Streak biases in decision making: Data and a memory model

  • Altmann E
  • Burns B
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Abstract

Streaks of past outcomes, for example of gains or losses in the stock market, are one source of information for a decision maker trying to predict the next outcome in the series. We examine how prediction biases based on streaks change as a function of length of the current streak. Participants experienced a sequence of 150 flips of a simulated coin. On the first of a streak of heads, participants showed positive recency, meaning that they predicted heads for the next outcome with a greater-than-baseline probability. As streak length increased, positive recency first decreased but then increased again, producing a quadratic trend. We explain these results in terms of outcome-prediction processes that are sensitive to the historical frequency of streak lengths and that make heuristic assumptions about changes in bias of the outcome-generating process (here, the coin). An ACT-R simulation captures the quadratic trend in positive recency, as well as the baseline heads bias, in two experimental conditions with different coin biases. We discuss our memory-based model in relation to a model from the domain of economics that posits explicit representation of an "urn" from which events are sampled without replacement. © 2004 Elsevier B.V. All rights reserved.

Author-supplied keywords

  • ACT-R
  • Cognitive simulation
  • Decision making
  • Gambler's fallacy
  • Hot hand
  • Memory
  • Streaks

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Authors

  • Erik M. Altmann

  • Bruce D. Burns

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