". Inference/chance nodes are red circles with outcomes as dashed red lines; outcomes are "1" and "2"; it's understood that each has 50% probability.
The expected utility of each decision is in grey at the end of the corresponding line. The fold-back value at each decision branch is in grey above the corresponding decision node.
If the first roll is "2" then it's clearly best to keep it, as the subsequent roll would yield a divisor and a 0$ outcome.
If the first roll is "1", the best decision apparently is to roll once more, and then, if the outcome is "2", keep the result.
Assume that the player stops in any case with "Keep" at the Nth decision, say N = 5. Then the expected utility of the previous "Roll" decision is 9$/2, which is less that the utility of the "Keep" decision, $7. So the previous decision should be "Keep". Folding backwards this situation remains up to the very first decision, for which the expected utility of "Roll" is 3$/2, whereas that for "Keep" is 1$.
It *seems* that the reasoning above would still apply if the player could continue indefinitely, since at each decision the utility of "Keep" is (1+2N)$, whereas that of "Roll" is (1.5 + N)$. But I'm not completely sure about this, there may be some logical gap.
Unfortunately this simplest case is too special, owing to its binary nature, to say something about your score-bound-strategy assumption. It'll be cool to check a 3-sided die :)
-
Thank you for this fun decision problem!
To start getting a picture I examined the much simpler case of a "two-sided die" or a coin with faces 1 and 2. Same rules otherwise.
Below is a sketch of a truncated decision tree for this case, when the initial roll is "1". Decision nodes are blue squares with decisions as solid blue lines; the decisions are "K" for "keep" and "R" for "roll". Inference/chance nodes are red circles with outcomes as dashed red lines; outcomes are "1" and "2"; it's understood that each has 50% probability.
The expected utility of each decision is in grey at the end of the corresponding line. The fold-back value at each decision branch is in grey above the corresponding decision node.
If the first roll is "2" then it's clearly best to keep it, as the subsequent roll would yield a divisor and a 0$ outcome.
If the first roll is "1", the best decision apparently is to roll once more, and then, if the outcome is "2", keep the result.
Assume that the player stops in any case with "Keep" at the Nth decision, say N = 5. Then the expected utility of the previous "Roll" decision is 9$/2, which is less that the utility of the "Keep" decision, $7. So the previous decision should be "Keep". Folding backwards this situation remains up to the very first decision, for which the expected utility of "Roll" is 3$/2, whereas that for "Keep" is 1$.
It *seems* that the reasoning above would still apply if the player could continue indefinitely, since at each decision the utility of "Keep" is (1+2N)$, whereas that of "Roll" is (1.5 + N)$. But I'm not completely sure about this, there may be some logical gap.
Unfortunately this simplest case is too special, owing to its binary nature, to say something about your score-bound-strategy assumption. It'll be cool to check a 3-sided die :)
-
Thank you for this fun decision problem!
To start getting a picture I examined the much simpler case of a "two-sided die" or a coin with faces 1 and 2. Same rules otherwise.
Below is a sketch of a truncated decision tree for this case, when the initial roll is "1". Decision nodes are blue squares with decisions as solid blue lines; the decisions are "K" for "keep" and "R" for "roll". Inference/chance nodes are red circles with outcomes as dashed red lines; outcomes are "1" and "2"; it's understood that each has 50% probability.
The expected utility of each decision is in grey at the end of the corresponding line. The fold-back value at each decision branch is in grey above the corresponding decision node.
If the first roll is "2" then it's clearly best to keep it, as the subsequent roll would yield a divisor and a 0$ outcome.
If the first roll is "1", the best decision apparently is to roll once more, and then, if the outcome is "2", keep the result.
Assume that the player stops in any case with "Keep" at the Nth decision, say N = 5. Then the expected utility of the previous "Roll" decision is 9$/2, which is less that the utility of the "Keep" decision, $7. So the previous decision should be "Keep". Folding backwards this situation remains up to the very first decision, for which the expected utility of "Roll" is 3$/2, whereas that for "Keep" is 1$.
It *seems* that the reasoning above would still apply if the player could continue indefinitely, since at each decision the utility of "Keep" is (1+2N)$, whereas that of "Roll" is (1.5 + N)$. But I'm not completely sure about this, there may be some logical gap.
Unfortunately this simplest case is too special, owing to its binary nature, to say something about your score-bound-strategy assumption. It'll be cool to check a 3-sided die :)
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Jon Baron shared Peter Wakker's annotated bibliography of #decisionTheory
> 9000 entries!
