MAMmodels are inherent in CAS as a holistic System. Multi-agent modeling is based on "down-up" Many surprising properties of the Economic Systems (such methodology, starting from the interaction of a multitude as sudden crises, jumps of macro-indices, catastrophe-like of "agents" to revealing the emergent properties of the changes of the system) can be understood deeper on the integral system.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. Adaptive mechanisms such as ubiquitous feedback and feedforward loops create non-linear relationships among its individual components and render the study of this biology at a molecular and cellular level nearly impossible. Computational modeling aims to overcome limitations of current analytical methods through in silico simulation of these complex interplays. We present herein an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 48 rules to define the interactions among 24 enzymes and factors of the CS. These rules simulate the interaction of each “agent”, such as substrates, enzymes, and cofactors, on a two-dimensional grid of ~3,000 cells and ~500,000 agents. Our ABMS method demonstrates the robustness of the physiologic CS system over large ranges of tissue factor (TF) concentrations. The system also demonstrates fragility as complete coagulation occurs at sufficiently high concentrations of TF. Removal of individual coagulation inhibitors from the physiologic system results in system fragility at relatively lower TF concentrations. The complete removal of coagulation inhibitors leads to a system that is incapable of controlling coagulation at all TF concentrations. The synergistic effects of the inhibitory pathways create an intricate regulatory mechanism that allows sufficient clot formation while preventing system wide activation of the CS; a robust system emerges.

Attitudes play a significant role in determining how individuals process information and behave. In this paper we have developed a new computational model of population wide attitude change that captures the social level: how individuals interact and communicate information, and the cognitive level: how attitudes and concept interact with each other. The model captures the cognitive aspect by representing each individuals as a parallel constraint satisfaction network. The dynamics of this model are explored through a simple attitude change experiment where we vary the social network and distribution of attitudes in a population.

In this paper we make a simple theoretical point using a practical issue as an example. The simple theoretical point is that robustness is not 'all or nothing': in asking whether a system is robust one has to ask 'robust with respect to what property?' and 'robust over what set of changes in the system?' The practical issue used to illustrate the point is an examination of degrees of linkage between sub-networks and a pointed contrast in robustness and fragility between the dynamics of (1) contact infection and (2) information transfer or belief change. Time to infection across linked sub-networks, it turns out, is fairly robust with regard to the degree of linkage between them. Time to infection is fragile and sensitive, however, with regard to the type of sub-network involved: total, ring, small world, random, or scale-free. Aspects of robustness and fragility are reversed where it is belief updating with reinforcement rather than infection that is at issue. In information dynamics, the pattern of time to consensus is robust across changes in network type but remarkably fragile with respect to degree of linkage between sub-networks. These results have important implications for public health interventions in realistic social networks, particularly with an eye to ethnic and socio-economic sub-communities, and in social networks with sub-communities changing in structure or linkage.

Here we present a network model in which self-sustaining recurrent activation emerges from simple cascades of activation. It is demonstrated that the ability to support such self-sustaining activation in our model depends on network connectivity as well as the ability to grow new links over time. Additionally, we explore how the probability of emergence of self-sustaining activity can be modulated by changing various network parameters and suggest potential applications of our findings.

In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic.In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities. Based on previous cultural algorithm approaches, we were to extend the existing models to produce a more general one that could be applied across all complexity classes. We produced a new version of the Cultural Algorithms Toolkit, CAT 2.0, which supported a variety of co-evolutionary features at both the Knowledge and Population levels. We then applied the system to the solution of a 150 randomly generated problems that ranged from simple to chaotic complexity classes. As a result we were able to produce the following conclusions: No homogeneous Social Fabric tested was dominant over all categories of complexity. As the complexity of problems increased, so did the complexity of the Social Fabric that was need to deal with it efficiently. In other words, there was experimental evidence that social structure can be related to the frequency and complexity type of the problems that presented to a cultural system.

A large body of research describes the importance of adaptability for systems to be resilient in the face of disruptions. However, adaptive processes can be fallible, either because systems fail to adapt in situations requiring new ways of functioning, or because the adaptations themselves produce undesired consequences. A central question is then: how can systems better manage their capacity to adapt to perturbations, and constitute intelligent adaptive systems? Based on studies conducted in different high-risk domains (healthcare, mission control, military operations, urban firefighting), we have identified three basic patterns of adaptive failures or traps: (1) decompensation – when a system exhausts its capacity to adapt as disturbances and challenges cascade; (2) working at cross-purposes – when sub-systems or roles exhibit behaviors that are locally adaptive but globally maladaptive; (3) getting stuck in outdated behaviors – when a system over-relies on past successes although conditions of operation change. The identification of such basic patterns then suggests ways in which a work organization, as an example of a complex adaptive system, needs to behave in order to see and avoid or recognize and escape the corresponding failures. The paper will present how expert practitioners exhibit such resilient behaviors in high-risk situations, and how adverse events can occur when systems fail to do so. We will also explore how various efforts in research related to complex adaptive systems provide fruitful directions to advance both the necessary theoretical work and the development of concrete solutions for improving systems’ resilience.

Complex systems are found all around us. Companies, societies, fields who study these complex systems using the tools and markets, and humans rarely stay in a stable, predictable techniques of complex adaptive systems. We will explore state for long. Yet all these systems are characterized phenomena related to resilience, robustness, and evolvability by the notable persistence of some key attributes across various disciplines as one avenue towards exposing which maintain their identities, even as their constituent common dynamics that are found in these disparate domains. In the past, knowledge gained in each domain about these - What is it about these systems that define their identity?

We propose a collaborative approach to the issue of resource creation for commonsense computing by developing a collaboratory application aimed at children. Human validation is enabled through a game-with-a-purpose (GWAP) interface, gathering reliability judgements of assertions that can be used to aid the process of resource validation. Our experiments confirm that children aged 10 to 12 can be valuable and reliable partners in building commonsense databases, due to their stage of mental development and their eagerness to play GWAPs. Results show that children adapt their word choice in the assertions they provide to the difficulty level of the stimuli words, and that the judgements gathered through in-game validation can help to validate about 30% of the gathered statements automatically.

Most information extraction research identifies the state of the world in text, including the entities and the relationships that exist between them. Much less attention has been paid to the understanding of dynamics, or how the state of the world changes over time. Because intelligent behavior seeks to change the state of the world in rational and utility-maximizing ways, common-sense knowledge about dynamics is essential for intelligent agents. In this paper, we describe a novel system, Prepost , that tackles the problem of extracting the preconditions and effects of actions and events, two important kinds of knowledge for connecting world state and the actions that affect it. In experiments on Web text, Prepost is able to improve by 79% over a baseline technique for identifying the effects of actions (64% improvement for preconditions).