representativeness would cause the investors to play against the Bayes’s rules. In stock markets‚ recent market prices become the representatives that help to explain the phenomenon for investors to chase the market. In fact‚ the representativeness heuristic showed us that people trust the movement of the market prices
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AP Psychology Exam Review Breakdown of Question Categories: 2-4% history – (prologue) 6-8% methods and approaches – (chapter 1) 8-10% biological bases of behavior – (chapter 2‚ 3‚ 14) 7-9% sensation and perception – (chapter 5‚ 6) 2-4% states of consciousness – (chapter 7) 7-9% learning – (chapter 8) 8-10% cognition – (chapter 9‚ 10) 7-9% motivation and emotion – (chapter 12‚ 13) 7-9% developmental psychology – (chapter 4) 6-8% personality – (chapter 15) 5-7% testing and individual
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Semiconductor manufacturing processes are very costly and complex. Every stage of the semiconductor in the supply chain can be ordered by customers. In addition‚ processing of semiconductors is very time consuming and therefore it is very hard to satisfy demands with different amplitude and priorities. Ordinary optimization methods are not best suited for this industry and will be explained below. To understand the IBM central planning engine (CPE) model‚ two essential knowledge must first be obtained
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Bounded rationality In game theory‚ bounded rationality is a concept based on the fact that rationality of individuals is limited by the information they have‚ the cognitive limitations of their minds‚ and the finite amount of time they have to make decisions. This contrasts with the concept of rationality as optimization.[1] Another way to look at bounded rationality is that‚ because decision-makers lack the ability and resources to arrive at the optimal solution‚ they instead apply their rationality
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A Simple Exploration of Bounded Rationality by Susan Fisk “Psychological theories of intuitive thinking cannot match the elegance and precision of formal normative models of belief and choice‚ but this is just another way of saying that rational models are psychologically unrealistic.” -Daniel Kahneman Sit back and imagine that the United States is preparing for the outbreak of an unusual disease‚ which is expected to kill 600 people. Two alternative programs to combat the disease have
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Problem-Solving Despite what folks accomplish as a profession or where they exist‚ most folks use the majority of their waking hours‚ at a workplace or at home‚ tackling situations. Most situations people challenge are little‚ some are substantial and complex‚ yet they need to be settled in a tasteful manner. There are a few definitions of a situation or how one individual may distinguish a situation. A situation is a chance for development. A situation may be a true break‚ the stroke of fortunes
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computing resource (Shang et al. 2015). Thus‚ a variety of algorithms are given by researchers to find sub-optimal solution of the problem in a limited time (Shang et al. 2015). In general‚ there are two categories: heuristics and meta-heuristics. Among them‚ the representative heuristic algorithms: in 1983‚ Golden et al. (1983) proposed Augment-Merge algorithm and Path-Scanning algorithm (Golden and Wong 1981). In Path-Scanning algorithm‚ all tasks will be inserted into the loop according to some
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Pearl described best-first search as estimating the promise of node n by a "heuristic evaluation function f(n) which‚ in general‚ may depend on the description of n‚ the description of the goal‚ the information gathered by the search up to that point‚ and most important‚ on any extra knowledge about the problem domain."[1] [2] Some authors have used "best-first search" to refer specifically to a search with a heuristic that attempts to predict how close the end of a path is to a solution‚ so that
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SECURITIES MARKETS Semester 1‚ 2010 / 2011 Ms. Viet Cao Topic 2 – Formal overview of investor psychology Part 1: Heuristic – driven biases 2010 Investment & Securities Markets 2 What are heuristics ? – Trial and error rules of thumb or “mental shortcuts” to simplify complex judgment or decisions – Includes intuitive “back-of-the-envelope” mental calculations – However‚ heuristics are imperfect and often leads to errors – Investors‚ like everyone else‚ commit decision errors as a result – Behavioral
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science. The problem was first formulated in 1930 and is one of the most intensively studied problems in optimization. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult‚[1] a large number of heuristics and exact methods are known‚ so that some instances with tens of thousands of cities can be solved. The TSP has several applications even in its purest formulation‚ such as planning‚ logistics‚ and the manufacture of microchips. Slightly modified
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