The Relationship of Participatory Democracy and City Councils: A Comparative Analysis Through Kırsehir and Yozgat City Councils1

AbstractParticipatory democracy describes that individuals of their own accord, active and versatile to be incorporated into management decisions and practices. Today, administrative participation applications such as participatory democracy and city councils as one of the most basic values of local governments realized on a local level hold great importance with respect to the fact that both fellow countrymen are actively involved in administrative process. This study as well, will help to understand how successfully city councils in Turkey have developed in terms of participatory democracy and to acquire concrete results in terms of testing theory-practice contention. To achieve this, present data (directives, meeting decisions, internet sites, etc.) related to corporate structure and function of Kırşehir and Yozgat City Councils have been utilized. Findings acquired within this scope indicate that Kırşehir City Council is more successful in terms of both structure and functioning and participatory democracy applications. However, both special administrations have attempted to realize change in positive direction and still, there are problems which need solving

Characterization and Learning of Causal Graphs with Small Conditioning Sets

Constraint-based causal discovery algorithms learn part of the causal graph structure by systematically testing conditional independences observed in the data. These algorithms, such as the PC algorithm and its variants, rely on graphical characterizations of the so-called equivalence class of causal graphs proposed by Pearl. However, constraint-based causal discovery algorithms struggle when data is limited since conditional independence tests quickly lose their statistical power, especially when the conditioning set is large. To address this, we propose using conditional independence tests where the size of the conditioning set is upper bounded by some integer $k$ for robust causal discovery. The existing graphical characterizations of the equivalence classes of causal graphs are not applicable when we cannot leverage all the conditional independence statements. We first define the notion of $k$-Markov equivalence: Two causal graphs are $k$-Markov equivalent if they entail the same conditional independence constraints where the conditioning set size is upper bounded by $k$. We propose a novel representation that allows us to graphically characterize $k$-Markov equivalence between two causal graphs. We propose a sound constraint-based algorithm called the $k$-PC algorithm for learning this equivalence class. Finally, we conduct synthetic, and semi-synthetic experiments to demonstrate that the $k$-PC algorithm enables more robust causal discovery in the small sample regime compared to the baseline PC algorithm.Comment: 30 page