Over time, I’ve found myself returning to a deceptively simple question: How do we actually evaluate which explanations are true, especially when we’re dealing with entire systems of belief rather than isolated claims?
We already have powerful tools for assessing truth. Bayesian reasoning helps us update beliefs in light of new evidence. Inference to the Best Explanation (IBE) provides a way to choose among competing hypotheses. Consilience highlights when independent lines of evidence converge on the same conclusion. These approaches have sharpened our thinking and reduced many obvious errors. In countless contexts, they work extremely well.
Yet they also tend to operate somewhat in parallel. You can update probabilities Bayesian-style. You can apply IBE to pick the explanation that best accounts for the data at hand. You can notice consilience when unrelated sources align. What has been less clear is how these methods should come together when the object of evaluation is not a single proposition or hypothesis, but a complete, interconnected worldview; a system that makes claims across prediction, explanation, history, consciousness, morality, and lived experience all at once.
This is where the fragmentation becomes noticeable. A particular claim might fit the data well in one domain while requiring heavy assumptions or ad-hoc adjustments in another. One framework might be elegantly simple but leave important phenomena unexplained. Another might align beautifully with historical patterns but struggle with the reality of conscious experience. When we evaluate these pieces separately, we can easily convince ourselves we are making progress, even as deeper structural tensions remain largely unexamined.
The result is a familiar kind of epistemic fragmentation: we score points on individual arguments, but rarely achieve clarity on which overall system is more robust. This gap matters because many of the most important questions we face (in science, ethics, AI alignment, and philosophy) ultimately depend on how well entire frameworks hold together, not just how well they perform on any single test.
I’ve been exploring whether a more structured, system-level approach could help bridge this gap. The core intuition is that genuine convergence across genuinely independent domains carries a different and stronger kind of epistemic weight than any isolated argument or piece of evidence on its own.
One framework for approaching this is the Worldview Evaluation Protocol (WEP), which falls within the broader framework of Convergent Epistemology.
At a high level, it evaluates worldviews by testing how well they perform and hold together across several domains simultaneously: predictive capacity (how effectively the system generates constrained, forward-looking expectations rather than retrofitting explanations), anomaly integration (how well it absorbs genuinely conflicting data without endless special pleading), knowledge production (its track record of generating reliable, expanding insight over time), macro-historical alignment (how coherently it accounts for large-scale patterns in human history and civilizational development), and experiential coherence (how well it fits with the felt reality of conscious, moral, meaning-seeking agents).
The central idea is convergence. When relatively independent lines of inquiry (each operating under its own constraints) consistently point in the same direction, that pattern carries substantial epistemic weight. When maintaining strength in one domain requires increasing flexibility or ad-hoc adjustments in another, those tensions become visible at the system level rather than staying hidden.
This is not presented as a finished method, but rather as an attempt to make the intuitive desire to “evaluate the whole system” more explicit and testable. The real test will be whether it helps surface hidden weaknesses that current approaches tend to miss, or whether it simply adds unnecessary complexity.
I’d be particularly interested in how others see this. Does the idea of cross-domain convergence add something meaningful beyond the tools we already have, or is it largely a restatement of existing ideas in a new language? When comparing entire systems rather than individual claims, what does a well-structured evaluation actually look like in practice?
