Results for "incentive engineering"

Truthful bidding is optimal strategy.

Crafting prompts to elicit desired behavior, often using role, structure, constraints, and examples.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep learning.

Decisions dependent on others’ actions.

Designing systems where rational agents behave as desired.

Field combining mechanics, control, perception, and AI to build autonomous machines.

Learning physical parameters from data.

A measurable property or attribute used as model input (raw or engineered), such as age, pixel intensity, or token ID.

When a model cannot capture underlying structure, performing poorly on both training and test data.

Average of squared residuals; common regression objective.

Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.

Practices for operationalizing ML: versioning, CI/CD, monitoring, retraining, and reliable production management.

Structured dataset documentation covering collection, composition, recommended uses, biases, and maintenance.

Central system to store model versions, metadata, approvals, and deployment state.

Tendency to trust automated suggestions even when incorrect; mitigated by UI design, training, and checks.

Optimization problems where any local minimum is global.

Extracting system prompts or hidden instructions.

Sequential data indexed by time.

End-to-end process for model training.

Centralized repository for curated features.

Converts constrained problem to unconstrained form.

Alternative formulation providing bounds.

Assigning a role or identity to the model.

Explicit output constraints (format, tone).

Breaking tasks into sub-steps.

Prompt augmented with retrieved documents.

Mechanism to disable AI system.

Governance of model changes.

Guaranteed response times.

Mathematical framework for controlling dynamic systems.