Lecture 25 Maher on Physical Probability

Similar documents
Lecture 9 Maher on Inductive Probability

A Few Basics of Probability

Lecture 8 The Subjective Theory of Betting on Theories

Math/Stats 425 Introduction to Probability. 1. Uncertainty and the axioms of probability

Chapter What is the probability that a card chosen from an ordinary deck of 52 cards is an ace? Ans: 4/52.

Conditional Probability, Independence and Bayes Theorem Class 3, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

Lesson 1. Basics of Probability. Principles of Mathematics 12: Explained! 314

WRITING PROOFS. Christopher Heil Georgia Institute of Technology

Definition and Calculus of Probability

Handout #1: Mathematical Reasoning

Chapter 3. Cartesian Products and Relations. 3.1 Cartesian Products

Mathematical goals. Starting points. Materials required. Time needed

E3: PROBABILITY AND STATISTICS lecture notes

2. How many ways can the letters in PHOENIX be rearranged? 7! = 5,040 ways.

Statistics 100A Homework 2 Solutions

ST 371 (IV): Discrete Random Variables

Cartesian Products and Relations

Discrete Mathematics and Probability Theory Fall 2009 Satish Rao, David Tse Note 10

Point and Interval Estimates

Elementary Statistics and Inference. Elementary Statistics and Inference. 16 The Law of Averages (cont.) 22S:025 or 7P:025.

7.S.8 Interpret data to provide the basis for predictions and to establish

6.3 Conditional Probability and Independence

CHAPTER 2. Logic. 1. Logic Definitions. Notation: Variables are used to represent propositions. The most common variables used are p, q, and r.

Accentuate the Negative: Homework Examples from ACE

Problem of the Month: Fair Games

Lecture 17 Newton on Gravity

Quine on truth by convention

Section 6.2 Definition of Probability

P1. All of the students will understand validity P2. You are one of the students C. You will understand validity

Science and Scientific Reasoning. Critical Thinking

Student Outcomes. Lesson Notes. Classwork. Discussion (10 minutes)

Chapter 4: Probability and Counting Rules

Basic Probability Concepts

Comparison of frequentist and Bayesian inference. Class 20, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

Oracle Turing machines faced with the verification problem

Measurement and Measurement Scales

STAT 319 Probability and Statistics For Engineers PROBABILITY. Engineering College, Hail University, Saudi Arabia

Feb 7 Homework Solutions Math 151, Winter Chapter 4 Problems (pages )

Chapter 4 Lecture Notes

Atomic structure. Resources and methods for learning about these subjects (list a few here, in preparation for your research):

Stat 20: Intro to Probability and Statistics

How To Find The Sample Space Of A Random Experiment In R (Programming)

1/9. Locke 1: Critique of Innate Ideas

13.0 Central Limit Theorem

Section 4.1 Rules of Exponents

1 Combinations, Permutations, and Elementary Probability

Formal Languages and Automata Theory - Regular Expressions and Finite Automata -

Contemporary Mathematics- MAT 130. Probability. a) What is the probability of obtaining a number less than 4?

Topic #6: Hypothesis. Usage

DIFFICULTIES AND SOME PROBLEMS IN TRANSLATING LEGAL DOCUMENTS

Chapter 16. Law of averages. Chance. Example 1: rolling two dice Sum of draws. Setting up a. Example 2: American roulette. Summary.

The Procedures of Monte Carlo Simulation (and Resampling)

Lab 11. Simulations. The Concept

AP: LAB 8: THE CHI-SQUARE TEST. Probability, Random Chance, and Genetics

The Basics of Graphical Models

Mathematical Induction

36 Odds, Expected Value, and Conditional Probability

Question of the Day. Key Concepts. Vocabulary. Mathematical Ideas. QuestionofDay

14.30 Introduction to Statistical Methods in Economics Spring 2009

Probability. Sample space: all the possible outcomes of a probability experiment, i.e., the population of outcomes

Decision Making under Uncertainty

Lesson Plans for (9 th Grade Main Lesson) Possibility & Probability (including Permutations and Combinations)

Self-locating belief and the Sleeping Beauty problem

LAB : THE CHI-SQUARE TEST. Probability, Random Chance, and Genetics

Concepts of Probability

Probability: Terminology and Examples Class 2, 18.05, Spring 2014 Jeremy Orloff and Jonathan Bloom

QUANTITATIVE METHODS BIOLOGY FINAL HONOUR SCHOOL NON-PARAMETRIC TESTS

Bayesian Tutorial (Sheet Updated 20 March)

An Innocent Investigation

One natural response would be to cite evidence of past mornings, and give something like the following argument:

AMS 5 CHANCE VARIABILITY

MATH10040 Chapter 2: Prime and relatively prime numbers

Lecture 16 : Relations and Functions DRAFT

1.6 The Order of Operations

NP-Completeness and Cook s Theorem

Library, Teaching and Learning. Writing Essays. and other assignments Lincoln University

Particle Soup: Big Bang Nucleosynthesis

Section 5-3 Binomial Probability Distributions

Basic Probability. Probability: The part of Mathematics devoted to quantify uncertainty

Short-Run Mechanistic Probability

General Philosophy. Dr Peter Millican, Hertford College. Lecture 3: Induction

FEGYVERNEKI SÁNDOR, PROBABILITY THEORY AND MATHEmATICAL

The Slate Is Not Empty: Descartes and Locke on Innate Ideas

3. Mathematical Induction

What is a P-value? Ronald A. Thisted, PhD Departments of Statistics and Health Studies The University of Chicago

Probabilistic Strategies: Solutions

2. Three dice are tossed. Find the probability of a) a sum of 4; or b) a sum greater than 4 (may use complement)

Section 6-5 Sample Spaces and Probability

Validity, Fairness, and Testing

Kant s Fundamental Principles of the Metaphysic of Morals

def: An axiom is a statement that is assumed to be true, or in the case of a mathematical system, is used to specify the system.

Probability: The Study of Randomness Randomness and Probability Models. IPS Chapters 4 Sections

Lecture 1. Basic Concepts of Set Theory, Functions and Relations

Correlational Research. Correlational Research. Stephen E. Brock, Ph.D., NCSP EDS 250. Descriptive Research 1. Correlational Research: Scatter Plots

5544 = = = Now we have to find a divisor of 693. We can try 3, and 693 = 3 231,and we keep dividing by 3 to get: 1

Thesis Statement & Essay Organization Mini-Lesson (Philosophy)

Using Patterns and Composite Propositions to Automate the Generation of Complex LTL

D irections. BMX Racing. by Bill Gutman

Basic Probability Theory II

Last May, philosopher Thomas Nagel reviewed a book by Michael Sandel titled

Transcription:

Lecture 25 Maher on Physical Probability Patrick Maher Philosophy 270 Spring 2010

Form of pp statements Experiments and outcomes Let an experiment be an action or event such as tossing a coin, weighing an object, or two particles colliding. We distinguish between experiment tokens and experiment types. An experiment token is a physical event at a particular place and time, e.g., a particular toss of a particular coin. An experiment type is an abstract categorization to which a token experiment may belong, e.g., toss of a coin. It doesn t have a place or time. Experiments have outcomes, such as a coin landing heads. Again there is a type/token distinction. An outcome token is a physical event at a particular place and time, e.g., a particular event of a coin landing heads. An outcome type is an abstract categorization to which a token outcome may belong, e.g., landing heads. It doesn t have a place or time.

Statements of pp A typical statement of pp: The pp of heads on a toss of this coin is 1/2. Here the pp relates three things: tossing this coin (an experiment type) the coin landing heads (an outcome type) 1/2 (a number). In general, an elementary sentence of pp can be represented in the form: The pp of X resulting in O is r where X is an experiment type, O is an outcome type, and r is a number. Notation: pp X (O) = r means the pp of experiment type X having outcome type O is r.

Compatibility with determinism Pp is compatible with determinism Determinism is the view that the state of the world at one time, together with the laws of nature, completely determine the state of the world at all later times. The way the concept of pp is used shows that pps can have non-extreme values even if determinism is true. People attribute non-extreme physical probabilities in games of chance, while believing that the outcome of such games is causally determined by the initial conditions. Scientific theories in statistical mechanics, genetics, and the social sciences postulate non-extreme physical probabilities in situations that are believed to be governed by underlying deterministic laws. Some of the most important statistical scientific theories were developed in the 19th century by scientists who believed that all events are governed by deterministic laws.

How it is compatible Determinism implies that, if X is sufficiently specific, then pp X (O) = 0 or 1; but X need not be this specific, in which case pp X (O) can have a non-extreme value even if the outcome of X is governed by deterministic laws. Example A token coin toss belongs to both the following types: X : Toss of this coin. X : Toss of this coin from such and such a position, with such and such force applied at a such and such a point, etc. Assuming that the outcome of tossing a coin is governed by deterministic laws, pp X (head) = 0 or 1; however, this is compatible with pp X (head) = 1/2.

Differences between pp and ip 1 Ip is logical; pp isn t. 2 The arguments of ip are two propositions; the arguments of pp are an experiment type and an outcome type. Propositions are true or false; experiment and outcome types aren t. 3 Ips exist for practically all pairs of propositions but there are many experiment types X and outcome types O for which pp X (O) doesn t exist. Example: X = placing a die on a table, in whatever way one wants, O = the die is placed with six facing up. 4 Ips often lack numeric values but if a pp exists at all then it has a numeric value. Example: X = randomly drawing a ball from an urn of unknown composition, O = the ball drawn is white. We don t know the value of pp X (O) but it has a numeric value.

Specification The specification principle (SP) If it is possible to perform X in a way that ensures it is also a performance of the more specific experiment type X, then pp X (O) exists only if pp X (O) exists and is equal to pp X (O). Example X = tossing a normal coin, X = tossing a normal coin on a Monday, O = the coin lands heads. It is possible to perform X in a way that ensures it is a performance of X (just toss the coin on a Monday), and pp X (O) exists, so SP implies that pp X (O) exists and equals pp X (O).

Corollary If it is possible to perform X in a way that ensures it is also a performance of the more specific experiment type X i, for i = 1, 2, and if pp X1 (O) pp X2 (O), then pp X (O) does not exist. Example Let b be an urn that contains only black balls and w an urn that contains only white balls. Let: X = selecting a ball from either b or w X b = selecting a ball from b X w = selecting a ball from w O = the ball selected is white. It is possible to perform X in a way that ensures it is also a performance of the more specific experiment type X b, likewise for X w, and pp Xb (O) = 0 while pp Xw (O) = 1, so the corollary implies that pp X (O) doesn t exist.

Another look at coin tossing Let X = tossing a normal coin, O = the coin lands heads. If this description of X was a complete specification of the experiment type, then X could be performed with apparatus that would precisely fix the initial position of the coin and the force applied to it, thus determining the outcome. It would then follow from SP that pp X (O) doesn t exist. This consequence of SP is correct. So when we say that pp X (O) does exist, we are tacitly assuming that the toss is made by a normal human without special apparatus that could precisely fix the initial conditions of the toss; a fully explicit specification of X would include this requirement. The existence of pp X (O) thus depends on an empirical fact about humans, namely, the limited precision of their perception and motor control.

Questions 1 Explain the difference between an experiment token and an experiment type; illustrate your answer with an example. 2 What is determinism? If determinism is true, does it follow that all pps are 0 or 1? Explain why, or why not. 3 State SP and its corollary. 4 Let X = tossing a fair coin, O = the coin lands heads. (a) Prove that pp X (O) doesn t exist, if nothing further is assumed about X. (b) Describe what could be added to X to make it true that pp X (O) = 1/2. 5 If X = placing a die on a table in whatever way one wants and O = the die is placed with six facing up, does pp X (O) exist? Explain why, or why not.

Reference Patrick Maher. Physical probability. In Clark Glymour, Wei Wang, and Dag Westerståhl, editors, Proceedings of the 13th International Congress of Logic, Methodology and Philosophy of Science. Kings College Publications, 2009. http://patrick.maher1.net/preprints/pp.pdf.