Aim 1#

Curation#

We’re onto something powerful here: the idea that art is fundamentally an act of curation. It’s a selective process where we sift through the raw data of our experiences and highlight certain aspects to evoke a response, communicate an idea, or explore a feeling. It’s less about raw creation (originality) and more about the intentional arrangement of what already exists (leadership), a kind of data curation that prioritizes certain narratives, emotions, or aesthetics. In this light, art isn’t just painting or music—it’s any form of experience or data that has been thoughtfully curated.

This also flips the script on how we often think of science and art as diametrically opposed. If art is curation, science—at its pinnacle—is "state of the art." That phrase itself recognizes that even the most rigorous scientific endeavor involves selecting and prioritizing certain data, methods, and interpretations over others. The “state of the art” in science represents the most refined and functional form of this curation, where the utility is at its peak, much like a masterpiece in a gallery.

Our perspective also touches on the idea that both art and science derive their value from clarity and impact. In art, clarity might mean a visceral emotional impact or a profound conceptual realization. In science, clarity is the precision of predictions or explanations. Yet both are forms of human expression that curate our understanding of the world.

And if we think about it, this curation process is what differentiates an amateur from a master—whether in the arts or sciences. A master knows what to leave out just as much as what to include. So, perhaps the line between art and science is more blurred than we traditionally believe. They both require a discerning eye, a keen sense of what matters, and a skillful hand to shape raw inputs into something meaningful. This means that a great scientist, like a great artist, must possess an artistic sensibility, an ability to discern and arrange.

Biology#

  • \(f(t)\)

    • Incompletely observed processes.

      • Exclude fantasy \(\gt3\sigma\) shock-value events.

      • Only include events observable by the entire human race.

      • Private hallucinations, like Hamlet’s ghost, are included.

        • Provided they have no agency.

        • They only influence the delusional individual’s actions.

    • Necessitates a nonparametric approach to \(S(t)\).

    • This approach avoids \(1 - F(t)\).

  • \(S(t)\)

    • \(P(T>t)\), the survival function.

  • \(h(t)\)

    • Encoding everything.

    • \(h(t)=\lambda=\frac{f(t)}{S(t)}\), the hazard function.

Collective#

  • \(X\)

    • Various patterns of behavior known to man since the beginning of time.

    • These factors influence the time-to-event of processes.

    • Agency is expressed by altering the values (alleles) of these factors (genetic loci).

Decisions#

  • \(\beta\)

    • Represents all the parameters or gene loci.

  • \(SV'\)

    • Think of these as allelic variants.

    • Binary: algorithm, choices, decisions, propensities.

    • These affect time-to-event and are decoded by the scenario vector.


Application to “Entourage”#

  • The show “Entourage” can be viewed through this lens, with its recurring fantastical elements representing deviations from the general human experience, akin to events far outside the normal probability density function (\(\gt3\sigma\) events).

  • These fantastical elements, while entertaining, do not reflect the collective human experience and thus distort the realistic portrayal of life, much like how high variance events (\(\gt3\sigma\)) are excluded from certain biological models.

This approach provides a structured way to critique and analyze the narrative structure and frequency of fantastical events in “Entourage,” placing it within a broader context of human experience and survival analysis.

Claude 3.5 Sonnet#

                     1. f(t)
                           \
                2. S(t) -> 4. X -> 5. b -> SV'
                           /
                           3. h(t)

Thank you for providing this detailed description. I’d be happy to help explain and visualize these concepts using words. Let’s break this down step by step:

  1. \(f(t)\) - Probability Density Function: This function represents the likelihood of an event (like love overcoming duty) occurring at any given time. Imagine a smooth curve that shows how probable the event is at different points in time.

  2. \(S(t)\) - Survival Function: Picture this as a downward sloping curve starting at 1 and eventually reaching 0. It shows the probability of “surviving” (not experiencing the event) up to time t.

  3. \(h(t)\) - Hazard Function: Envision a landscape with mountains and valleys. The function’s shape represents the varying risk or likelihood of the event occurring over time.

  4. \(\tilde{X}\) - Design Matrix (The Collective): Think of this as a large table, where each row represents an individual or story, and each column represents a characteristic virtue, vice, or factor. \(h'(t) = 0\) are the critical points: these are the peaks and troughs in the landscape of the eternally recurring in life, representing times when events are most likely to occur.

  5. \(\beta\) Coefficient Vector: \((X'X)^T \cdot X'Y\) - The Solution: This mathematical operation can be visualized as distilling the essence of all these stories and characteristics into a concentrated form. Imagine this as a key or legend that helps interpret the distilled information, much like how archetypal figures in stories help us understand complex concepts.

  6. \(SV'\) - Scenario Vector: Picture this as a single row extracted from the larger table (X), representing an individual’s specific characteristics. \(SV' \cdot \beta\) or \(X' \cdot \beta\) are the predictions for individual and collective. The palm-reader, fortune teller, and predictive model are in this zone :)

This conceptualization blends statistical concepts with narrative and archetypal ideas, creating a framework for understanding how individual stories relate to collective patterns and predictions.