About Perfinitive Research

Perfinitive Research is an independent human-first research program led by Micheal Charles Preble, Independent Researcher (ORCID 0009-0005-3906-6028).

The work focuses on the conditions emerging around people as artificial intelligence becomes persistent, adaptive, intermediary, and embedded across work, learning, institutions, and everyday decision-making.

The research does not begin from the assumption that every important phenomenon is novel. Many components are already described across HCI, cognitive science, organizational research, law, causal inference, AI memory, joint action, education, and governance. The task is often to preserve those components with enough fidelity to see the human-facing structure they produce together.

Perfinitive Research develops methods for discovering, discriminating, localizing, reducing, and governing those structures without collapsing them prematurely into familiar categories.

Preserve Before You Compress

Perfinitive Research treats premature categorization as a methodological risk. A complex human-AI condition should not be reduced to a familiar label merely because that label is available.

A phenomenon described as “memory,” “trust,” “dependency,” “personalization,” “automation,” or “bias” may contain materially distinct dimensions that matter for mechanism, prediction, falsification, governance, or human consequence.

No candidate structure should be reduced to a familiar category until the reduction has been shown not to erase a consequential distinction.

This principle does not reject simplification. It requires simplification to be earned.