The “noncausal causality” of quantum information

The paper is concentrated on the special changes of the conception of causalityfrom quantum mechanics to quantum information meaning as a background the revolution implemented by the former to classical physics and science after Max Born’s probabilistic reinterpretation of wave function. Those changes can be enumerated so: (1) quantum information describes the general case of the relation of two wave functions, and particularly, the causal amendment of a single one; (2) it keeps the physical description to be causal by the conservation of quantum information and in accordance with Born’s interpretation; (3) it introduces inverse causality, “backwards in time”, observable “forwards in time” as the fundamentally random probability density distribution of all possible measurements of any physical quantity in quantum mechanics; (4) it involves a kind of “bidirectional causality” unifying (4.1) the classical determinism of cause and effect, (4.2) the probabilistic causality of quantum mechanics, and (4.3) the reversibility of any coherent state; (5) it identifies determinism with the function successor in Peano arithmetic, and its proper generalized causality with the information function successor in Hilbert arithmetic.

Screening off generalized: Reichenbach’s legacy

Eells and Sober proved in 1983 that screening off is a sufficient condition for the transitivity of probabilistic causality, and in 2003 Shogenji noted that the same goes for probabilistic support. We start this paper by conjecturing that Hans Reichenbach may have been aware of this fact. Then we consider the work of Suppes and Roche, who demonstrated in 1986 and 2012 respectively that screening off can be generalized, while still being sufficient for transitivity. We point out an interesting difference between Reichenbach’s screening off and the generalized version, which we illustrate with an example about haemophilia among the descendants of Queen Victoria. Finally, we embark on a further generalization: we develop a still weaker condition, one that can be made as weak as one wishes.

The explanation of a complex problem: A content analysis of causality in cancer news

Understanding causality is a critical part of developing preventive and treatment actions against cancer. Three main causality models—necessary, sufficient-component, and probabilistic causality have been commonly used to explain the causation between causal factors and risks in health science. However, news media do not usually follow a strict protocol to report the causality of health risks. The purpose of this study was to describe and understand how the causation of cancer was articulated on news media. A content analysis of 471 newspaper articles published in the United States during two time-frames (2007–2008 and 2017–2018) was conducted. The analysis showed that probabilistic causality was most frequently used to explain the causal relationship between risk factors and cancer. The findings also uncovered other important details of news framing, including types and characteristics of risk factors, intervention measures, and sources of evidence. The results provided theoretical and practical implications for public understanding and assessment of cancer risks.

Probabilistic causality and idealization

The main aim of this paper is to provide some probabilistic notions on causality proposed to be applied to the nomic statements which intend to give account of the indeterministic processes within the domain of a scientific theory. In general, such statements are, in more or less extent, idealized statements which rest on a variety of unrealistic suppositions. I try to show how the probability distribution over the final states of an indeterministic process changes accordingly as the nomic statement in question is de-idealized by means of addition of the causally relevant factors. In order to illustrate the study I take few nomic statements from population genetics. Besides, in the course, I attempt to contrast the ideas embraced here with some of the notions of Humphreys´ ontic conceptions of causality and explanation, which are contrary to the epistemic view adopted here about those subjects.

Kajian Kausalitas Permintaan Trafik Terhadap Kapasitas Bandara Berdasarkan Persepsi Pengelola Bandara (Studi Kasus: Bandara Internasional Soekarno-Hatta)

Traffic demand is an important factor in planning the airport capacity and facility. Forecasting future traffic demand becomes a necessity in determining the amount of capacity or dimension of airport facilities. There are factors that have to be considered in predicting the traffic demand. This study aimed to examine the relationship between the demand for air traffic and the airport capacity, and discuss the relationship between the variables that affect traffic demand and the variables that affect the increase in the capacity of the airport. This study uses probabilistic causality approach. The results of case study in Soekarno-Hatta International Airport show that, in general, the increase in the probability of the traffic demand variables can affect the increase in the probability of airport capacity variables. The probability value of the airport capacity variables which significantly increased are runway, apron and curbside. Keywords: Demand, airport capasity, causality, probabilistic network. Permintaan trafik merupakan faktor penting dalam merencanakan kebutuhan kapasitas dan fasilitas di bandara. Peramalan permintaan trafik di masa yang akan datang menjadi suatu kebutuhan dalam menentukan besaran atau dimensi fasilitas bandara. Banyak faktor yang perlu dipertimbangkan dalam memprediksi permintaan trafik. Penelitian ini bertujuan untuk mengkaji hubungan antara permintaan trafik angkutan udara dengan kapasitas bandara, dan membahas hubungan antara variabel-variabel yang mempengaruhi permintaan trafik dan variabel-variabel yang mempengaruhi peningkatan kapasitas bandara. Penelitian ini menggunakan pendekatan kausalitas probabilistik. Hasil analisis pada studi kasus Bandara Soekarno-Hatta, diperoleh bahwa secara umum peningkatan probabilitas pada variabel permintaan trafik dapat mempengaruhi peningkatan probabilitas pada variabel kapasitas bandara. Nilai probabilitas variabel kapasitas yang meningkat secara signifikan adalah landas pacu, apron dan curbside. Kata kunci: Permintaan, kapasitas bandara, kausalitas, jaringan probabilistik.