Incentive-Based Control of Asynchronous Best-Response Dynamics on Binary Decision Networks
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Bibliographic record
Abstract
Various populations of interacting decision-making agents can be modeled by asynchronous best-response dynamics or, equivalently, linear threshold dynamics. Building upon recent convergence results in the absence of control, we now consider how such a network can be efficiently driven to a desired equilibrium state by offering payoff incentives or rewards for using a particular strategy, either uniformly or targeted to individuals. We begin by showing that strategy changes are monotone following an increase in payoffs in coordination games, and that the resulting equilibrium is unique. Based on these results, for the case when a uniform incentive is offered to all agents, we show how to compute the optimal incentive using a binary search algorithm. When different incentives can be offered to each agent, we propose a new algorithm to select which agents should be targeted based on maximizing a ratio between the cascading effect of a strategy switch by each agent and the incentive required to cause the agent to switch. Simulations show that this algorithm computes near-optimal targeted incentives for a wide range of networks and payoff distributions in coordination games and can also be effective for anticoordination games.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it