What Is a Cognitive Bias? When we are making judgments and decisions about the world around us, we like to think that we are objective, logical, and capable of taking in and evaluating all the information that is available to us. The reality is, however, that our judgments and decisions are often riddled with errors and influenced by a wide variety of biases. The human brain is both remarkable and powerful, but certainly subject to limitations. One type of fundamental limitation on human thinking is known as a cognitive bias. A cognitive bias is a type of error in thinking that occurs when people are processing and interpreting information in the world around them. Cognitive biases can be caused by a number of different things. These biases are not necessarily all bad, however. Cognitive Bias vs. People sometimes confuse cognitive biases with logical fallacies, but the two are not the same. A Few Types of Cognitive Biases More Psychology Definitions: The Psychology Dictionary Browse the Psychology Dictionary
Endowment effect In behavioral economics, the endowment effect (also known as divestiture aversion) is the hypothesis that people ascribe more value to things merely because they own them.[1] This is illustrated by the observation that people will tend to pay more to retain something they own than to obtain something owned by someone else—even when there is no cause for attachment, or even if the item was only obtained minutes ago. Examples[edit] One of the most famous examples of the endowment effect in the literature is from a study by Kahneman, Knetsch & Thaler (1990)[2] where participants were given a mug and then offered the chance to sell it or trade it for an equally priced alternative good (pens). Kahneman et al. (1990)[2] found that participants' willingness to accept compensation for the mug (once their ownership of the mug had been established) was approximately twice as high as their willingness to pay for it. Background[edit] Theoretical explanations[edit] Loss aversion[edit] Criticisms[edit]
Inductive bias From Wikipedia, the free encyclopedia Assumptions for inference in machine learning The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered.[1] Inductive bias is anything which makes the algorithm learn one pattern instead of another pattern (e.g. step-functions in decision trees instead of continuous function in a linear regression model). In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. A classical example of an inductive bias is Occam's razor, assuming that the simplest consistent hypothesis about the target function is actually the best. Approaches to a more formal definition of inductive bias are based on mathematical logic. Types[edit] The following is a list of common inductive biases in machine learning algorithms. Shift of bias[edit] See also[edit] References[edit]
List of cognitive biases Systematic patterns of deviation from norm or rationality in judgment Cognitive biases are systematic patterns of deviation from norm and/or rationality in judgment. They are often studied in psychology, sociology and behavioral economics.[1] Although the reality of most of these biases is confirmed by reproducible research,[2][3] there are often controversies about how to classify these biases or how to explain them.[4] Several theoretical causes are known for some cognitive biases, which provides a classification of biases by their common generative mechanism (such as noisy information-processing[5]). Gerd Gigerenzer has criticized the framing of cognitive biases as errors in judgment, and favors interpreting them as arising from rational deviations from logical thought.[6] Explanations include information-processing rules (i.e., mental shortcuts), called heuristics, that the brain uses to produce decisions or judgments. Belief, decision-making and behavioral[edit] Anchoring bias[edit]
New Orleans five years later FIVE YEARS AGO today, hell was unleashed on New Orleans. The storm surge created by the winds and rains of Hurricane Katrina overwhelmed the city's levee system, which the Army Corps of Engineers had poorly designed and poorly maintained. For days, with much of the city flooded, people pleaded for help from rooftops, the Superdome and the convention center. Scenes of depravation, desperation and death shocked the nation and the world. Of course, New Orleans wasn't the only site of devastation. Still, when Air Force One touches down Sunday, President Obama will alight on a New Orleans that is recovering -- from Hurricane Katrina, the recession and this year's oil spill in the Gulf of Mexico as well. People who fled are coming home. Potential improvements in the public education system are perhaps the most encouraging aspect of New Orleans's recovery. Many challenges remain. Coastal restoration is another challenge.
No free lunch in search and optimization Average solution cost is the same with any method The problem is to rapidly find a solution among candidates a, b, and c that is as good as any other, where goodness is either 0 or 1. There are eight instances ("lunch plates") fxyz of the problem, where x,y, and z indicate the goodness of a, b, and c, respectively. In the "no free lunch" metaphor, each "restaurant" (problem-solving procedure) has a "menu" associating each "lunch plate" (problem) with a "price" (the performance of the procedure in solving the problem). Overview[edit] "The 'no free lunch' theorem of Wolpert and Macready," as stated in plain language by Wolpert and Macready themselves, is that "any two algorithms are equivalent when their performance is averaged across all possible problems To make matters more concrete, consider an optimization practitioner confronted with a problem. Theorems[edit] A "problem" is, more formally, an objective function that associates candidate solutions with goodness values. . and Origin[edit]
Being Really, Really, Ridiculously Good Looking “I’m pretty sure there’s a lot more to life than being really, really, ridiculously good looking. And I plan on finding out what that is.”~Derek Zoolander, Zoolander Humans like attractive people. Those blessed with the leading man looks of Brad Pitt or the curves of Beyonce can expect to make, on average, 10% to 15% more money over the course of their life than their more homely friends. This insight is not lost on Madison Avenue or Hollywood. Abercrombie & Fitch might be able to sell more clothes by having good-looking sales associates, but is that legal? The surprising answer is none. Is that a problem? The Science of Beauty Beauty is often considered subjective and “in the eye of the beholder.” To some extent this is true. However, academic work on beauty finds that much of what we find attractive is consistent over time and across cultures. More evidence of a universal, objective basis for beauty comes from studies of babies presented with pictures of different faces. The Halo Effect
Choosing a Methodological Path: Reflections on the Constructivist Turn | Grounded Theory Jenna P. Breckenridge, Queen Margaret University, Derek Jones, Northumbria University, Ian Elliott, Queen Margaret University, Margaret Nicol, Queen Margaret University Abstract Researchers deciding to use grounded theory are faced with complex decisions regarding which method or version of grounded theory to use: Classic, straussian, feminist or constructivist grounded theory. Particularly for beginning PhD researchers, this can prove challenging given the complexities of the inherent philosophical debates and the ambiguous and conflicting use of grounded theory ‘versions’ within popular literature. The aim of this article is to demystify the differences between classic and constructivist grounded theory, presenting a critique of constructivist grounded theory that is rooted in the learning experiences of the first author as she grappled with differing perspectives during her own PhD research. Introduction Constructivist grounded theory The interpretive understanding of subjects’ meanings