Bibliographic record
Abstract
Abstract Recent studies in psychology and neuroscience offer systematic evidence that fictional works exert a surprisingly strong influence on readers and have the power to shape their opinions and worldviews. Building on these findings, we study ‘Potterian economics’, the economic ideas, insights and structure, found in Harry Potter books, to assess how the books might affect economic literacy. A conservative estimate suggests that more than 7.3% of the world’s population has read the Harry Potter books, and millions more have seen their movie adaptations. These extraordinary figures underscore the importance of the messages the books convey. We explore the Potterian economic model and compare it to professional economic models to assess the consistency of the Potterian economic principles with the existing economic models. We find that some of the principles of Potterian economics are consistent with economists’ models. Many other principles, however, are distorted and contain numerous inaccuracies, contradicting professional economists’ views and insights. We conclude that Potterian economics can teach us about the formation and dissemination of folk economics—the intuitive notions of naïve individuals who see market transactions as a zero-sum game, who care about distribution but fail to understand incentives and efficiency and who think of prices as allocating wealth but not resources or their efficient use. ‘I think the writers [of popular literature] are not particularly sympathetic to or don’t understand how a market works. It’s not easy to convey that to a child. It’s not always easy to convey it to grown-ups.’ Gary Becker (New York Times, August 21, 2011, p. SR5). ‘With all due respect to Richard Posner, Cass Sunstein, or Peter Schuck [reference to the books these scholars published in 2005], no book released in 2005 will have more influence on what kids and adults around the world think about government than [Rowling’s] The Half-Blood Prince.’ Benjamin Barton (Michigan Law Review, 2006, p. 1525). ‘As economic theorists, we organize our thoughts using what we call models. The word “model” sounds more scientific than “fable” or “fairy tale” although I do not see much difference between them. The author of a fable draws a parallel to a situation in real life. He has some moral he wishes to impart to the reader. The fable is an imaginary situation that is somewhere between fantasy and reality. Any fable can be dismissed as being unrealistic or simplistic, but this is also the fable’s advantage. Being something between fantasy and reality, a fable is free of extraneous details and annoying diversions. In this unencumbered state, we can clearly discern what cannot always be seen in the real world. On our return to reality, we are in possession of some sound advice or a relevant argument that can be used in the real world.” Ariel Rubinstein (Econometrica, 2006, p. 881). ‘An investigation of novels and [economic] models…may help us better understand how the public thinks about economic issues.’ Tyler Cowen (The Street Porter and the Philosopher: Conversations on Analytical Egalitarianism, 2008, p. 321).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".