How much time do we have before catastrophic disclosure occurs?

Abstract: Claims of the retrieval of crashed craft or vehicles from non-human intelligence(s) (NHI) abound in the popular culture and media. For this article, the number of unsubstantiated claims is utilized to estimate the time expected until a "catastrophic disclosure" occurs. The term was defined at the 2023 Sol Foundation's inaugural conference as an accidental disclosure of strong evidence of the existence of NHI. The phrase refers to this occurring outside the control of major human institutions, such as governments and militaries. One possible example of this is the crash of a piloted (space)craft or ET probe in the middle of a busy metropolis (such as the New York City Times Square). The distribution of humans across the Earth's surface, the population as a function of time, and the fraction of individuals owning a camera-phone, also versus time, are each taken into consideration as a foundation for a rigorous statistical analysis. This author adopts a skeptical and agnostic approach and does not conclude NHI or ET are real, but applies standard statistical distributions as educational examples of critical thinking to an issue which captures the imagination of the public as almost no other issue does. Making the extraordinary assumptions that sentient species other than humans exist, are capable of constructing vehicles for transportation, and are sufficiently fallible that their technology can malfunction, it becomes possible to quantify some potential answers to the question of how long it might be before smartphone imagery and/or video evidence appears on the web and becomes irrevocable via classification in the modern era. Results of simulations of numerous potential scenarios with varying degrees of optimism indicate that, if NHI are real, catastrophic disclosure may actually happen accidentally rather soon, with the mean expected year being 2040 +/- 20 under the default assumptions.

• The paper utilizes Monte Carlo simulations, incorporating factors like population density and hypothetical crash rates, to statistically model the potential for accidental NHI disclosure via civilian tech.
• Simulation results suggest a mean expected year for accidental catastrophic disclosure is around 2040 ± 20, with a probable timeframe between the present and 2050 under default assumptions.
• The study offers a statistical framework for analyzing UAP/NHI claims and highlights the need for rigorous data collection and potential public-civilian collaboration in this field.

A Quantitative Analysis of the Potential for Catastrophic Disclosure of Non-Human Intelligences

The paper "How much time do we have before catastrophic disclosure occurs?" by Matthew Szydagis offers a statistical analysis aimed at predicting the timeframe of a potential accidental disclosure of conclusive evidence concerning non-human intelligences (NHI) and unidentified aerial phenomena (UAP). The study assesses the likelihood of such an event occurring via a statistically driven approach that draws from contemporary claims and societal conditions, incorporating parameters such as global population density and smartphone ownership.

Methodological Approach

The research employs a Monte Carlo simulation framework to model the conditions under which a NHI-related event might be involuntarily disclosed through civilian-operated technologies, such as smartphones. Three primary assumptions underpin the study:

1. The potential existence of NHIs or advanced terrestrial groups equipped with extraordinary transportation technology.
2. The capacity of these entities to operate on or near Earth's surface.
3. The fallibility of their technologies, thereby allowing for possible accidents such as crashes.

The simulations account for varying crash rates, ranging from as low as 1 per century to as high as 100 per century. The simulation incorporates population density data, smartphone proliferation, and hypothetical crash visibility radii. Various statistical distributions, including skew-Gaussian for population density and Poisson for crash events, are applied to derive meaningful probability metrics related to disclosure events.

Results and Statistical Findings

The primary outcome of the simulations suggested that should NHIs exist and their technology be fallible, the mean expected year for an accidental catastrophic disclosure to occur under default assumptions is around 2040 ± 20. The analysis indicated that scenarios with higher crash rates and observation radii predict earlier disclosure, potentially as early as the mid-21st century. However, scenarios assuming fewer crashes or smaller observation areas extend the timeline, potentially into the 22nd century.

Moreover, several assumptions yield unsustainable results when compared against current empirical observations (e.g., no disclosure by 2024). By setting these scenarios aside, the insights primarily provide a window between the present period and 2050 as a statistically viable timeframe for potential disclosure, assuming no mitigating actions are taken by authorities.

Implications for Future Research and Policy

This research bears significant implications for both scientific inquiries into UAPs/NHIs and the sociopolitical handling of such phenomena. The probabilistic approach does not only predict disclosure windows but also offers a methodology for revisiting claims using statistical limits and can thereby serve to substantiate or refute claims over time. Consequently, it underscores the importance of integrating scientific rigor and investigative methodologies analogous to those deployed in mainstream scientific endeavors (e.g., dark matter research).

As the scientific discourse around UAPs and potential NHIs continues to evolve, this research highlights the necessity for garnering empirical evidence through technological means accessible to the civilian populace, aligning public interest with rigorous data collection paradigms. Moving forward, prioritizing openness in data and fostering collaborative research between governmental bodies, civilian scientists, and the public could ameliorate potential societal disruptions associated with such disclosures.

In conclusion, this paper sets a precedent for statistically modeling exogenous phenomena previously relegated to the conjectural domain, thereby providing a scaffold from which further scientific explorations—both methodological and empirical—can build toward greater understanding and integration of potential NHI interactions.