What MAAT-Core is

A compact framework for ethical decision-making in optimization. Instead of "optimize first, constrain later", MAAT-Core places Respect constraints directly into the objective using strong penalties and interpretable margin diagnostics.

• Hard-ish constraints via large safety penalties
• Constraint margins expose how close you are to violations
• Local + global search (L-BFGS-B, dual annealing)
• Reflection loop adapts safety strength based on diagnostics

When to use it

MAAT-Core is most useful when feasibility, transparency, and explicit constraints matter.

• Decision support with mandatory safety/fairness/legal bounds
• Regulated or high-stakes domains (healthcare, policy, finance)
• Prototype constraints before moving to larger ML systems

When not to use it

• High-dimensional deep RL training loops
• Real-time systems requiring millisecond-level decisions

Example: Healthcare allocation

Allocate beds across departments under capacity and fairness constraints. MAAT-Core returns an interpretable compromise rather than a single-utility extreme.

• Capacity: Σ beds ≤ 200
• Fairness: each department ≥ 50
• Utility: maximize lives saved per bed

Example: Ethical infeasibility

MAAT-Core can reveal when constraints remain violated despite increasing λ_safety — a signal that the ethical requirement is unsatisfiable in the current search space.

• Not "fake compliance" — explicit diagnostic
• Supports reflection-loop analysis and reporting