DocX http://personal.eur.nl/wakker/refs/webrfrncs.docx
PDF http://personal.eur.nl/wakker/refs/webrfrncs.pdf
BibTeX (no annotations, I merged redundancies) https://www.dropbox.com/scl/fi/vbkki82h62ydq0fol1g8d/Decision-Theory.bib?rlkey=84m8zx3tyaa4uy6p0zptkybnx&st=qx9myebb&dl=0
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Jon Baron shared Peter Wakker's annotated bibliography of #decisionTheory
> 9000 entries!
DocX http://personal.eur.nl/wakker/refs/webrfrncs.docx
PDF http://personal.eur.nl/wakker/refs/webrfrncs.pdf
BibTeX (no annotations, I merged redundancies) https://www.dropbox.com/scl/fi/vbkki82h62ydq0fol1g8d/Decision-Theory.bib?rlkey=84m8zx3tyaa4uy6p0zptkybnx&st=qx9myebb&dl=0
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Jon Baron shared Peter Wakker's annotated bibliography of #decisionTheory
> 9000 entries!
DocX http://personal.eur.nl/wakker/refs/webrfrncs.docx
PDF http://personal.eur.nl/wakker/refs/webrfrncs.pdf
BibTeX (no annotations, I merged redundancies) https://www.dropbox.com/scl/fi/vbkki82h62ydq0fol1g8d/Decision-Theory.bib?rlkey=84m8zx3tyaa4uy6p0zptkybnx&st=qx9myebb&dl=0
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Jon Baron shared Peter Wakker's annotated bibliography of #decisionTheory
> 9000 entries!
DocX http://personal.eur.nl/wakker/refs/webrfrncs.docx
PDF http://personal.eur.nl/wakker/refs/webrfrncs.pdf
BibTeX (no annotations, I merged redundancies) https://www.dropbox.com/scl/fi/vbkki82h62ydq0fol1g8d/Decision-Theory.bib?rlkey=84m8zx3tyaa4uy6p0zptkybnx&st=qx9myebb&dl=0
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Jon Baron shared Peter Wakker's annotated bibliography of #decisionTheory
> 9000 entries!
DocX http://personal.eur.nl/wakker/refs/webrfrncs.docx
PDF http://personal.eur.nl/wakker/refs/webrfrncs.pdf
BibTeX (no annotations, I merged redundancies) https://www.dropbox.com/scl/fi/vbkki82h62ydq0fol1g8d/Decision-Theory.bib?rlkey=84m8zx3tyaa4uy6p0zptkybnx&st=qx9myebb&dl=0
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What is #Intelligence?
In a nutshell: If Life is about #survival and #reproduction, Intelligence is the art of making decisions in a complex world that support these goals, through:
🧩 #Modeling – simplify the world
💓 #Feeling – value what matters🔗 https://medium.com/@modfeel/modfeel-coming-soon-in-english-800a209238e5
#modfeel #AI #Philosophy #Cognition #Complexity #DecisionTheory #Evolution #biology #science
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What is #Intelligence?
In a nutshell: If Life is about #survival and #reproduction, Intelligence is the art of making decisions in a complex world that support these goals, through:
🧩 #Modeling – simplify the world
💓 #Feeling – value what matters🔗 https://medium.com/@modfeel/modfeel-coming-soon-in-english-800a209238e5
#modfeel #AI #Philosophy #Cognition #Complexity #DecisionTheory #Evolution #biology #science
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What is #Intelligence?
In a nutshell: If Life is about #survival and #reproduction, Intelligence is the art of making decisions in a complex world that support these goals, through:
🧩 #Modeling – simplify the world
💓 #Feeling – value what matters🔗 https://medium.com/@modfeel/modfeel-coming-soon-in-english-800a209238e5
#modfeel #AI #Philosophy #Cognition #Complexity #DecisionTheory #Evolution #biology #science
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To be further accurate, you would also not know how many people are on each track, or whether there are any people at all on each track, or how many tracks there are.
#PascalsWager #trolleyProblem #decisionTheory #apologetics
h/t @AnswersInReason
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To be further accurate, you would also not know how many people are on each track, or whether there are any people at all on each track, or how many tracks there are.
#PascalsWager #trolleyProblem #decisionTheory #apologetics
h/t @AnswersInReason
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To be further accurate, you would also not know how many people are on each track, or whether there are any people at all on each track, or how many tracks there are.
