We define zero-sum games and show that they can be modeled with matrices. We find optimal strategies for two types of such games: (1) strictly determined games which have a saddle point, and (2) 2x2 non-strictly determined games, for which a calculus computation finds the optimal strategy
This document provides an introduction to game theory and outlines key concepts such as payoff matrices, expected value, and optimal strategies. It discusses examples of zero-sum games including matching dice and a game of chance involving biased dice. Strictly and non-strictly determined games are introduced. The document also provides an example of a non-zero-sum game involving two TV networks choosing programming for a time slot and the optimal strategies that maximize each network's minimum expected viewership.
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
This document provides an overview of game theory concepts taught in a university course. It defines game theory as the mathematics of human interactions and decision making. Key concepts discussed include Nash equilibrium, where each player adopts the optimal strategy given other players' strategies. Examples of applications are given in fields like economics, politics and biology. Different types of games and solutions concepts like mixed strategies are also introduced.
The document provides an overview of game theory, including definitions of key concepts. It discusses:
1) Game theory as the mathematical analysis of conflict situations where players make rational decisions. It aims to find optimal strategies.
2) Key concepts in game theory including games, moves, strategies, information, payoffs, extensive and normal forms, and equilibria such as Nash equilibrium.
3) Examples of games and equilibrium concepts including prisoners' dilemma, mixed strategies, and maximin strategies. Game theory has applications in economics, politics, and military strategy.
This document provides an introduction to game theory concepts including Nash equilibrium. It discusses how game theory can be used to model market situations with two players making strategic decisions. John Nash developed an important concept called Nash equilibrium where no player has incentive to unilaterally change their strategy. The document demonstrates finding Nash equilibrium in sample games using payoff matrices and shows how equilibrium can change depending on whether decisions are made simultaneously or sequentially.
This document provides an introduction to decision making using game theory. It defines game theory as attempting to mathematically model strategic situations where an individual's success depends on the choices of others. It outlines the basic constituents of games including players, actions/strategies, rules, types of games, and branches of game theory. Game theory can be applied to management areas like industrial organization strategies, corporate finance, and mechanism/auction design.
This document summarizes key concepts in unconstrained optimization of functions with two variables, including:
1) Critical points are found by taking the partial derivatives and setting them equal to zero, generalizing the first derivative test for single-variable functions.
2) The Hessian matrix generalizes the second derivative, with its entries being the partial derivatives evaluated at a critical point.
3) The second derivative test classifies critical points as local maxima, minima or saddle points based on the signs of the Hessian matrix's eigenvalues.
4) Taylor polynomial approximations in two variables involve partial derivatives up to second order, analogous to single-variable Taylor series.
5) An example classifies the critical points
This document provides an introduction to game theory and outlines key concepts such as payoff matrices, expected value, and optimal strategies. It discusses examples of zero-sum games including matching dice and a game of chance involving biased dice. Strictly and non-strictly determined games are introduced. The document also provides an example of a non-zero-sum game involving two TV networks choosing programming for a time slot and the optimal strategies that maximize each network's minimum expected viewership.
This presentation is an attempt to introduce Game Theory in one session. It's suitable for undergraduates. In practice, it's best used as a taster since only a portion of the material can be covered in an hour - topics can be chosen according to the interests of the class.
The main reference source used was 'Games, Theory and Applications' by L.C.Thomas. Further notes available at: http://bit.ly/nW6ULD
This document provides an overview of game theory concepts taught in a university course. It defines game theory as the mathematics of human interactions and decision making. Key concepts discussed include Nash equilibrium, where each player adopts the optimal strategy given other players' strategies. Examples of applications are given in fields like economics, politics and biology. Different types of games and solutions concepts like mixed strategies are also introduced.
The document provides an overview of game theory, including definitions of key concepts. It discusses:
1) Game theory as the mathematical analysis of conflict situations where players make rational decisions. It aims to find optimal strategies.
2) Key concepts in game theory including games, moves, strategies, information, payoffs, extensive and normal forms, and equilibria such as Nash equilibrium.
3) Examples of games and equilibrium concepts including prisoners' dilemma, mixed strategies, and maximin strategies. Game theory has applications in economics, politics, and military strategy.
This document provides an introduction to game theory concepts including Nash equilibrium. It discusses how game theory can be used to model market situations with two players making strategic decisions. John Nash developed an important concept called Nash equilibrium where no player has incentive to unilaterally change their strategy. The document demonstrates finding Nash equilibrium in sample games using payoff matrices and shows how equilibrium can change depending on whether decisions are made simultaneously or sequentially.
This document provides an introduction to decision making using game theory. It defines game theory as attempting to mathematically model strategic situations where an individual's success depends on the choices of others. It outlines the basic constituents of games including players, actions/strategies, rules, types of games, and branches of game theory. Game theory can be applied to management areas like industrial organization strategies, corporate finance, and mechanism/auction design.
This document summarizes key concepts in unconstrained optimization of functions with two variables, including:
1) Critical points are found by taking the partial derivatives and setting them equal to zero, generalizing the first derivative test for single-variable functions.
2) The Hessian matrix generalizes the second derivative, with its entries being the partial derivatives evaluated at a critical point.
