Decision
Overview
Decision (Korean: 결정, 決定) refers to the cognitive process of selecting one among multiple possible alternatives and putting it into action. Narrowly, it denotes an individual's act of choice; broadly, it encompasses collective judgment such as in organizations, policy, and investment. A decision goes through the stages of information gathering, alternative evaluation, selection, implementation, and ex post evaluation, and takes place amid the tension between rationality and uncertainty.
Main Content
Rational Choice Theory
Classical economics assumes humans as 'rational actors' who maximize utility based on complete information. From this perspective, decision is formalized as an optimization problem that maximizes expected utility, and von Neumann–Morgenstern expected utility theory is a representative example. The logic is to assign probabilities to the outcomes of each alternative, compare their weighted sums, and choose the alternative that gives the highest value.
Bounded Rationality
Herbert Simon pointed out that human cognitive ability and information-processing capacity are finite, and proposed 'bounded rationality'. Actual decisions do not explore all alternatives and stop at a 'satisficing' alternative instead of an optimal solution. This began from the reflection that the assumption of complete rationality cannot explain reality.
Behavioral Economics and Biases
Daniel Kahneman and Amos Tversky revealed cognitive biases through which humans make systematically irrational judgments. Representative examples include confirmation bias, loss aversion, anchoring effect, availability heuristic, and overconfidence. These reduce the quality of decisions when information is insufficient or time pressure is high. Kahneman explained this with dual-process theory: the fast, intuitive 'System 1' and the slow, analytical 'System 2'.
Decision Models
At the organizational level, several models have been proposed. The rational model emphasizes selecting the optimal alternative, while the satisficing model emphasizes realistic compromise. The garbage can model shows that decisions are made coincidentally in organizations where goals, means, and participants are mixed together. In addition, the incrementalist model argues for stepwise adjustment rather than radical change, and the mixed scanning model argues for a combination of rationality at the broad framework level and incrementalism in details.
The Decision Process
Generally, a decision has a cyclical structure of problem recognition → information search → alternative generation → evaluation and selection → implementation → feedback. In the evaluation stage, cost-benefit analysis, multi-criteria decision analysis (MCDA), decision trees, Monte Carlo simulation, and similar methods are used. When uncertainty is high, the real options approach is useful.
Recent Trends
In 2024–2025, AI-based decision support spread rapidly. Scenario analysis, forecasting, and automated alternative evaluation using large language models (LLMs) are being introduced in finance, healthcare, and public policy. At the same time, discussions on algorithmic bias, explainability, and accountability are active. In behavioral economics, 'nudges' and choice architecture have become established as policy tools, and research on the attention economy and decision fatigue in digital environments is drawing attention. In addition, brain science and neuroeconomics are identifying the neural basis of decision-making, strengthening interdisciplinary approaches.
Related Topics
- [[Behavioral economics]]
- [[Cognitive bias]]
- [[Bounded rationality]]
- [[Expected utility theory]]
- [[Decision tree]]
- [[Nudge]]