Optimoijan

Optimoijan is a theoretical construct in decision theory and artificial intelligence that describes an agent designed to optimize a composite objective: conventional utility and a justification quality measure. In this model, actions are evaluated not only by their expected outcomes but also by how well they can be justified under a given normative or epistemic standard.

The model uses state space S, action space A, outcome U(s,a) and justification score J(s,a) in [0,1]. The agent chooses action maximizing O = α U(s,a) + (1-α) J(s,a), with α in [0,1] representing emphasis on performance versus justification. In sequential settings, dynamic programming may be used with a horizon h; at each step, Ot is considered with future expected scores.

Measurement of J includes consistency with model assumptions, transparency of inference paths, and parsimony; practically, J encodes explanations comprehensible to stakeholders. Variants may incorporate probabilistic assessments to handle uncertainty and may impose constraints to maintain alignment with normative standards or safety requirements.

Optimoijan is proposed as a framework for interpretable AI, policy design, and risk management, emphasizing accountability and transparent decision processes. The approach supports different emphases on performance and justification, and can be extended with multi-agent settings or adaptive weighting to reflect changing norms or objectives.

Criticism centers on the difficulty of quantifying J, the potential for justification to come at the expense of objective performance, and the possibility of gaming justification metrics. The term appeared in speculative AI ethics discussions in the early 2020s and remains primarily a theoretical concept in current debates.

See also Explainable AI; Interpretable machine learning; Decision theory; Value alignment.