3) The second derivative test classifies critical points as local maxima, minima or saddle points based on the signs of the Hessian matrix's eigenvalues.
4) Taylor polynomial approximations in two variables involve partial derivatives up to second order, analogous to single-variable Taylor series.
5) An example classifies the critical points
The document is notes for a lesson on tangent planes. It provides definitions of tangent lines and planes, formulas for finding equations of tangent lines and planes, and examples of applying these concepts. Specifically, it defines that the tangent plane to a function z=f(x,y) through the point (x0,y0,z0) has normal vector (f1(x0,y0), f2(x0,y0),-1) and equation f1(x0,y0)(x-x0) + f2(x0,y0)(y-y0) - (z-z0) = 0 or z = f(x0,y0) +
The document provides a review outline for Math 1a Midterm II covering topics including: differentiation using product, quotient, and chain rules; implicit differentiation; logarithmic differentiation; applications such as related rates and optimization; and the shape of curves including the mean value theorem and extreme value theorem. It also lists learning objectives and provides details on key concepts like L'Hopital's rule and the closed interval method for finding extrema.
This document contains:
1) An announcement about an assigned problem set due November 28th and office hours.
2) A summary of the regression theorem for finding local maxima, minima, and saddle points of functions with two variables.
3) An example of classifying critical points of a function.
4) A discussion of finding the line of best fit to a set of data points by minimizing the sum of squared errors between the data points and fitted line.
This document summarizes a lesson on implicit differentiation. It discusses implicit differentiation in two dimensions using both the "old school" and "new school" methods. It also covers applications of implicit differentiation, generalization to more than two dimensions, and the second derivative. Examples are provided to illustrate implicit differentiation of a utility function and calculating slopes along indifference curves.
We look at the area problem of finding areas of curved regions. Archimedes had a method for parabolas, Cavalieri had a method for other graphs, and Riemann generalized the whole thing. It doesn't just work for areas, any "product law" such as distance=rate x time can be generalized to a similar computation
The document summarizes key concepts from Lesson 28 on Lagrange multipliers, including:
1) Restating the method of Lagrange multipliers and providing justifications through symbolic, graphical, and other perspectives.
2) Discussing second order conditions for constrained optimization problems, noting the importance of compact feasibility sets.
3) Providing the definition of compact sets and stating the compact set method for finding extreme values of a function over a compact domain.
Game Theory for Business: Overcoming Rivals and Gaining AdvantageeCornell
The application of game theory in business is a natural, as it serves to answer the central question “What are my opponents thinking and what is their next move?” Chess masters know how to think a few moves ahead, and put themselves in the shoes of their rival.
The game theory approach in business can give you a clear advantage and position you for growth in highly competitive markets. In this one-hour webinar, you’ll learn to:
Get inside the motivations and strategies of your rivals
Exploit their weaknesses and bring more value to your business proposition
Identify concepts for maximizing the size of the piece of the pie you grab in business dealings
This discussion is tailored for CEOs, execs, VPs, upper-level management and anyone involved in competitive analysis and overall business strategy.
About Prof. Johnson
Prof. Justin Johnson received his PhD from MIT and is an associate professor of economics at the Samuel Curtis Johnson Graduate School of Management at Cornell University. He has written extensively on real-world applications of economics and game theory, teaches in Cornell’s Executive MBA program, and is the faculty author of eCornell’s online certificate program Business Strategy: Achieving Competitive Advantage.
Lesson 25: Indeterminate Forms and L'Hôpital's RuleMatthew Leingang
This document discusses indeterminate forms and L'Hopital's rule. It introduces indeterminate forms as limits that can have different values depending on the approach, such as 0/0 or infinity/infinity forms. It then presents L'Hopital's rule, which states that if the limit of the numerator and denominator of a quotient both approach 0, infinity, or negative infinity, the limit can be evaluated by taking the derivative of the numerator and denominator and rearranging terms. Examples are provided to demonstrate how L'Hopital's rule can be used to evaluate indeterminate forms. The document also provides biographical information about Guillaume de l'Hopital, after whom the rule is named.
The document discusses several key topics:
1) The First Fundamental Theorem of Calculus, which states that if f is continuous on [a,b] and F is an antiderivative of f, then the integral of f from a to x is equal to F(x) - F(a).
2) Examples of differentiating functions defined by integrals, including area functions and the error function (Erf).
3) The Second Fundamental Theorem of Calculus (weak form), which relates the integral of a continuous function f to antiderivatives F of f, stating that the integral of f from a to b is equal to F(b) - F(a).
This document summarizes key topics from a lesson on quadratic forms, including:
1) It defines a quadratic form in two variables as a function of the form f(x,y) = ax^2 + 2bxy + cy^2.
2) It classifies quadratic forms as positive definite, negative definite, or indefinite based on the sign of f(x,y) for all non-zero (x,y) points.
3) It gives examples of quadratic forms and classifies them, such as f(x,y) = x^2 + y^2 being positive definite.
This document provides an overview of lessons on the chain rule in calculus. It introduces the chain rule for functions of one variable and then extends it to functions of multiple variables. Examples are provided to demonstrate how to use the chain rule to calculate derivatives of composite functions. Formulas for the chain rule are stated for reference. The document also discusses using tree diagrams to visualize applications of the chain rule and introduces matrix expressions of the chain rule.
