To help realize the potential of complex systems models we need new measures appropriate for capturing processes that exhibit feedback, nonlinearity, heterogeneity, and emergence. As part of a larger research project encompassing several categories of dynamical properties this paper provides formal and general definitions of tipping point-related phenomena. For each tipping concept this paper provides a probabilistic definition derived from a Markov model representation. We start with the basic features of Markov models and definitions of the foundational concepts of system dynamics. Then several tipping point-related concepts are described, defined, measured, and illustrated with a simplified graphical example. The paper finishes with several branches of future work involving new measures for complex systems and the fusion of research domains.

A state is not alive, yet it performs many of the central enjoys few bonds of kinship: and residence depends upon functions of life like replication and adaptation to new conditions occupational specialization rather than blood relations. A to balance social protection and opportunity. As a modern state can declare war on behalf of the entire collectivity, lifelike system the rise of the modern state raises four sets reserving the right to declare mandatory participation of fundamental questions about its evolutionary design. A and to contract the area of private vengeance. They proclaim first set concerns how it became a sustainable, autonomously a monopoly of force and of law, while requiring citizens to replicating system, capable of evolution. All non-state agglomerations forgo violence; vengeance is not the responsibility of the offended such as empires or chiefdoms eventually stagnate party. Almost any crime against one member is a because they are closed systems that break down over crime against the state. Subgroups seeking vengeance are time (Weber). A state is an open system that must able to viewed as threatening to the order of the state.

Consciousness is a major feature of mammalian nervous significant "news" is demanding to be heard by the systems. Recent evidence indicates it may extend from established powers in the society of mind. The only known mammals to birds and even cephalopods (Edelman & Seth, function of slow-wave sleep, for example, is to consolidate 2009). Since all major biological adaptations are episodic memories (i.e., memories of conscious events that functional, or sequelae of biofunctions, and since brains took place during the conscious period before sleep). They be to "reapportion" cortical functions based on the are based on biochemistry and eukaryotic cells, which previous day's experiences, consistent with the last two impose narrow limits on such features as the peak rate of decades of work on cortical plasticity.

A human subject experiment was conducted to investigate caffeine’s effect on appraisal and performance of a mental serial subtraction task. Serial subtraction performance data was collected from three treatment groups: placebo, 200, and 400 mg caffeine. The data were analyzed by caffeine treat ment group and how subjects appraised the task (as challenging or threatening). A cognitive model of the serial subtraction task was developed. The model was fit to the human performance data using a parallel genetic algorithm. How the model’s parameters change to fit the data suggest how cognition changes due to caffeine and appraisal. Over all, the cognitive modeling and optimization results suggest that the speed of vocalization varies the most along with changes to declarative memory. This approach provides a way to compute how cognitive mechanisms change due to moderators.

We describe some experiments which simulate a grounded approach to the acquisition of the cognitive and linguistic competence required to communicate propositional logic sentences. This encompasses both the construction of a conceptualisation of its environment by each individual agent and of a shared language by the population. The processes of conceptualisation and language acquisition in each individual agent are based on general purpose cognitive capacities, such as categorisation, discrimination, invention, adoption and induction. The construction of a shared language by the population is achieved using a particular type of linguistic interaction, known as the evaluation game, which gives rise to a common set of linguistic conventions through a process of self-organisation. This work addresses the problem of the acquisition of both the semantics and the syntax of propositional logic. Trying to learn these two aspects at the same time is more difficult than learning the semantics or the syntax of propositional logic separately. Because the agents must coordinate their linguistic behaviour taking into account only the subset of objects which constitutes the topic of a particular linguistic interaction. This means that a pair of agents can communicate successfully about a particular subset of objects (a topic) even if they use different conceptualisations (formulas) in order to identify the same topic. And this introduces a high degree of ambiguity in the interpretation process the agents have to deal with when they try to construct a shared communication language. In spite of this, the results of the experiments show that at the end of the simulation runs the individual agents build different conceptualisations and grammars, but that the conceptualisations and grammars of the agents in the population are compatible in the sense that they guarantee the unambiguous communication of propositional logic sentences.

Dramatic advances in the last six months in High Definition Fiber Tracking (HDFT) make it possible to image the fiber connectivity from source to destination mapping hundreds of thousands fiber tracks with sufficient resolution to identify the cable level circuit diagram of the human brain. Brain activity imaging studies using functional Magnetic Resonance Imagining (fMRI) identify differential activation patterns as a function of task and level of practice. These data show subnetworks with communication of high bandwidth vector associations, scalar priority and control signals, and interactions with control and meta cognition. The connectivity and activity data support a triarchic cognitive architecture. Processing is the synergistic interaction of three interlinked cognitive computational systems with differential computation role and evolutionary history. These data provided a detailed diagram to guide reverse engineering of the systems levels of the human brain.

Automated health behavior coaches (HBCs) potentially can provide a widely accessible, cost-effective means of promoting health behavior. Coaches are intelligent agents that “converse” with users, offering tailored feedback, advice, and empathy. Research subjects like coaches and comply with target behaviors, but interest and adherence wane over time. More research is needed on next-generation HBCs to improve coaching techniques, enhance user engagement, and extend adherence. However, the necessary technical tools and expertise reside in only a few research labs. In an effort to expand and accelerate research, we are developing an HBC Kit that will extend and specialize our more general Imp™ Kit. We propose 7 innovations for next-generation HBCs, demonstrate them in a lifestyle coach, and characterize authoring with the Imp Kit. We discuss planned extensions for the HBC Kit to enable a larger and more diverse community to create and evaluate a broader range of coaches.

Unfortunately, many students stop using these beneficial learning practices as soon Presented by Stephen Grossberg, Department of Cognitive as the metatutoring ceases. Apparently, the metatutors were and Neural Systems, Center for Adaptive Systems, and Center nagging rather than convincing. This talk will present a of Excellence for Learning in Education, Science, and study of Pyrenees, a metatutor that coaches students to focus Technology, Boston University, Boston, MA 02215 on learning domain principles rather than solutions to A deep and rational understanding of the factors that influence examples. It was convincing, in that students who were effective education and learning technologies depends taught probability with Pyrenees used principle-based problem on a corresponding understanding of how the brain in health solving on post-test more so than students taught by Andes, and disease controls learned behaviors. There has been a which did not focus students on principles. Moreover, revolution in discovering new computational paradigms, organizational when all students were transferred to Andes for learning principles, mechanisms, and models of how of physics, those who were metatutored used the principlefocused learning processes enable brains to give rise to minds.

In this paper we propose a new distance metric for signals that admit a sparse representation in a known basis or dictionary. The metric is derived as the length of the sparse geodesic path between two points, by which we mean the shortest path between the points that is itself sparse. We show that the distance can be computed via a simple formula and that the entire geodesic path can be easily generated. The distance provides a natural similarity measure that can be exploited as a perceptually meaningful distance metric for natural images. Furthermore, the distance has applications in supervised, semi-supervised, and unsupervised learning settings.

The anthropological and economic history of humanity gives evidence of a progression of cognitive frameworks. There are three cognitive perspectives, in order: living in the present, living in the past, and living in the future. They correspond to three levels of competency with abstract thought: concrete thought only, abstract thought with correlations, and abstract thought with both correlations and causality. This appears to explain the fundamental differences between primitive cultures, traditional cultures, and modern cultures: differences in economics, politics, personality, and anthropological differences in general. So, not only does this theory succinctly explain a wide range of human behavior, but because it does, it appears to be a valid theory and a promising way to decompose abstract thought into its component parts for future cognitive research. These frameworks are discussed along with their implications of exploiting this progression to simplify the problem of developing an AI.