How do we evaluate future gambles? Experimental evidence on path dependency in risky intertemporal choice
Bibliographic record
Abstract
Abstract This study reports three experiments which demonstrate path dependency in risky intertemporal choice. Consider a lottery to be resolved and paid in a future time period. One can obtain the present value of this lottery in three different ways: (1) eliciting directly the present certainty equivalent (CE) of the future lottery (direct path); (2) eliciting the future CE and then discounting this amount to the present (risk‐time path); and (3) eliciting the present value of the risky prospect and then determining the CE of this current lottery (time‐risk path). Standard rational choice models such as the discounted expected utility model, assume a multiplicative model, where all three methods mentioned above would yield the same value. We conducted three studies to examine if this is the case: Experiments 1 and 2 were based on a set of matching‐task questions and Experiment 3 used a process‐tracing design to analyze the natural sequence of decision making by the subjects. These three studies show that the evaluation of future gambles is path‐dependent. The present values elicited under the time‐risk and direct paths are, on average, higher than those reported under the risk‐time path. In addition, we found evidence for a two‐stage evaluation of risky future prospects: When evaluating a future gamble, individuals first assess the present value of the gamble (time discounting) and then they determine a certainty equivalent (probability discounting). Copyright © 2008 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".