Bargaining in Legislatures: An Experimental Investigation of Open versus Closed Amendment Rules
Bibliographic record
Abstract
We investigate the differential effects of open versus closed amendment rules within the framework of a distributive model of legislative bargaining. The data show that there are longer delays in distributing benefits and a more egalitarian distribution of benefits under the open amendment rule, the proposer gets a larger share of the benefits than coalition members under both rules, and play converges toward minimal winning coalitions under the closed amendment rule. However, there are important quantitative differences between the theoretical model underlying the experiment (Baron and Ferejohn 1989) and data, as the frequency of minimal winning coalitions is much greater under the closed rule (the theory predicts minimal winning coalitions under both rules for our parameter values) and the distribution of benefits between coalition members is much more egalitarian than predicted. The latter are consistent with findings from shrinking pie bilateral bargaining game experiments in economics, to which we relate our results.Research support from the Economics Division and the DRMS Divisions of NSF and the University of Pittsburgh is gratefully acknowledged. We have benefited from comments by David Cooper, Massimo Morelli, Jack Ochs, and seminar participants at Carnegie Mellon University, École des Hautes Études Commerciales, Harvard University, Indiana University, ITAM, Université de Montreal, Universite du Québec a Montréal, University of Pittsburgh, Joseph L. Rotman School, University of Toronto, Ohio State University, Texas \widehat{{\rm A}{\&}{\rm M}} University, Tilburg University CENTER, Western Michigan University, The Wharton School, University of Pennsylvania, the 2000 Public Choice Meetings, the 2000 Summer Institute in Behavioral Economics, the 2000 Econometric Society World Congress meetings, and the CEA 35th Annual Meetings. We are responsible for all remaining errors.
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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 | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 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".