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Quizzes > Quizzes for Business > Business

Test Your Decision-Making and Uncertainty Knowledge Quiz

Gauge Your Risk Assessment Skills in This Quiz

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art depicting questions and answers for a decision-making and uncertainty knowledge quiz.

Dive into this dynamic decision-making and uncertainty quiz crafted for learners seeking to sharpen their risk assessment and probabilistic reasoning skills. Whether you're an aspiring analyst or a curious student, this free knowledge quiz offers clear scenarios and insightful feedback to enhance your strategic thinking. You can also explore our Financial Decision-Making Quiz or the Interactive Decision-Making Quiz for more specialized practice. Every question is fully editable in our intuitive editor, empowering you to tailor the quizzes to your learning objectives. Join in and elevate your understanding of uncertainty today!

Which best describes decision-making under uncertainty?
Using only deterministic algorithms for decisions.
Setting fixed goals before any analysis.
Choosing actions when outcomes are not fully known.
Selecting options with guaranteed results.
Decision-making under uncertainty involves choosing actions without full knowledge of outcomes. It contrasts with decisions under certainty where outcomes are known in advance.
What is 'risk' in the context of decision-making under uncertainty?
A fixed penalty assigned before deciding.
The emotional fear of making a choice.
A guaranteed loss in every decision.
Variability of possible outcomes.
Risk refers to the variability or dispersion of possible outcomes around an expected value. It highlights that different results may occur, some more favorable than others.
What is a cognitive bias?
A systematic deviation in thinking affecting decisions.
A random error with no predictable pattern.
A purely emotional reaction unconnected to reasoning.
The use of rigorous statistical methods.
A cognitive bias is a systematic pattern of deviation from norm or rationality in judgment. These biases can lead decision-makers to make illogical or suboptimal choices.
When you calculate expected value, you are finding:
The total sum of all outcomes.
The single worst-case result.
The weighted average of all possible outcomes.
Only the most likely outcome.
Expected value is computed by multiplying each outcome by its probability and summing those products. It provides the long-run average if the decision is repeated.
Which of the following is a strategy for informed decision-making under uncertainty?
Random guesswork.
Relying solely on gut feelings.
Ignoring available data.
Scenario analysis.
Scenario analysis involves constructing possible future states and evaluating decisions under each. It helps decision-makers prepare for a range of uncertainties.
After hearing about a high-profile plane crash, you overestimate the danger of flying. Which cognitive bias is at work?
Availability heuristic.
Confirmation bias.
Anchoring bias.
Overconfidence effect.
The availability heuristic causes people to judge probabilities based on how easily examples come to mind. Dramatic or recent events are more salient, leading to inflated risk perceptions.
Believing that a fair coin is 'due' to land tails after several heads is an example of:
Gambler's fallacy.
Hot-hand fallacy.
Base rate neglect.
Hindsight bias.
The gambler's fallacy is the mistaken belief that independent random events will balance out in the short term. Past outcomes have no influence on future independent events.
A disease has a prevalence of 2%. A test is 90% sensitive and 5% false positive. If you test positive, what is roughly the probability you actually have the disease?
About 2%.
About 27%.
About 95%.
About 90%.
Using Bayes' theorem: P(D|+) = (0.02à - 0.90)/[(0.02à - 0.90)+(0.98à - 0.05)] ≈ 0.018/0.067 ≈ 27%. Low prevalence leads to a moderate positive predictive value.
In a simple decision tree, Option A yields $100 with 50% probability or $0 otherwise, while Option B yields $60 for sure. Which option has the higher expected value?
Cannot determine without more data.
Option B.
They have equal expected value.
Option A.
Option A's expected value is 0.5Ã - 100 + 0.5Ã - 0 = $50, while Option B's expected value is $60 for sure. Therefore, Option B has the higher expected value.
Choosing a guaranteed $50 instead of a 90% chance to win $100 exemplifies which preference?
Loss averse.
Risk neutral.
Risk averse.
Risk seeking.
A risk-averse individual prefers a certain outcome over a gamble with a higher expected value. This reflects diminishing marginal utility of gains.
Which statistical measure quantifies the dispersion of uncertain outcomes around an expected value?
Standard deviation.
Mode.
Skewness.
Correlation.
Standard deviation measures how much individual outcomes deviate from the mean. It is a key metric for assessing the variability or risk in a distribution.
Reading a description of Linda and concluding she's more likely a feminist bank teller illustrates which heuristic?
Representativeness heuristic.
Anchoring heuristic.
Overconfidence bias.
Framing effect.
The representativeness heuristic causes people to judge probabilities by how much one thing resembles another, leading to conjunction errors like the Linda problem.
Preferring bets with known probabilities over those with unknown probabilities is an example of:
Anchoring bias.
Ambiguity aversion.
Overconfidence.
Endowment effect.
Ambiguity aversion describes the preference for options with known risks over those with unknown or vague probabilities. This is illustrated by the Ellsberg paradox.
Which theory integrates risk preferences by using a utility function instead of monetary value?
Expected utility theory.
Prospect theory.
Markov decision process.
Game theory.
Expected utility theory uses a utility function to represent a decision-maker's risk preferences. It contrasts with expected monetary value by accounting for diminishing marginal utility.
Which method assesses how changes in input parameters affect decision outcomes?
Scenario denial.
Heuristic evaluation.
Bias correction.
Sensitivity analysis.
Sensitivity analysis systematically varies input values to see their impact on outcomes. It identifies which parameters most influence decision results.
What is the formula for the expected value of perfect information (EVPI)?
EVPI = Expected utility with risk aversion âˆ' EV without risk aversion.
EVPI = Expected value with perfect information âˆ' Maximum expected monetary value without information.
EVPI = Probability of best outcome à - Utility of outcome.
EVPI = Maximum EV without information âˆ' EV with perfect information.
EVPI measures the worth of knowing future states by comparing the expected value with perfect information against the best expected value achievable without it. It quantifies the maximum one should pay for information.
Which technique is used to solve a decision tree by evaluating from the end back to the start?
Heuristic pruning.
Monte Carlo sampling.
Backward induction.
Forward simulation.
Backward induction, also called rollback analysis, solves decision trees by starting at terminal nodes and working back to the root. It determines the optimal choice at each decision node.
Which decision criterion focuses on minimizing the maximum regret across outcomes?
Maximin utility.
Minimax regret.
Maximax strategy.
Laplace criterion.
The minimax regret criterion selects the option that minimizes the worst-case difference between the payoff obtained and the best payoff that could have been obtained. It emphasizes reducing potential regret.
According to prospect theory, which concept describes how individuals overweight small probabilities and underweight large probabilities?
Probability weighting function.
Endowment effect.
Loss aversion.
Reference dependence.
Prospect theory posits a probability weighting function that transforms objective probabilities into decision weights. This function typically overweights small probabilities and underweights large ones.
Which framework prioritizes maximizing the robustness of decisions against severe parameter uncertainty?
Expected utility theory.
Game-theoretic equilibrium.
Bayesian decision analysis.
Info-gap decision theory.
Info-gap decision theory focuses on finding decisions that remain viable under the widest range of uncertainty. It emphasizes robustness rather than optimality under assumed probabilities.
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Learning Outcomes

