Clinical practice guidelines are a critical medium for the standardization of practices within the overall medical community. However, several studies have shown that, in general, there is a significant delay in the adoption of recommendations in such guidelines. Surveys have identified multiple barriers, including clinical inertia, organizational culture/incentives, access to information and peer influence on guideline dissemination and adoption. Although modeling techniques, especially agent-based models, have shown promise, a rigorous computational model for guideline dissemination that incorporates the intricacies of medical decision making and interactions of healthcare workers, and can identify more effective dissemination strategies, is needed. Similar modeling and simulation issues are also prevalent in many other domains such as opinion diffusion, innovation, and technology adoption. In this paper, we introduce a novel overarching computational modeling and simulation framework called the Culturally Infused Agent Based Modeling (CI-ABM) Framework. CI-ABM is a generalizable framework that provides the capability to model a wide range of real-world complex scenarios. To validate the framework, we focus on modeling and analyzing the dissemination of a Type 2 diabetes guideline that recommends individualizing glycemic (A1C) goals. Using existing cross-sectional surveys from physicians across the US, we demonstrate how our methodology for incorporating various socio-cultural and other related factors in agent based models lead to better posterior probability-based analysis and prediction of guideline dissemination behaviors.

Great strides have been made in computational methods for social systems. However, real-world modeling problems require novel computational techniques to support modeling and analyses with time and resource constraints. The emergence of parallel architectures, in the form of multicore/manycore processors, and distributed platforms, have led to the formulation of new approaches for large-scale information processing, modeling and simulation, and new tools for analysis. Due to the inherent interdisciplinary nature of the research in computational social systems, techniques that utilize parallel and distributed algorithms, systems, and methodologies continue to evolve in diverse disciplines. This special issue provides a platform to bring together these researchers to showcase innovative research in computational social systems that leverage the emerging trends in parallel and distributed processing, algorithms, computational modeling, and high-performance computing. The call for papers was issued in 2017, with the submission deadline of December 29, 2017.We received a number of strong submissions. The papers went through a rigorous review process and six high-quality papers were selected. The special issue papers are distributed across multiple issues of the TRANSACTIONS, specifically the last two issues of 2018 and this issue. We provide brief descriptions of the papers below.

One of the primary challenges of modeling the social resilience of communities is that resilience may depend on a number of multidomain factors, ranging from ecological to political. In addition, the impacts and interactions of the relevant factors may not be fully understood. There is also the challenge of representing the intricate behaviors of the social actors, both individuals and groups, in order to model their responses to perturbations in the environment and to internal changes. In light of these challenges, current works tend to make a number of simplifying assumptions. In this paper, we propose a computational framework to formulate multiple resilience functions, each modeling a particular hypothesis about the system's resilience. One of our key contributions is the ability to use social theories to compose these individual resilience functions into an umbrella resilience function, while providing qualitative analysis. We validate our framework by modeling the resilience of a fishing community in Somalia over the period of 1999-2012, as it underwent a series of dramatic ecological, political, and economic changes. We formulated resilience functions to computationally model the competing support for the community's traditional occupation of fishing and alternatively for taking up piracy on the high seas. We then provide an overall resilience function by combining these individual resilience functions using social theories such as the social norm theory and risk theory.

Discusses the purpose, scope and articles that will appear in the IEEE Transactions on Computational Social Systems.

Social Network Analysis (SNA) is a powerful tool for analyzing social phenomena that is based on studying how actors are connected or interact with each other. All Social Networks (SNs) are inherently embedded in particular cultures. However, the effect of cultural influence is often missing from SNA techniques. Moreover, to incorporate culture, modeling approaches have to deal with inaccurate, unrealistic, and incomplete cultural data. In order to address this problem, we propose a generic approach to systematically represent culture in the form of relevant factors and relationships, while leveraging relevant social theories, and to infuse them into SNs in order to obtain more realistic and complete analyses. Using two sets of experiments, we validate the effectiveness of our approach and demonstrate the significant advantages obtained through culturally infused SNA.

