1.2 Basic concepts of decision-making under uncertainty
2 min read•july 24, 2024
Decision-making involves weighing alternatives, considering external factors, and evaluating potential outcomes. This process varies depending on the level of certainty, from clear-cut choices to situations with unknown probabilities.
Different criteria guide decisions under uncertainty, from optimistic to conservative approaches. calculations provide a numerical basis for comparison, though they have limitations in certain scenarios.
Decision-Making Components and Types
Components of decision problems
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- Alternatives represent possible courses of action available to decision-maker mutually exclusive options (invest in stocks, bonds, real estate)
- States of nature encompass external factors or conditions affecting outcome beyond decision-maker's control (economic recession, natural disasters)
- Payoffs denote outcomes or consequences associated with each combination of alternative and state of nature often expressed monetarily or as utility values (profit, loss, customer satisfaction)
Decision-making under different conditions
- Certainty involves perfect information about outcomes in deterministic environment with only one state of nature (fixed-rate mortgage)
- Risk entails known probabilities of states of nature with multiple possible outcomes utilizing expected values and probabilistic analysis (insurance pricing)
- Uncertainty occurs when probabilities of states of nature are unknown with limited information about potential outcomes relying on decision criteria and subjective judgment (new product launch)
Criteria for decisions under uncertainty
- adopts optimistic approach selecting alternative with best possible outcome ignoring potential losses (high-risk investment strategy)
- employs conservative approach choosing alternative with best worst-case scenario focusing on minimizing potential losses (diversified portfolio)
- uses regret-based approach calculating difference between best possible outcome and actual outcome selecting alternative that minimizes maximum regret (career choice)
Role of expected value
- Expected value calculated as weighted average of all possible outcomes provides single value to compare alternatives (investment returns)
- Useful for decision-making under risk forms basis for advanced decision analysis techniques (portfolio optimization)
- Limitations include not accounting for may not suit one-time decisions or extreme outcomes (lottery ticket purchase)
Key Terms to Review (19)
Bayesian inference: Bayesian inference is a statistical method that applies Bayes' theorem to update the probability of a hypothesis as more evidence or information becomes available. This approach allows decision-makers to incorporate prior knowledge along with new data, refining their beliefs and predictions about uncertain events. By combining prior distributions with likelihoods, Bayesian inference offers a flexible framework for making informed decisions under uncertainty.
Bounded rationality: Bounded rationality refers to the idea that when making decisions, individuals are limited by the information they have, the cognitive limitations of their minds, and the time available to make a choice. This concept suggests that while people strive to make rational decisions, they often settle for a solution that is good enough rather than the optimal one due to these constraints. Bounded rationality emphasizes that decision-making occurs in an environment filled with uncertainty, where not all factors can be known or considered.
Decision risk: Decision risk refers to the potential negative outcomes associated with making a choice under uncertain conditions. This uncertainty stems from various factors, including incomplete information, changing environments, and unpredictable variables. Understanding decision risk is crucial for making informed choices and effectively managing uncertainty in decision-making processes.
Decision tree: A decision tree is a visual representation of possible outcomes and decisions, often used to evaluate the potential consequences of various choices under uncertainty. It helps in mapping out different decision paths, taking into account their probabilities and potential payoffs, making it easier to visualize the impact of decisions. By incorporating discrete probability distributions, a decision tree can aid in analyzing situations where outcomes are not certain.
Diminishing Marginal Utility: Diminishing marginal utility refers to the decrease in the added satisfaction or utility a consumer derives from consuming additional units of a good or service. This concept is important for understanding consumer behavior, as it explains why individuals may not continue to consume a good indefinitely, even if it is available. The principle highlights that as consumption increases, the incremental benefit from each additional unit consumed tends to decline, affecting decisions made under uncertainty.
Environmental Uncertainty: Environmental uncertainty refers to the lack of predictability in external factors that can affect decision-making processes in organizations. This uncertainty can arise from various sources, such as market fluctuations, changes in regulations, technological advancements, and social dynamics, making it challenging for managers to make informed choices.
Expected Value: Expected value is a fundamental concept in probability and statistics that represents the average outcome of a random variable when considering all possible outcomes, each weighted by its probability of occurrence. It helps in making informed decisions under uncertainty by providing a single summary measure that reflects the anticipated result of a decision or gamble. By incorporating different probabilities and potential payoffs, expected value connects deeply to various decision-making scenarios involving risk, uncertainty, and strategic analysis.
