boundedoptimality

Bounded optimality is a concept in decision theory and artificial intelligence that describes a scenario where an agent or system does not always make the globally optimal decision but instead achieves performance that is "good enough" for practical purposes. Unlike traditional optimization approaches that seek perfect solutions, bounded optimality acknowledges that perfect decisions may be computationally expensive, impossible to achieve, or unnecessary in many real-world contexts.

The idea was introduced to address limitations in rational decision-making models, such as those proposed by Herbert Simon in his work on "satisficing." Simon argued that humans and systems often operate under constraints like time, cognitive capacity, or incomplete information, leading them to adopt heuristics or rules of thumb rather than exhaustive search for optimal outcomes. Bounded optimality extends this notion by formalizing the trade-off between computational effort and decision quality.

In computational models, bounded optimality can be implemented through techniques like local search, approximation algorithms, or reinforcement learning with limited exploration. For example, a system might use a greedy algorithm that selects the best available option at each step rather than evaluating all possible future states. This approach ensures near-optimal performance while reducing complexity.

The concept is widely applied in fields such as economics, psychology, and machine learning. In economics, bounded optimality helps explain why agents may accept suboptimal solutions due to transaction costs or information asymmetry. In psychology, it provides a framework for understanding human decision-making under cognitive constraints. In machine learning, it informs the design of efficient algorithms that balance accuracy and computational feasibility.

Critics of bounded optimality note that it may lead to systematic biases or suboptimal long-term outcomes, particularly in dynamic environments where small initial errors can compound over time. However, proponents argue that it offers a more realistic and scalable alternative to strict optimality in complex, uncertain settings.