Keum Joo Kim

Modeling complex social scenarios using Culturally Infused Social Networks

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.

Capturing User Intent for Analytic Process

We are working on the problem of modeling an analysts intent in order to improve collaboration among intelligence analysts. Our approach is to infer the analysts goals, commitment, and actions to improve the effectiveness of collaboration. This is a crucial problem to ensure successful collaboration because analyst intent provides a deeper understanding of what analysts are trying to achieve and how they are achieving their goals than simply modeling their interests. The novelty of our approach relies on modeling the process of committing to a goal as opposed to simply modeling topical interests. Additionally, we dynamically generate a goal hierarchy by exploring the relationships between concepts related to a goal. In this short paper, we present the formal framework of our intent model, and demonstrate how it is used to detect the common goals between analysts using the APEX dataset.

Satisfying constraint sets through convex envelopes

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.

Incorporating Social Theories in Computational Behavioral Models

Computational social science methodologies are increasingly being viewed as critical for modeling complex individual and organizational behaviors in dynamic, real world scenarios. However, many challenges for identifying, representing and incorporating appropriate socio-cultural behaviors remain. Social theories provide rules, which have strong theoretic underpinnings and have been empirically validated, for representing and analyzing individual and group interactions. The key insight in this paper is that social theories can be embedded into computational models as functional mappings based on underlying factors, structures and interactions in social systems. We describe a generic framework, called a Culturally Infused Social Network (CISN), which makes such mappings realizable with its abilities to incorporate multi-domain socio-cultural factors, model at multiple scales, and represent dynamic information. We explore the incorporation of different social theories for added rigor to modeling and analysis by analyzing the fall of the Islamic Courts Union (ICU) regime in Somalia during the latter half of 2006. Specifically, we incorporate the concepts of homophily and frustration to examine the strength of the ICU’s alliances during its rise and fall. Additionally, we employ Affect Control Theory (ACT) to improve the resolution and detail of the model, and thus enhance the explanatory power of the CISN framework.

Modeling emergent border-crossing behaviors during pandemics

Modeling real-world scenarios is a challenge for traditional social science researchers, as it is often hard to capture the intricacies and dynamisms of real-world situations without making simplistic assumptions. This imposes severe limitations on the capabilities of such models and frameworks. Complex population dynamics during natural disasters such as pandemics is an area where computational social science can provide useful insights and explanations. In this paper, we employ a novel intent-driven modeling paradigm for such real-world scenarios by causally mapping beliefs, goals, and actions of individuals and groups to overall behavior using a probabilistic representation called Bayesian Knowledge Bases (BKBs). To validate our framework we examine emergent behavior occurring near a national border during pandemics, specifically the 2009 H1N1 pandemic in Mexico. The novelty of the work in this paper lies in representing the dynamism at multiple scales by including both coarse-grained (events at the national level) and finegrained (events at two separate border locations) information. This is especially useful for analysts in disaster management and first responder organizations who need to be able to understand both macro-level behavior and changes in the immediate vicinity, to help with planning, prevention, and mitigation. We demonstrate the capabilities of our framework in uncovering previously hidden connections and explanations by comparing independent models of the border locations with their fused model to identify emergent behaviors not found in either independent location models nor in a simple linear combination of those models.

Bayesian knowledge modelling for healthcare practices

Abstract Healthcare situations are ever increasingly complex: team performance can easily deteriorate when medical procedures are delivered by teams composed of individuals having different intentions. In fact, medical errors resulting in catastrophic outcomes are often due to the conflicting goals, plans, or intentions among those individuals who make up teams. To improve patient safety, we propose a computational framework to model and simulate the healthcare professionals decision-making processes. We also provide a methodology to evaluate team performance by analysing gaps among individuals whose goals are deduced from their perceptions and observations through intent inferencing. In particular, we focus on the dynamic changes in the healthcare professionals’ decision-making processes when the patient condition is changing over time, while accounting for the various healthcare providers’ individual differences. Understanding, analysing and aiding individuals to make better decisions for improving pat...

Distributed caching strategy

When compared to biological experiments, using computational protein models can save time and effort in identifying native conformations of proteins. Nonetheless, given the sheer size of the conformation space, identifying the native conformation remains a computationally hard problem - even in simplified models such as hydrophobic-hydrophilic (HP) models. Distributed systems have become the focus of protein folding, providing high performance computing power to accommodate the conformation space. To use a distributed system efficiently (with limited resources), an appropriate strategy should be designed accordingly. Communication incurs overhead but can provide useful information in distributed systems through careful consideration. Our study focuses on understanding the behavior of distributed systems and developing an efficient communication strategy to save computational effort in order to obtain good solutions. In this paper, we propose a distributed caching strategy, which reuses partial results of computations and transmits the cached and reusable information among neighboring inter-connected processors. In order to validate this idea in a practical setting, we present algorithms to retrieve and restore the cached information and apply them to 2D triangular HP lattice models through coarse-grained parallel genetic algorithms (CPGAs). Our experimental results demonstrate the time savings as well as the limits in caching improvements for our distributed caching strategy.