What Can Be Learned from Computer Modeling? Comparing Expository and Modeling Approaches to Teaching Dynamic Systems Behavior

Computer modeling has been widely promoted as a means to attain higher order learning outcomes. Substantiating these benefits, however, has been problematic due to a lack of proper assessment tools. In this study, we compared computer modeling with expository instruction, using a tailored assessment designed to reveal the benefits of either mode of instruction. The assessment addresses proficiency in declarative knowledge, application, construction, and evaluation. The subscales differentiate between simple and complex structure. The learning task concerns the dynamics of global warming. We found that, for complex tasks, the modeling group outperformed the expository group on declarative knowledge and on evaluating complex models and data. No differences were found with regard to the application of knowledge or the creation of models. These results confirmed that modeling and direct instruction lead to qualitatively different learning outcomes, and that these two modes of instruction cannot be compared on a single “effectiveness measure”

Declarative vs Rule-based Control for Flocking Dynamics

The popularity of rule-based flocking models, such as Reynolds' classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational models. We introduce a very simple control law for flocking based on a cost function capturing cohesion (agents want to stay together) and separation (agents do not want to get too close). We refer to it as {\textit declarative flocking} (DF). We use model-predictive control (MPC) to define controllers for DF in centralized and distributed settings. A thorough performance comparison of our declarative flocking with Reynolds' model, and with more recent flocking models that use MPC with a cost function based on lattice structures, demonstrate that DF-MPC yields the best cohesion and least fragmentation, and maintains a surprisingly good level of geometric regularity while still producing natural flock shapes similar to those produced by Reynolds' model. We also show that DF-MPC has high resilience to sensor noise.Comment: 7 Page

Learning from Semantic Inconsistencies as the Origin of Dynamic Capabilities in MNCs: Evidence from Pharmaceutical MNCs

This paper focuses on origins of dynamic capabilities in multinational corporations (MNCs). Building on literature in the area of organizational memory and organizational learning, we investigate factors that contribute to subsidiaries of MNCs ability to detach themselves from obsolete knowledge and practices. To construct the theoretical framework, 11 extensive interviews with marketing and sales executives from three pharmaceutical MNCs operated in Iran were conducted. We test our hypotheses using statistical quantitative analysis of data related to 459 observations from subsidiaries of 51 pharmaceutical MNCs during years 2005-2009. We examine the quality of corrective actions taken by subsidiaries of pharmaceutical MNCs subsequent to subsidiaries failing to meet expected performance objectives. Our findings confirm a moderating role for internationalization, span, and the composition of human resources on the quality of corrective actions pursued

Advances in semantic representation for multiscale biosimulation: a case study in merging models

As a case-study of biosimulation model integration, we describe our experiences applying the SemSim methodology to integrate independently-developed, multiscale models of cardiac circulation. In particular, we have integrated the CircAdapt model (written by T. Arts for MATLAB) of an adapting vascular segment with a cardiovascular system model (written by M. Neal for JSim). We report on three results from the model integration experience. First, models should be explicit about simulations that occur on different time scales. Second, data structures and naming conventions used to represent model variables may not translate across simulation languages. Finally, identifying the dependencies among model variables is a non-trivial task. We claim that these challenges will appear whenever researchers attempt to integrate models from others, especially when those models are written in a procedural style (using MATLAB, Fortran, etc.) rather than a declarative format (as supported by languages like SBML, CellML or JSim’s MML)

A Survey of Languages for Specifying Dynamics: A Knowledge Engineering Perspective

A number of formal specification languages for knowledge-based systems has been developed. Characteristics for knowledge-based systems are a complex knowledge base and an inference engine which uses this knowledge to solve a given problem. Specification languages for knowledge-based systems have to cover both aspects. They have to provide the means to specify a complex and large amount of knowledge and they have to provide the means to specify the dynamic reasoning behavior of a knowledge-based system. We focus on the second aspect. For this purpose, we survey existing approaches for specifying dynamic behavior in related areas of research. In fact, we have taken approaches for the specification of information systems (Language for Conceptual Modeling and TROLL), approaches for the specification of database updates and logic programming (Transaction Logic and Dynamic Database Logic) and the generic specification framework of abstract state machine

Answer Set Programming Modulo `Space-Time'

We present ASP Modulo `Space-Time', a declarative representational and computational framework to perform commonsense reasoning about regions with both spatial and temporal components. Supported are capabilities for mixed qualitative-quantitative reasoning, consistency checking, and inferring compositions of space-time relations; these capabilities combine and synergise for applications in a range of AI application areas where the processing and interpretation of spatio-temporal data is crucial. The framework and resulting system is the only general KR-based method for declaratively reasoning about the dynamics of `space-time' regions as first-class objects. We present an empirical evaluation (with scalability and robustness results), and include diverse application examples involving interpretation and control tasks

The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges