Chapter1: Making Economics Decisions
Section: Chapter Questions
Problem 1QTC
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
Transcribed Image Text:**Question 14:**
What is the difference between risk and uncertainty? How do we model each in game theory? Provide an example of each.
---
**Explanation:**
In the context of game theory and decision-making, both risk and uncertainty refer to situations with unknown outcomes, but they have distinct definitions:
1. **Risk:** This occurs when the probability of each outcome is known. In game theory, risk can be modeled using probability distributions where the likelihood of different outcomes is quantified. For example, in a poker game, a player can calculate the probability of winning a hand based on known odds.
2. **Uncertainty:** This arises when the probabilities of outcomes are unknown or not well-defined. In game theory, uncertainty is often tackled using strategies like maximin and minimax, or by employing Bayesian approaches to update beliefs based on new information. An example of uncertainty is entering a new market where a company does not have historical data to estimate the competitors' responses.
Both concepts are pivotal in strategic decision-making, influencing how players formulate and adjust their strategies in various games and real-world scenarios.
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