The document provides an overview of constrained optimization using Lagrange multipliers. It begins with motivational examples of constrained optimization problems and then introduces the method of Lagrange multipliers, which involves setting up equations involving the functions to optimize and constrain and a Lagrange multiplier. Examples are worked through to demonstrate solving these systems of equations to find critical points. Caution is advised about dividing equations where one side could be zero. A contour plot example visually depicts the constrained critical points.
The document summarizes a lesson on game theory and linear programming. It discusses using linear programming to find optimal strategies in zero-sum games represented by payoff matrices. It provides examples of solving for optimal strategies in Rock-Paper-Scissors and another sample game. The key steps of formulating the column player's problem as a linear program to minimize the maximum payoff for the row player are outlined.
These are the slides from the review session. THE FILE IS BIG AND MAY HAVE BEEN CORRUPTED. IF YOU CAN'T SEE IT THROUGH THE FLASH INTERFACE, JUST CLICK THE "DOWNLOAD" LINK and view it on your own computer.
Game theory is a mathematical approach to modeling strategic interactions between rational decision-makers. It assumes humans seek the best outcomes and makes predictions based on payoff matrices showing players' rewards for different strategy combinations. Common applications include economics, politics, and analyzing conflict and cooperation situations like the Prisoner's Dilemma. Game theory also studies concepts like Nash equilibrium, mixed strategies, and evolutionary stable strategies.
We define the definite integral as a limit of Riemann sums, compute some approximations, then investigate the basic additive and comparative properties
The document discusses related rates problems in mathematics. It provides examples of how to solve related rates problems using derivatives and the chain rule. In one example, the radius of an oil slick is increasing and the volume is known to be increasing at a rate of 10,000 liters per second. The problem is to determine the rate of change of the radius. The solution uses derivatives and the geometry of the situation to set up and solve an equation relating the rates of change. A second example involves determining the rate at which two people walking away from each other are increasing their distance apart.
The document is a lesson on implicit differentiation and related concepts:
1) Implicit differentiation allows one to take the derivative of an implicitly defined relation between x and y, even if y is not explicitly defined as a function of x.
2) Examples are provided to demonstrate implicit differentiation, such as finding the slope of a tangent line to a curve.
3) The van der Waals equation is introduced to describe non-ideal gas properties, and implicit differentiation is used to find the isothermal compressibility of a van der Waals gas.
This document provides an outline and review for a midterm exam in Math 20. It covers topics like vectors, matrices, vector and matrix algebra, geometry of lines and planes, and determinants. There will be a midterm exam on October 18th from 7-8:30pm in Hall A. Old exams and solutions are available online, and there are review sessions being held on Wednesday.
The document is notes for a lesson on tangent planes. It provides definitions of tangent lines and planes, formulas for finding equations of tangent lines and planes, and examples of applying these concepts. Specifically, it defines that the tangent plane to a function z=f(x,y) through the point (x0,y0,z0) has normal vector (f1(x0,y0), f2(x0,y0),-1) and equation f1(x0,y0)(x-x0) + f2(x0,y0)(y-y0) - (z-z0) = 0 or z = f(x0,y0) +
The document provides a review outline for Math 1a Midterm II covering topics including: differentiation using product, quotient, and chain rules; implicit differentiation; logarithmic differentiation; applications such as related rates and optimization; and the shape of curves including the mean value theorem and extreme value theorem. It also lists learning objectives and provides details on key concepts like L'Hopital's rule and the closed interval method for finding extrema.
This document contains:
1) An announcement about an assigned problem set due November 28th and office hours.
2) A summary of the regression theorem for finding local maxima, minima, and saddle points of functions with two variables.
3) An example of classifying critical points of a function.
4) A discussion of finding the line of best fit to a set of data points by minimizing the sum of squared errors between the data points and fitted line.
This document summarizes a lesson on implicit differentiation. It discusses implicit differentiation in two dimensions using both the "old school" and "new school" methods. It also covers applications of implicit differentiation, generalization to more than two dimensions, and the second derivative. Examples are provided to illustrate implicit differentiation of a utility function and calculating slopes along indifference curves.
We look at the area problem of finding areas of curved regions. Archimedes had a method for parabolas, Cavalieri had a method for other graphs, and Riemann generalized the whole thing. It doesn't just work for areas, any "product law" such as distance=rate x time can be generalized to a similar computation
The document summarizes key concepts from Lesson 28 on Lagrange multipliers, including:
1) Restating the method of Lagrange multipliers and providing justifications through symbolic, graphical, and other perspectives.
2) Discussing second order conditions for constrained optimization problems, noting the importance of compact feasibility sets.
3) Providing the definition of compact sets and stating the compact set method for finding extreme values of a function over a compact domain.
Game Theory for Business: Overcoming Rivals and Gaining AdvantageeCornell
The application of game theory in business is a natural, as it serves to answer the central question “What are my opponents thinking and what is their next move?” Chess masters know how to think a few moves ahead, and put themselves in the shoes of their rival.
