# Decision Theory

 brook v1.3.0Aug 11th 2020 (+66/-11) Ruby v1.2.0Apr 11th 2020 (+143/-24) Ruby v1.1.0Apr 11th 2020 (+253/-2922) Caspar42 v0.0.44Oct 6th 2017 (+80) Deku-shrub v0.0.43May 12th 2017 (+26/-50) /* Commonly discussed decision theories */ lifelonglearner v0.0.42May 12th 2017 /* Commonly discussed decision theories */ lifelonglearner v0.0.41May 12th 2017 (-30) /* Commonly discussed decision theories */ lifelonglearner v0.0.40May 12th 2017 (+80/-1) /* Commonly discussed decision theories */ lifelonglearner v0.0.39May 12th 2017 (+6) /* Commonly discussed decision theories */ notsonewuser v0.0.38Apr 5th 2013 (+43) /* Blog posts */ added Luke's recent "Decision Theory FAQ"
brookv1.3.0Aug 11th 2020 (+66/-11)

Decision theory is the study of principles and algorithms for making correct decisions—decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals [1].

The best known decision theories are Causal Decision Theory (CDT) and Evidential Decision Theory (EDT). On LessWrong, an alternative family of decision theories has been discussed heavily with the varying names of Updateless Decision Theory (UDT), Timeless Decision Theory (TDT), and Functional Decision Theory (FDT).

Rubyv1.2.0Apr 11th 2020 (+143/-24)

Decision theory is the formal study of principles and algorithms for making correct decisions—that is, decisions that allow an agent's choicesagent to achieve better outcomes with respect to its goals [1].

Rubyv1.1.0Apr 11th 2020 (+253/-2922)

Decision theory is the formal study of principlesan agent's choices [1].

The best known decision theories are Causal Decision Theory (CDT) and algorithms for making correct decisions—that is, decisions that allowEvidential Decision Theory (EDT). On LessWrong, an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologiesalternative family of decision theories can be analyzed by consideringhas been discussed heavily with the decisions they suggest in the certain idealized situations that stretch the limitsvarying names of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include:

## Commonly discussed decision theories

Theories invented by researchers associated with MIRI and LW:

Other decision theories are listed in A comprehensive list of decision theories (FDT).

## Sequence by orthonormal (Decision Theories: A Semi-Formal Analysis)

Caspar42v0.0.44Oct 6th 2017 (+80)

Decision theory is the study of principles and algorithms for making correct decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologies of decision theories can be analyzed by considering the decisions they suggest in the certain idealized situations that stretch the limits of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include:

## Commonly discussed decision theories

Theories invented by researchers associated with MIRI and LW:

Other decision theories are listed in A comprehensive list of decision theories.

## Sequence by orthonormal (Decision Theories: A Semi-Formal Analysis)

Deku-shrubv0.0.43May 12th 2017 (+26/-50) /* Commonly discussed decision theories */

Decision theory is the study of principles and algorithms for making correct decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologies of decision theories can be analyzed by considering the decisions they suggest in the certain idealized situations that stretch the limits of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include:

## Commonly discussed decision theories

Theories invented by researchers associated with MIRI and LW:

## Sequence by orthonormal (Decision Theories: A Semi-Formal Analysis)

lifelonglearnerv0.0.41May 12th 2017 (-30) /* Commonly discussed decision theories */

Decision theory is the study of principles and algorithms for making correct decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologies of decision theories can be analyzed by considering the decisions they suggest in the certain idealized situations that stretch the limits of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include:

## Commonly discussed decision theories

Theories invented by researchers associated with MIRI and LW:

## Sequence by orthonormal (Decision Theories: A Semi-Formal Analysis)

lifelonglearnerv0.0.40May 12th 2017 (+80/-1) /* Commonly discussed decision theories */

Decision theory is the study of principles and algorithms for making correct decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologies of decision theories can be analyzed by considering the decisions they suggest in the certain idealized situations that stretch the limits of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include:

## Commonly discussed decision theories

Theories invented by researchers associated with MIRI and LW:

## Sequence by orthonormal (Decision Theories: A Semi-Formal Analysis)

lifelonglearnerv0.0.39May 12th 2017 (+6) /* Commonly discussed decision theories */

Decision theory is the study of principles and algorithms for making correct decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologies of decision theories can be analyzed by considering the decisions they suggest in the certain idealized situations that stretch the limits of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include:

## Commonly discussed decision theories

Theories invented by researchers associated with MIRI and LW:

## Sequence by orthonormal (Decision Theories: A Semi-Formal Analysis)

notsonewuserv0.0.38Apr 5th 2013 (+43) /* Blog posts */ added Luke's recent "Decision Theory FAQ"

Decision theory is the study of principles and algorithms for making correct decisions—that is, decisions that allow an agent to achieve better outcomes with respect to its goals. Every action at least implicitly represents a decision under uncertainty: in a state of partial knowledge, something has to be done, even if that something turns out to be nothing (call it "the null action"). Even if you don't know how you make decisions, decisions do get made, and so there has to be some underlying mechanism. What is it? And how can it be done better? Decision theory has the answers.

A core idea in decision theory is that of expected utility maximization, usually intractable to directly calculate in practice, but an invaluable theoretical concept. An agent assigns utility to every possible outcome: a real number representing the goodness or desirability of that outcome. The mapping of outcomes to utilities is called the agent's utility function. (The utility function is said to be invariant under affine transformations: that is, the utilities can be scaled or translated by a constant while resulting in all the same decisions.) For every action that the agent could take, sum over the utilities of the various possible outcomes weighted by their probability: this is the expected utility of the action, and the action with the highest expected utility is to be chosen.

## Thought experiments

The limitations and pathologies of decision theories can be analyzed by considering the decisions they suggest in the certain idealized situations that stretch the limits of decision theory's applicability. Some of the thought experiments more frequently discussed on LW include: