Introduction to Philosophy Lecture 6 Pascal’s wager By David Kelsey.

Slides:



Advertisements
Similar presentations
Probability How likely is an event to occur?
Advertisements

Expected Value. When faced with uncertainties, decisions are usually not based solely on probabilities A building contractor has to decide whether to.
Utility Theory.
Probability Three basic types of probability: Probability as counting
1 Decision Making and Utility Introduction –The expected value criterion may not be appropriate if the decision is a one-time opportunity with substantial.
Introduction to Philosophy Lecture 6 Pascal’s wager
A measurement of fairness game 1: A box contains 1red marble and 3 black marbles. Blindfolded, you select one marble. If you select the red marble, you.
Game Theory, Part 1 Game theory applies to more than just games. Corporations use it to influence business decisions, and militaries use it to guide their.
Clear your desk for your quiz. Unit 2 Day 8 Expected Value Average expectation per game if the game is played many times Can be used to evaluate and.
Risk Attitude Dr. Yan Liu
4 Why Should we Believe Politicians? Lupia and McCubbins – The Democratic Dilemma GV917.
Take out a coin! You win 4 dollars for heads, and lose 2 dollars for tails.
Decision Analysis Introduction Chapter 6. What kinds of problems ? Decision Alternatives (“what ifs”) are known States of Nature and their probabilities.
Games of probability What are my chances?. Roll a single die (6 faces). –What is the probability of each number showing on top? Activity 1: Simple probability:
1-3A Experimental Probability Warm-up (IN) Learning Objective: to develop a sense that probability is a ration that measures the chance that an event will.
1 Utility Theory. 2 Option 1: bet that pays $5,000,000 if a coin flipped comes up tails you get $0 if the coin comes up heads. Option 2: get $2,000,000.
PSY 5018H: Math Models Hum Behavior, Prof. Paul Schrater, Spring 2005 Normative Decision Theory A prescriptive theory for how decisions should be made.
Lectures in Microeconomics-Charles W. Upton Minimax Strategies.
Expected Value.  In gambling on an uncertain future, knowing the odds is only part of the story!  Example: I flip a fair coin. If it lands HEADS, you.
Chapter 7 Expectation 7.1 Mathematical expectation.
Rights and Wrongs of Belief II Pascal, Blackburn.
Copyright © 2010, 2007, 2004 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Using Tinkerplots Ruth Kaniuk Endeavour Teacher Fellow, 2013.
What are the chances of that happening?. What is probability? The mathematical expression of the chances that a particular event or outcome will happen.
Warm up: Solve each system (any method). W-up 11/4 1) Cars are being produced by two factories, factory 1 produces twice as many cars (better management)
Pascal’s Wager / Divine Foreknowledge. Pascal’s Wager ❏ Blaise Pascal ❏ Pascal's Wager is an argument that belief in the existence of God is in a rational.
Decision-making under uncertainty. Introduction Definition of risk Attitudes toward risk Avoiding risk: Diversification Insurance.
Decision Analysis (cont)
Complexity and Emergence in Games (Ch. 14 & 15). Seven Schemas Schema: Conceptual framework concentrating on one aspect of game design Schemas: –Games.
1-1 Copyright © 2015, 2010, 2007 Pearson Education, Inc. Chapter 13, Slide 1 Chapter 13 From Randomness to Probability.
● Uncertainties abound in life. (e.g. What's the gas price going to be next week? Is your lottery ticket going to win the jackpot? What's the presidential.
Chapter 5 Uncertainty and Consumer Behavior. ©2005 Pearson Education, Inc.Chapter 52 Q: Value of Stock Investment in offshore drilling exploration: Two.
Section 5.1 Discrete Probability. Probability Distributions x P(x)1/4 01/83/8 x12345 P(x)
Decision making Under Risk & Uncertainty. PAWAN MADUSHANKA MADUSHAN WIJEMANNA.
Chapter 1: Religion Pascal’s Wager Introducing Philosophy, 10th edition Robert C. Solomon, Kathleen Higgins, and Clancy Martin.
Lecture 15 – Decision making 1 Decision making occurs when you have several alternatives and you choose among them. There are two characteristics of good.
Salvaging Pascal’s Wager Liz Jackson and Andy Rogers.
What is Probability?. The Mathematics of Chance How many possible outcomes are there with a single 6-sided die? What are your “chances” of rolling a 6?
ProbabilitiesProbabilities. Introduction Human life is full of uncertainties. In our day to day life very often we make guess & use statements like :”possibility.
Expected Value.
Game Theory, Part 2 Consider again the game that Sol and Tina were playing, but with a different payoff matrix: H T Tina H T Sol.
Probability Evaluation 11/12 th Grade Statistics Fair Games Random Number Generator Probable Outcomes Resources Why Fair Games? Probable Outcome Examples.
Probability How likely is an event to occur? What are the chances of that happening??!!
Decision theory under uncertainty
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 14 From Randomness to Probability.
From Randomness to Probability Chapter 14. Dealing with Random Phenomena A random phenomenon is a situation in which we know what outcomes could happen,
Lecture 12. Game theory So far we discussed: roulette and blackjack Roulette: – Outcomes completely independent and random – Very little strategy (even.
Fair and Unfair Games Laura Smiley. What makes a game… FairUnfair.
WOULD YOU PLAY THIS GAME? Roll a dice, and win $1000 dollars if you roll a 6.
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U.
MATH 256 Probability and Random Processes Yrd. Doç. Dr. Didem Kivanc Tureli 14/10/2011Lecture 3 OKAN UNIVERSITY.
President UniversityErwin SitompulPBST 4/1 Dr.-Ing. Erwin Sitompul President University Lecture 4 Probability and Statistics
On Investor Behavior Objective Define and discuss the concept of rational behavior.
Cognitive Processes PSY 334 Chapter 10 – Reasoning & Decision-Making.
Binomial Distributions Chapter 5.3 – Probability Distributions and Predictions Mathematics of Data Management (Nelson) MDM 4U Authors: Gary Greer (with.
Lecture 2 Probability By Aziza Munir. Summary of last lecture Why QBA What is a model? Why to develop a model Types of models Flow chart of transformation.
Copyright © 2010 Pearson Education, Inc. Chapter 14 From Randomness to Probability.
Money and Banking Lecture 11. Review of the Previous Lecture Application of Present Value Concept Internal Rate of Return Bond Pricing Real Vs Nominal.
Philosophical Problems January 11, 2015 Pascal's Wager.
1 Chapter 4 Mathematical Expectation  4.1 Mean of Random Variables  4.2 Variance and Covariance  4.3 Means and Variances of Linear Combinations of Random.
Uncertainty and Games (Ch. 15). Uncertainty If a game outcome is certain can it achieve meaningful play? –Example of such a game? Two kinds of uncertainty:
Unit 7: Chance Variation
Decisions under uncertainty and risk
Lecture 13.
Chapter Randomness, Probability, and Simulation
Critical Thinking Lecture 14 Pascal’s wager
Introduction to Philosophy Lecture 6 Pascal’s wager
Fun… Tree Diagrams… Probability.
From Randomness to Probability
WARM-UP 3/20 Why is number 7 lucky?
Presentation transcript:

