Freedom to Choose and Choice X-inefficiencies: Human and Consumer Rights, and Positive and Normative Implications of Choice Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is important to model choice behavior in a manner that captures the reality that choices made by individuals are often not utility maximizing or satisficing from the perspective of the person doing the choosing. If an individual’s choice behavior is not welfare maximizing, as it is always assumed to be in the traditional economics narrative, then the revealed preferences of individuals need not be a metric for the good life. Rather, choice behavior might instead reflect the preferences of others, misleading or incomplete information, or poor education. A key point made in this paper is that only in an environment where individuals are free to choose and have effective voice, irrespective of income, gender, ethnicity, or religion, can choice behavior be a metric for the good life. It is possible for rational agents who are characterized by optimal or true preferences to be unable to realize or manifest these preferences for social or institutional reasons. This has important implications for economic theory as well as for public policy, especially for institutional design. The perspective adopted here also explicitly takes the ethical and normative stance that one should respect the free choices of individuals and that free choice is an important component of the good life or socio-economic wellbeing (Sen 2009). The narrative model builds on insights from Isaiah Berlin (1968), John Harsanyi (1982), Martha Nussbaum (1999, 2000), and Amartya Sen (1990, 1999, 2000, 2009), amongst others, who argue that the manner in which preferences are formed and expressed is important for understanding the welfare implications of expressed preferences and the choices made by individuals. Insights of contemporary behavioral economics, which find that the manner in which choices are framed impact on the choices individuals make, need to be considered as well (Tversky and Kahneman 1981; Thaler and Sunstein 2003, 2008). <br/><br/>In the alternative modeling, the preferences of agents are not necessarily the true or objective preferences that reflect the objective wants and desires of the individual. True preferences are those preferences individuals would construct under ideal but reasonably practical circumstances (Harsanyi 1982; Tomer 1995). Even if the individual is characterized by true preferences, individuals may lack the capacity to realize these preferences. Not unlike the conventional wisdom, however, it is argued that individuals have the capacity to engage in reasoned choice, which can be realized under particular circumstances. The alternative model of preference formation and choice asks ‘‘What are necessary conditions under which true or objective preferences can be constructed?’’
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it