roposta de Submissã\;o para o Fó\;rum Internacional da Sabedoria
\nTí\;tulo da Apresentaç\;ã\;o:
\nDa Totalidade Aberta à\; Infraestrutura Material: O Fluxo de Neutrinos Có\;smicos como o Hardware de Registro do Ser
\nResumo (Abstract):
\nA filosofia contemporâ\;nea e o pensamento pó\;s-filosó\;fico de Alexis Karpouzos convergem para a superaç\;ã\;o dos sistemas metafí\;sicos fechados\, introduzindo o conceito de Totalidade Aberta e cogê\;nese relacional. No entanto\, a transiç\;ã\;o da filosofia como mera representaç\;ã\;o abstrata para uma sabedoria experiencial integrada exige a identificaç\;ã\;o do substrato fí\;sico que viabiliza essa rede de relaç\;õ\;es vivas.
\nEsta apresentaç\;ã\;o introduz a Ontologia do Estar (V7.0) como a resposta de engenharia có\;smica a esse horizonte aberto. Demonstra-se que o Fundo Có\;smico de Neutrinos\, operando em uma densidade de saturaç\;ã\;o está\;vel de $n_{\\nu} \\approx 340 \\text{ cm}^{-3}$\, funciona como um hardware de registro passivo\, contí\;nuo e incompressí\;vel. Sob essa ó\;tica\, os paradoxos de nã\;o-localidade e entrelaç\;amento quâ\;ntico deixam de ser anomalias "fantasmagó\;ricas" e passam a ser compreendidos como protocolos de sincronizaç\;ã\;o de rede nativa em um meio saturado (a metá\;fora da Montanha Submersa).
\nAtravé\;s da formulaç\;ã\;o matemá\;tica da constante geomé\;trica Mariana ($\\mu_{\\text{inf}} = H_0/c$)\, propõ\;e-se que os desvios espectrais observados no universo profundo nã\;o decorrem de uma expansã\;o caó\;tica\, mas sim de um processo de atenuaç\;ã\;o por fricç\;ã\;o mecâ\;nica (Rutilâ\;ncia Espectral) em um cosmos autocontido com conservaç\;ã\;o estrita de informaç\;ã\;o ($dS = 0$). O trabalho oferece a infraestrutura material que ancora o pensamento meta-ontoló\;gico\, unificando a ontologia do processo com a fí\;sica de alta performance.
\nIdentificadores do Autor para Inclusã\;o:
\nAutor: Ricardo Mariano de Oliveira Dias
\nORCIDA: 0009-0005-0182-0021
\nBase de Ancoragem: Zenodo DOI 10.5281/zenodo.21220716
\nThe term &ldquo\;randomness&rdquo\; often appears in the context of Quantum Mechanics. The behaviour of quantum systems is said to be random\, the outcomes of quantum mechanical experiments are said to be random\, certain devices based on quantum processes are said to operate in a random way&hellip\; However\, the concept of randomness is rarely made precise in these contexts. Meanwhile\, in another branch of science &ndash\; computability theory\, also called recursion theory &ndash\; a fully precise concept of randomness has been developed\, termed &ldquo\;algorithmic randomness&rdquo\;. How are these two uses of the term &ldquo\;randomness&rdquo\; related? Is the concept of algorithmic randomness relevant to Quantum Mechanics? The aim of this workshop is to address various facets of this question in an interdisciplinary gathering. The event will take place at the Jagiellonian University in Krakó\;w on August 31st and September 1st\, 2026. The format of the workshop will be hybrid: it is possible to participate either in person or online (but the organizers strongly encourage in-person participation).
\nOur keynote speakers are:
\nIn addition to talks by our invited speakers\, we plan a few contributed talks. We invite submissions concerning any aspect of the relationship between algorithmic randomness and Quantum Mechanics\, including (but not limited to) the following questions and topics:
\nAbstracts of about 500 words should be sent to joanna.luc@uj.edu.pl by 10.07.2026.
\nTo participate without giving a talk (either in person or online)\, please register by sending an e-mail to joanna.luc@uj.edu.pl.
ORGANIZER;CN=Joanna Luc;CN=Tomasz Placek: METHOD:PUBLISH END:VEVENT BEGIN:VEVENT DTSTAMP:20260711T184047Z DTSTART;TZID=Europe/London:20261215T230000 DTEND;TZID=Europe/London:20261215T230000 SUMMARY:Synthese Topical Collection: Severity and Learning from Error UID:20260712T092601Z-iCalPlugin-Grails@fe80:0:0:0:5864:16ff:fe1a:92fe%3 TZID:Europe/London DESCRIPTION:Synthese Topical Collection: Severity and Learning from Error
\nCall for Papers
\nThis Topical Collection examines how inquiry learns from error by focusing on a basic principle of evidence in science\, statistics\, medicine\, law\, epistemology\, and day-to-day learning: a claim is not well-tested\, known or epistemically warranted\, if it is based on a method that makes it easy to accept\, conclude or infer the claim\, even if it is false. Such a claim may accord well with the data\, but it has not passed a stringent or severe test. While this overarching intuition is widely shared\, the problem of how to understand or satisfy it remains unsolved. C. S. Peirce emphasizes randomization and (what is now called) pre-designation to achieve self-correcting methods. Popper viewed severity in terms of satisfying novel predictive success and surviving stringent attempts at falsification. Deborah Mayo (1996\, 2018) combines elements from Popper and Peirce with the use of error probabilities from statistical methods: proposed solutions to problems earn warrant by surviving probes that were capable of showing them wrong or inadequate. This Topical Collection takes &ldquo\;severity&rdquo\; to be a broad meta-level concept according to which a claim &ndash\; whether a report of a perception\, a prediction\, a hypothesis\, or part of a model &ndash\; is assessed according to whether\, and how readily\, its errors and inadequacies would have been found\, if present.
