Abstract. We established a Complex Systems Digital Campus(CS-DC) e-laboratory "Open Systems Exploration for Ecosystems Leveraging" in view of redesigning sustainable social-ecological systems related to food production ranging over food, health, community, economy, and environment. 6 projects have begun to collaborate in e-laboratory, namely Synecoculture, P2P Food Lab, Open Systems Data Analytics, The Bee Laboratory, Open Systems Simulation and One-Health Food Lab. As a transversal methodology we apply open systems science to deepen scientific understanding and for a continuous amelioration of the management. The projects involve scientists, engineers, artists, citizens and are open to collaboration inside and outside of the e-laboratory. This article summarizes foundational principles of these projects and reports initial steps in operation.
Maximum likelihood method for estimating parameters of Bayesian networks (BNs) is efficient and accurate for large samples. However, the method suffers from overfitting when the sample size is small. Bayesian methods, which are effective to avoid overfitting, present difficulties for determining optimal hyperparameters of prior distributions with good balance between theoretical and practical points of view when no prior knowledge is available. As described in this paper, we propose an alternative estimation method of the parameters on BNs. The method uses a principle, rooted in thermodynamics, of minimizing free energy (MFE). We define internal energies, entropies, and temperature, which constitute free energies. Especially for temperature, we propose a "data temperature" assumption and some explicit models. This approach can treat the maximum likelihood principle and the maximum entropy principle in a unified manner of the MFE principle. For assessments of classification accuracy, our method shows higher accuracy than that obtained using the Bayesian method with normally recommended hyperparameters. Moreover, our method exhibits robustness for the choice of introduced hyperparameters.
Constraint-based search methods, which are a major approach to learning Bayesian networks, are expected to be effective in causal discovery tasks. However, such methods often suffer from impracticality of classical hypothesis testing for conditional independence when the sample size is insufficiently large. We propose a new conditional independence (CI) testing method that is effective for small samples. Our method uses the minimum free energy principle, which originates from thermodynamics, with the "Data Temperature" assumption recently proposed for relating probabilistic fluctuation to virtual thermal fluctuation. We define free energy using Kullback-Leibler divergence in a manner corresponding to an information-geometric perspective. This CI method incorporates the maximum entropy principle and converges to classical hypothesis tests in asymptotic regions. We provide a simulation study, the results of which show that our method improves the learning performance of the well known PC algorithm in some respects.
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