Probability assessment: Continuous quantities and probability decomposition

Abstract

There has been a great deal of research conducted in the areas of probability assessment and calibration. However, a limited amount of research has been conducted using continuous random variables where participants construct subjective probability distributions (SPD?s) and a dearth of research exploring probability decomposition. The current research had three main goals: 1) to examine accuracy for various predictions of NFL and NCAA football game outcomes, 2) to compare different methods of elicitation for SPD?s, and 3) to examine probability decomposition using stimuli from a real-world domain. It was hypothesized that participants would demonstrate the most accuracy in prediction for questions regarding game winners and for questions about winners against the point spread, since such events are the most common for those who regularly follow sports. It was also hypothesized that participants would be more accurate for SPD?s based on a simultaneous 4-interval forced-consistency method versus a sequential 2-interval method, since all possible outcomes are presented at once using the 4-interval method. Participants were recruited online through various sources and responded to online surveys regarding games in the 2009 NFL and NCAA football season. Results demonstrated participants were not very accurate in assessments of game winners or of picking winners against the point spread. Also, participants were more accurate constructing SPD?s using the sequential method. Additionally, probability decomposition did not lead to more accurate assessments. These results are most likely due to the difficulties in conducting probability assessment via online survey. Implications, potential limitations, and recommendations for future research are discussed.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 1 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.