Autoregression and decision making under uncertainty
Bibliographic record
Abstract
There are innumerable social and economic situations in which we are influenced in our decision making by what others are doing. Under uncertainty it’s general tendency of an individual to get inspired by decisions of others or mass opinion. However, such herd behavior many times leads to autoregressive affect i.e. output at some moment is weighted average of past few observation. Hence can autoregressive models be used to predict the outcomes in the situations exhibiting such behavior? Studies have already been done on herd behavior in financial market. So, can models used to forecast financial markets be used to predict general decision making under uncertainty. To prove the validity of the point we conduct a small experiment of human decision making under uncertainty and try to forecast future responses using autoregressive models. A group of students were surveyed such that they can also look upon previous responses which would promote herding. A unique financial market type framework is used to quantify the responses and time series models of auto regression are used to forecast mass opinion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".