The game theory approach in business can give you a clear advantage and position you for growth in highly competitive markets. In this one-hour webinar, you’ll learn to:
Get inside the motivations and strategies of your rivals
Exploit their weaknesses and bring more value to your business proposition
Identify concepts for maximizing the size of the piece of the pie you grab in business dealings
This discussion is tailored for CEOs, execs, VPs, upper-level management and anyone involved in competitive analysis and overall business strategy.
About Prof. Johnson
Prof. Justin Johnson received his PhD from MIT and is an associate professor of economics at the Samuel Curtis Johnson Graduate School of Management at Cornell University. He has written extensively on real-world applications of economics and game theory, teaches in Cornell’s Executive MBA program, and is the faculty author of eCornell’s online certificate program Business Strategy: Achieving Competitive Advantage.
Lesson 25: Indeterminate Forms and L'Hôpital's RuleMatthew Leingang
This document discusses indeterminate forms and L'Hopital's rule. It introduces indeterminate forms as limits that can have different values depending on the approach, such as 0/0 or infinity/infinity forms. It then presents L'Hopital's rule, which states that if the limit of the numerator and denominator of a quotient both approach 0, infinity, or negative infinity, the limit can be evaluated by taking the derivative of the numerator and denominator and rearranging terms. Examples are provided to demonstrate how L'Hopital's rule can be used to evaluate indeterminate forms. The document also provides biographical information about Guillaume de l'Hopital, after whom the rule is named.
The document discusses several key topics:
1) The First Fundamental Theorem of Calculus, which states that if f is continuous on [a,b] and F is an antiderivative of f, then the integral of f from a to x is equal to F(x) - F(a).
2) Examples of differentiating functions defined by integrals, including area functions and the error function (Erf).
3) The Second Fundamental Theorem of Calculus (weak form), which relates the integral of a continuous function f to antiderivatives F of f, stating that the integral of f from a to b is equal to F(b) - F(a).
This document summarizes key topics from a lesson on quadratic forms, including:
1) It defines a quadratic form in two variables as a function of the form f(x,y) = ax^2 + 2bxy + cy^2.
2) It classifies quadratic forms as positive definite, negative definite, or indefinite based on the sign of f(x,y) for all non-zero (x,y) points.
3) It gives examples of quadratic forms and classifies them, such as f(x,y) = x^2 + y^2 being positive definite.
This document provides an overview of lessons on the chain rule in calculus. It introduces the chain rule for functions of one variable and then extends it to functions of multiple variables. Examples are provided to demonstrate how to use the chain rule to calculate derivatives of composite functions. Formulas for the chain rule are stated for reference. The document also discusses using tree diagrams to visualize applications of the chain rule and introduces matrix expressions of the chain rule.
The document provides an overview of constrained optimization using Lagrange multipliers. It begins with motivational examples of constrained optimization problems and then introduces the method of Lagrange multipliers, which involves setting up equations involving the functions to optimize and constrain and a Lagrange multiplier. Examples are worked through to demonstrate solving these systems of equations to find critical points. Caution is advised about dividing equations where one side could be zero. A contour plot example visually depicts the constrained critical points.
The document summarizes a lesson on game theory and linear programming. It discusses using linear programming to find optimal strategies in zero-sum games represented by payoff matrices. It provides examples of solving for optimal strategies in Rock-Paper-Scissors and another sample game. The key steps of formulating the column player's problem as a linear program to minimize the maximum payoff for the row player are outlined.
These are the slides from the review session. THE FILE IS BIG AND MAY HAVE BEEN CORRUPTED. IF YOU CAN'T SEE IT THROUGH THE FLASH INTERFACE, JUST CLICK THE "DOWNLOAD" LINK and view it on your own computer.
Game theory is a mathematical approach to modeling strategic interactions between rational decision-makers. It assumes humans seek the best outcomes and makes predictions based on payoff matrices showing players' rewards for different strategy combinations. Common applications include economics, politics, and analyzing conflict and cooperation situations like the Prisoner's Dilemma. Game theory also studies concepts like Nash equilibrium, mixed strategies, and evolutionary stable strategies.
We define the definite integral as a limit of Riemann sums, compute some approximations, then investigate the basic additive and comparative properties
The document discusses related rates problems in mathematics. It provides examples of how to solve related rates problems using derivatives and the chain rule. In one example, the radius of an oil slick is increasing and the volume is known to be increasing at a rate of 10,000 liters per second. The problem is to determine the rate of change of the radius. The solution uses derivatives and the geometry of the situation to set up and solve an equation relating the rates of change. A second example involves determining the rate at which two people walking away from each other are increasing their distance apart.
The document is a lesson on implicit differentiation and related concepts:
1) Implicit differentiation allows one to take the derivative of an implicitly defined relation between x and y, even if y is not explicitly defined as a function of x.
2) Examples are provided to demonstrate implicit differentiation, such as finding the slope of a tangent line to a curve.
3) The van der Waals equation is introduced to describe non-ideal gas properties, and implicit differentiation is used to find the isothermal compressibility of a van der Waals gas.
