Consumer preferences are based on how much utility or satisfaction consumers derive from different bundles of goods, given their budget constraints. The document outlines a model of consumer choice involving three stages: 1) stating the consumer's objectives of maximizing utility, 2) mapping the consumer's preferences through indifference curves, and 3) incorporating the consumer's budget constraint to examine choices. Consumer preferences are defined as their subjective tastes for bundles of goods, independent of income or prices, and can be modeled using indifference curves to rank bundles according to their utility.
This part is an introduction to the theory of Consumer behavior which is very key in microeconomics since the consumer sits at the center of microeconomics
rinciples of Microeconomics Course Description In this course, students will study the price system, market structures, and consumer theory. Topics covered include supply and demand, price controls, public policy, the theory of the firm, cost and revenue concepts, forms of competition, elasticity, and efficient resource allocation, among others.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
This part is an introduction to the theory of Consumer behavior which is very key in microeconomics since the consumer sits at the center of microeconomics
rinciples of Microeconomics Course Description In this course, students will study the price system, market structures, and consumer theory. Topics covered include supply and demand, price controls, public policy, the theory of the firm, cost and revenue concepts, forms of competition, elasticity, and efficient resource allocation, among others.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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1. CONSUMER PREFERENCES
The underlying foundation of demand, therefore, is a model of how consumers behave. The
individual consumer has a set of preferences and values whose determination are outside the
realm of economics. They are no doubt dependent upon culture, education, and individual
tastes, among a plethora of other factors. The measure of these values in this model for a
particular good is in terms of the real opportunity cost to the consumer who purchases and
consumes the good. If an individual purchases a particular good, then the opportunity cost of
that purchase is the forgone goods the consumer could have bought instead. We develop a
model in which we map or graphically derive consumer preferences. These are measured in
terms of the level of satisfaction the consumer obtains from consuming various combinations
or bundles of goods. The consumer’s objective is to choose the bundle of goods which provides
the greatest level of satisfaction as they the consumer define it. But consumers are very much
constrained in their choices. These constraints are defined by the consumer’s income, and the
prices the consumer pays for the goods. We will formally present the model of consumer
choice. As we go along, we will establish a vocabulary in order to explain the model.
Development of the model will be in three stages. After a formal statement of the consumer’s
objectives, we will map the consumer’s preferences. Secondly, we present the consumer’s
budget constraint; and lastly, combine the two in order to examine the consumer’s choices of
goods.
2. THE THEORY OF THE CONSUMER
Consumer make decisions by allocating their scarce income across all possible goods in order to
obtain the greatest satisfaction. Formally, we say that consumers maximize their utility subject
to budget constraint. Utility is defined as the satisfaction that a consumer derives from the
consumption of a good. As noted above, utility’s determinants are decided by a host of
noneconomic factors. Consumer value is measured in terms of the relative utilities between
goods. These reflect the consumer’s preferences
Theory of Consumer Preferences
Consumer preferences are defined as the subjective (individual) tastes, as measured by utility,
of various bundles of goods. They permit the consumer to rank these bundles of goods
according to the levels of utility they give the consumer. Note that preferences are independent
of income and prices. Ability to purchase goods does not determine a consumer’s likes or
dislikes. One can have a preference for Porsches over Fords but only have the financial means
to drive a Ford. These preferences can be modeled and mapped through the use of indifference
curves. In order to graphically portray consumer preferences, we need to define some terms.
First, since we will be working in two dimensions (2-d graphs), we assume a two good world.
These could be anytwo goods. One common treatment is to define one good, say food, and let
the other good be a composite of all other goods. For expository simplicity (making things
easier for me), let’s define the two goods as Good X and Good Y. The axes of the graph then
measure amounts of Good X on the horizontal, and amounts of Good Y on the vertical. Each
point in this Cartesian space then defines some combination of goods X and Y. We call these
combinations commodity bundles. The goal of the theory of preferences is for the consumer to
be able to rank these commodity bundles according to the amount of utility obtained from
them. In other words, the consumer has different preferences over the different combinations
of goods defined by the set of commodity bundles. In order to develop a model we need to
make some assumptions about the consumer’s preferences . There are four assumptions. The
first is decisiveness. Here, given any two commodity bundles in commodity space, the
consumer must be able to rank them. In Figure 1, suppose we randomly chose two commodity
bundles A and B. This assumption means that the consumer must be able to say that they
prefer commodity bundle A over B, or B over A, or that bundles A and B provide the same level
of utility.