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pdf:docinfo:title: How Estimation Can Benefit From an Imbalanced World - Oxford Scholarship
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subject: This chapter analyzes how valuable the assumption of systematic environment imbalance is for performing rough-and-ready intuitive estimates, which people regularly do when inferring the quantitative value of an object (e.g., its frequency, size, value, or quality). The chapter outlines how systematic environment imbalance can be quantified using the framework of power laws. It investigates to what extent power-law characteristics and other statistical properties of real-world environments can be allies of two simple estimation heuristics, QuickEst and the mapping heuristic. The analyses, which involve comparing the estimation performances of the heuristics relative to more complex strategies, demonstrate that QuickEst could be particularly suited for deriving rough-and-ready estimates in skewed distributions with highly dispersed cue validities, whereas the mapping heuristic might be most suited when the cues have similar validities.
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title: How Estimation Can Benefit From an Imbalanced World - Oxford Scholarship
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dc:title: How Estimation Can Benefit From an Imbalanced World - Oxford Scholarship
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cp:subject: This chapter analyzes how valuable the assumption of systematic environment imbalance is for performing rough-and-ready intuitive estimates, which people regularly do when inferring the quantitative value of an object (e.g., its frequency, size, value, or quality). The chapter outlines how systematic environment imbalance can be quantified using the framework of power laws. It investigates to what extent power-law characteristics and other statistical properties of real-world environments can be allies of two simple estimation heuristics, QuickEst and the mapping heuristic. The analyses, which involve comparing the estimation performances of the heuristics relative to more complex strategies, demonstrate that QuickEst could be particularly suited for deriving rough-and-ready estimates in skewed distributions with highly dispersed cue validities, whereas the mapping heuristic might be most suited when the cues have similar validities.
pdf:docinfo:subject: This chapter analyzes how valuable the assumption of systematic environment imbalance is for performing rough-and-ready intuitive estimates, which people regularly do when inferring the quantitative value of an object (e.g., its frequency, size, value, or quality). The chapter outlines how systematic environment imbalance can be quantified using the framework of power laws. It investigates to what extent power-law characteristics and other statistical properties of real-world environments can be allies of two simple estimation heuristics, QuickEst and the mapping heuristic. The analyses, which involve comparing the estimation performances of the heuristics relative to more complex strategies, demonstrate that QuickEst could be particularly suited for deriving rough-and-ready estimates in skewed distributions with highly dispersed cue validities, whereas the mapping heuristic might be most suited when the cues have similar validities.
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