This document provides an outline and review for a midterm exam in Math 20. It covers topics like vectors, matrices, vector and matrix algebra, geometry of lines and planes, and determinants. There will be a midterm exam on October 18th from 7-8:30pm in Hall A. Old exams and solutions are available online, and there are review sessions being held on Wednesday.
Lesson03 Dot Product And Matrix Multiplication Slides NotesMatthew Leingang
The document discusses the dot product and matrix multiplication. It defines the dot product of two vectors p and q as the sum of the element-wise products of corresponding entries. The dot product is a scalar. Matrix-vector multiplication is defined as taking the dot product of each row of the matrix with the vector to produce another vector, with the dimensions working out properly. An example calculates the matrix-vector product of a given matrix and vector.
This document discusses CP violation in B meson decays as studied by the Belle experiment. It provides background on CP violation and describes how CP violation is observed in the interference between decays of neutral B mesons and their mixing into opposite B mesons. As an example, the document discusses CP violation measured in the golden mode of B0 decays to J/ψKS, which has small direct CP violation and allows extraction of the CKM parameter using the interference between decay and mixing.
The document discusses financial information including revenues and expenditures. It mentions total revenues of 556,050 and expenditures of 10,000. It also notes revenues of 64,900 and total expenditures of 145,500. Various expenditures are listed under different categories. Total revenues for the period were 72,000 and 162,000 while total expenditures were 483,591.
The document provides steps for solving locus problems:
1) Find the coordinates of the point whose locus is being found
2) Look for the relationship between x and y values by eliminating parameters
3) If the relationship is not obvious, use a previously proven relationship between parameters
It then works through an example problem, finding that if chord PQ passes through (0,a), the locus of point R is the parabola y = a(x2/a2 - 1).
Representing molecules as atomic-scale electrical circuits with fluctuating-c...Jiahao Chen
This document describes fluctuating charge models that represent molecules as electrical circuits with atomic charges that fluctuate. It introduces the QTPIE model, which improves upon previous models like QEq by including an overlap integral that introduces an explicit notion of distance, allowing it to better describe charge transfer and polarization. The QTPIE model is shown to predict correct dissociation behavior and charge distributions compared to ab initio results.
Similar to Lesson 34: Introduction To Game Theory (7)
This document provides guidance on developing effective lesson plans for calculus instructors. It recommends starting by defining specific learning objectives and assessments. Examples should be chosen carefully to illustrate concepts and engage students at a variety of levels. The lesson plan should include an introductory problem, definitions, theorems, examples, and group work. Timing for each section should be estimated. After teaching, the lesson can be improved by analyzing what was effective and what needs adjustment for the next time. Advanced preparation is key to looking prepared and ensuring students learn.
Streamlining assessment, feedback, and archival with auto-multiple-choiceMatthew Leingang
Auto-multiple-choice (AMC) is an open-source optical mark recognition software package built with Perl, LaTeX, XML, and sqlite. I use it for all my in-class quizzes and exams. Unique papers are created for each student, fixed-response items are scored automatically, and free-response problems, after manual scoring, have marks recorded in the same process. In the first part of the talk I will discuss AMC’s many features and why I feel it’s ideal for a mathematics course. My contributions to the AMC workflow include some scripts designed to automate the process of returning scored papers
back to students electronically. AMC provides an email gateway, but I have written programs to return graded papers via the DAV protocol to student’s dropboxes on our (Sakai) learning management systems. I will also show how graded papers can be archived, with appropriate metadata tags, into an Evernote notebook.
This document discusses electronic grading of paper assessments using PDF forms. Key points include:
- Various tools for creating fillable PDF forms using LaTeX packages or desktop software.
- Methods for stamping completed forms onto scanned documents including using pdftk or overlaying in TikZ.
- Options for grading on tablets or desktops including GoodReader, PDFExpert, Adobe Acrobat.
- Extracting data from completed forms can be done in Adobe Acrobat or via command line with pdftk.
Integration by substitution is the chain rule in reverse.
NOTE: the final location is section specific. Section 1 (morning) is in SILV 703, Section 11 (afternoon) is in CANT 200
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
g(x) represents the area under the curve of f(t) between 0 and x.
.
x
What can you say about g? 2 4 6 8 10f
The First Fundamental Theorem of Calculus
Theorem (First Fundamental Theorem of Calculus)
Let f be a con nuous func on on [a, b]. Define the func on F on [a, b] by
∫ x
F(x) = f(t) dt
a
Then F is con nuous on [a, b] and differentiable on (a, b) and for all x in (a, b),
F′(x
Lesson 26: The Fundamental Theorem of Calculus (slides)Matthew Leingang
The document discusses the Fundamental Theorem of Calculus, which has two parts. The first part states that if a function f is continuous on an interval, then the derivative of the integral of f is equal to f. This is proven using Riemann sums. The second part relates the integral of a function f to the integral of its derivative F'. Examples are provided to illustrate how the area under a curve relates to these concepts.
Lesson 27: Integration by Substitution (handout)Matthew Leingang
This document contains lecture notes on integration by substitution from a Calculus I class. It introduces the technique of substitution for both indefinite and definite integrals. For indefinite integrals, the substitution rule is presented, along with examples of using substitutions to evaluate integrals involving polynomials, trigonometric, exponential, and other functions. For definite integrals, the substitution rule is extended and examples are worked through both with and without first finding the indefinite integral. The document emphasizes that substitution often simplifies integrals and makes them easier to evaluate.