Introduction to Philosophy Lecture 6 Pascal’s wager By David Kelsey

Pascal Blaise Pascal lived from He was a famous mathematician and a gambler. He invented the theory of probability.

Probability and decision theory Pascal thinks that we can’t know for sure whether God exists. Decision theory: used to study how to make decisions under uncertainty, I.e. when you don’t know what will happen. –Lakers or Knicks: –Rain coat: Rule for action: when making a decision under a time of uncertainty always perform that action that has the highest expected utility!

Expected Utility The expected utility for any action: the payoff you can expect to gain on each attempt if you continued to make attempts... –It is the average gain or loss per attempt. To compute the expected value of an action: –((The prob. of a success) x (The payoff of success)) + ((the prob. of a loss) x (the payoff of a loss)) Which game would you play? –The Big 12: pay 1$ to roll two dice. –Lucky 7: pay 1$ to roll two dice. –E.V. of Big 12: –E.V. of Lucky 7:

Payoff matrices Gamble: Part of the idea of decision theory is that you can think of any decision under uncertainty as a kind of gamble. Payoff Matrix: used to represent a scenario in which you have to make a decision under uncertainty. –On the left: our alternative courses of action. –At the top: the outcomes. –Next to each outcome: add the probability that it will occur. –Under each outcome: the payoff for that outcome Calling a coin flip: –If you win it you get a quarter and if you lose it you lose a quarter. The coin comes up heads: ___ It comes up tails: ___ You call heads ___ ___ You call tails ___ ___

The Expected Utility of the coin flip So when making a decision under a time of uncertainty: construct a payoff matrix –To compute the expected value of an action: ((The prob. of a success) x (The payoff of success)) + ((the prob. of a loss) x (the payoff of a loss)) For our coin tossing example: –The EU of calling head: –The EU of calling tails: –Which action has the higher expected utility?

Taking the umbrella to work Do you take an umbrella to work? You live in Seattle. There is a 50% chance it will rain. –Taking the Umbrella: a bit of a pain. You will have to carry it around. Payoff = -5. –If it does rain & you don’t have the umbrella: you will get soaked payoff of -50. –If it doesn’t rain then you don’t have to lug it around: payoff of 10. It rains (___) It doesn’t rain (___) Take umbrella ___ ___ Don’t take umbrella ___ ___ EU (take umbrella) = … EU (don’t take umbrella) = … Take the umbrella to work!

Pascal’s wager Choosing to believe in God: Pascal thinks that choosing whether to believe in God is like choosing whether to take an umbrella to work in Seattle. –It is a decision made under a time of uncertainty: –But We can estimate the payoffs: Believing in God is a bit of pain whether or not he exists: An infinite Reward: … Infinite Punishment: …

Pascal’s payoff matrix God exists (___) God doesn’t exist (___) Believe ____ ____ Don’t believe ____ ____ Assigning a probability to God’s existence: –A bit tricky since we don’t know. –For Pascal: since we don’t know if God exists we know the probability of his existence is greater than 0. –EU (believe) = … –EU (don’t believe) = … Which action has greater expected utility?

Pascal’s argument Pascal’s argument: –1. You can either believe in God or not believe in God. –2. Believing in God has greater EU than disbelieving in God. –3. You should perform whatever action has the greatest EU. –4. Thus, you should believe in God.

Denying premise 1 The first move: –Can you choose to believe? The second move: –Would God reward selfish believers?

Denying premise 2 Deny premise 2: –Infinite payoff’s make no sense: –Can we even assign a non-zero probability to God’s existence?

The Many Gods objection We could Deny premise 2 in another way: –The Many Gods objection: Catholic God exists (L) Muslim God exists (M) Jewish God exists (N) God doesn’t exist (1-L-M-N) Believe in: Catholic God infinity neg. infinity neg. infinity -5 Muslim God neg. infinity infinity neg. infinity-5 Jewish God neg. infinity neg. infinity infinity -5 Don’t believe neg. infinity neg. infinity neg. infinity+5

The Perverse Master The perverse master objection: God exists (m) Perverse Master exists (n) Neither exists (1-m-n) Believe infinity neg. infinity -5 Don’t Believe neg. infinity infinity 5 Disbelief seems no worse off than belief: Is it less likely that the perverse Master exists than does God?