Regret
Regret is the difference between the reward or utility obtained from a choice and that obtainable from the best alternative under the same circumstances, often accumulated across decisions.
Experimentation for Engineers
Regret is the lost outcome relative to the outcome that would have been obtained by choosing the best alternative throughout the decision process.
- How it is used
- Frames the business cost of exploration while comparing A/B testing and multi-armed bandit policies.
- How deep it goes
- Defines and calculates regret, compares policies numerically, and states asymptotic bounds without deriving their proofs.
- Examples
- Uses ad click-through rates, simulated A/B tests, epsilon-greedy runs, and decaying-epsilon comparisons.
- What you can do after
- Evaluate experimental policies by quantifying lost business-metric performance against the best alternative.
Optimization Algorithms: AI techniques for design, planning, and control problems
Regret is the cumulative difference between the reward obtainable by always choosing the best action and the reward actually obtained.
- How it is used
- Measures the cost of action selection while comparing exploration and exploitation strategies in multi-armed bandits.
- How deep it goes
- Calculates per-trial and average regret with a worked example and Python implementations, but does not develop formal regret bounds.
- Examples
- Uses three slot machines, 90 trials, payoff distributions, strategy pseudocode, and average-regret outputs.
- What you can do after
- Diagnose bandit strategies by implementing cumulative and average regret calculations in NumPy.
Causal AI
Regret is the difference between the utility or reward realized by an action and the utility or reward that would have been realized by an alternative action.
- How it is used
- Uses counterfactual outcome differences to learn from decisions and improve future policies.
- How deep it goes
- Defines regret, models counterfactual uncertainty probabilistically, samples expected regret, and extends it to cumulative policy optimization.
- Examples
- Uses portfolio backtesting, failed deals, game simulations, player purchases, and recommendation-policy comparisons.
- What you can do after
- Evaluate alternative actions or policies and estimate which would minimize cumulative regret.
Related concepts
- study designproduced by · Study design is the planned arrangement of procedures, conditions, measurements, and comparisons used to investigate a question and support valid conclusions.
- reward signalmeasures · A reward signal is an evaluative feedback value representing the desirability or payoff of an action, outcome, or state.
- minimax regret methodused by · A decision method that selects the alternative with the smallest maximum regret relative to the best alternative under each possible scenario.
- exploration-exploitation trade-offmeasures · The exploration-exploitation trade-off is the decision problem of balancing actions that gather information about uncertain alternatives with actions chosen for their currently expected payoff.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.