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Mathematical models in the social sciences have become increasingly sophisticated and widespread in the last decade. This period has also seen many critiques, most lamenting the sacrifices incurred in pursuit of mathematical rigor. If, as critics argue, our ability to understand the world has not improved during the mathematization of the social sciences, we might want to adopt a different paradigm. This book examines the three main fields of mathematical modeling - game theory, statistics, and computational methods - and proposes a new framework for modeling. Unlike previous treatments which view each field separately, the treatment provides a framework that spans and incorporates the different methodological approaches. The goal is to arrive at a new vision of modeling that allows researchers to solve more complex problems in the social sciences. Additionally, a special emphasis is placed upon the role of computational modeling in the social sciences

Mathematical models in the social sciences have become increasingly sophisticated and widespread in the last decade. This period has also seen many critiques, most lamenting the sacrifices incurred in pursuit of mathematical rigor. If, as critics argue, our ability to understand the world has not improved during the mathematization of the social sciences, we might want to adopt a different paradigm. This book examines the three main fields of mathematical modeling - game theory, statistics, and computational methods - and proposes a new framework for modeling. Unlike previous treatments which view each field separately, the treatment provides a framework that spans and incorporates the different methodological approaches. The goal is to arrive at a new vision of modeling that allows researchers to solve more complex problems in the social sciences. Additionally, a special emphasis is placed upon the role of computational modeling in the social sciences

10 pages, 8 figures, 2 tables and 1 additional file. ; [Background] Efficient and safe transection of biological tissue in liver surgery is strongly dependent on the ability to address both parenchymal division and hemostasis simultaneously. In addition to the conventional clamp crushing or finger fracture methods other techniques based on radiofrequency (RF) currents have been extensively employed to reduce intraoperative blood loss. In this paper we present our broad research plan for a new RF-assisted device for bloodless, rapid resection of the liver. ; [Methods] Our research plan includes computer modeling and in vivo studies. Computer modeling was based on the Finite Element Method (FEM) and allowed us to estimate the distribution of electrical power deposited in the tissue, along with assessing the effect of the characteristics of the device on the temperature profiles. Studies based on in vivo pig liver models provided a comparison of the performance of the new device with other techniques (saline-linked technology) currently employed in clinical practice. Finally, the plan includes a pilot clinical trial, in which both the new device and the accessory equipment are seen to comply with all safety requirements. ; [Results] The FEM results showed a high electrical gradient around the tip of the blade, responsible for the maximal increase of temperature at that point, where temperature reached 100°C in only 3.85 s. Other hot points with lower temperatures were located at the proximal edge of the device. Additional simulations with an electrically insulated blade produced more uniform and larger lesions (assessed as the 55°C isotherm) than the electrically conducting blade. The in vivo study, in turn, showed greater transection speed (3 ± 0 and 3 ± 1 cm2/min for the new device in the open and laparoscopic approaches respectively) and also lower blood loss (70 ± 74 and 26 ± 34 mL) during transection of the liver, as compared to saline-linked technology (2 ± 1 cm2/min with P = 0.002, and 527 ± 273 mL with P = 0.001). ; This work was partially supported by a medical research grant from the Spanish Government (PETRI 2005/0353), from the "Programa de Promoción de la Investigación Biomédica y en Ciencias de la Salud del Ministerio de Sanidad y Consumo" of Spain (PI052498), and by the "Spanish Plan Nacional de I+D+I del Ministerio de Ciencia e Innovación" (TEC2008-01369/TEC). Finally, the publication cost were provided by the "Programa de Apoyo a la Investigación y el Desarrollo" of the Universidad Politécnica de Valencia. ; Peer reviewed

Small group researchers are increasingly called to engage the computational sciences. One challenge in answering this call is the lack of information concerning what the study of small groups looks like in these domains. This paper addresses this challenge through a prospecting review of research that computationally models or trains computers to learn small group and team behavior and is published in computing disciplines from 2016 to 2023. This review investigated how groups were modeled, for what purpose, what group elements were modeled, and whether social science informed the model design. Qualitative methods were used to analyze 119 published articles. Suggestions are presented for increasing the influence of small group research on the computational modeling of groups and teams, particularly for researchers with limited access to large research teams or resources. This review helps bridge the gap between small group research and computational sciences to advance the understanding of small groups and teams.

