SEMFILOQUIUM #1 | Art vs Science | Maximillian Schich, Carlos Zapata-Carratalá @SEMF

SEMF | SEMFILOQUIUM #1 | Art vs Science | Maximillian Schich, Carlos Zapata-Carratalá @SEMF | Uploaded November 2021 | Updated October 2024, 6 hours ago.
A conversation about the similarities and synergies between art and science, with guest speaker Maximilian Schich and SEMF's moderator Carlos Zapata Carratalá.

semf.org.es/events/semfiloquia.html

Are art and science two ends of the same spectrum? This conversation explores the parallels between art and science from the point of view of modern cultural data analytics and multidisciplinary research collaborations. Topics include: the influence of modern computational frameworks in art history studies, the limitations of quantitative analysis, the future of social science, the prospect of society in the wake of the computational revolution, new paradigms of science, science-art feedback loops, etc.

MAXIMILIAN SCHICH

Max is a multidisciplinary scientist investigating the phenomenon of art and culture, he has had a long trajectory as a researcher applying quantitative techniques to history of art and culture studies and he is the current ERA Chair at the Cultural Data Analytics Open Lab at Tallinn University in Estonia. To give a sense of Max’s work, here is a description from the CUDAN Open Lab website: “Our project aims at building a new analytical approach, called ‘Cultural Data Analytics’ that integrates quantitative and qualitative methods, including aspects of network science, complexity science, computational social science, science of science, machine learning, data science, design research, visualization, art history, cultural semiotics, digital culture studies, and creative industries’ studies to work with digitized cultural heritage as well as with born-digital data acquired from contemporary platforms.”

Tallinn University Profile: https://cudan.tlu.ee/team/max/
Personal website: schich.info
Google Scholar: scholar.google.com/citations?user=kB7vzdIAAAAJ
LinkedIn: linkedin.com/in/maximilianschich
Twitter: twitter.com/schichmax

CARLOS ZAPATA CARRATALÁ

PhD in Mathematical Physics, research on the mathematical foundations of science, particularly physics and cognitive science, founder and director of SEMF.

Personal website: czapatacarratala.wixsite.com/home
ResearchGate: researchgate.net/profile/Carlos_Zapata-Carratala2
YouTube: youtube.com/channel/UCkO2AJrRnRtWyaUi8yWrXew
Twitter: twitter.com/ZapataCarratala
BRCP profile: basic-research.org/members/Zaphys

SEMF NETWORKS
Website: semf.org.es
Twitter: twitter.com/semf_nexus
LinkedIn: linkedin.com/company/semf-nexus
Instagram: instagram.com/semf.nexus
Facebook: facebook.com/semf.nexus

MUSIC
youtube.com/user/Baroquenoise

Emergence and the Role of Observeres in Coarse-Graining | Sean Carroll, Stephen Wolfram

Michael Levin | Cell Intelligence in Physiological and Morphological Spaces

Community Livestream | The Limits of Science

Interdisciplinary Summer School 2023 | Dietz

Transdisciplinary Mathematics (1/5) | Carlos Zapata | Summer School 2022

Interdisciplinary Summer School 2023 | Kozlov

Dmitri Tymoczko | Visualizing Musical Structure | SPACIOUS SPATIALITY 2022

Interdisciplinary School 2024 | Froese, Moya, Andrews

Concept Collider | Geometry of Data and Neural Correlates

$Causal Specificity, Functional Maps, Bijective Functions | Ameer Sarwar | Numerosity Workshop 2021 Session kindly contributed by Ameer Sarwar in SEMFs 2021 Numerous Numerosity Workshop: https://semf.org.es/numerosity/ ABSTRACT Neuroscientists are interested in understanding neural populations. This often involves comparing the effect of an intervention on population P1 with a controlled population P2. Since it is the only difference between populations, the intervention is taken to have causal powers (Waters, 2007; Woodward, 2003). Yet, neuroscientists are not interested in merely comparing manipulations or (statistical) differences. They instead want to understand the mechanisms that explain those differences, i.e., the ways in which causes influence their effects. Broadly speaking, causal influence comes in two varieties. On the one hand, it can be ‘coarse-grained.’ For example, neurosurgeons may lesion a brain area in order to reduce neural hyperactivity, e.g., during epilepsy. Indeed, this was done to patient H.M. when his medial temporal lobes were bilaterally removed. On the other hand, causal influence can be ‘fine-tuned.’ Rather than lesioning hyperactive areas, the medical doctor may, for instance, prescribe medication that inhibit neural firing, e.g., GABAergic. Neural hyperactivity, then, can be reduced in both ways, yet it makes a great deal of difference whether this occurs in a coarse-grained or fine-tuned manner. Philosophers of science have proposed ‘causal specificity’ as a way of understanding relative causal control (Weber, 2017a). According to this view, the causal set with the highest number of functional mappings onto the effect is most fine-tuned. What does this mean? Let A:{x, y, z} denote the set of sub-causes that influence population P. Here, A is the cause and x, y, and z are sub-causes. Let P:{f, g, h} denote the set of sub-effects influenced by A. Here, P is the effect and f, g, and h are sub-effects. Let us place these variables within an example. The cause may be an anticonvulsant (A), which acts via psychoactive proteins (x, y, z) that bind to postsynaptic receptors (f, g, h), thereby reducing neural hyperactivity (P). So, according to causal specificity, the degree to which the sub-causes of A—the values of the domain—map onto those of P—the values of the co-domain—constitutes the degree of causal control A exercises over P (relative to P’s other causes). Indeed, the A-to-P mapping must be functional: (1) All values of the domain must map onto at least one value of the co-domain, and (2) each value of the domain must map to no more than one value of the co-domain. The proponents of this view advance a numerical interpretation of ‘functional mapping,’ according to which “the number of values that the variables on both sides of the relation can take is vastly higher … than that of any other causal variables that bear the [same] relation” (Weber, 2017b, p. 32). My presentation will be a systematic critique of this view. First, the framework fails to capture the phenomena of dual effects, wherein a sub-cause influences two sub-effects. Second, the framework fails to capture causal redundancy, wherein a sub-cause fails to map onto any sub-effect in P. Third, the framework is incoherent when the numerical interpretation is advanced alongside the assertion that bijective functions are most specific. Finally, I want to suggest that the framework includes incommensurability. Accordingly, it seems that the very problem that motivated the discussion, namely whether a schematic can be developed for capturing relative causal control, cannot be accommodated. As a result, causal specificity as a view of causal control is misguided. AMEER SARWAR University of Toronto University of Oxford ResearchGate: https://www.researchgate.net/profile/Ameer-Sarwar Google Scholar: https://scholar.google.ca/citations?user=3L3z7rsAAAAJ Academia.edu: https://utoronto.academia.edu/AmeerSarwar SEMF NETWORKS Website: https://semf.org.es Twitter: https://twitter.com/semf_nexus LinkedIn: https://www.linkedin.com/company/semf-nexus Instagram: https://www.instagram.com/semf.nexus Facebook: https://www.facebook.com/semf.nexus$