Title AI-Assisted Hypothesis Generation: From Fragmented Manifestations to the Morphology of War Contamination
Author(s) Laboratory of Military Ecotones
Affiliation New Euro Vision: exhibitions, marketing, research s.r.o.
Country Czech Republic
Contribution type conceptual paper; methodological paper
Thematic area • Theory, Methodology, and Evidence
Conference framework connection • Analytical Track AT-01 — provisional title to be defined• Analytical Track AT-02 — provisional title to be defined• Analytical Track AT-03 — provisional title to be defined• Analytical Track AT-04 — provisional title to be defined• Analytical Track AT-05 — provisional title to be defined• Analytical Track AT-06 — provisional title to be defined• Analytical Track AT-07 — provisional title to be defined
Abstract War-related environmental contamination is a complex, long-term, and only partially observable process. Researchers usually encounter separate manifestations: a damaged industrial facility, an ammunition disposal site, an abnormal concentration in a soil sample, a change in freshwater quality, unexploded ordnance, disturbed land, or a delayed health observation. No individual manifestation contains the complete process. Connections between the source, environmental transformation, transport, exposure, and long-term consequence must therefore be reconstructed under substantial uncertainty.
Under these conditions, hypotheses are not optional additions to established facts. They are necessary working material of the cognitive process. A hypothesis temporarily connects manifestations, proposes a possible causal structure, and identifies the evidence that should exist if the proposed structure is correct. Hypotheses must consequently be inexpensive to formulate, open to comparison, easy to revise, and possible to abandon.
Traditional research often reduces hypotheses until they satisfy strict disciplinary admissibility. A chemical hypothesis remains chemical, a hydrological hypothesis remains hydrological, and a historical hypothesis remains historical. Such formulations may become methodologically precise and institutionally acceptable while losing the capacity to represent relationships and development. The perfected disciplinary hypothesis becomes a terminal destination rather than an expendable instrument of inquiry.
A second constraint is the implicit requirement of recognition by the scientific community. Recognition may refer to publication, peer review, citation, institutional status, or compatibility with an accepted paradigm. These different processes are often combined into a single social judgement. This contribution separates recognition from scientific evaluation. Silence, non-citation, or institutional exclusion does not constitute logical refutation. The production of recognition must itself become an object of research.
The expert–AI system creates a practical alternative. The expert defines the object, evaluates plausibility, and accepts responsibility. AI expands the capacity to compare literatures, languages, historical cases, causal sequences, and alternative explanations. The objective is not to maximize the number of hypotheses, but to maintain a controlled portfolio of competing hypotheses and derive observations capable of distinguishing among them.
This working standard directs research beyond collections of unique cases toward recognition of the recurring morphology of long-term war contamination.
Key points • Hypotheses are indispensable working material when the investigated process is complex, developing, and only partially observable.
• Strict disciplinary admissibility can isolate a hypothesis from the relationships required to understand the complete process.
• Recognition by a scientific community is an institutional condition and cannot substitute for logical and empirical evaluation.
• The expert–AI system should develop and compare competing hypotheses rather than protect a single preferred explanation.
• The purpose of hypothesis generation is to identify discriminating observations and reconstruct the morphology of the process.
Keywords artificial intelligence; hypothesis generation; expert–AI system; war-related environmental contamination; military ecotones; scientific recognition; disciplinary admissibility; causal reconstruction; process morphology; uncertainty
Main discussion question How can the expert–AI system use competing hypotheses to move from fragmented manifestations toward recognition of the long-term morphology of war-related environmental contamination?
OJS publication link https://pollution-diseases-ojs.org/index.php/pd/article/view/76
Note. Analytical TracksIn addition to the main thematic areas, the conference programme will include several cross-cutting analytical tracks. These tracks will be defined during the preparation of the programme, based on the submitted abstracts and the emerging links between presentations.At the preliminary stage, abstracts may be assigned to provisional analytical tracks marked as AT-01 to AT-07. Final track titles will be announced after the Scientific Committee has reviewed the submitted materials.