Glossary

11.1 Decision-making under uncertainty and risk

2 min read•july 24, 2024

Decision-making involves navigating and . Uncertainty lacks outcome probabilities, while risk uses known probabilities. This distinction shapes how we approach choices in various scenarios, from new markets to investment portfolios.

Different criteria help make decisions under uncertainty. Maximax focuses on best outcomes, maximin on worst-case scenarios, and minimizes potential regret. and also play crucial roles in shaping our choices and preferences.

Decision-Making Concepts

Uncertainty vs risk in decisions

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  • Decision-making under uncertainty involves no knowledge of outcome probabilities and limited information available leads to challenges when entering new markets or developing products (electric cars)

  • Decision-making under risk utilizes known or estimable outcome probabilities and more information available enables informed choices for insurance policies or investment portfolios (stocks)

  • Key differences stem from probability information availability affects ability to quantify potential outcomes and shapes analysis methods used ()

Decision criteria for uncertainty

  • focuses on best possible outcome selects alternative with highest potential payoff suits optimistic decision-makers (venture capital investments)

  • emphasizes worst possible outcome chooses alternative with best worst-case scenario appeals to conservative decision-makers (emergency preparedness)

  • Minimax regret criterion aims to minimize potential regret calculates difference between best possible and actual outcomes selects alternative with lowest maximum regret (career choices)

  • Application steps:

    1. Identify alternatives and possible outcomes
    2. Create
    3. Apply chosen criterion
    4. Select best alternative

Expected value in decision-making

  • Expected value (EV) represents weighted average of all possible outcomes calculated using formula EV=∑i=1npi×viEV = \sum_{i=1}^{n} p_i \times v_i where pip_i is probability of outcome i viv_i is value of outcome i and n is number of possible outcomes

  • Steps to calculate EV:

    1. Identify all possible outcomes
    2. Determine probability of each outcome
    3. Assign value to each outcome
    4. Multiply outcome value by probability
    5. Sum all products

  • Decision rule suggests choosing alternative with highest expected value but has limitations assumes risk neutrality may not account for extreme outcomes (lottery tickets)

Risk aversion's impact

  • Risk aversion reflects preference for certainty over uncertainty leads individuals to accept lower expected value to avoid risk influences decisions in financial planning (conservative vs aggressive portfolios)

  • measures satisfaction or desirability of outcomes accounts for individual risk preferences helps explain seemingly irrational choices (insurance purchases)

  • represents amount decision-maker willing to pay to avoid risk calculated as difference between expected value and certainty equivalent affects pricing of financial instruments (bonds)

  • Impact on decision-making causes risk-averse individuals to choose safer alternatives may lead to suboptimal decisions based solely on expected value (missed investment opportunities)

  • Methods to incorporate risk aversion include utility functions risk-adjusted discount rates and scenario analysis with enhance decision-making process in uncertain environments (project evaluations)

Key Terms to Review (12)

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.
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: Minimax regret is a decision-making criterion used in situations of uncertainty that focuses on minimizing the maximum possible regret that could result from a chosen alternative. This approach helps individuals and managers to make rational choices when faced with uncertain outcomes by evaluating potential regret from not selecting the best possible option after the fact. It emphasizes a risk-averse attitude, where decision-makers consider worst-case scenarios to avoid feelings of regret associated with poor choices.
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.
Payoff matrix: A payoff matrix is a structured representation that outlines the potential outcomes of different strategies chosen by players in a decision-making scenario, specifically under uncertainty or risk. It helps to visualize the relationship between the choices made and their respective payoffs, allowing for a systematic evaluation of possible outcomes. This tool is essential in analyzing how different strategies might interact and influence one another in competitive or cooperative situations.
Risk: Risk is the potential for loss or adverse outcomes resulting from uncertain events or decisions. It is a critical concept in decision-making, especially when individuals or organizations are faced with uncertain conditions, as it involves evaluating probabilities and impacts of various scenarios before making choices.
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.
Risk premium: The risk premium is the additional return that investors require for taking on the risk of an investment compared to a risk-free asset. This concept highlights the relationship between risk and expected return, where higher uncertainty in returns typically demands a higher premium to entice investors into accepting that risk.
Sensitivity testing: Sensitivity testing is a technique used to determine how the variation in the output of a model or decision-making process can be attributed to different variations in its inputs. This approach helps in understanding which variables have the most influence on outcomes, allowing for better risk assessment and decision-making under uncertainty. By identifying critical factors, sensitivity testing enables managers to focus on the most impactful areas, leading to more informed choices and strategies.
Uncertainty: Uncertainty refers to the lack of complete knowledge about the outcomes of a decision, making it difficult to predict future events or results. In decision-making, uncertainty is a critical factor that managers must navigate, as it influences the evaluation of risks and potential rewards associated with various choices.
Utility Theory: Utility theory is a framework used in economics and decision-making that quantifies the satisfaction or value an individual derives from a particular outcome or choice. This theory helps in understanding how people make decisions under uncertainty and risk by analyzing preferences and trade-offs between different options. It plays a crucial role in assessing the desirability of various outcomes and supports various analytical methods for improving decision-making processes.
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