Lesson 26: The Fundamental Theorem of Calculus (handout)Matthew Leingang
1) The document discusses lecture notes on Section 5.4: The Fundamental Theorem of Calculus from a Calculus I course. 2) It covers stating and explaining the Fundamental Theorems of Calculus and using the first fundamental theorem to find derivatives of functions defined by integrals. 3) The lecture outlines the first fundamental theorem, which relates differentiation and integration, and gives examples of applying it.
This document contains notes from a calculus class lecture on evaluating definite integrals. It discusses using the evaluation theorem to evaluate definite integrals, writing derivatives as indefinite integrals, and interpreting definite integrals as the net change of a function over an interval. The document also contains examples of evaluating definite integrals, properties of integrals, and an outline of the key topics covered.
This document contains lecture notes from a Calculus I class covering Section 5.3 on evaluating definite integrals. The notes discuss using the Evaluation Theorem to calculate definite integrals, writing derivatives as indefinite integrals, and interpreting definite integrals as the net change of a function over an interval. Examples are provided to demonstrate evaluating definite integrals using the midpoint rule approximation. Properties of integrals such as additivity and the relationship between definite and indefinite integrals are also outlined.
Lesson 24: Areas and Distances, The Definite Integral (handout)Matthew Leingang
We can define the area of a curved region by a process similar to that by which we determined the slope of a curve: approximation by what we know and a limit.
Lesson 24: Areas and Distances, The Definite Integral (slides)Matthew Leingang
We can define the area of a curved region by a process similar to that by which we determined the slope of a curve: approximation by what we know and a limit.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
At times it is useful to consider a function whose derivative is a given function. We look at the general idea of reversing the differentiation process and its applications to rectilinear motion.
This document contains lecture notes from a Calculus I class discussing optimization problems. It begins with announcements about upcoming exams and courses the professor is teaching. It then presents an example problem about finding the rectangle of a fixed perimeter with the maximum area. The solution uses calculus techniques like taking the derivative to find the critical points and determine that the optimal rectangle is a square. The notes discuss strategies for solving optimization problems and summarize the key steps to take.
Uncountably many problems in life and nature can be expressed in terms of an optimization principle. We look at the process and find a few good examples.
The document discusses curve sketching of functions by analyzing their derivatives. It provides:
1) A checklist for graphing a function which involves finding where the function is positive/negative/zero, its monotonicity from the first derivative, and concavity from the second derivative.
2) An example of graphing the cubic function f(x) = 2x^3 - 3x^2 - 12x through analyzing its derivatives.
3) Explanations of the increasing/decreasing test and concavity test to determine monotonicity and concavity from a function's derivatives.
The document contains lecture notes on curve sketching from a Calculus I class. It discusses using the first and second derivative tests to determine properties of a function like monotonicity, concavity, maxima, minima, and points of inflection in order to sketch the graph of the function. It then provides an example of using these tests to sketch the graph of the cubic function f(x) = 2x^3 - 3x^2 - 12x.
Lesson 20: Derivatives and the Shapes of Curves (slides)Matthew Leingang
This document contains lecture notes on derivatives and the shapes of curves from a Calculus I class taught by Professor Matthew Leingang at New York University. The notes cover using derivatives to determine the intervals where a function is increasing or decreasing, classifying critical points as maxima or minima, using the second derivative to determine concavity, and applying the first and second derivative tests. Examples are provided to illustrate finding intervals of monotonicity for various functions.
Lesson 20: Derivatives and the Shapes of Curves (handout)Matthew Leingang
This document contains lecture notes on calculus from a Calculus I course. It covers determining the monotonicity of functions using the first derivative test. Key points include using the sign of the derivative to determine if a function is increasing or decreasing over an interval, and using the first derivative test to classify critical points as local maxima, minima, or neither. Examples are provided to demonstrate finding intervals of monotonicity for various functions and applying the first derivative test.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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Lesson 34: Introduction To Game Theory
1. Lesson 34 (KH, Section 11.4)
Introduction to Game Theory
Math 20
December 12, 2007
Announcements
Pset 12 due December 17 (last day of class)
next OH today 1–3 (SC 323)
2. Outline
Games and payoffs
Matching dice
Vaccination
The theorem of the day
Strictly determined games
Example: Network programming
Characteristics of an Equlibrium
Two-by-two strictly-determined games
Two-by-two non-strictly-determined games
Calculation
Example: Vaccination
Other
3. A Game of Chance
You and I each have a
six-sided die
We roll and the loser
pays the winner the
difference in the numbers
shown
If we play this a number
of times, who’s going to
win?
4. The Payoff Matrix
Lists each player’s
C ’s outcomes
outcomes versus
1 2345 6
the other’s
1 0 -1 -2 -3 -4 -5
Each aij represents
R’s outcomes
2 1 0 -1 -2 -3 -4
the payoff from C
3 2 1 0 -1 -2 -3
to R if outcomes i
4 3 2 1 0 -1 -2
for R and j for C
5 4 3210 -1
occur (a zero-sum
6 5 4321 0
game).
