The way of science, in a nutshell, is to assume a theory is true then see if it predicts what happens. If it works, we accept it, if it doesn’t, we reject it. This is how quantum theory arose. In contrast, the way of faith is to find facts that support a belief and ignore those that don’t.
Computing is a science because it designs a logical model, implements it, then tests it against expectations. Design science uses this approach, as information systems are designed in theory, built in practice, then tested against requirements, in an iterative process (Hevner et al., 2004).
Reverse engineering is a subset of design science that discovers the processes behind an application. The method is to first specify the outputs, best-guess the processes involved, then check the outputs predicted against those observed, and repeat until consistently correct. Quantum simulations use this method to predict atomic events. Note that the proposed design must be falsifiable.
Reverse engineering the physical world then involves the following steps:
1. Specify: Specify the physical world outputs (physics).
2. Design: Design processes that could produce those outputs (computer science).
3. Validate: Compare expected with actual outputs (experiments).
4. Repeat: Repeat steps 1-3 to achieve design consistency (quantum realism).
The consistency constraint is critical, as while it is easy to satisfy one requirement, satisfying many is much harder. In addition, the design should:
1. Follow best practices. Use established computer science principles.
2. Satisfy Occam’s razor. Given a design choice, take the simpler option.
The aim is to derive the laws of physics from processing first principles, step by step. A scientific theory can’t choose what it explains, so this method must explain all current physics, including space, time, energy, matter, gravity, magnetism, spin, and charge. Cherry-picking cases to show that selected programs mimic some world properties isn’t a new kind of science but an old kind of bias (Wolfram, 2002). Reverse engineering the physical world could reveal this approach to be:
1. Spurious. Spurious models add no value because they need new assumptions or parameters to explain every new fact.
2. Coincidental. Coincidental models work for a while, by luck, but fail over time, as they cherry-pick cases to support the model and ignore those that contradict it.
3 Useful. Useful models aren’t actually true, but are stepping-stones to new research that increases knowledge.
4. Valid. Valid models not only match observed reality in many ways, but also predict new effects that contradict established models and are later found to be true.
If physics describes physical events and computer science describes processing events, whether the physical world is generated by quantum processes is a question that design science can evaluate.