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The Universal Plausibility Metric (UPM) & Principle (UPP)
1Department of ProtoBioCybernetics/ProtoBioSemiotics, The Gene Emergence Project of The Origin of Life Science Foundation, Inc, 113-120 Hedgewood Dr, Greenbelt, MD 20770-1610, USA. life@us.net
Theoretical Biology & Medical Modelling
|December 5, 2009
Summary
Scientific plausibility can now be objectively measured using the Universal Plausibility Metric (UPM) and falsified with the Universal Plausibility Principle (UPP). This provides a rigorous standard for evaluating chance hypotheses, especially in origin-of-life models.
Area of Science:
- Astrobiology
- Origin of Life Research
- Scientific Methodology
Background:
- Scientific plausibility requires more than mere possibility, necessitating a defined bound for operational falsification.
- Quantifying subjective plausibility is challenging but achievable.
- The Universal Plausibility Metric (UPM) and Universal Plausibility Principle (UPP) offer objective measures.
Purpose of the Study:
- To introduce a quantifiable method for assessing the plausibility of chance hypotheses.
- To establish a universal standard for falsifying hypotheses based on their plausibility.
- To address the need for objective evaluation in fields like origin-of-life research.
Main Methods:
- Development of the Universal Plausibility Metric (UPM) for objective measurement.
- Introduction of the Universal Plausibility Principle (UPP) with a falsification inequality (xi < 1).
- Demonstration that UPM and UPP are independent of specific experimental designs and data.
Main Results:
- A method for objectively measuring the plausibility of any chance hypothesis (UPM) is presented.
- A numerical inequality (UPP) is provided for definitive falsification of chance hypotheses (UPM metric xi < 1).
- The UPM and UPP are shown to be universally applicable and pre-existing.
Conclusions:
- No low-probability hypothetical plausibility assertion should pass peer-review without formal falsification using the UPP inequality (xi < 1).
- The UPM and UPP provide a critical standard for scientific rigor in hypothesis evaluation.
- These principles are essential for advancing fields reliant on chance-based models, such as abiogenesis.
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