5. Expected Value
Let the probabilities of R’s outcomes and C ’s outcomes be
given by probability vectors
q1
q2
p = p1 p2 · · · pn q=.
..
qn
6. Expected Value
Let the probabilities of R’s outcomes and C ’s outcomes be
given by probability vectors
q1
q2
p = p1 p2 · · · pn q=.
..
qn
The probability of R having outcome i and C having outcome
j is therefore pi qj .
7. Expected Value
Let the probabilities of R’s outcomes and C ’s outcomes be
given by probability vectors
q1
q2
p = p1 p2 · · · pn q=.
..
qn
The probability of R having outcome i and C having outcome
j is therefore pi qj .
The expected value of R’s payoff is
n
E (p, q) = pi aij qj = pAq
i,j=1
8. Expected Value
Let the probabilities of R’s outcomes and C ’s outcomes be
given by probability vectors
q1
q2
p = p1 p2 · · · pn q=.
..
qn
The probability of R having outcome i and C having outcome
j is therefore pi qj .
The expected value of R’s payoff is
n
E (p, q) = pi aij qj = pAq
i,j=1
A “fair game” if the dice are fair.
11. Strategies
What if we could
choose a die to be
C ’s outcomes
as biased as we
1 2345 6
wanted?
1 0 -1 -2 -3 -4 -5
In other words,
R’s outcomes
2 1 0 -1 -2 -3 -4
what if we could
3 2 1 0 -1 -2 -3
choose a strategy
4 3 2 1 0 -1 -2
p for this game?
5 4 3210 -1
Clearly, we’d want 6 5 4321 0
to get a 6 all the
time!
12. Flu Vaccination
Suppose there are two flu
strains, and we have two
flu vaccines to combat
them.
We don’t know
distribution of strains Strain
1 2
Neither pure strategy is
Vacc
1 0.85 0.70
the clear favorite
2 0.60 0.90
Is there a combination of
vaccines (a mixed
strategy) that
maximizes total
immunity of the
population?
13. Outline
Games and payoffs
Matching dice
Vaccination
The theorem of the day
Strictly determined games
Example: Network programming
Characteristics of an Equlibrium
Two-by-two strictly-determined games
Two-by-two non-strictly-determined games
Calculation
Example: Vaccination
Other
14. Theorem (Fundamental Theorem of Zero-Sum Games)
There exist optimal strategies p∗ for R and q∗ for C such that for
all strategies p and q:
E (p∗ , q) ≥ E (p∗ , q∗ ) ≥ E (p, q∗ )
15. Theorem (Fundamental Theorem of Zero-Sum Games)
There exist optimal strategies p∗ for R and q∗ for C such that for
all strategies p and q:
E (p∗ , q) ≥ E (p∗ , q∗ ) ≥ E (p, q∗ )
E (p∗ , q∗ ) is called the value v of the game.
16. Reflect on the inequality
E (p∗ , q) ≥ E (p∗ , q∗ ) ≥ E (p, q∗ )
In other words,
E (p∗ , q) ≥ E (p∗ , q∗ ): R can guarantee a lower bound on
his/her payoff
E (p∗ , q∗ ) ≥ E (p, q∗ ): C can guarantee an upper bound on
how much he/she loses
This value could be negative in which case C has the
advantage
17. Fundamental problem of zero-sum games
Find the p∗ and q∗ !
The general case we’ll look at next time (hard-ish)
There are some games in which we can find optimal strategies
now:
Strictly-determined games
2 × 2 non-strictly-determined games
18. Outline
Games and payoffs
Matching dice
Vaccination
The theorem of the day
Strictly determined games
Example: Network programming
Characteristics of an Equlibrium
Two-by-two strictly-determined games
Two-by-two non-strictly-determined games
Calculation
Example: Vaccination
Other
19. Example: Network programming
Suppose we have two
networks, NBC and CBS
Each chooses which
program to show in a
certain time slot
Viewer share varies
depending on these
combinations
How can NBC get the
most viewers?
20. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
21. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is NBC’s strategy?
22. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is NBC’s strategy?
NBC wants to maximize NBC’s minimum share
23. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is NBC’s strategy?
NBC wants to maximize NBC’s minimum share
In airing Dateline, NBC’s share is at least 45
24. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is NBC’s strategy?
NBC wants to maximize NBC’s minimum share
In airing Dateline, NBC’s share is at least 45
This is a good strategy for NBC
25. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is CBS’s strategy?
26. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is CBS’s strategy?
CBS wants to minimize NBC’s maximum share
27. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is CBS’s strategy?
CBS wants to minimize NBC’s maximum share
In airing CSI, CBS keeps NBC’s share no bigger than 45
28. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
What is CBS’s strategy?
