The Application of Complex Systems Science to Political Philosophy

Although complex systems science is relevant to problems of political philosophy, the intersection of these two disciplines has not been studied in depth. Complex systems are made up of multiple interdependent parts whose interactions create emergent properties. This interdependence makes these systems “fat-tailed”: low-probability events can have a major impact on the system. Complex systems engineers have formulated a series of rules of thumb for approximating an “evolutionary” environment. Contemporary human civilization is a complex system; because of this, governments need to become adaptable and approximate the evolutionary environment by fostering policy innovation while at the same time promoting mechanisms for altering or abolishing “toxic” policies. The best way to apply the techniques of complex systems engineering to government is for there to be a preference for smaller jurisdictions, decentralized governance, bottom-up policy creation, and discretionary policy implementation. However, the goal of making governments adaptable must be balanced against the other goals of government. Thus, there are situations in which larger jurisdictions, etc. are appropriate—primarily, cases which involve risk of grave moral harm or otherwise insoluble collective action problems. The complex systems science approach to political philosophy grounds many widely-held intuitions, but also provides some support for the political philosophy of Anglo-American conservatism

Multilayer Networks

In most natural and engineered systems, a set of entities interact with each other in complicated patterns that can encompass multiple types of relationships, change in time, and include other types of complications. Such systems include multiple subsystems and layers of connectivity, and it is important to take such "multilayer" features into account to try to improve our understanding of complex systems. Consequently, it is necessary to generalize "traditional" network theory by developing (and validating) a framework and associated tools to study multilayer systems in a comprehensive fashion. The origins of such efforts date back several decades and arose in multiple disciplines, and now the study of multilayer networks has become one of the most important directions in network science. In this paper, we discuss the history of multilayer networks (and related concepts) and review the exploding body of work on such networks. To unify the disparate terminology in the large body of recent work, we discuss a general framework for multilayer networks, construct a dictionary of terminology to relate the numerous existing concepts to each other, and provide a thorough discussion that compares, contrasts, and translates between related notions such as multilayer networks, multiplex networks, interdependent networks, networks of networks, and many others. We also survey and discuss existing data sets that can be represented as multilayer networks. We review attempts to generalize single-layer-network diagnostics to multilayer networks. We also discuss the rapidly expanding research on multilayer-network models and notions like community structure, connected components, tensor decompositions, and various types of dynamical processes on multilayer networks. We conclude with a summary and an outlook.Comment: Working paper; 59 pages, 8 figure

From the Ground Up: A Complex Systems Approach to Climate Change Adaptation in Agriculture

Climate change presents an unprecedented challenge to global agriculture and food security. Small farms are especially vulnerable to the local impacts of large-scale drivers of change. Effective adaptation in agriculture requires working across scales, and geographic, political, and disciplinary boundaries to address barriers. I use elements of case study, agent-based modeling and serious games, to design a model of farmer decision-making using the sociocognitive framework of climate change adaptation. I examine how adaptation functions as a process, how complex dynamics influence farmer behavior, and how individual decisions influence collective behavior in response to climate change. This novel approach to adaptation research in agriculture examines the relationships between the contextual, compositional, and cognitive elements of the sociocognitive theory. The tools developed for this research have broad practical and theoretical future applications in climate adaptation research and policymaking. This dissertation is available in open access at AURA (https://aura.antioch.edu) and OhioLINK ETD Center (https://etd.ohiolink.edu)

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201

BRAIN Program and Promotion of Self-Regulation for Students with Emotional and Behavioral Disorders: A Case Study

The purpose of this qualitative case study is to explore, through the lens of self-regulated learning theory, the interrelationship of the BRAIN program and the development of positive classroom behaviors for students with emotional and behavioral disorders in a selected Midwestern school district. This study used purposeful sampling to select five school sites implementing the BRAIN program. The study participants were principals and BRAIN teachers at the five school sites. Data were collected through interviews of four principals, five BRAIN teachers, observations, and documents. Identification of self-regulated learning theory espoused by Zimmerman and Campillo (2003), Zimmerman (2000), and Pintrich and Zusho (2002) occurred prior to conducting the study, providing a lens through which to present and analyze the implementation of the BRAIN program at the five school sites. Findings indicated the BRAIN program is a district-led program implemented with consistency at five school sites for grade levels K-8. The BRAIN team at each site has autonomy in flexing the program to meet the needs of students with support from the district BRAIN team. Self-regulated learning theory helps to explain the interrelationship of the BRAIN program and the facilitation of the development of positive classroom behaviors. Through the cycle of forethought, performance or practice, and self-reflection, students learn to self-regulate behaviors and gain control in the general education classrooms. As this cycle continues, students become more confident in their abilities and are intrinsically motivated toward greater autonomy in controlling the behaviors. Additional research could focus on BRAIN students as they progress and exit the program to better understand their perceptions on their ability to self-regulate behaviors

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin