Utility Maximization
... Marginal utility per dollar indicates addition in total utility from spending an additional dollar on a commodity If marginal utility per dollar for one commodity is higher than that for another commodity Household can increase overall utility by • Spending one less dollar on commodity with lower ...
... Marginal utility per dollar indicates addition in total utility from spending an additional dollar on a commodity If marginal utility per dollar for one commodity is higher than that for another commodity Household can increase overall utility by • Spending one less dollar on commodity with lower ...
Logistic Regression Models for Ordinal Response Variables
... For many response variables in education and the social sciences, ordinal scales provide a simple and convenient way to distinguish between possible outcomes that can best be considered as rank-ordered. The primary characteristic of ordinal data is that the numbers assigned to successive categories ...
... For many response variables in education and the social sciences, ordinal scales provide a simple and convenient way to distinguish between possible outcomes that can best be considered as rank-ordered. The primary characteristic of ordinal data is that the numbers assigned to successive categories ...
Introduction to Network Utility Maximization (NUM)
... need to know users’ applications and utility functions in practice ...
... need to know users’ applications and utility functions in practice ...
x 1 + x 2
... with a > 0 and b > 0 is called a CobbDouglas utility function (very useful family of functions, as it exhibits nice properties and serves several purposes). E.g. U(x1,x2) = x11/2 x21/2 (a = b = 1/2) V(x1,x2) = x1 x23 ...
... with a > 0 and b > 0 is called a CobbDouglas utility function (very useful family of functions, as it exhibits nice properties and serves several purposes). E.g. U(x1,x2) = x11/2 x21/2 (a = b = 1/2) V(x1,x2) = x1 x23 ...
Solving Hybrid Influence Diagrams with Deterministic Variables
... IDs where all chance and decision variables are continuous. The continuous chance variables have conditional linear Gaussian (CLG) distributions, and the utility function is quadratic. Such IDs are called Gaussian IDs. These assumptions ensure that the joint distribution of all chance variables is m ...
... IDs where all chance and decision variables are continuous. The continuous chance variables have conditional linear Gaussian (CLG) distributions, and the utility function is quadratic. Such IDs are called Gaussian IDs. These assumptions ensure that the joint distribution of all chance variables is m ...
expected marginal utility approach
... she owns to get into this bet. • It must be that people make decisions by criteria other than maximizing expected monetary payoff. Slide 5 ...
... she owns to get into this bet. • It must be that people make decisions by criteria other than maximizing expected monetary payoff. Slide 5 ...
Technological Standardization with and without
... say little, if anything, about the spatial structure of technology use. Spatial patterns in economic activity arise through local interactions, and these are missing in the early models of technology choice. Because part of the concern in this literature is with the eciency implications of technolo ...
... say little, if anything, about the spatial structure of technology use. Spatial patterns in economic activity arise through local interactions, and these are missing in the early models of technology choice. Because part of the concern in this literature is with the eciency implications of technolo ...
All Models are Right
... Thad: “I just fit a line to the body fat percentage (y) versus weight data.” Fellow Statistician: “Tarpey, your model is wrong...under-specified – there are other variables that also predict body fat percentage; your estimated slope will be biased. You need more predictors” ...
... Thad: “I just fit a line to the body fat percentage (y) versus weight data.” Fellow Statistician: “Tarpey, your model is wrong...under-specified – there are other variables that also predict body fat percentage; your estimated slope will be biased. You need more predictors” ...
Uncertainty
... Making decisions under uncertainty Suppose I believe the following: P(A25 gets me there on time | …) P(A90 gets me there on time | …) P(A120 gets me there on time | …) P(A1440 gets me there on time | …) ...
... Making decisions under uncertainty Suppose I believe the following: P(A25 gets me there on time | …) P(A90 gets me there on time | …) P(A120 gets me there on time | …) P(A1440 gets me there on time | …) ...
Choosing among risky alternatives
... Note here that for all x the area under f(x) is less than the are under g(x) so f dominates g. Why? Requires for all x the cdf for f is always to the right of cdf for g or that for every x the cumulative probability of that level of wealth or higher is greater under f than g. Notice in Figure 1 that ...
... Note here that for all x the area under f(x) is less than the are under g(x) so f dominates g. Why? Requires for all x the cdf for f is always to the right of cdf for g or that for every x the cumulative probability of that level of wealth or higher is greater under f than g. Notice in Figure 1 that ...
Utility Theory
... assumptions could imply that any decision-makers' risk preferences should be consistent with utility theory. In this section, we discuss a simplified version of their argument, which justifies our use of utility theory. To illustrate the logic of the argument, let us begin with an example. Suppose t ...
... assumptions could imply that any decision-makers' risk preferences should be consistent with utility theory. In this section, we discuss a simplified version of their argument, which justifies our use of utility theory. To illustrate the logic of the argument, let us begin with an example. Suppose t ...
3. Generalized linear models
... expected value of Y is E(Y) = and the variance of Y is Var(Y) = (1-). The goal in this section to find a GLM to model at specific values of explanatory variables (x’s) For example, suppose you want to estimate the probability of success, , of a field goal. The value of will probably be diff ...
... expected value of Y is E(Y) = and the variance of Y is Var(Y) = (1-). The goal in this section to find a GLM to model at specific values of explanatory variables (x’s) For example, suppose you want to estimate the probability of success, , of a field goal. The value of will probably be diff ...
The shape of incomplete preferences
... least one probability/utility pair, analogous to SSK’s result establishing one-way agreement between a partially ordered preference relation and a nonempty set of probability/utility pairs. However, those assumptions are too weak to yield two-way agreement in which every extremal preference has an a ...
... least one probability/utility pair, analogous to SSK’s result establishing one-way agreement between a partially ordered preference relation and a nonempty set of probability/utility pairs. However, those assumptions are too weak to yield two-way agreement in which every extremal preference has an a ...
Random Expected Utility,
... A random choice rule is extreme if extreme points of the choice set are chosen with probability 1. Extreme points are those elements of the choice problem that are unique optima for some von Neumann-Morgenstern utility function. Hence, if a random utility is regular, then the corresponding random c ...
... A random choice rule is extreme if extreme points of the choice set are chosen with probability 1. Extreme points are those elements of the choice problem that are unique optima for some von Neumann-Morgenstern utility function. Hence, if a random utility is regular, then the corresponding random c ...
13. Acting under Uncertainty Maximizing Expected Utility
... Performance of the agent is measured by the sum of rewards for the states visited. To determine an optimal policy we will first calculate the utility of each state and then use the state utilities to select the optimal action for each state. The result depends on whether we have a finite or infinite ...
... Performance of the agent is measured by the sum of rewards for the states visited. To determine an optimal policy we will first calculate the utility of each state and then use the state utilities to select the optimal action for each state. The result depends on whether we have a finite or infinite ...
ImpactScore: A Novel Credit Score for Social Impact
... and Herzegovina” by Augsburg, De has, Harmgart, and Meghir (2015). We choose this dataset for various reasons. First, it contains both baseline and follow-up survey responses from loan borrowers, thus enabling a detailed panel study on their characteristics. Second, it focuses on individual loans in ...
... and Herzegovina” by Augsburg, De has, Harmgart, and Meghir (2015). We choose this dataset for various reasons. First, it contains both baseline and follow-up survey responses from loan borrowers, thus enabling a detailed panel study on their characteristics. Second, it focuses on individual loans in ...
A Definition of Subjective Probability FJ Anscombe
... If our construction of subjective probabilities is applied to a set of exclusive and exhaustive outcomes hi of some trial, such that each outcome has a known chance f i , the "horse lotteries" degenerate into "roulette lotteries." (Formally this means that we are assuming [R1 , . . . , R,] (flR1 , . ...
... If our construction of subjective probabilities is applied to a set of exclusive and exhaustive outcomes hi of some trial, such that each outcome has a known chance f i , the "horse lotteries" degenerate into "roulette lotteries." (Formally this means that we are assuming [R1 , . . . , R,] (flR1 , . ...
Name: Your EdX Login
... Part A: Allen lives in some County C1, e.g Alameda. Our model has three possible scenarios for C1: ● Scenario —m: marijuana is not legalized regardless of Allen's vote (probability 1/2) ● Scenario +m: marijuana is legalized regardless of Allen’s vote (probability 1/4) ● Scenario @: marijuana ...
... Part A: Allen lives in some County C1, e.g Alameda. Our model has three possible scenarios for C1: ● Scenario —m: marijuana is not legalized regardless of Allen's vote (probability 1/2) ● Scenario +m: marijuana is legalized regardless of Allen’s vote (probability 1/4) ● Scenario @: marijuana ...
Making Choices in Risky Situations
... less, how do consumers compare different “bundles” of goods that may contain more of one good but less of another? Microeconomists have identified a set of conditions that allow a consumer’s preferences to be described by a utility function. ...
... less, how do consumers compare different “bundles” of goods that may contain more of one good but less of another? Microeconomists have identified a set of conditions that allow a consumer’s preferences to be described by a utility function. ...