CBS wants to minimize NBC’s maximum share
In airing CSI, CBS keeps NBC’s share no bigger than 45
This is a good strategy for CBS
29. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
Equilibrium
30. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
Equilibrium
(Dateline,CSI) is an equilibrium pair of strategies
31. The payoff matrix and strategies
CBS
es
r
ut
ea
CS r
ivo
in
D
M
rv
s,
I
Ye
Su
60
My Name is Earl 60 20 30 55
NBC
Dateline 50 75 45 60
Law & Order 70 45 35 30
Equilibrium
(Dateline,CSI) is an equilibrium pair of strategies
Assuming NBC airs Dateline, CBS’s best choice is to air CSI,
and vice versa
32. Characteristics of an Equlibrium
Let A be a payoff matrix. A saddle point is an entry ars
which is the minimum entry in its row and the maximum
entry in its column.
A game whose payoff matrix has a saddle point is called
strictly determined
Payoff matrices can have multiple saddle points
33. Pure Strategies are optimal in Strictly-Determined Games
Theorem
Let A be a payoff matrix. If ars is a saddle point, then er is an
optimal strategy for R and es is an optimal strategy for C.
34. Pure Strategies are optimal in Strictly-Determined Games
Theorem
Let A be a payoff matrix. If ars is a saddle point, then er is an
optimal strategy for R and es is an optimal strategy for C.
Proof.
If q is a strategy for C, then
n n
arj qj ≥
E (er , q) = er Aq = ars qj = ars = E (er , es )
j=1 j=1
If p is a strategy for R, then
m m
pi ais ≤
E (er , es ) = pAes = pi ars = E (er , es )
i=1 i=1
So for any p and q, we have
E (er , q) ≥ E (er , es ) ≥ E (er , es )
35. Outline
Games and payoffs
Matching dice
Vaccination
The theorem of the day
Strictly determined games
Example: Network programming
Characteristics of an Equlibrium
Two-by-two strictly-determined games
Two-by-two non-strictly-determined games
Calculation
Example: Vaccination
Other
36. Finding equilibria by gravity
If C chose strategy 2,
and R knew it, R would
1 3
definitely choose 2
This would make C
choose strategy 1
but (2, 1) is an
2 4
equilibrium, a saddle
point.
37. Finding equilibria by gravity
2 3
Here (1, 1) is an equilibrium
position; starting from there
neither player would want to
deviate from this.
1 4
38. Finding equilibria by gravity
2 3
What about this one?
4 1
39. Outline
Games and payoffs
Matching dice
Vaccination
The theorem of the day
Strictly determined games
Example: Network programming
Characteristics of an Equlibrium
Two-by-two strictly-determined games
Two-by-two non-strictly-determined games
Calculation
Example: Vaccination
Other
41. Two-by-two non-strictly-determined games
Calculation
In this case we can compute E (p, q) by hand in terms of p1 = p
and q1 = q:
E (p, q) = pa11 q + pa12 (1 − q) + (1 − p)a21 q + (1 − p)a22 (1 − q)
The critical points are when
∂E
= a11 q + a12 (1 − q) − a21 q − a22 (1 − q)
0=
∂p
∂E
= pa11 − pa12 + (1 − p)a21 − (1 − p)a22
0=
∂q
42. Two-by-two non-strictly-determined games
Calculation
In this case we can compute E (p, q) by hand in terms of p1 = p
and q1 = q:
E (p, q) = pa11 q + pa12 (1 − q) + (1 − p)a21 q + (1 − p)a22 (1 − q)
The critical points are when
∂E
= a11 q + a12 (1 − q) − a21 q − a22 (1 − q)
0=
∂p
∂E
= pa11 − pa12 + (1 − p)a21 − (1 − p)a22
0=
∂q
So
a22 − a12 a22 − a21
p= q=
a11 + a22 − a21 − a22 a11 + a22 − a21 − a12
These are in between 0 and 1 if there are no saddle points in the
matrix.
43. Examples
13
, then p = 2 ? Doesn’t work because A has a
If A = 0
24
saddle point.
23 3
If A = , p = 2 ? Again, doesn’t work.
14
2 3
, p = −3 = 3/4, while q = −4 = 1/2. So R
−2
If A = −4
4 1
should pick 1 half the time and 2 the other half, while C
should pick 1 3/4 of the time and 2 the rest.
44. Further Calculations
Also
∂2E ∂2E
=0 =0
∂p 2 ∂q 2
So this is a saddle point!
Finally,
a11 a22 − a12 a21
E (p, q) =
a11 + a22 − a21 − a22
45. Example: Vaccination
We have
0.9 − 0.6 2
p1 = = Strain
0.85 + 0.9 − 0.6 − 0.7 3
1 2
0.9 − 0.7 4
q1 = =
Vacc
1 0.85 0.70
0.85 + 0.9 − 0.6 − 0.7 9
2 0.60 0.90
(0.85)(0.9) − (0.6)(0.7)
≈ 0.767
v=
0.85 + 0.9 − 0.6 − 0.7
We should give 2/3 of the population vaccine 1 and the rest
vacine 2
The worst case scenario is a 4 : 5 distribution of strains
We’ll still cover 76.7% of the population
46. Outline
Games and payoffs
Matching dice
Vaccination
The theorem of the day
Strictly determined games
Example: Network programming
Characteristics of an Equlibrium
Two-by-two strictly-determined games
Two-by-two non-strictly-determined games
Calculation
Example: Vaccination
Other
47. Other Applications of GT
War
the Battle of the
Bismarck Sea
Business
product introduction
pricing
Dating