“Acausal” is used as a contrast to Causal Decision Theory (CDT). CDT states that decisions should be evaluated with respect to their causal consequences; ie if there’s no way for a decision to have a causal impact on something, then it is ignored. (More precisely, in terms of Pearl’s Causality, CDT is equivalent to having your decision conduct a counterfactual surgery on a Directed Acyclic Graph that represents the world, with the directions representing causality, then updating nodes affected by the decision.) However, there is a class of decisions for which your decision literally does have an acausal impact. The classic example is Newcomb’s Problem, in which another agent uses a simulation of your decision to decide whether or not to put money in a box; however, the simulation took place before your actual decision, and so the money is already in the box or not by the time you’re making your decision.
“Acausal” refers to anything falling in this category of decisions that have impacts that do not result causally from your decisions or actions. One example is, as above, Newcomb’s Problem; other examples include:
The Prisoner’s Dilemma, or any other symmetrical game, when played against the same algorithm you are running. You know that the other player will make the same choice as you, but your choice has no causal impact on their choice.
In EDT, which originates in academia, casuality is completely ignored, and only correlations are used. This leads to the correct answer on Newscomb’s Problem, but fails on others- for example, the Smoking Lesion. UDT is essentially EDT, but with an agent that has access to its own code. (There’s a video and transcript explaining this in more detail here).
TDT, like CDT, relies on causality instead of correlation; however, instead of having agents chose a decision that is implemented, it has agents first chose a platonic computation that is instantiated in, among other things, the actual decision maker; however, is is also instantiated in every other algorithm is equal, acausally, to the decision maker’s algorithm, including simulations, other agents, etc. And, given all of these instantiations, the agent then choses the utility-maximizing algorithm.
“Acausal” is used as a contrast to Causal Decision Theory (CDT). CDT states that decisions should be evaluated with respect to their causal consequences; ie if there’s no way for a decision to have a causal impact on something, then it is ignored. (More precisely, in terms of Pearl’s Causality, CDT is equivalent to having your decision conduct a counterfactual surgery on a Directed Acyclic Graph that represents the world, with the directions representing causality, then updating nodes affected by the decision.) However, there is a class of decisions for which your decision literally does have an acausal impact. The classic example is Newcomb’s Problem, in which another agent uses a simulation of your decision to decide whether or not to put money in a box; however, the simulation took place before your actual decision, and so the money is already in the box or not by the time you’re making your decision.
“Acausal” refers to anything falling in this category of decisions that have impacts that do not result causally from your decisions or actions. One example is, as above, Newcomb’s Problem; other examples include:
Acausal romance: romances where interaction is impossible
The Prisoner’s Dilemma, or any other symmetrical game, when played against the same algorithm you are running. You know that the other player will make the same choice as you, but your choice has no causal impact on their choice.
There are a number of acausal decision theories: Evidential Decision Theory (EDT), Updateless Decision Theory (UDT), Timeless Decision Theory (TDT), and Ambient Decision Theory (ADT).
In EDT, which originates in academia, casuality is completely ignored, and only correlations are used. This leads to the correct answer on Newscomb’s Problem, but fails on others- for example, the Smoking Lesion. UDT is essentially EDT, but with an agent that has access to its own code. (There’s a video and transcript explaining this in more detail here).
TDT, like CDT, relies on causality instead of correlation; however, instead of having agents chose a decision that is implemented, it has agents first chose a platonic computation that is instantiated in, among other things, the actual decision maker; however, is is also instantiated in every other algorithm is equal, acausally, to the decision maker’s algorithm, including simulations, other agents, etc. And, given all of these instantiations, the agent then choses the utility-maximizing algorithm.
ADT...I don’t really know, although the wiki says that it is “variant of updateless decision theory that uses first order logic instead of mathematical intuition module (MIM), emphasizing the way an agent can control which mathematical structure a fixed definition defines, an aspect of UDT separate from its own emphasis on not making the mistake of updating away things one can still acausally control.”