Holistic Computation (HC) is a production framework that unifies modern statistics and AI with a multidimensional view of value so organizations can turn predictive work into dependable decision systems. It aligns goals with 4Capital (Material, Intellectual, Social, Spiritual), makes assumptions explicit with causal maps and estimands, and ships results through a disciplined 4Es loop—Equation → Estimation → Evaluation → Execution—backed by monitoring for performance, fairness, drift, and continuous learning.
Why use HC now
Projects succeed more often when value trade-offs are explicit and ecosystem collaboration is built in.
HC adds ethical, auditable practice to AI work—improving trust, reuse, and outcomes.
What to optimize:
outcomes and affordability (Material), cognition/performance (Intellectual),
connection/support (Social), and meaning/well-being (Spiritual)
at the same time.
How it works: integrate physical, mental, social, and spiritual health data;
formalize decisions (eligibility, timing, intensity) in
Equation; fit calibrated
and uncertainty-aware models in
Estimation; prove policy
value and fairness in
Evaluation; deploy with audit trails and drift monitoring in
Execution.
What to optimize:
revenue & reliability with asset health (Material), forecasting/operational
intelligence (Intellectual), equity & stakeholder outcomes (Social), and
ESG/CO₂e (Purpose). HC encodes these goals as a utility-under-constraints
problem, then selects safe, auditable actions (e.g., BESS dispatch, PV/Wind
O&M, equity-aware DR/EV orchestration).
Scenario simulation and
off-policy evaluation
de-risk rollouts; execution is staged with rollback.
Why it fits HC: climate/environmental operations are a named application
area, and the Operational Blueprint provides the exact artifacts—policy
cards, utility/constraint specs, guardrails, and monitoring.
What to optimize:
portfolio-level 4Capital goals with clear roles, causal rigor, and
reproducible pipelines. Quick-start outputs include 4Capital OKRs,
DAG+estimands, leak-free datasets aligned to causal roles, calibrated
models, policy mapping, evaluation reports (predictive + policy value +
fairness + robustness), and model/policy cards with a monitoring plan.
How to run: set 4Capital KPIs and constraints; specify DAG/estimands;
prepare data aligned to the DAG with leakage guards; run the
4Es; govern with
auditability, recourse, and compliance; scale through shared templates,
registries, and quarterly portfolio reviews.
The framework embeds Ethical & Responsible AI and a Four-Dimensional AI lens so capability, behavior, interaction, and values are designed together—plus governance with incident response, recourse, and compliance mapping.