Modeling Voting Decisions in Governance Networks for Agents with Heterogeneous Mental Models and Alternate Network Structures
Bibliographic record
Abstract
Public1 and private sector partnerships have proliferated to address wicked and complex planning problems, resulting in the rise of "governance networks." Governance networks draw actors from the public, private and non-profit sectors cutting across geographic, social and administrative boundaries. Empirical examples of governance networks include, but are not limited to, watershed partnerships, airshed partnerships, regional transportation and land-use planning networks and climate action partnerships. For this study of watershed governance networks, we develop agent-based models to examine how agents holding diverse beliefs interact under different assumptions for network structure. Using a simulation methodology, we address three research questions: (1) How do voting outcomes in watershed partnerships differ when planning proposals with low, medium and high scores on decision criteria regarding the environment, market and local government are introduced for discussion and voting by agents with heterogeneous mental models? (2) How sensitive are decision making outcomes to changes in the tolerance of a network members' beliefs to other members' beliefs in small world versus like-minded networks? (3) How sensitive are decision-making outcomes to changes in the average number of connections per agent in small world versus tolerance of belief difference in connections in like-minded networks? Results from a survey of watershed stakeholders are used to initialize the simulated beliefs of six stakeholder groups in an agent-based model: environmentalists, farmers, business people, government officials, and water and forestry experts. Simulated voting outcomes are sensitive to both stakeholder beliefs and simulated social networks among stakeholders. Increasing an agent's tolerance of other's beliefs increases the likelihood of majority or consensus voting on planning proposals. Counter to our expectations, simulated group consensus emerged more readily in small world networks than like-minded networks in which stakeholders had narrow thresholds of tolerance for other beliefs. As the diversity of stakeholder connections increases, consensus becomes more likely for watershed and other environmental planning governance networks.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".