On the Evolution of Symbols and Prediction Models

Abstract

The ability of predicting upcoming events or conditions in advance offers substantial selective advantage to living beings. The most successful systematic tool for fairly reliable prognoses is the use of dynamical causal models in combination with memorised experience. Surprisingly, causality is a fundamental but rather controversially disputed concept. For both models and memory, symbol processing is requisite. Symbols are a necessary and sufficient attribute of life from its very beginning; the process of their evolutionary emergence was discovered by Julian Huxley a century ago. In behavioural biology, this universal symmetry-breaking kinetic phase transition became known as ritualisation. Symbol use for predicting future dynamical processes has culminated in the unprecedented complexity of mental models used in science and technology, coining the historical ascent of modern humans. Observation and measurement transform structural information of physical exchange processes into symbolic information from which state quantities are derived by means of mental models. However, phylogenetically inherited models such as naïve realism do not necessarily explain the sophisticated insights revealed by modern experiments with, say, entangled quantum states. It is suggested to carefully distinguish observed exchange quantities from predicted unobservable state quantities, and physical reality from mental models thereof.

TEOS-10 and the Climatic Relevance of Ocean-Atmosphere Interaction

“…"The obsolete hypothesis of heat being a substance is excluded" (Sommerfeld 1988: p. 6). This distinction is qualitatively fundamental (Feistel 2023). Physical conservation quantities such as energy or mass have the key property that the change of that quantity in a volume equals the flux of that quantity across the boundary Lifschitz 1966, Glansdorff andPrigogine 1971), but this does not apply to "heat".…”

“…It is a plausible working hypothesis that this correlation could also indicate a causal relation between the two phenomena. However, such correlations imply hen-and-egg problems (Rapp 2014): putative causality relations between those trends cannot be derived from observation but only be concluded from reliable prediction models (Feistel 2023). May the observed systematic reduction of global cloudiness (Fasullo and Trenberth 2012) actually be responsible for the currently recorded excessive ocean warming (You 2024)?…”

“…According to recent model comparison studies, many of those "models fail to provide as much heat into the ocean as observed" (Weller et al 2022). Dynamical models, rather than observed correlations, are the most reliable tools for the detection and verification of causal relations (Feistel 2023), however, such as in this case of air-sea interaction, large uncertainties may prevent any significant conclusions to be drawn regarding the causes of the observed ocean warming rate of 1.3 W m -2 .…”

Self-Organisation of Prediction Models

“…A striking distinction between Figures 1 and 2 is the one between symbolic information and structural information (Ebeling and Feistel 1994, Feistel and Ebeling 2011, Feistel 2017a, 2023) [11][12][13][14]; see Sections 5 and 6. Entropy may serve as an example demonstrating the difference.…”

“…The entropy proposed by Shannon and Weaver (1964) [21], by contrast, is a measure of the amount of symbolic information (or physically free information); it does not depend on the physical nature of the particular information carriers, be those neural nerve pulses, electronic computer bits, or ink-printed letters (Brillouin 2013, Feistel 2017a [13,15,22]. A striking distinction between Figures 1 and 2 is the one between symbolic information and structural information (Ebeling and Feistel 1994, Feistel and Ebeling 2011, Feistel 2017a [11][12][13][14]; see Sections 5 and 6. Entropy may serve as an example demonstrating the difference.…”