Given the challenges of modeling multi-scale social phenomena, hybrids may hold the key to unlocking social complexity dynamics. We introduce hybrid system modeling from engineering, as a means to capture complex dynamics within interacting, multi-scale, and global social systems. Whereby hybrid modeling is used in industrial processes and automated control systems, this research uses world cup soccer tournament simulations to demonstrate successful applications. Agent-based modeling for soccer games and cellular automatons for crowd and bettor emotional reactions are modeled on each side of a playing field. A predator-prey theoretical approach is applied with self-organizing soccer teams represented as predators and the soccer ball as prey. Simulations of multiple soccer tournaments of thirty-two teams were conducted with pre-game betting and without betting as a pseudo-control measure. Tournaments conducted with pre-game betting resulted in the final tournament games having the wining team demonstrating strong defensive playing styles and scoring by a large margin. Divergence of playing styles did not develop in tournaments without pre-game betting. Hybrids offer a means to explore complexity with evolutionary learning by players, corresponding emotional reactions of spectators, and betting interacting, resulting in patterns of emergent behavior and unique evolutionary behavioral responses to complexity.

The paper provides an investigation and identification of the values and assumptions that influence the applications and results derived from the applications of game theoretical models to solve logistical problems. The ultimate goal is to identify and begin to eliminate prejudices and blind spots within the formulation of the game theory constraints and the application itself. We explore the investigation and identification of blind spots through the application of game theoretical models to solve problems concerning resource allocation. In particular, the paper investigates the application of game theoretical models to allocate resources in crisis situations.

Scone is a knowledge-base system developed specifically to support human-like common-sense reasoning and the understanding of human language. One of the unusual features of Scone is its multiple-context system. Each context represents a distinct world-model, but a context can inherit most of the knowledge of another context, explicitly representing just the differences. We explore how this multiple-context mechanism can be used to emulate some aspects of human mental behavior that are difficult or impossible to emulate in other representational formalisms. These include reasoning about hypothetical or counter-factual situations; understanding how the world model changes over time due to specific actions or spontaneous changes; and reasoning about the knowledge and beliefs of other agents, and how their mental state may affect the actions of those agents.

Millions of people globally have been forcibly displaced from their homes due to reasons beyond their control such as conflict, political upheaval, and environmental catastrophes. In many cases, these forced migrants seek temporary refuge in camps managed by nongovernmental organizations (NGOs). Although responsibility for refugees’ well-being within camps belongs mainly to the NGOs and host government, the density of the camp population and lack of resources of service providers leads to a high degree of insecurity. Building off successful models of mHealth, or utilizing mobile technologies to address healthcare needs, this paper explores the possibility of using communication technologies to address personal security issues. Using agent based modeling techniques, this paper examines the ways in which information about incidents of violence are communicated through a closed population. In this way, the authors advocate for the use of mobile phones in an mSecurity context that empowers forced migrants to become active members in reducing incidents of violence within refugee and internally displaced persons camps.

According to Baddeley, "The episodic buffer is assumed to be a limitedcapacity Our research team has been working on the development of a language analysis model (Ball, 2011; Ball, Heiberg & temporary storage system that is capable of Silber, 2007) within the ACT-R cognitive architecture integrating information from a variety of sources…the (Anderson, 2007) since 2002 (Ball, 2004). The focus is on buffer provides not only a mechanism for modeling the development of a general-purpose, large-scale, functional environment, but also for creating new cognitive model (Ball, 2008; Ball et al., 2010) that adheres to well representations" (ibid, p. 421). A key empirical result which established cognitive constraints on human language motivated Baddeley to introduce the episodic buffer after 25 processing (HLP) as realized by ACT-R.

Evidence indicates that episodic memory plays an important role in general cognition. A modest body of research exists for creating artificial episodic memory systems. To date, research has focused on exploring their benefits. As a result, existing episodic memory systems rely on a small, relevant memory cue for effective memory retrieval. We present Ziggurat, a domain-independent episodic memory structure and accompanying episodic learning algorithm that learns the temporal context of recorded episodes. Ziggurat's context-based memory retrieval means that it does not have to rely on relevant agent cues for effective memory retrieval; it also allows an agent to dynamically make plans using past experiences. In our experimental trials in two different domains, Ziggurat performs as well or better than an episodic memory implementation based on most other systems.

Complex adaptive systems (CAS) promise to be useful in modeling and understanding real-world phenomena, but remain difficult to validate and verify. The authors present an adaptive, tool-chain-based approach to continuous validation and verification that allows the subject matter experts (SMEs) and modelers to interact in a useful manner. A CAS simulation of the ICU at the Mayo Clinic is used as a working example to illustrate the method and its benefits.

This paper describes a prototype system that generates mathematical word problems from ontologies in unrestricted domains. It builds on an existing ontology verbaliser that renders logical statements written in Web Ontology Language (OWL) as English sentences. This kind of question is more complex than those normally attempted by question generation systems, since mathematical word problems consist of a number of sentences that communicate a short narrative (in addition to providing the relevant numerical information required to solve the underlying mathematical problem). Thus, they embody many research issues that do not crop up with single-sentence questions. As well as describing the prototype system, I discuss five ways in which the difficulty of the generated questions may be controlled automatically during generation.

People acquire new knowledge in various ways and this helps them to adapt to changing environment properly. In this paper, we investigatethe interoperation of multiple learning mechanisms within a single system. We extend a cognitive architecture, ICARUS, to have three different modes of learning. Through experiments in a modified Blocks World and a route generation domain, we test and demonstrate the system's ability to get synergistic effects from these learning mechanisms.

In this paper, we address the problem of communicating, interpreting,and executing complex yet abstract instructions to a robot teammember. This requires specifying the tasks in an unambiguous manner,translating them into operational procedures, and carrying outthose procedures in a persistent yet reactive manner. We reportour response to these issues, after which we demonstrate theircombined use in controlling a mobile robot in a multi-room officesetting on tasks similar to those in search-and-rescue operations.We conclude by discussing related research and suggesting directionsfor future work.