Bibliographic record
Abstract
This paper investigates the effects of a limited consumer memory on the price competition between firms. It studies a specific aspect of memory—namely, the categorization of available price information that the consumers may need to recall for decision making. This paper analyzes competition between firms in a market with uninformed consumers who do not compare prices, informed consumers who compare prices but with limited memory, and informed consumers who have perfect memory. Consumers, aware of their memory limitations, choose how to encode the prices into categories, whereas firms take the limitations of consumers into account in choosing their pricing strategies. Two distinct types of categorization processes are investigated: (1) a symmetric one in which consumers compare only the labels of price categories from the competing firms and (2) an asymmetric one in which consumers compare the recalled price of one firm with the actual price of the other. We find that the equilibrium partition for the consumers calls for finer categorization toward the bottom of the price distribution. Thus consumers have a motivation to invest in greater memory resources in encoding lower prices to induce firms to charge more favorable prices. The interaction between the categorization strategies of the consumers and the price competition between the firms is such that small initial improvements in recall move the market outcomes quickly toward the case of perfect recall. Even with few memory categories, the expected price consumers pay and their surplus is close to the case of perfect recall. There is thus a suggestion in this model that market competition adjusts to the memory limitations of consumers.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".