Title: The Causal Frame Problem: An Algorithmic Perspective,
Abstract: The Frame Problem (FP) is a puzzle in philosophy of mind and epistemology,
articulated by the Stanford Encyclopedia of Philosophy as follows: "How do we
account for our apparent ability to make decisions on the basis only of what is
relevant to an ongoing situation without having explicitly to consider all that
is not relevant?" In this work, we focus on the causal variant of the FP, the
Causal Frame Problem (CFP). Assuming that a reasoner's mental causal model can
be (implicitly) represented by a causal Bayes net, we first introduce a notion
called Potential Level (PL). PL, in essence, encodes the relative position of a
node with respect to its neighbors in a causal Bayes net. Drawing on the
psychological literature on causal judgment, we substantiate the claim that PL
may bear on how time is encoded in the mind. Using PL, we propose an inference
framework, called the PL-based Inference Framework (PLIF), which permits a
boundedly-rational approach to the CFP to be formally articulated at Marr's
algorithmic level of analysis. We show that our proposed framework, PLIF, is
consistent with a wide range of findings in causal judgment literature, and
that PL and PLIF make a number of predictions, some of which are already
supported by existing findings.