\nSeveral questions arise: What errors matter for a given aim? What would it take for a method to be capable of detecting them? How in actual practice can inquirers show they have engaged in responsible error probing when there are no formal probability models? Addressing questions like these is of urgent importance today as we face high-powered methods that make it easy to find impressive looking eUects that are spurious and non-replicating\, or to arrive at well-fitting models that do not predict well\, do not replicate\, or do not provide substantive scientific understanding. These questions arise in debates about methodological shifts in AI/ML\, randomized clinical trials\, legal evidence\, climate modeling\, statistical inference\, and error-prone inference in general. We seek to bring these metascience debates into direct contact and to ask what is often left hidden: What errors are now being controlled\, and which have quietly dropped out of view? By bringing together philosophers\, statisticians\, and scientists\, we aim to develop a shared set of problems and tools with a forward-looking goal: to shape emerging practices\, rather than merely react to them with retrospective commentary.
\nWe welcome submissions on any topic that broadly relates to severity or learning from error. We invite contributions that develop\, apply or challenge severity-based reasoning\, or that develop alternative approaches\, Bayesian\, frequentist\, machine-learning and other\, which engage the same underlying concern: how inquiry learns from error\, and how claims earn warrant by surviving probes that were capable of showing them wrong or inadequate. We encourage contributions that explore connections between concepts of severity in diUerent fields. Notably\, the concepts of sensitivity and safety in contemporary epistemology can be understood through the lens of severity\, and both are redolent of stability in AI. We also welcome discussions of how contemporary manifestations of severity interrelate with the traditional notions of severity from Popper and Peirce\, and how concepts of severity may help in tackling fundamental problems of induction\, falsification\, underdetermination\, and realism in philosophy of science.
\nThe collection is partly motivated by the thirtieth anniversary of Deborah Mayo&rsquo\;s (1996\, Chicago) Error and the Growth of Experimental Knowledge (Lakatos Prize 1998) and the development of its account of severe testing.
\nAppropriate Topics for Submission include\, among others:
\nSeverity and philosophy of statistics
\n&bull\;Do recent controversies about the uses of error probabilities in statistics (and metastatistics) present a challenge to severity-based reasoning?
\n&bull\;Do the new fields of post-selection inference (in AI and other disciplines) allow for error control despite data-driven constructions? Or do they shift attention to diUerent errors?
\n&bull\;How does severity link to such notions as calibration\, security\, and stability\, and statistical techniques that promote such notions as robustness analyses\, and multiverse analyses?
\nSeverity and philosophy of science
\n&bull\;What does it mean for a method\, or for science itself\, to be self-correcting or error-correcting? Does it fit best with a pragmatist philosophy?
\n&bull\;How does severe probing take place in the historical sciences\, e.g.\, climate science\, geology? Can claims be well probed without being replicable?
\n&bull\;Rather than probing for falsity\, how can we severely probe if a model is adequate for a purpose or problem of interest?
\nSeverity and contemporary epistemology
\n&bull\;Can a useful cross-cutting epistemology that links science\, statistics\, and applied epistemology be built around the concept of severity?
\n&bull\;Do features of severity (e.g.\, auditing of assumptions) point to ways to avoid problems of sensitivity and safety in epistemology?
\n&bull\;Does requiring severity explain why legal epistemology resists mere base-rates and &ldquo\;naked statistics&rdquo\;? Does it solve proof paradoxes in legal epistemology?
\nTracking shifts in error control
\n&bull\;How does AI/ML shift from modeling data-generating mechanisms in statistics to optimizing predictive performance in machine learning.
\n&bull\;How do changing guidelines for RCTs shift trials from probing biological mechanisms to predicting average treatment eUects over a population?
\n&bull\;What are the social\, epistemic\, ethical\, and political consequences of shifting regimens of error control?
\nThe value of probing error
\n&bull\;How can adversarial collaborations and stress-testing advance science?
\n&bull\;How can error repertoires be built and eUectively employed to facilitate severity in measurement and experiment?
\n&bull\;How does learning from error enter outside science (e.g.\, in art\, architecture and life drawing)?
\nSubmissions via: https://www.editorialmanager.com/synt/default.aspx
Under the drop-down menu\, select Severity and Learning from Error.
\;
Submitted papers will undergo the usual Synthese review process.
\;
For further information\, please contact the guest editors: Deborah Mayo (mayod@vt.edu)\, Wendy Parker (wendyparker@vt.edu)\, Danië\;l Lakens (D.Lakens@tue.nl)\, Kent Staley (kent.staley@slu.edu).
\;
The deadline for submissions is the 15th of December\, 2026
Conditionals lie at the heart of reasoning in philosophy\, mathematics\, and everyday discourse\, yet the logical rules governing them remain an object of philosophical debate. The workshop \;Ifs\, Thens\, and Otherwises: New Perspectives on the Logic of Conditionals \;will bring together contributions addressing the semantics\, pragmatics\, and logic of conditionals\, including counterfactual conditionals and other related kinds of expressions\, from a wide range of perspectives. These include approaches from philosophical logic\, formal semantics\, epistemology\, and related areas.
\nThis workshop will be part of the \;1st Andean Congress for Epistemology and Logic \;(CAELO 1)\, to take palce from \;22 to 28 February 2027 in Lima. \;
ORGANIZER: METHOD:PUBLISH END:VEVENT END:VCALENDAR