Wednesday, September 13th @ 10:00am in KC 149 or Join us on Zoom!
Abstract: There has been a proliferation of different interpretations of quantum mechanics, some of which are vague, incomplete, or internally inconsistent. There is also a group of operational theories based only on regularities in empirical data (laws), which explicitly avoid specifying any ontology, and yet their proponents make claims about them, like nonlocality or superdeterminism, that can only be true or false given an ontology, which is a fundamental logic error. We present the generative programs framework (GPF), which encompasses any and all physical theories that explain the empirical data for a given scenario or model, as a standard for describing physical theories, whether ontological or operational, which prevents ambiguity, incompleteness, and inconsistency. A generative program is a set of instructions that starts from nothing and generates all of the relevant empirical data. The instructions are executed in a logical order independent of space and time, and may include the creation of intermediate entities, like functions and their inputs, which participate in the generation of the data. An operationalist will interpret such a program as a useful summary of the data and some regular laws that it obeys, but will ascribe it no ontological significance. A realist will interpret a program as describing true ontological machinery that creates the empirical data, such that what is `physical' is defined as what appears in the instructions of the program, rendering all other facts about the data incidental. Yes/no questions about ontology like, "is this theory local?", or "is this theory superdeterministic?" can only be answered for programs which are considered to be ontologically real, since these are questions about the physical machinery of the ontology. Laws that appear in empirical data describe correlations, from which causality can never be inferred, meaning that the existence of causality is another yes/no question about an ontological program, and causality is thus independent of realism. The information flow in a generative program can be represented as a directed-acyclic graph (DAG) of ontological priority describing the logical order in which entities are generated in the process of the generation of the empirical data. Parts of the ontological priority DAG may also represent causality for an ontologically real program, but this is not required. The GPF is a unifying framework for evaluating and comparing physical theories, in that, a) no physical theory can be taken seriously unless its program can be specified, and b) given the programs for various theories, we can rigorously analyze their ontological properties and then apply our many subjective heuristics (locality is good, superdeterminism is bad, fine-tuning is bad, causality is good, Occam's razor, identity of indiscernibles, etc.) to argue why one should be preferred over another.