The ACDT algorithm is similar, except that when it cuts the causal links to its decision, it also adds potential links from that decision node to all the other nodes in the graph. In this situation, the CDT agent will always two-box, since it treats Ω's decision as fixed, and in that case two-boxing dominates, since you get whatever's in the first box, plus the consolation prize. Essentially all the incoming arrows to the decision node are cut (though the CDT agent keeps track of any information gained that way), then the CDT agent maximises its utility by choosing its action: The CDT agent uses the ' d o' operator from Pearl's Causality.

The green node is a utility node, whose value the agent cares about. The dark red node is the decision node, which the agent can affect. In terms of causal graphs, we can represent it this way: There is always a small consolation prize in the second box. If Ω predicts you will one-box, it had put a large prize in that first box otherwise that box is empty. In the Newcomb problem, there is a predictor Ω who leaves two boxes, and predicts whether you will take one ('one-box') or both ('two-box'). Defining ACDT CDT and the Newcomb problem

The drawback is its lack of elegance the advantage, if it works, is that it's simple to specify and focuses attention on the important aspects of deducing the graph.

It is, essentially, CDT which can draw extra, acausal arrows on the causal graphs, and which attempts to figure out which graph represents the world it's in. Inspired by my post on problems with causal decision theory (CDT), here is a hacked version of CDT that seems to be able to imitate timeless decision theory (TDT) and functional decision theory (FDT), as well as updateless decision theory (UDT) under certain circumstances.Ĭall this ACDT, for (a)causal decision theory.

Decision 3 hack