  1. Analyze various decision-making scenarios under uncertainty.
  2. Evaluate risk factors and their impact on outcomes.
  3. Identify cognitive biases affecting uncertain choices.
  4. Apply probabilistic reasoning to real-world problems.
  5. Demonstrate strategies for informed decision-making.
  6. Master techniques for assessing ambiguous situations.

Cheat Sheet

  1. Decision-making under uncertainty - Ever wish you had a crystal ball? In this section, you'll learn how to assess ambiguous scenarios and manage risks like a seasoned strategist, turning unknowns into calculated moves. Dive into the fundamentals
  2. Common cognitive biases - Biases like the availability heuristic and confirmation bias can sneak into your choices without you noticing. Discover how to spot these mental shortcuts and keep your brain on the straight and narrow. Explore heuristics and biases
  3. Ellsberg Paradox - Why do people prefer a known risk over an unknown one? This paradox exposes our love-hate relationship with ambiguity and shows how uncertainty can distort our preferences. Read the study
  4. Bayesian reasoning - Think of this as the ultimate mindset shift: update your beliefs as new facts roll in. Learn to calculate probabilities on the fly and watch your decision accuracy skyrocket. Unlock Bayesian thinking
  5. Role of heuristics - Heuristics are mental shortcuts that save time but can lead you astray. Gain the skills to balance fast thinking with careful analysis and dodge systematic errors. Master mental shortcuts
  6. Overconfidence bias - Sometimes we think we know more than we actually do - welcome to overconfidence. Learn strategies to check your ego, calibrate your confidence, and make wiser calls. Tackle overconfidence
  7. Loss aversion - The pain of losing often outweighs the joy of winning. Explore how this quirk steers your risk appetite and find out how to flip the script on your fear of losses. Understand loss aversion
  8. Framing effects - A simple twist in wording can make a choice look like a jackpot or a trap. Discover how presentation biases your decisions and learn to reframe problems like a pro. See framing in action
  9. Decision-making strategies - Stuck on a tough choice? Techniques like "pre-mortems" and devil's advocacy will help you break free from analysis paralysis and spot blind spots. Boost your toolkit
  10. Probabilistic thinking - Swap absolute "yes/no" answers for probability-based insights to predict outcomes more accurately. Embrace uncertainty by thinking in percentages and make every forecast count. Develop probabilistic mindsets
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