Heuristics: Heuristics are mental shortcuts or rules of thumb that simplify decision-making processes, especially in situations involving uncertainty. They help individuals make quick judgments and decisions by reducing the cognitive load required to analyze complex information. While heuristics can lead to efficient problem-solving, they can also result in biases and errors in judgment when applied inappropriately.
Maximax criterion: The maximax criterion is a decision-making approach that focuses on maximizing the maximum possible payoff. It is primarily used in situations where decision-makers are optimistic and are willing to take risks to achieve the highest potential rewards. This criterion emphasizes a forward-thinking perspective, encouraging individuals to choose the alternative that offers the best possible outcome among all options, regardless of the likelihood of those outcomes occurring.
Maximin Criterion: The maximin criterion is a decision-making approach used under conditions of uncertainty, where the objective is to maximize the minimum possible payoff or outcome. This strategy prioritizes securing the best worst-case scenario, making it particularly useful for managers who aim to minimize risk and protect against potential losses in uncertain environments.
Minimax regret criterion: The minimax regret criterion is a decision-making approach that aims to minimize the maximum possible regret a decision-maker might feel after making a choice. It does this by evaluating the potential regret of each decision based on the best outcome that could have been achieved had different choices been made, allowing for a systematic evaluation of options under uncertainty. This approach connects with concepts such as risk assessment, where understanding potential outcomes is crucial, and sensitivity analysis, which examines how changes in assumptions can impact decisions.
Monte Carlo simulation: Monte Carlo simulation is a statistical technique that uses random sampling and repeated simulations to model and analyze complex systems or processes, particularly under conditions of uncertainty. This method helps decision-makers understand the impact of risk and uncertainty by generating a range of possible outcomes, enabling informed decision-making.
Opportunity Cost: Opportunity cost refers to the value of the next best alternative that is forgone when a decision is made to pursue a certain option. It emphasizes the idea that every choice has a cost, not just in monetary terms but also in terms of time, resources, and potential benefits that could have been gained from the alternative decision. Understanding opportunity cost is crucial in decision-making, especially when dealing with uncertainty, as it helps evaluate the trade-offs involved in each choice.
Probability distribution: A probability distribution describes how probabilities are assigned to the possible outcomes of a random variable. It provides a comprehensive overview of the likelihood of various outcomes, allowing for the analysis and understanding of uncertainty in decision-making processes. By modeling the various potential results and their associated probabilities, it forms the backbone of statistical theory and informs techniques such as simulation, enabling effective management under uncertainty.
Risk aversion: Risk aversion refers to the preference of individuals or organizations to avoid uncertainty and potential loss, often leading them to choose safer options over riskier ones, even when the riskier choice may have a higher expected return. This behavior is crucial in decision-making as it influences how decisions are made under conditions of uncertainty and risk, affecting everything from investment choices to policy formulation.
Scenario analysis: Scenario analysis is a strategic planning tool used to evaluate the potential outcomes of various future events by considering different possible scenarios. It helps organizations assess how uncertainties might impact their decisions and operations, enabling them to make more informed choices. This method is closely linked with other analytical techniques, as it can enhance decision-making processes by providing a clearer picture of risks and opportunities in various contexts.
Sensitivity Analysis: Sensitivity analysis is a technique used to determine how different values of an independent variable can impact a particular dependent variable under a given set of assumptions. It plays a crucial role in assessing the risk and uncertainty in decision-making, helping managers understand which variables have the most influence on their outcomes and decisions.
Strategic Uncertainty: Strategic uncertainty refers to the unpredictability in the outcomes of decisions due to the actions and strategies of other decision-makers. It arises when individuals or organizations must consider how their choices will affect, and be affected by, the choices of others, making it a crucial aspect in decision-making under uncertain conditions.
Utility function: A utility function is a mathematical representation that assigns a numerical value to the level of satisfaction or happiness an individual derives from consuming goods and services. This concept helps to quantify preferences, allowing for comparisons between different choices under conditions of uncertainty. By modeling decision-making behavior, utility functions serve as a crucial tool in analyzing how individuals make choices that maximize their overall satisfaction.