In this paper an agent-based model of endogenously evolving migrant networks is developed to identify the determinants of migration and return decisions. Individuals are connected by links, the strength of which declines over time and distance. Methodologically, this paper combines parameterization using data from the Mexican Migration Project with calibration. It is shown that expected earnings, an idiosyncratic home bias, network ties to other migrants, strength of links to the home country and age have a significant impact on circular migration patterns. The model can reproduce spatial patterns of migration as well as the distribution of number of trips of migrants. It is shown how it can also be used for computational experiments and policy analysis. ; In dieser Studie wird ein agentenbasiertes Modell zum Migrationskreislauf mexikanischer Migranten in die USA eingeführt. Es handelt sich um ein vollständig empirisch fundiertes Modell, d.h. alle Parameter basieren auf empirischen Schätzungen. Insbesondere wurden die Koeffizienten der Verhaltensregeln der Individuen mit geläufigen ökonometrischen Methoden geschätzt. Hierbei wurde das Mexican Migration Project (MMP) verwendet, ein großer Haushaltsdatensatz. In einem ersten Schritt wird gezeigt, dass erwartetes Einkommen, eine idiosynkratische Heimatpräferenz und Netzwerkbeziehungen zu anderen Migranten die wichtigsten Determinanten der Migrationsentscheidung von Angehörigen einer Generation mexikanischer Migranten sind. Die Anzahl und Stärke der Beziehungen in das Heimatland beeinflusst hingegen die Rückkehrentscheidung. Es wird zudem gezeigt, dass die Verteilung der Migranten über die Städte der USA hinweg einer Power-Law-Verteilung folgt. Dies wird erklärt durch einen Preferential-Attachment'-Prozess, in dem Migranten häufig die Städte als Zielort wählen, in denen sie Bekannte und Verwandte haben. Die Verteilung der Anzahl der Migrationsbewegungen ist negativ binomialverteilt, was dadurch zu erklären ist, dass es viel wahrscheinlicher ist, dass Migranten nach der ersten Migrationsbewegung eine weitere Migrationsbewegung durchführen, als dass sie das erste Mal migrieren. Der Grund hierfür ist, dass sich die Entscheidung, zum zweiten Mal zu migrieren, stark von der unterscheidet, zum ersten Mal auszuwandern, weil migrationsspezifische Erfahrungen die Entscheidung erleichtern. Das agentenbasierte Modell ist in der Lage, beide Verteilungen und zwei aggregierte Zeitreihen nachzubilden. Daher wird es für geeignet befunden, Politikanalysen durchzuführen. Es wird gezeigt, wie mit Hilfe des Modells der Effekt einer Erhöhung der mexikanischen Löhne und einer Intensivierung der Grenzkontrollen untersucht werden kann.

AbstractComputational models have been underutilized as tools for formal theory development, closing off theoretical analysis of complex substantive scenarios that they would well serve. I argue that this occurs for two reasons, and provide resolutions for each. First, computational models generally do not employ the language or modes of analysis common to game‐theoretic models, the status quo in the literature. I detail the types of insights typically derived from game‐theoretic models and discuss analogues in computational modeling. Second, there are not widely established procedures for analysis of deductive computational models. I present a regularized method for deriving comparative statics from computational models that provides insights comparable to those arising from game‐theoretic analyses. It also serves as a framework for building theoretically tractable computational models. Together, these contributions should enhance communication between models of social science and open up the tool kit of deductive computational modeling for theory building to a broader audience.

"Computational Models of Political Decision Making" published on by Oxford University Press.