Beyond Maximizing: Exploring Diverse Decision‑Making Perspectives

We usually set goals when making decisions or taking actions, aiming to align outcomes with those goals. In research, goals might be testing a hypothesis, solving a technical problem, or proposing a new theory.

Even when goals are not explicitly stated, they often guide our behavior, such as choosing a product with a good price‑performance ratio. Many decisions appear casual but ultimately satisfy a need or solve a problem.

Personally, I often set "maximization" goals—maximizing knowledge gain, time efficiency, or economic benefit—reflecting an aggressive, proactive attitude. However, is an aggressive "maximization" goal always the best choice?

1. Maximization: An Aggressive Decision Choice

In mathematics, a maximization problem is expressed as an optimization model that seeks a decision yielding the highest value of an objective function (e.g., profit, utility, efficiency). This approach aims to obtain the highest possible benefit in daily life, research, or finance.

Nevertheless, maximization is not always the sole guiding principle; uncertainty and complexity can make it impractical, prompting the exploration of alternative goal settings.

2. Satisficing: A Practical Compromise

When resources are limited, pursuing the absolute optimum may be costly or time‑consuming. Instead, decision makers may aim for a "good enough" outcome—a concept known as satisficing, introduced by Herbert A. Simon.

Knowledgeable people are rich; forceful people have ambition.

Satisficing sets a threshold of acceptable utility; once reached, further improvement is unnecessary. This approach is effective under high uncertainty and limited resources, such as buying a house within a budget and core requirements rather than seeking perfection.

3. Minimizing Risk: A Conservative Decision Choice

In uncertain or high‑risk situations, decision makers may prioritize reducing potential loss rather than maximizing gain, employing a minimax loss model that minimizes the worst‑case loss.

Rule 1: Never lose money. Rule 2: Never forget Rule 1.

This conservative perspective is common in finance and insurance, where investors may accept lower returns to limit possible losses.

4. Balancing: Multi‑Objective Trade‑Offs

Single‑goal maximization or minimization often cannot capture all aspects of a decision. Organizations may need to balance profit, employee satisfaction, product quality, and social responsibility, leading to multi‑objective optimization.

Multi‑objective optimization seeks solutions where no objective can be improved without worsening another, known as Pareto optimal solutions. For example, car manufacturers may simultaneously aim to reduce fuel consumption and increase performance, finding a balanced design.

5. Minimizing Regret: A Psychological Perspective

Beyond physical or economic factors, decision makers consider psychological outcomes such as regret. Minimizing regret involves choosing actions that would cause the least remorse if the future turned out differently, modeled by the minimax regret framework.

Prominent figures like Jeff Bezos have used regret minimization when deciding to leave a stable job to start Amazon, focusing on whether they would later regret not trying.

6. Conclusion

Decision optimization is not limited to a single "maximization" viewpoint. Depending on context, alternatives such as satisficing, risk minimization, multi‑objective optimization, and regret minimization provide valuable frameworks for navigating complex, uncertain decisions.

Broadening our perspectives helps avoid overly narrow or isolated viewpoints and encourages a wider, more balanced outlook.