A small consortium of philosophers has begun work on the implications of epistemic networks (Zollman 2008 and forthcoming; Grim 2006, 2007; Weisberg and Muldoon forthcoming), building on theoretical work in economics, computer science, and engineering (Bala and Goyal 1998, Kleinberg 2001; Amaral et. al., 2004) and on some experimental work in social psychology (Mason, Jones, and Goldstone, 2008). This paper outlines core philosophical results and extends those results to the specific question of thresholds. Epistemic maximization of certain types does show clear threshold effects. Intriguingly, however, those effects appear to be importantly independent from more familiar threshold effects in networks.

A peloton is a group of cyclists whose individual and collective energy expenditures are reduced when cyclists ride behind others in zones of reduced air pressure; this effect is known in cycling as ‘drafting’. As an aggregate of biological agents (human), a peloton is a complex dynamical system from which patterns of collective behaviour emerge, including phases and transitions between phases, through which pelotons oscillate. Coupling of cyclists’ energy expenditures when drafting is the basic peloton property from which self-organized collective behaviours emerge. Shown here are equations that model coupling behaviours. Environmental constraints are further parameters that affect peloton dynamics. Phases are defined by thresholds of aggregate energy expenditure; shown here are two different, but consistent, conceptual descriptions of these phase transitions. The first is an energetic model that describes phases in terms of individual, bi-coupled and globally-coupled energy output thresholds that define four observable changes in peloton behaviour. A second, economic model incorporates competition and cooperation dynamics: cooperation increases as power outputs and course constraints increase and population diminishes, and where competition and cooperation for resources results in peloton divisions into sub-pelotons whose average fitness levels are more closely homogeneous.

One of the open issues in developing large-scale computational models of the brain is how the transfer of information between communicating cortical regions is controlled. Here, we present a model where the basal ganglia implement such a conditional information routing system. The basal ganglia are a set of subcortical nuclei that play a central role in cognition. Like a switchboard, the model basal ganglia direct the communication between cortical regions by alerting the destination regions to the presence of important signals coming from the source regions. This way, they can impose serial control on the massive parallel communication between cortical areas. The model also incorporates a possible mechanism by which subsequent transfers of information control the release of dopamine. This signal is used to produce novel stimulus-response associations by internalizing the representation being transferred in the striatum. We discuss how this neural circuit can be seen as a biological implementation of a production system. This comparison highlights the basal ganglia as bridge between computational models of small-size brain circuits and high-level characterizations of complex cognition, such as cognitive architectures.

Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to inte-raction only with those objects that it has previously ob-served. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe action selection in uncertain situations, and to allow reward-maximizing selection of behaviors.

We present an interactive learning method that enables a user to iteratively refine a regression model. The user examines the output of the model, visualized as the vertical axis of a 2D scatterplot, and provides corrections by repositioning individual data points to the correct output level. Each repositioned data point acts as a control point for altering the learned model, using the geometry underlying the data. We capture the underlying structure of the data as a manifold, on which we compute a set of basis functions as the foundation for learning. Our results show that manifold-based interactive learning achieves dramatic improvement over alternative approaches.

Many biological models of human motivation and behavior posit a functional division between those subsystems respon- sible for approach and avoidance behaviors. Gray and McNaughton's (2000) revised Reinforcement Sensitivity Theory (RST) casts this distinction in terms of a Behavioral Activation System (BAS) and a Fight-Flight-Freeze System (FFFS), mediated by a third, conflict resolution system — the Behavioral Inhibition System (BIS). They argued that these are fundamental, functionally distinct systems. The model has been highly influential both in personality psychology, where it provides a biologically-based explanation of traits such as extraversion and neuroticism, and in clinical psychology wherein state disorders such as Major Depressive Disorder and Generalized Anxiety Disorder can be modeled as differences in baseline sensitivities of one or more of the systems. In this paper, we present work in progress on implementing a simplified simulation of RST in a set of embodied virtual characters. We argue that RST provides an interesting and potentially powerful starting point for cognitive architectures for various applications, including interactive entertainment, in which simulation of human-like affect and personality is important.

The evolution in health care to greater support for self-managed care is escalating the demand for e-health systems in which patients can access their personal health information in order to ultimately partner with providers in the management of their health and wellness care. At present, unfortunately, patients are seldom able to easily access their own health information so, as a result, it is often difficult for patients to enter into a dialogue with their healthcare providers about treatment and other options. One truism seems to be constantly ignored: it is not possible for patients to actively manage their health without the requisite information. Health information should be made available through "any time, anywhere" delivery: outside the physician's office or hospital, in the home or other personal setting, on a variety of multimedia information devices. We believe that personalization of health information will be a key element in effective self-managed healthcare.

Recent research identifies stochastic dominance as critical for understanding the relationship between network structure and diffusion. This paper introduces the concept of stochastic dominance, explains the theory linking stochastic dominance and diffusion, and applies this theory to a number of diffusion studies in the literature. The paper illustrates how the theory connects observations from different disciplines, and details when and how those observations can be generalized to broader classes of networks.

The coagulation system (CS) is a complex, inter-connected biological system with major physiological and pathological roles. The CS may be viewed as a complex adaptive system, in which individual components are linked through multiple feedback and feedforward loops. The non-linear relationships between the numerous coagulation factors and the interplay among the elements of the CS render the study of this biology at a molecular and cellular level nearly impossible. We present an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. Our ABMS approach utilizes a subset of 52 rules to define the interactions among 33 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 ~12,000 cells and ~300,000 agents. Our ABMS method successfully reproduces the initiation, propagation, and termination of thrombin formation due to the activation of the extrinsic pathway. Furthermore, the ABMS is able to demonstrate the emergence of a threshold effect for thrombin generation as a result of the synergistic effect of combining anticoagulant systems.

The Institute of Education Sciences (IES) provides funding opportunities for researchers to bring their knowledge of learning, cognitive science, and technology to bear on education practice. This panel describes opportunities available through the National Center for Education Research and the National Center for Special Education Research.