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Mutually beneficial cooperation is a common part of economic systems as firms in partial cooperation with others can often make a higher sustainable profit. Though cooperative games were popular in 1950s, recent interest in noncooperative games is prevalent despite the fact that cooperative bargaining seems to be more useful in economic and political applications. In this paper we assume that the strategy space and time are inseparable with respect to a contract. Furthermore, it is assumed that each firm's strategy polygon is a geodesic polygon which changes its shape every point of time with the stubbornness strategy surface of firm's executive board follow a Gaussian free field. This gives us more flexibility to deal with generalized geodesic cooperative games which is the main contribution of this paper. Under this environment we show that the strategy spacetime is a dynamic curved Liouville-like 2-brane quantum gravity surface under asymmetric information and that traditional Euclidean geometry fails to give a proper feedback Nash equilibrium. Cooperation occurs when two firms' strategies fall into each other's influence curvature in this strategy spacetime. Small firms in an economy dominated by large firms are subject to the influence of large firms. We determine an optimal feedback semicooperation of the small firm in this case using a Liouville-Feynman path integral method. [ABSTRACT FROM AUTHOR]

This article is a tribute to Thomas J. Fararo, a pioneer in the field of mathematical sociology. Fararo believed that mathematical sociology should focus on the theoretical unification of different theories and frameworks. The article provides biographical details about Fararo's life and his contributions to the field. It also includes papers from three scholars who discuss Fararo's impact on sociological theory through mathematical modeling and the prospects for achieving his vision of unification. The article emphasizes the importance of communication and collaboration across research groups for successful theoretical integration. [Extracted from the article]

Concept class analysis (CoCA) is a method for recovering cultural schemas in texts using a combination of word embedding and community detection models. Like survey-based forms of schematic class analysis (SCA), however, interpreting results can be difficult. Some of these interpretive difficulties are applicable across types of SCA, while others are unique to CoCA. In this paper, we propose a complete workflow for interpreting and analyzing CoCA output. We use the case of social identity schemas in a collection of over 13,000 U.S. political blog posts to outline a number of interpretive and analytical strategies and a robustness check to make sense of the cultural schemas recovered from texts. [ABSTRACT FROM AUTHOR]

Motivated by debates about California's net migration loss, we employ valued exponential-family random graph models to analyze the inter-county migration flow network in the United States. We introduce a protocol that visualizes the complex effects of potential underlying mechanisms and perform in silico knockout experiments to quantify their contribution to the California Exodus. We find that racial dynamics contribute to the California Exodus, urbanization ameliorates it, and political climate and housing costs have little impact. Moreover, the severity of the California Exodus depends on how one measures it, and California is not the state with the most substantial population loss. This paper demonstrates how generative statistical models can provide mechanistic insights beyond simple hypothesis-testing. [ABSTRACT FROM AUTHOR]

Latent class analysis (LCA) is a cross-sectional latent variable mixture modeling (LVMM) approach. Like all LVMM approaches, LCA aims to find heterogeneity within the population by identifying homogenous subgroups of individuals, with each subgroup (called latent class) possessing a unique set of characteristics that differentiate it from other subgroups. LCA can be carried out with categorical latent and indicator variables. But, LCA is unable to examine the association between respective items and the latent variable among categories of individuals. Multiple-group LCA, in particular, is a useful extension of LCA which enables the testing of homogeneity of the class patterns between groups of the individual through a series of constraints. In this paper, we have performed a multi-group latent class analysis for measuring self reported academic dishonesty among the students of University of Jammu. From the analysis, three general behaviors of academic cheaters are identified as rare, frequent, and instant cheaters. Further, from the multi-group LCA, it is envisaged that female students of University of Jammu are more instantaneous cheaters than male students. Students who are self-reported cheaters from sciences and humanities of the University of Jammu are persistent in cheating whereas from professional courses they are more occasional. [ABSTRACT FROM AUTHOR]