#PascalsWager #trolleyProblem #decisionTheory #apologetics
h/t @AnswersInReason
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Based on work by psychologists Daniel Kahneman and Amos Tversky, who have shown bad feelings about losses are stronger than good feelings we have about gains, Schwartz argues that as you’re presented with countless choices, your pleasure at the prospect of more options is canceled out by the anticipated loss of making a wrong choice.
#Choice #DecisionTheory #dating #optimization #socialnetworking
https://nautil.us/the-problem-with-modern-romance-is-too-much-choice-236135/ -
Based on work by psychologists Daniel Kahneman and Amos Tversky, who have shown bad feelings about losses are stronger than good feelings we have about gains, Schwartz argues that as you’re presented with countless choices, your pleasure at the prospect of more options is canceled out by the anticipated loss of making a wrong choice.
#Choice #DecisionTheory #dating #optimization #socialnetworking
https://nautil.us/the-problem-with-modern-romance-is-too-much-choice-236135/ -
Based on work by psychologists Daniel Kahneman and Amos Tversky, who have shown bad feelings about losses are stronger than good feelings we have about gains, Schwartz argues that as you’re presented with countless choices, your pleasure at the prospect of more options is canceled out by the anticipated loss of making a wrong choice.
#Choice #DecisionTheory #dating #optimization #socialnetworking
https://nautil.us/the-problem-with-modern-romance-is-too-much-choice-236135/ -
Based on work by psychologists Daniel Kahneman and Amos Tversky, who have shown bad feelings about losses are stronger than good feelings we have about gains, Schwartz argues that as you’re presented with countless choices, your pleasure at the prospect of more options is canceled out by the anticipated loss of making a wrong choice.
#Choice #DecisionTheory #dating #optimization #socialnetworking
https://nautil.us/the-problem-with-modern-romance-is-too-much-choice-236135/ -
Based on work by psychologists Daniel Kahneman and Amos Tversky, who have shown bad feelings about losses are stronger than good feelings we have about gains, Schwartz argues that as you’re presented with countless choices, your pleasure at the prospect of more options is canceled out by the anticipated loss of making a wrong choice.
#Choice #DecisionTheory #dating #optimization #socialnetworking
https://nautil.us/the-problem-with-modern-romance-is-too-much-choice-236135/ -
DECISION MAKING ADVICE:
Whenever someone complains that a kid is doing something, respond with, "It would be better if they were doing drugs." because the absurdity of that response is directly proportional to the absurdity of the complaint.
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DECISION MAKING ADVICE:
Whenever someone complains that a kid is doing something, respond with, "It would be better if they were doing drugs." because the absurdity of that response is directly proportional to the absurdity of the complaint.
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DECISION MAKING ADVICE:
Whenever someone complains that a kid is doing something, respond with, "It would be better if they were doing drugs." because the absurdity of that response is directly proportional to the absurdity of the complaint.
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DECISION MAKING ADVICE:
Whenever someone complains that a kid is doing something, respond with, "It would be better if they were doing drugs." because the absurdity of that response is directly proportional to the absurdity of the complaint.
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DECISION MAKING ADVICE:
Whenever someone complains that a kid is doing something, respond with, "It would be better if they were doing drugs." because the absurdity of that response is directly proportional to the absurdity of the complaint.
