A Reductionist Framework for Human Happiness

Is it possible to define happiness as a set of non-linear basis functions that capture societal interaction effects?

Formalizing Happiness

Sinusoidal Functions: Emotional Battery

  • Career progress.
  • The state of one’s physical & mental health.
  • The quality of one’s friendships & romantic relationships.
  • The depth of one’s thought of philosophical or religious connections.
  • One’s sense of purpose & community.
  • One’s sense of novelty & excitement.



Interactive Complexity

Defining Happiness

  1. We consider happiness a latent manifestation of simpler, observable phenomena (relationship satisfaction, career growth, spiritual enlightenment, level of societal acceptance, etc) — thus quantifying these attributes highly correlates with happiness.

Sampling Happiness

Data Generation Summary

  1. One’s happiness can be adequately represented by a set of observable metrics (work/relationship satisfaction, health, blood pleasure (think nonlinearly), level of novelty/excitement or peace/belonging, etc)) — we call these attributes one’s Emotional Battery.
  2. One’s Emotional Battery is readily observable through wearables, psychometrics, discussions, etc — thus we are able to generate data on one’s emotional state!
  3. We can present this Battery as a stationary process of cyclical, non-linear, highly interactive basis functions (this adds very little restrictions however allows for a sound mathematical framework).
  4. We can now model this data by defining some statistical machinery ;).

Gaussian Processes

Weight Space View

Function Space View

Pyro Mødel

Happiness Data Generation

  1. Work performance
  2. Sleeping cycle
  3. Closeness of relationships
  4. Strength of faith

Aggregate Happiness Process

Let’s take a look at what happiness looks like!

Our AHP: a process that governs one’s happiness at any given time. The dark blue line is the final (convolved) Aggregate Happiness Process. The fainter, dotted, functions are one’s emotional battery (when combined produce the AHP). Access to this underlying process provides insight into one’s emotional state.
  • Administering surveys
  • Measuring physical, psychological & emotional conditions, etc.
Sampled data from the target function. Representing what we might actually observe in an individual.

Model Fitting IN Pyro!

Optimization Methodology + Loss Function

Blue line: the true data generating process (target function). Red line: the predictive model (samples from our GP). Purple Area: credibility (confidence) intervals around out mean estimate (the red line). Purple dots: the data.


Cracking GPs

Left: In a data-sparse environment, the model adequately (and intuitively) learns a smoother functional fit. Right: In areas with insufficient data (no samples) the model captures vast uncertainty.
Left: The data generating process, generate many samples in the first half of the function & very few samples over the span of the second half. A single model fit over both the data-rich & data-sparse environment: note the far smoother, less confident fit over the latter continuum.

Philosophical Consideration





Statistician, scientist, technologist — writing about stats, data science, math, philosophy, poetry & any other flavours that occupy my mind. Get in touch

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Zach Wolpe

Zach Wolpe

Statistician, scientist, technologist — writing about stats, data science, math, philosophy, poetry & any other flavours that occupy my mind. Get in touch

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