5.1 Phase transitions
Since nature often implementens abstract designs only within feasibility regions, there will almost always be borderline situations in which the implementation of an abstract design is on the verge of breaking down. These borderline situations frequently manifest as what we call phase transitions—regions or points (related to a parameter such as size, speed, temperature, and pressure) where multiple distinct and incompatible abstractions may to be implemented.
Newton’s laws fail at both the quantum level and at relativistic speeds. If as Laughlin suggests, the Newtonian abstraction is not an approximation of quantum theory, phase transitions should appear as one approaches the quantum realm. As explained by Sachdev , the transition from a Newtonian gas to a Boise-Einstein condensate (such as super-fluid liquid helium) illustrates such a phase transition.
“At room temperature, a gas such as helium consists of rapidly moving atoms, and can be visualized as classical billiard balls which collide with the walls of the container and occasionally with each other. As the temperature is lowered, the atoms slow down [and] their quantum-mechanical characteristics become important. Now we have to think of the atoms as occupying specific quantum states which extend across the entire volume of the container. … [Since helium] atoms are ‘bosons’ … an arbitrary number of them can occupy any single quantum state. … If the temperature is low enough … every atom will occupy the same lowest energy … quantum state.”
On the other hand, since Newton’s laws are an approximation of relativistic physics, there are no Newtonian-related phase transitions as one approaches relativistic speeds.
These considerations suggest that whenever data suggests a phase transition, one should look for two or more abstractions with overlapping or adjacent feasibility regions.
The entropy of a mass-based entity is strictly lower than the entropy of the entity’s components when not bound together as an entity. Whatever binds the components together limits the states they may assume and hence lowers the overall entropy. But since entropy cannot decrease overall, the entropy of the new entity’s environment must increase.
[Abstraction/Emergence as loss of information in the macro-level?]
Categorization of online platforms, from Nick Srnicek’s talk at Goldsmiths, 6 Feb 2017
Robert Plutchik’s wheel of emotions
Emotional response to viral content
1. Negative emotions were less commonly found in highly viral content than positive emotions, but viral success was still possible when negative emotion also evoked anticipation and surprise.
2. Certain specific emotions were extremely common in highly viral content, while others were extremely uncommon. Emotions that fit into the surprise and anticipation segments of Plutchik’s wheel were overwhelmingly represented. Specifically:
3. The emotion of admiration was very commonly found in highly shared content, an unexpected result.
The aggregate results of all images combined:
Tension between surprise and anticipation… Could this also be related to aesthetics? If surprise relates to orientation (a new stimulus makes someone stop) and anticipation relates to exploration (see below), these emotions also seem to be the ones most related to information theory.
Robert Plutchik’s (1979) theory views defences as derivatives of basic emotions, which in turn relate to particular diagnostic structures.
According to his theory:
- Reaction formation ⟶ Joy (and manic features)
- Denial ⟶ Acceptance (and histrionic features)
- Repression ⟶ Fear (and passivity)
- Regression ⟶ Surprise (and borderline traits)
- Compensation ⟶ Sadness (and depression)
- Projection ⟶ Disgust (and paranoia)
- Displacement ⟶ Anger (and hostility)
- Intellectualization ⟶ Anticipation (and obsessionality)
Theories of Emotion – Adaptations Shared by All Animals: Plutchik
||Cause & Effect
“The Future of Text and Image: Collected Essays on Literary and Visual Conjunctures”, Ofra Amihay, Lauren Walsh (“Introduction: Image X Text”, W.J.T. Mitchell) http://www.cambridgescholars.com/download/sample/60858
Bilimsel bir kuramdan bahsederken “anomali” dediğimizde, alışılageldik olandan, normdan sapmayı anlıyoruz. Aslında anomali, kurmuş olduğumuz normların, oluşturduğumuz kuramların hatalarını işaretliyor. Aynı zamanda da bilimsel yönteme bağlı kuramların sürekli değişip kendilerini yenilediklerini gözardı eden bir dil kullanımı. Bug kelimesinin kullanımı da benzer bir şekilde kurulan modeldeki hatayı öteleme semptomu gösteriyor: Bilgisayar programcısı yazdığı hatalı programdan bahsederken “hata yapmışım” veya “programlama hatası” demek yerine “programda bug var” diyerek insan hatasının üzerini örtüyor, makinaya ve hataya ayrı birer hayat veriyor. Bilimsel kuramların öngördüklerine uymayan gözlemlere anomali denmesi de aynen bu paralelde: Anomali aslında kuram çalıştırılırken ortaya çıkan bir bug ve kuramın tekrardan gözden geçirilmesi gerektiğinin göstergesi. Buna bir çeşit psikolojik yansıtma denebilir mi? Eğer öyleyse motivasyonu nedir, sürecin ucu açık olmasının, ne modelin ne de modeli oluşturanların mükemmel olmadığının üzerinin örtülerek inanılırlık sağlamak mı? Veya sadece “bu model hatalı ama çözümü de bulamadık, bir süre hataları dış etkenmiş gibi kabul edip bu yanlış modeli kullanmaya devam edelim” demenin bir başka yolu mu?