Bibliographic record
Abstract
Harsanyi and Selten [1988] embarked on a project to find conditions that would plausibly select a unique noncooperative equilibrium point to be the solution to any matrix game. Central to this approach was the acceptance of … [John] Nash’s [1951] formal concept of a noncooperative equilibrium as the central necessary property of any solution. The viewpoint espoused here is that the search for a unique noncooperative equilibrium solution to all games poses many interesting philosophical problems in an abstract world inhabited by abstract von Neumann game players with unlimited intelligence and perception and no passions or personality traits. These players act in an institution free world where context is implicitly accounted for in the matrix game or the extensive form of the game. Unfortunately, as a portrayal of human decision-making it fails to appreciate the fundamental limitations in attempting to portray an open evolving system where the dynamics are context dependent and the institutions of any society are the carriers of process. Martin Shubik [2012, p. 2] The obstacle facing anyone who wishes to discuss any limitations of using game theory to build economic models or even criticize game theory or game-theoretic models is the bifurcation of the proponents. On the one hand, we have the economists who are building game-theoretic models in the hopes that they can overcome one of the short-comings of both Marshallian partial-equilibrium analysis, which looks only at the behaviour of singular, price-taking individuals who are just minding their own business, and those who complain about Walrasian general-equilibrium models, which do not recognize the diversity in an economy or the interaction between individuals beyond buying and selling in the markets. On the other hand, we have the mathematics-oriented game theorists who lead the way in game-theoretic analysis and who, regardless of realism, are willing to assume anything that helps them construct their proofs or find solutions for their equilibrium models. Most of the critical questions I discussed at the end of Chapter 3 are the result of these mathematics-oriented assumptions. This chapter will focus on whether the mathematical devices and assumptions that have been invented to answer those questions are as useful as game theorists think or as limited as some critical economic model builders think.
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.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.009 | 0.023 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".