Modeling Simultaneous Multiple Goal Pursuit and Adaptation in Consumer Choice
Bibliographic record
Abstract
Goals are constructs that direct choice behavior by guiding a decision maker toward desirable (or away from undesirable) end states. Often, consumers are motivated to satisfy multiple goals within a single choice. Although previous research has recognized this possibility, it has not directly formulated models of choice as a multigoal problem. The authors develop such a model, referred to as the multiple-goal-based choice model, which incorporates (1) simultaneous multiple goal pursuit and (2) context-driven goal adaptation but (3) does not require a priori identification of the number or nature of the goals. Goal adaptation within a single choice instance, allied to repeated choices, is the key to empirical identification of multiple latent goals. The proposed model is tested and supported using discrete choice experimental data on digital cameras through multiple validation exercises. The model can lead to significantly different policy implications with regard to consumers’ valuation for new product designs, compared with extant utility-based choice models.
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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 | 0.014 | 0.011 |
| 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".