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Been having a bit of existential dread these days. I've got tremendous buckets of skill and talent in building mathematical models and doing decision making calculations and etc, but when I hear about people actually doing that stuff its like "I ran this linear regression and now I don't know how to use the outcomes and what sort of p value can I calculate to tell me if what I did was significant?" Or whatever. The world doesn't WANT competence. #bayesian #decisionTheory #statistics
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Been having a bit of existential dread these days. I've got tremendous buckets of skill and talent in building mathematical models and doing decision making calculations and etc, but when I hear about people actually doing that stuff its like "I ran this linear regression and now I don't know how to use the outcomes and what sort of p value can I calculate to tell me if what I did was significant?" Or whatever. The world doesn't WANT competence. #bayesian #decisionTheory #statistics
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Been having a bit of existential dread these days. I've got tremendous buckets of skill and talent in building mathematical models and doing decision making calculations and etc, but when I hear about people actually doing that stuff its like "I ran this linear regression and now I don't know how to use the outcomes and what sort of p value can I calculate to tell me if what I did was significant?" Or whatever. The world doesn't WANT competence. #bayesian #decisionTheory #statistics
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Been having a bit of existential dread these days. I've got tremendous buckets of skill and talent in building mathematical models and doing decision making calculations and etc, but when I hear about people actually doing that stuff its like "I ran this linear regression and now I don't know how to use the outcomes and what sort of p value can I calculate to tell me if what I did was significant?" Or whatever. The world doesn't WANT competence. #bayesian #decisionTheory #statistics
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Been having a bit of existential dread these days. I've got tremendous buckets of skill and talent in building mathematical models and doing decision making calculations and etc, but when I hear about people actually doing that stuff its like "I ran this linear regression and now I don't know how to use the outcomes and what sort of p value can I calculate to tell me if what I did was significant?" Or whatever. The world doesn't WANT competence. #bayesian #decisionTheory #statistics
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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from the standpoint of model selection, parsimony often boils down to dimensionality reduction
#modelSelection #parsimony #OccamsRazor #dimensionalityReduction #degreesOfFreedom #complexity #informationTheory #biasVarianceTradeoff #overfitting #underfitting #optimization #parameterTuning #crossValidation #inverseProblems #inference #statisticalLearning #machineLearning #ML #dataScience #modeling #decisionTheory #fitting #regression #classification #residualError #costFunction #performanceLoss
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A super late #introduction
Hi Mastodon, and thank you @kinozhao for running @fediphilosopher !
I'm Xin Hui, a PhD student in #philosophy shuttling between the University of #Pittsburgh and #MIT. My main philosophical interests lie in the intersection of #decisiontheory and #feminist epistemology, particularly how decision models bear on our #social and #political agency.
You may know me as thyacinth on #twitter!
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A super late #introduction
Hi Mastodon, and thank you @kinozhao for running @fediphilosopher !
I'm Xin Hui, a PhD student in #philosophy shuttling between the University of #Pittsburgh and #MIT. My main philosophical interests lie in the intersection of #decisiontheory and #feminist epistemology, particularly how decision models bear on our #social and #political agency.
You may know me as thyacinth on #twitter!
-
A super late #introduction
Hi Mastodon, and thank you @kinozhao for running @fediphilosopher !
I'm Xin Hui, a PhD student in #philosophy shuttling between the University of #Pittsburgh and #MIT. My main philosophical interests lie in the intersection of #decisiontheory and #feminist epistemology, particularly how decision models bear on our #social and #political agency.
You may know me as thyacinth on #twitter!
-
A super late #introduction
Hi Mastodon, and thank you @kinozhao for running @fediphilosopher !
I'm Xin Hui, a PhD student in #philosophy shuttling between the University of #Pittsburgh and #MIT. My main philosophical interests lie in the intersection of #decisiontheory and #feminist epistemology, particularly how decision models bear on our #social and #political agency.
You may know me as thyacinth on #twitter!
-
A super late #introduction
Hi Mastodon, and thank you @kinozhao for running @fediphilosopher !
I'm Xin Hui, a PhD student in #philosophy shuttling between the University of #Pittsburgh and #MIT. My main philosophical interests lie in the intersection of #decisiontheory and #feminist epistemology, particularly how decision models bear on our #social and #political agency.
You may know me as thyacinth on #twitter!
-
You have a problem: you currently pick thresholds for model-based actions using some arbitrary heuristic.
Your solution: pick the threshold that maximizes expected utility (e.g. revenue, profit, ROI, …) instead. That’s the definition of the rational decision, right?
Hmm, for some reason you now seem to have several more problems.
#DecisionTheory #Optimization #rationality #AppliedML -
You have a problem: you currently pick thresholds for model-based actions using some arbitrary heuristic.
Your solution: pick the threshold that maximizes expected utility (e.g. revenue, profit, ROI, …) instead. That’s the definition of the rational decision, right?
Hmm, for some reason you now seem to have several more problems.
#DecisionTheory #Optimization #rationality #AppliedML -
You have a problem: you currently pick thresholds for model-based actions using some arbitrary heuristic.
Your solution: pick the threshold that maximizes expected utility (e.g. revenue, profit, ROI, …) instead. That’s the definition of the rational decision, right?
Hmm, for some reason you now seem to have several more problems.
#DecisionTheory #Optimization #rationality #AppliedML -
You have a problem: you currently pick thresholds for model-based actions using some arbitrary heuristic.
Your solution: pick the threshold that maximizes expected utility (e.g. revenue, profit, ROI, …) instead. That’s the definition of the rational decision, right?
Hmm, for some reason you now seem to have several more problems.
#DecisionTheory #Optimization #rationality #AppliedML