International law is a singular exception to the top-down systems of law within nations. It presents the puzzle of how the law can be created or changed in the absence of authoritative rule-making institutions. The present paper is part of a work in progress that locates the law-making apparatus of international law in a complex adaptive system. Herein the focus is on thresholds. The first and most detailed threshold describes the emergence of the complex adaptive system. The second threshold consists of the transformation of international law from the voluntary to the automatic. The third threshold is here but has not yet been crossed: actualizing human rights as enforceable claims by individuals against States.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.

Irregular Warfare (IW), with its emphasis on social and cognitive phenomena such as population sentiment, is a major new focus of the Department of Defense (DoD). One of the most important classes of IW action is Information Operations (IO), the use of information to influence sentiment. With the DoD’s new focus on IW comes the new need to analyze and forecast the effects of IO actions on population sentiment. Analysts at the DoD traditionally use Modeling and Simulation to analyze and forecast the effects of conventional warfare’s actions on the outcome of wars, but IW and IO in particular are far more complex than conventional physics-based simulations. DoD analysts are in the early stages of looking for scientifically rigorous methods in the Modeling and Simulation of IO’s complex effects. This paper presents the state of IO modeling and simulation in the DoD, using examples from several computer models now being used, in these early stages of IW analysis. It discusses how the ideas of Complex Adaptive Systems (CAS) and threshold events in particular may be incorporated into IO modeling in order to increase its scientific rigor, fidelity, and validity.

Conceptual Blending through the process of Double Scope Blending provides an account for human creativity. We show how computational creativity can be modeled after Double Scope Blending for machine generation of scenarios, stories, hypotheses, etc. This paper describes an application of this process to the generation of novel and creative scenarios in the maritime security domain.

Early studies of public budgeting emphasized uncertainty Two of us (BJ and FB) have published a set of papers, in the decision-making environment. Budgeting in the books focusing on annual budget changes (Jones and absence of information about the impacts of decisions led Baumgartner 2005b). Leptokurtic distribution of percentual to an adjustment process rooted in simple decision rules budget changes were observed in a broad range of settings: and bargaining among interests. This led to marginal or small increases and small decreases of budgets and budget incremental adjustments from the budgetary status quo, components are the most frequent, but time to time large with all major actors wary of big changes to the budgetary increases and cutoffs are observed as well.

The purpose of this study was to develop and test a platform that would assist the Environmental Protection Agency (EPA), and the scientific community at large, in the generation of a human activity and energy expenditure database of sufficient detail to accurately predict human exposures and dose to various pollutants. The monitoring system developed is easily extendable to the collection of other health-related data. Our protocol tested the use of a digital voice recorder to collect activity/location diary data assuming it to be a less burdensome and a more reliable method than using paper and pencil diaries or hand-held computers. We expected the data to be more complete and reliable than retrospective reports (diaries filled out at the end of day) because the recorders are easy to use, the diary entries are made as the events occur, and we expected that participants would be more likely to complete the study because of the reduced burden. The data collection plan was also expected to show that the cost of the transcription of the diary can be reduced substantially by using speech and language processing to translate the digital diaries into the EPA’s Comprehensive Human Activity Database (CHAD).

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.

The present historical epoch is unique in the sense that now The present work takes a shot at this target. The author's people may have the opportunity to create something equal answer to the first question should be clear from the above to them, if not greater: machines capable of humanlike and can be formulated concisely as follows: the goal is to intellectual and cultural development. The reason is not design a human-level learner. Yet, this statement needs a only that the hardware available today is compatible in its further clarification. Its limited interpretation could be, raw computational capacities with the human brain. The e.g.: "The goal of a human-level learner is to take complex, main reason is the emergent understanding of how the noisy information from multiple modalities and distill this human mind works. It appears that implementing the same experience into a representation that supports prediction principles of the human mind in a machine would not take about and manipulation of the world" (Shrobe et al., 2006, yet unavailable today computer resources.

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 present a cognitive model of frequency-based decision-making applied to the task of landmine detection. The model is implemented in the ACT-R cognitive architecture and is strongly constrained by the cognitive primitives of the architecture. We then generalize the model to another task in the domain of macroeconomic decision-making using the same architecture, pursuing theoretical parsimony. We describe each model's representation requirements, assess their fits to the data, and analyze their performance scaling as a function of task and architectural parameters. Efforts to generalize the landmine detection model to macroeconomic decision making showed that reasonable fits to the macro-economic performance data could be achieved by models based either on procedural knowledge or declarative knowledge. This finding underscores the importance of distinguishing between processing strategies employed to execute tasks. Such detail appears needed to understand the neural foundations of frequency-based decision-making.