Knowledge acquisition is an essential process in improving the problem-solving capabilities of existing knowledge-based systems through the absorption of new information and facilitating change in current knowledge. However, without a verification mechanism, these changes could result in violations of semantic soundness of the knowledge causing inconsistencies and ultimately, contradictions. Therefore, maintaining semantic consistency is of primary concern, especially when dealing with incompleteness and uncertainty. In this paper, we consider the semantic completability of a knowledge system as a means of ensuring long-term semantic soundness. In particular, we focus on how to preserve semantic completability as the knowledge evolves over time. Among numerous methods of knowledge representation under uncertainty, we examine Bayesian Knowledge-Bases, which are a rule-based probabilistic model that allows for incompleteness and cycles between variables. A formal definition of full/partial completability of BKB is first introduced. A principle to check the overall completability of a BKB is then formulated with a formal proof of correctness. Furthermore, we show how to use this principle as a guide for maintaining semantic soundness and completability during incremental knowledge acquisition. In particular, we consider two primary modifications to the knowledge base: 1) adding/fusing knowledge, and 2) changing/tuning conditional probabilities.

Modeling complex real world scenarios require representing and analyzing information from multiple domains including social, economic and political aspects. However, most of the current frameworks in social networks are not generic enough to incorporate multi-domain information or to be applied in different scenarios. Current frameworks also make simplifications in other modeling aspects such as incorporating dynamism and providing multi-scale analyses. Representing culture is critical to truly capture the nuances of various social processes. It also helps to make the framework generic enough to be applied in multiple application domains.We will leverage a novel framework called the Culturally Infused Social Network (CISN) to represent culture using probabilistic reasoning networks called Bayesian Knowledge Bases (BKBs), in representations known as cultural fragments. Cultural fragments model the intent of actors by relating their actions to underlying beliefs and goals. CISN also supports analysis algorithms to make predictions and provide explanations. We validate CISN by simulating the 2006 Somali conflict involving the Islamic Court Union (ICU). The Somali conflict is a complex scenario requiring deep understanding of myriad factors. We focus on analyzing the group stability of ICU, how changing alliance caused conflicts and led to its ultimate demise. We define a metric to measure instability in a group, identify critical factors that led to instability in ICU and provide analyses.

One of the greatest challenges in accurately modeling a human system is the integration of dynamic, fine-grained information in a meaningful way. A model must allow for reasoning in the face of uncertain and incomplete information and be able to provide an easy to understand explanation of why the system is behaving as it is. To date, work in multi-agent systems has failed to come close to capturing these critical elements. Much of the problem is due the fact that most theories about the behavior of such a system are not computational in nature, they come from the social sciences. It is very difficult to successfully get from these qualitative social theories to meaningful computational models of the same phenomena. We focus on analysis of human populations where discerning the opinions of the members of the populace is integral in understanding behavior on an individual and group level. Our approach allows the easy aggregation and de-aggregation of information from multiple sources and in multiple data types into a unified model. We also present an algorithm that can be used to automatically detect the variables in the model that are causing changes in opinion over time. This gives our model the capability to explain why swings in opinion may be experienced in a principled, computational manner. An example is given based on the 2008 South Carolina Democratic Primary election. We show that our model is able to provide both predictions of how the population may vote and why they are voting this way. Our results compare favorably with the election results and our explanation of the changing trends compares favorably with the explanations given by experts.

Social network analysis (SNA) is a set of broadly used techniques designed for analyzing structural information contained in interactions. However, current SNA tools have a poor ability to handle large and dynamic social networks. One particular problem of interest is that of maximal clique enumeration used for studying modularity/community. Critical challenges for this problem include limited scalability and poor ability for handling dynamism. In this paper, we design and implement an anytime anywhere approach for maximal clique enumeration problem in SNA. Through a set of experiments on random graphs, we validate and demonstrate the effectiveness and efficiency of our approach.

Modeling or simulating Complex Adaptive Systems (CASs) is a very important and challenging endeavor. Previously, we introduced a generic framework for addressing this problem, and included a number of critical criteria including emergence, self-organization, adaptivity, and others. In this paper, we present the methodology used for designing a particularly key component of our framework: the short-term adaptivity model. We test our short-term adaptivity model and framework within the biological science application domain, which have a number of critical CASs. In particular we model the aggregation process of Dictyostelium. The comparison between established biological experimental results and our simulation results validate the effectiveness of our model and framework.

