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Fast-and-frugal trees are descriptive or prescriptive models of decision making under uncertainty. For instance, an analysis or court decisions reported that the best model of how London magistrates make bail decisions is a fast and frugal tree. [37]
System 1 is a bottom-up, fast, and implicit system of decision-making, while system 2 is a top-down, slow, and explicit system of decision-making. [78] System 1 includes simple heuristics in judgment and decision-making such as the affect heuristic , the availability heuristic , the familiarity heuristic , and the representativeness heuristic .
Thinking, Fast and Slow is a 2011 popular science book by psychologist Daniel Kahneman. The book's main thesis is a differentiation between two modes of thought : "System 1" is fast, instinctive and emotional ; "System 2" is slower, more deliberative , and more logical .
Gigerenzer & Gaissmaier (2011) state that sub-sets of strategy include heuristics, regression analysis, and Bayesian inference. [14]A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods (Gigerenzer and Gaissmaier [2011], p. 454; see also Todd et al. [2012], p. 7).
By contrast, fast unconscious automaticity is constituted by unregulated simulatory biases, which induce errors in subsequent algorithmic processes. The phrase ‘rubbish in, rubbish out' is used to explain errorful heuristic processing: errors will always occur if the accuracy of initial retrieval and location of data is poorly self-regulated.
Escalation of commitment, irrational escalation, or sunk cost fallacy, where people justify increased investment in a decision, based on the cumulative prior investment, despite new evidence suggesting that the decision was probably wrong. G. I. Joe fallacy, the tendency to think that knowing about cognitive bias is enough to overcome it. [65]
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In psychology, the take-the-best heuristic [1] is a heuristic (a simple strategy for decision-making) which decides between two alternatives by choosing based on the first cue that discriminates them, where cues are ordered by cue validity (highest to lowest). In the original formulation, the cues were assumed to have binary values (yes or no ...