Dissertation
Investigation of motivated forecasting in binary event sequences
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Summer 2024
DOI: 10.25820/etd.007629
Abstract
People rely on patterns of past outcomes to inform their expectations for future ones, sometimes resulting in an unwarranted belief that a change is imminent or that a trend will persist after observing a streak. This dissertation explores how individuals use binary outcome sequences to guide their expectations, particularly when they have motivated concerns about the outcomes they observe—an aspect largely overlooked in the literature.
Five related studies build on a mental model framework suggested by Rao & Hastie (2023), which demonstrates that people generally engage in reasonable belief updating (i.e., predicting that streaks will continue) as long as their mental schema about the outcome generator assumes a variable or shifting base rate. Studies 1-3 replicated and extended their findings without introducing outcome desirability, addressing critical methodological concerns in the original research. These studies consistently supported the idea that people update their beliefs based on the streaks they observe when they are aware of the distribution of potential base rates. A novel finding was that while belief updating occurs, there is a persistent tendency to expect a reversal of streaks compared to mathematically identical sequences that do not end in streaks.
Studies 4 and 5 investigated how outcome desirability influences predictions within this framework. Both studies demonstrated a robust desirability bias—participants expected desired streaks to continue more than undesired streaks. Moreover, outcome desirability and streak length interacted to affect predictions differently across various contexts. When outcomes were generated by a random device with an unknown base rate, the desirability bias diminished with longer streaks. Conversely, when the base rate was known to be 50% or involved human athletic performance, the desirability bias remained consistent regardless of streak length.
These findings highlight the intricacies of human predictive behaviors, showing that while expectations of future outcomes are partly shaped by reasonable, evidence-based reasoning, persistent biases continue to impact how we make predictions.
Details
- Title: Subtitle
- Investigation of motivated forecasting in binary event sequences
- Creators
- Inkyung Park
- Contributors
- Paul D. Windschitl (Advisor)J Toby Mordkoff (Committee Member)Jodie Plumert (Committee Member)Dorit Kliemann (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Psychology
- Date degree season
- Summer 2024
- DOI
- 10.25820/etd.007629
- Publisher
- University of Iowa
- Number of pages
- ix, 166 pages
- Copyright
- Copyright 2024 Inkyung Park
- Language
- English
- Date submitted
- 07/02/2024
- Description illustrations
- illustrations, graphs, tables
- Description bibliographic
- Includes bibliographical references (pages 139-146).
- Public Abstract (ETD)
- People often use past outcomes to predict future ones, sometimes believing a change or continuation is imminent after observing a streak. This dissertation explores how individuals use sequences of binary outcomes to form expectations, especially when they care about the results—an area not well-explored before. Five studies build on a framework by Rao & Hastie (2023), showing that people generally predict streaks will continue if they think the base rate of the outcome generator are known to vary. Studies 1-3 confirmed that people update their beliefs based on observed streaks when they understand that base rates are not fixed. A new finding was that people expect streaks to reverse more often than not. Studies 4 and 5 examined how the desirability of outcomes affects predictions. They found a strong desirability bias—people expected streaks of desired outcomes to continue more than undesired ones. This bias changed in different contexts. When outcomes came from a random source with an unknown pattern, the bias decreased with longer streaks. However, when the pattern was known or involved human performance, the bias remained constant. These findings highlight that while our predictions are partly based on logical reasoning, biases still play a significant role in how we expect future outcomes.
- Academic Unit
- Psychological and Brain Sciences
- Record Identifier
- 9984698152702771
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