With the broad application of electronic communication monitoring tools and data-sharing techniques, the size of networks to be studied by Social Network Analysis (SNA) has grown rapidly. However, current SNA techniques are not particularly scalable. For example, even centrality, which is one of the most frequently used SNA parameters, cannot be measured by most current SNA software when the network is large. This paper presents the design of an effective and scalable anytime anywhere parallel methodology for SNA with large-scale networks emphasizing centrality measurement algorithms. The efficiency and effectiveness of the methodology is validated by experiments of centrality analysis for large networks.

In this article, we present a general representation for constraint satisfaction problems with disjunctive relations called cluster constraint systems (CCS). For this representation, we develop a novel and simple approach for solving CCSs using convex envelopes. These envelopes can be used to decompose the feasible space of the CCS through convex approximations. We explore interval reasoning as a case study of CCS. Our experimental results demonstrate that such CCS can be effectively and efficiently solved through convex enveloping with very modest branching requirements in comparison to other generic as well as specialized algorithms for interval reasoning. In fact, convex enveloping solves significantly more cases and more efficiently than other methods used in our test bed.

Predicting the native conformation using computational protein models requires a large number of energy evaluations even with simplified models such as hydrophobic-hydrophilic (HP) models. Clearly, energy evaluations constitute a significant portion of computational time. We hypothesize that given the structured nature of algorithms that search for candidate conformations such as stochastic methods, energy evaluation computations can be cached and reused, thus saving computational time and effort. In this paper, we present a caching approach and apply it to the triangular 2D-HP lattice model. We provide theoretical analysis and prediction of the expected savings from caching as applied this model. We conduct experiments using a sophisticated evolutionary algorithm that contains elements of local search, memetic algorithms, diversity replacement, etc. in order to verify our hypothesis and demonstrate a significant level of savings in computational effort and time that caching can provide.

Summary form only given. We design parallel Monte Carlo algorithms for the Ising spin model on a hierarchical cluster. A hierarchical cluster can be considered as a cluster of homogeneous nodes which are partitioned into multiple supernodes such that communication across homogenous clusters are represented by a supernode topological network. We consider different data layouts and provide equations for choosing the best data layout under such a network paradigm. We show that the data layouts designed for a homogeneous cluster will not yield as good results as layouts designed for a hierarchical cluster. We derive theoretical results of the performance of the algorithms on a modified version of the LogP model that represents such tiered networking, and present simulation results to analyze the utility of the theoretical design and analysis. Furthermore, we consider the 3D Ising model and design parallel algorithms for sweep spin selection for them on both homogeneous and hierarchical clusters.

A vast body of theoretical research has focused either on overly simplistic models of parallel computation, notably the PRAM, or overly specific models that have few representatives in the real world. Both kinds of models encourage exploitation of formal loopholes, rather than rewarding development of techniques that yield performance across a range of current and future parallel machines. This paper offers a new parallel machine model, called LogP, that reflects the critical technology trends underlying parallel computers. it is intended to serve as a basis for developing fast, portable parallel algorithms and to offer guidelines to machine designers. Such a model must strike a balance between detail and simplicity in order to reveal important bottlenecks without making analysis of interesting problems intractable. The model is based on four parameters that specify abstractly the computing bandwidth, the communication bandwidth, the communication delay, and the efficiency of coupling communication and computation. Portable parallel algorithms typically adapt to the machine configuration, in terms of these parameters. The utility of the model is demonstrated through examples that are implemented on the CM-5.

In many distributed-memory parallel computers the only built-in communication primitive is point-to-point message transmission, and more powerful operations suchas broadcast and synchronization must be realized using this primitive. Within the LogP model of parallel computation we present algorithms that yield optimal communication schedulesfor several broadcastand synchronization operations. Most of our algorithms are the absolutely best possible in that not even the constant factors can be improved upon. For one particular broadcast problem, called continuous broadcast, the optimality of our algorithm is not yet completely proven, although proofs have been achieved for a certain range of parameters. We also devise an optimal algorithm for summing or, more generally, applying a non-commutative associative binary operator to a set of operands. 1 Introduction Most models of parallel computation reflect the communication bottlenecks of real parallel machines inadequately. The PRAM [11],...