Effects of behavioral bias regarding demand forecasting in a competitive market
Bibliographic record
Abstract
Abstract Considerable human judgment is involved in demand forecasting. When managers judge demands under uncertainty, they inevitably use signals to update their demand information. These signals are seldom perfect; hence, managers hold behavioral bias about the signal fidelity, that is, over‐ or under‐estimating the signal fidelity. This article models managers' behavioral bias about signal fidelity in Bayesian demand forecasting and explores its impact on competitive firms. We find that no matter whether the competitor's manager is unbiased or biased, a firm can benefit from its manager's slight overestimation, but the competitor can benefit from the firm's manager's underestimation. However, when one firm's manager is biased, improving the signal fidelity may not constantly improve firms' profits, revealing the potential risk of behavioral bias on the efficiency of the forecasting systems. We further consider the diversity of biased managers and the information asymmetry regarding the bias. Except that the benefits of behavioral bias exist, we additionally find that managers' heterogeneous behavioral bias can form a hedge effect and bring a win‐win situation. Under asymmetric information, managers' inference bias on the competitor's type may benefit firms by easing the negative impact of managers' behavioral bias about signal fidelity. We finally analyze the social welfare and consumer surplus, check the robustness of the main results and deliver additional findings by considering competing firms, different signal fidelity measures, and the signal‐dependent behavioral bias.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".