Detection of Homophilic Communities and Coordination of Interacting Meta-Agents: A Game-Theoretic Viewpoint
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Bibliographic record
Abstract
This paper studies two important signal processing aspects of homophilic behavior namely, detection of homophilic communities and the distributed coordination of meta-agents, which interact with the detected homophilic communities. First, the theory of revealed preferences from microeconomics is used to construct a nonparametric decision test for homophilic behavior using only the time series of external influences and associated agents' responses. These tests rely on rationalizing the dataset of agents' actions as the play from the Nash equilibrium of a concave potential game. A stochastic gradient algorithm is given to optimize the external influence signal in real time to minimize the Type-II error probabilities of the detection test subject to specified Type-I error probability. Using the decision test, methods are provided to detect for homophilic communities. Subsequently, a nonparametric algorithm is presented that uses the constructed potential function for the potential game to predict the preferences of the detected homophilic communities. Second, we present a non-cooperative game model for interaction of meta-agents that interact with the communities and propose an algorithm that prescribes meta-agents how to take actions based on the preference of the communities and past interaction information with other meta-agents. The proposed algorithm has two timescales: the slow timescale is the nonparametric preference learning presented in the first part, and the fast timescale is a regret-matching stochastic approximation algorithm. It is shown that, if all meta-agents follow the proposed algorithm, their collective behavior is attracted to the correlated equilibria set of the game. This means that meta-agents can co-ordinate their strategies in a distributed fashion as if there exists a centralized coordinating device that they all trust to follow. We provide a real-world example using the energy market, and a numerical example to detect malicious agents in an online social network.
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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 | 0.000 | 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 | 0.001 |
| 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