Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

Vection in depth during treadmill walking

Vection has typically been induced in stationary observers (ie conditions providing visual-only information about self-motion). Two recent studies have examined vection during active treadmill walking--one reported that treadmill walking in the same direction as the visually simulated self-motion impaired vection (Onimaru et al, 2010 Journal of Vision 10(7):860), the other reported that it enhanced vection (Seno et al, 2011 Perception 40 747-750; Seno et al, 2011 Attention, Perception, & Psychophysics 73 1467-1476). Our study expands on these earlier investigations of vection during observer active movement. In experiment 1 we presented radially expanding optic flow and compared the vection produced in stationary observers with that produced during walking forward on a treadmill at a 'matched' speed. Experiment 2 compared the vection induced by forward treadmill walking while viewing expanding or contracting optic flow with that induced by viewing playbacks of these same displays while stationary. In both experiments subjects' tracked head movements were either incorporated into the self-motion displays (as simulated viewpoint jitter) or simply ignored. We found that treadmill walking always reduced vection (compared with stationary viewing conditions) and that simulated viewpoint jitter always increased vection (compared with constant velocity displays). These findings suggest that while consistent visual-vestibular information about self-acceleration increases vection, biomechanical self-motion information reduces this experience (irrespective of whether it is consistent or not with the visual input)

Synergistic Model of Cardiac Function with a Heart Assist Device

The breakdown of cardiac self-organization leads to heart diseases and failure, the number one cause of death worldwide. The left ventricular pressure–volume relation plays a key role in the diagnosis and treatment of heart diseases. Lumped-parameter models combined with pressure–volume loop analysis are very effective in simulating clinical scenarios with a view to treatment optimization and outcome prediction. Unfortunately, often invoked in this analysis is the traditional, time-varying elastance concept, in which the ratio of the ventricular pressure to its volume is prescribed by a periodic function of time, instead of being calculated consistently according to the change in feedback mechanisms (e.g., the lack or breakdown of self-organization) in heart diseases. Therefore, the application of the time-varying elastance for the analysis of left ventricular assist device (LVAD)–heart interactions has been questioned. We propose a paradigm shift from the time-varying elastance concept to a synergistic model of cardiac function by integrating the mechanical, electric, and chemical activity on microscale sarcomere and macroscale heart levels and investigating the effect of an axial rotary pump on a failing heart. We show that our synergistic model works better than the time-varying elastance model in reproducing LVAD–heart interactions with sufficient accuracy to describe the left ventricular pressure–volume relation

STREETS: an agent-based pedestrian model.

8-11 Septembe

Simulating city growth by using the cellular automata algorithm

The objective of this thesis is to develop and implement a Cellular Automata (CA) algorithm to simulate urban growth process. It attempts to satisfy the need to predict the future shape of a city, the way land uses sprawl in the surroundings of that city and its population. Salonica city in Greece is selected as a case study to simulate its urban growth. Cellular automaton (CA) based models are increasingly used to investigate cities and urban systems. Sprawling cities may be considered as complex adaptive systems, and this warrants use of methodology that can accommodate the space-time dynamics of many interacting entities. Automata tools are well-suited for representation of such systems. By means of illustrating this point, the development of a model for simulating the sprawl of land uses such as commercial and residential and calculating the population who will reside in the city is discussed

Integrated urban evolutionary modeling

Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin