Report an observation

A Classification Method: The Judicial evidence

An investigation method for UAP is to objectify the facts, establish:

1. An evaluation of all reliable proofs and facts that constitute statements: the Robustness [R] level
2. The strength of these statements in a regard of standardized hypothesis: the Strangeness [S] level

Finally, we consider that a phenomenon is objective when:

New concepts: Strangeness, Robustness

Relations between R, S and A, B, C, D

The Strangeness Concept

The Strangeness value (𝑆= 0.550) measures the distance of the observed phenomena from what is currently known or understood. It is calculated as the complement to 1 of the best-scoring hypothesis (in this case, 0.450).

• 𝑆 = 0 indicates something perfectly explainable and well-documented, with all necessary information and knowledge available to classify it conclusively.
• 𝑆 = 1 represents something entirely indescribable, so far removed from existing knowledge and understanding that it defies any categorization.

Importantly, the strangeness evaluation does not follow a linear progression. Small increases in the strangeness value may represent disproportionately larger gaps in understanding, particularly as the value approaches 1. This non-linear behavior reflects the increasing difficulty of explaining phenomena as they deviate further from known frameworks.

The GEIPAN's methodology incorporates this concept for several reasons:

It bridges known and unknown realms

By defining strangeness as the complement to the best available hypothesis, we provide a quantitative framework to measure how far a phenomenon deviates from established knowledge.

This helps frame the unknown in a systematic way, offering a starting point for further investigation while avoiding premature conclusions.

It scales the uncertainty

The use of a non-linear progression in the strangeness metric is particularly important, as it captures the exponential challenges of explaining phenomena as they move farther from known parameters.

This approach aligns well with how human understanding tends to work—where small gaps in knowledge are easier to close, but larger gaps require exponentially more effort, resources, and breakthroughs.

It is objective but flexible

Tying the strangeness value to the complement of the best hypothesis score creates an objective baseline for evaluating phenomena.

At the same time, the methodology remains adaptive, allowing for refinement as new hypotheses or data become available.

It supports multidisciplinary analysis

This approach can facilitate discussions across disciplines, providing a common language for scientists, sociologists, engineers, and policymakers to evaluate the « distance » of phenomena from what is known in their respective domains.

It encourages humility and open-mindedness

Acknowledging a high strangeness value reflects intellectual humility—it recognizes the limits of current knowledge while maintaining an openness to explore new possibilities. This is particularly important in UAP research, where preconceived notions can often skew analyses.

The Robustness Concept: The Need for Reliable Evidence

While Strangeness is an essential dimension for evaluating a case, it is not sufficient on its own to draw conclusions. Strangeness measures how far a phenomenon deviates from known frameworks, but without corroborating evidence, it remains speculative. Equally important is the dimension of Robustness, which quantifies the Quantity of Reliable information available. Robustness can be expressed as:

𝑅 = λ x ρ

where represents Robustness, λ is the Quantity of useful Information collected, and ρ is the Reliability of that information, both ranging from 0 to 1. The product illustrates a key principle:

• Collecting large amounts of data is meaningless if it is unreliable (ρ = 0, so 𝑅 = 0).
• Conversely, even the most reliable source (ρ = 1) is useless if it provides no meaningful or verifiable data (λ = 0, so 𝑅 = 0).

Like Strangeness, Robustness is also ranging from 0 to 1, with a non-linear progression. This balance ensures that robustness reflects both the extent and the reliability of the available evidence.

Unlike a mathematical proof, where conclusions are absolute, evaluating unexplained phenomena resembles a judicial process: a convergence of facts must collectively align to form the most plausible explanation, while uncertainties inevitably remain due to the complexity of the real world.

As Carl Sagan famously stated, « Extraordinary claims require extraordinary evidence », and so to close a case we need to ensure that 𝑅 > 𝑆

In this sense, as Strangeness increases, so too must Robustness be reinforced proportionally to maintain a grounded and rigorous analysis.

Thus, while plausible explanations are explored, the unresolved aspect of robustness—due to incomplete or unsourced testimonies—should remain a key focus to complete this work. Evaluating robustness demands access to sourced evidence, such as direct testimonies, imagery, or physical data. Without it, even the most striking phenomena risk falling into conjecture.

Hypothesis

Selection Method

Evaluation Method

Each hypothesisis broken down in atable of argumentsmade of a standardizedlist ofcore elements.Each element is a unique descriptive criteria, orthogonal to the others: size, color, shape, trajectory, etc…

• The validity of each element is assessed for and against the hypothesis with « Pros » and « Cons » arguments.
• For each of them a value between [-1, 1] is given. Ranging from -1 totally incoherent to +1 perfectly coherent. This is similar to a correlation factor.
• This is the most fragile elements (« weak link ») that discriminate and limit the level of validity ofthe hypothesis. This is due to the fact thatonly one element can change the issue (see below).
• The list of elements is not limitative:each specific hypothesis may have specific elements of verification

Evaluation of an hypothesis. Example.

The weakiest value is translated to a score between [0, 1]: this is the reliability of our hypothesis:

Hyp = (Min( [Arg] ) + 1) / 2

Given the uncertainties associated with the data collected, it could be usefull to take the average of the two weakiest elements.

If several hypothesis are computed, then, the best one give us the Strangeness:

S = 1 - Max( [Hyp] )

Analytical stance

In summary, the array of arguments allows:

• To help in the evaluation to define the pros & cons of the hypothesis, and encourage self-criticism: adopt the posture of the « judge » not of the « lawyer ».
• To quickly identify the weaknesses of your hypothesis, measure for eachelement its degree of coherence within its observational context.
• Precisely, calculate the value of the hypothesis.

Finally, for themost complex cases (with higher strangeness ~ 2-5%), a panel of experts (like a jury) may be requested to cross-check the investigation and ensure that no hypothesis or elements were forgotten.