#### List of Notation  
G,H,E,P,S,C,M,U,R,A,... specific models/theories defined in Section 2  
\(T\)\(\in\) {G,H,E,P,S,C,M,U,R,A,...} theory/model  
ToE Theory of Everything (in any sense)  
ToE candidate a theory that might be a partial or perfect or wrong ToE  
UToE Universal ToE  
CToE Complete ToE (i+e+l+n+o)  
UCToE Universal Complete ToE  
theory model which can explain\(\approx\)describe\(\approx\)predict\(\approx\)compress observations  
universe typically refers to visible/observed universe  
multiverse un- or only weakly connected collection of universes  
predictive power precision and coverage  
precision the accuracy of a theory  
coverage how many phenomena a theory can explain/predict  
prediction refers to unseen, usually future observations  
computability assumption: that our universe is computable  
\(q^{T}\)\(\in\) {0,1}\({}^{*}\) the program that generates the universe modeled by theory \(T\)  
\(u^{q}\)\(\in\) {0,1}\({}^{\infty}\) the universe generated by program \(q\): \(u^{q}\)\(=\) UTM(\(q\))  
UTM Universal Turing Machine  
\(s\)\(\in\) {0,1}\({}^{*}\) observation model/program. Extracts \(o\) from \(u\). \(o^{qs}\)\(\in\) {0,1}\({}^{\infty}\) Subject's \(s\) observations in universe \(u^{q}\): \(o^{qs}\)\(=\) UTM(\(s\),\(u^{q}\))  
\(o_{1:t}^{true}\) True past observations  
\(\ddot{q}\),\(\ddot{u}\) Program and universe of UToE  
\(S(o|u)\) Probability of observing \(o\) in universe \(u\)  
\(Q(u)\) Probability of universe \(u\) (according to some prob. theory \(T\))  
\(P(o)\) Probability of observing \(o\)Introduction  
This paper uses an _information-theoretic_ and _computational_ approach for addressing the _philosophical_ problem of judging theories (of everything) in _physics_. In order to keep the paper generally accessible, I've tried to minimize field-specific jargon and mathematics, and focus on the core problem and its solution. By _theory_ I mean any _model_ which can explain\(\approx\)describe\(\approx\)predict\(\approx\)compress [1] our observations, whatever the form of the model. Scientists often say that their model _explains_ some phenomenon. What is usually meant is that the model _describes_ (the relevant aspects of) the observations more compactly than the raw data. The model is then regarded as capturing a law (of nature), which is believed to hold true also for unseen/future data. This process of inferring general conclusions from example instances is call _inductive reasoning_. For instance, observing 1000 black ravens but no white one supports but cannot prove the hypothesis that all ravens are black. In general, induction is used to find properties or rules or models of past observations. The ultimate purpose of the induced models is to use them for making predictions, e.g., that the next observed raven will also be black. Arguably inductive reasoning is even more important than deductive reasoning in science and everyday life: for scientific discovery, in machine learning, for forecasting in economics, as a philosophical discipline, in common-sense decision making, and last but not least to find theories of everything. Historically, some famous, but apparently misguided philosophers [2, 3], including [PERSON] and [PERSON], even disputed the existence, necessity or validity of inductive reasoning. Meanwhile it is well-known how minimum encoding length principles [4, 5], rooted in (algorithmic) information theory [6], quantify [PERSON]'s razor principle, and lead to a solid pragmatic foundation of inductive reasoning [7]. Essentially, one can show that the more one can _compress_, the better one can _predict_, and vice versa. A deterministic theory/model allows from initial conditions to determine an observation sequence, which could be coded as a bit string. For instance, [PERSON] mechanics maps initial planet positions+velocities into a time-series of planet positions. So a deterministic model with initial conditions is \"just\" a compact representation of an infinite observation string.