Inference to the Best Explanation: Powerful — But Incomplete

Most of what we believe isn’t proven.

It’s chosen.

Not at random — but by selecting the explanation that seems to make the most sense of the available evidence.

We do this constantly.

A scientist choosing between competing theories. A detective reconstructing a crime. A doctor diagnosing a patient.

In each case, the goal isn’t absolute certainty.

It’s something else:

finding the explanation that best fits the facts.

This method has a name.

It’s called inference to the best explanation.

At its core, inference to the best explanation is a simple idea:

When multiple explanations are possible, we should prefer the one that best accounts for the available evidence.

Not just any explanation — but the one that is:

• most coherent
• most comprehensive
• and most consistent with what we observe

This form of reasoning is often referred to as abductive reasoning, and it has been widely discussed in philosophy of science and epistemology as a central way humans actually form beliefs about the world.

Philosophers like Gilbert Harman helped formalize the concept, arguing that much of our reasoning — both scientific and everyday — relies not on strict proof, but on selecting the explanation that best makes sense of the data.

In other words:

We don’t usually arrive at truth by eliminating all doubt.

We arrive by choosing what seems to explain reality better than the alternatives.

This way of reasoning isn’t abstract.

It’s how decisions are made in the real world.

In science, researchers rarely prove a theory in the strict sense. Instead, they compare competing explanations and adopt the one that best accounts for the data — balancing explanatory scope, consistency, and predictive success.

In medicine, a diagnosis is not usually a deduction. A doctor evaluates symptoms, test results, and patient history, then selects the explanation that most coherently accounts for all of them.

In law, juries are not asked to achieve absolute certainty. They are asked to determine which explanation of the evidence is most convincing given the available facts.

Even in everyday life, we do the same thing.

We observe patterns, consider possibilities, and settle on the explanation that seems to make the most sense of what we see.

In this way, inference to the best explanation is not just a philosophical concept.

It is one of the primary ways human beings navigate reality.

The Problem: What Does “Best” Actually Mean?

For all its power, inference to the best explanation has a critical weakness.

It relies on a word that sounds precise — but often isn’t:

“best.”

What makes one explanation better than another?

Is it simplicity? Explanatory scope? Internal consistency? Predictive success?

In practice, the answer is usually some combination of these — but rarely in a clearly defined or consistently applied way.

Different people prioritize different criteria.

One person may value simplicity above all else. Another may prioritize explanatory depth. A third may focus on predictive accuracy, even if the explanation becomes more complex.

The result is that “best explanation” often becomes a kind of informal balancing act — one that depends heavily on subjective weighting.

And this introduces a deeper problem.

Because explanations are rarely evaluated across all domains at once.

They are usually judged within limited contexts — scientific, philosophical, experiential, or practical — without a clear mechanism for integrating those domains into a unified evaluation.

This creates a situation where multiple competing explanations can all appear “best”… depending on which aspects of reality are being emphasized.

And once that happens, the method begins to lose resolution.

Not because the idea is wrong —

but because it lacks a structured way to compare explanations at the level of entire systems.

The System-Level Problem

The deeper issue isn’t just that “best” is loosely defined.

It’s that explanations are rarely evaluated as complete systems.

In practice, most comparisons happen within isolated domains.

A theory may perform well scientifically but struggle philosophically. It may align with empirical data but conflict with human experience. It may offer strong explanatory depth in one area while relying on weak or ad hoc reasoning in another.

And yet, these tensions often go unresolved.

Because instead of evaluating how an explanation performs across all domains simultaneously, we tend to assess it piece by piece — focusing on its strengths while minimizing its weaknesses.

This creates a fragmented form of evaluation.

Different explanations appear strongest in different contexts, and without a unified framework, there is no consistent way to compare them as wholes.

The result is familiar.

Debates stall. Positions harden. And multiple competing explanations continue to coexist, each appearing persuasive within its own domain.

But this raises a more demanding question:

What happens when we stop evaluating explanations in isolation —

and instead ask how well they hold together as integrated systems?

Because reality itself is not divided into separate categories.

Scientific, historical, experiential, and philosophical dimensions are not independent silos — they are interconnected aspects of the same world.

And any explanation that aims to describe reality as a whole must ultimately account for that interconnectedness.

This is where the limits of inference to the best explanation become most visible.

It is effective at selecting between competing explanations within a given domain.

But it does not, by itself, provide a clear method for evaluating how those explanations perform when all domains are brought together at once.

The Missing Layer: Convergence

If the problem is fragmentation, then the solution begins to come into focus.

Not just better explanations —

but converging ones.

An explanation becomes significantly more powerful when it doesn’t just perform well in one domain, but begins to align across multiple independent domains at once.

When scientific evidence, historical patterns, experiential data, and philosophical reasoning all begin pointing in the same direction, something important happens:

The explanation becomes harder to dismiss — not because any single line of evidence is decisive, but because the combined weight becomes increasingly difficult to account for any other way.

This is the underlying intuition behind consilience.

But when we return to inference to the best explanation, a gap becomes clear.

IBE allows us to choose between competing explanations.

But it does not give us a structured way to evaluate how deeply those explanations converge across domains, or how much pressure they can withstand when all forms of evidence are considered together.

This matters because convergence is not binary.

It exists in degrees.

Some explanations align loosely across domains, requiring adjustment or reinterpretation along the way. Others align tightly, with minimal tension and increasing constraint as more evidence is considered.

And that difference is not trivial.

It is the difference between an explanation that can survive scrutiny —

and one that becomes stronger because of it.

As convergence increases, interpretive flexibility decreases.

And that is where explanatory power becomes most visible.

The question, then, is no longer simply:

Which explanation is best within a given domain?

It becomes:

Which explanation demonstrates the strongest, most constrained convergence across all domains of reality?

From Explanation to Evaluation

At this point, the limitation becomes clear.

Inference to the best explanation helps us choose between competing ideas.

Consilience shows us that convergence across independent domains strengthens those ideas.

But neither provides a fully structured way to evaluate how well an entire explanatory system holds together under that convergence.

And that is where a shift begins to take place.

From selecting explanations…

to evaluating systems.

This is the space where frameworks like the Worldview Evaluation Protocol (WEP) begin to emerge.

Rather than asking which explanation is best within a limited context, WEP attempts to evaluate how well a system performs across multiple independent domains at once — such as predictive capacity, anomaly integration, knowledge production, macro-historical alignment, and experiential coherence.

The focus is not on isolated success.

It is on how those domains interact under constraint.

Because once a system is evaluated as a whole, a different pattern begins to appear.

Explanations that rely on flexibility tend to fragment when forced to maintain consistency across domains. They require reinterpretation, adjustment, or selective emphasis to preserve coherence.

Explanations that are more tightly aligned with the structure of reality tend to show the opposite pattern.

As more domains are introduced, they do not collapse under pressure — they become more stable.

More constrained. More integrated. More difficult to replace.

In that sense, the question is no longer simply:

What is the best explanation?

It becomes:

Which system demonstrates the strongest convergence across all domains of reality — and continues to hold under pressure as those domains expand?

That is a different kind of evaluation.

And it may represent the next step in how we determine what is true.

If you’re interested in a deeper breakdown of system-level evaluation and convergent frameworks, you can explore more at convergentepistemology.com.