The Large Space of Information Structures
Bibliographic record
Abstract
We revisit the question of modeling incomplete information among 2 Bayesian players, following an ex-ante approach based on values of zero-sum games. $K$ being the finite set of possible parameters, an information structure is defined as a probability distribution $u$ with finite support over $K\times \mathbb{N} \times \mathbb{N}$ with the interpretation that: $u$ is publicly known by the players, $(k,c,d)$ is selected according to $u$, then $c$ (resp. $d$) is announced to player 1 (resp. player 2). Given a payoff structure $g$, composed of matrix games indexed by the state, the value of the incomplete information game defined by $u$ and $g$ is denoted $\mathrm{val}(u,g)$. We evaluate the pseudo-distance $d(u,v)$ between 2 information structures $u$ and $v$ by the supremum of $|\mathrm{val}(u,g)-\mathrm{val}(v,g)|$ for all $g$ with payoffs in $[-1,1]$, and study the metric space $Z^*$ of equivalent information structures. We first provide a tractable characterization of $d(u,v)$, as the minimal distance between 2 polytopes, and recover the characterization of Peski (2008) for $u \succeq v$, generalizing to 2 players Blackwell's comparison of experiments via garblings. We then show that $Z^*$, endowed with a weak distance $d_W$, is homeomorphic to the set of consistent probabilities with finite support over the universal belief space of Mertens and Zamir. Finally we show the existence of a sequence of information structures, where players acquire more and more information, and of $\varepsilon>0$ such that any two elements of the sequence have distance at least $\varepsilon$: having more and more information may lead now here. As a consequence, the completion of $(Z^*,d)$ is not compact, hence not homeomorphic to the set of consistent probabilities over the states of the world {\it \`a la} Mertens and Zamir. This example answers by the negative the second (and last unsolved) of the three problems posed by J.F. Mertens in his paper ``Repeated Games", ICM 1986.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".