HAAS Studio: A New Tool to Simulate Human-AI Work Allocation

Vicente Pelechano's HAAS Studio turns the HAAS framework into an interactive simulation and decision-support environment for human-AI work allocation. Before introducing AI into a workflow, teams can compare allocation strategies, inspect governance tradeoffs, and derive a defensible task-level operating model. The tool uses a five-dimensional cognitive representation of subtasks and a five-mode collaboration spectrum. It implements adaptive allocation with multi-armed bandit algorithms (UCB1, Discounted UCB, LinUCB, and Thompson Sampling), plus oracle counterfactual regret analysis and contract-based governance with four independent guards.

HAAS Studio includes three pre-built domain packs: software engineering, manufacturing, and healthcare. Each provides a task catalog, worker profiles, and KPI vocabulary. The architecture allows new domains to be added without modifying the core. The release also includes 16 company profiles and six governance benchmark suites. A decision-guidance layer translates benchmark outputs into deployment decisions through structured patterns, heuristics, and a decision matrix. The tool also monitors deskilling risk through sliding-window exposure metrics and supports persistent worker modeling via Live Twin and Planning modules.