Ted Carmichael

An Agent-Based Model of Solid Tumor Progression

Simulation techniques used to generate complex biological models are recognized as promising research tools especially in oncology. Here, we present a computer simulation model that uses an agent-based system to mimic the development and progression of solid tumors. The model includes influences of the tumors own features, the host immune response and level of tumor vascularization. The interactions among those complex systems were modeled using a multi-agent modeling environment provided by Netlogo. The model consists of a hierarchy of active objects including cancer cells, immune cells, and energy availability. The simulations conducted indicate the key importance of the nutrient needs of the tumor cells and of the initial responsiveness of the immune system in the tumor progression. Furthermore, the model strongly suggests that immunotherapy treatment will be efficient in individual with sustained immune responsiveness.

A Computer Simulation Laboratory for Social Theories

We present an agent-based model that allows the user to employ different social theories to try to explain and predict social changes. The model is set in the context of an armed insurgency in a developing country. We demonstrate the capabilities of the model by showing how it simulates a news report-based scenario under different theories and combinations of theories.

Towards a General Tool for Studying Threshold Effects Across Diverse Domains

Most interesting phenomena in natural and social systems include transitions and oscillations among their various phases. A new phase begins when the system reaches a threshold that marks the point of no return. These threshold effects are found all around us. In economics, this could be movement from a bull market to a bear market; in sociology, it could be the spread of political dissent, culminating in rebellion; in biology, the immune response to infection or disease as the body moves from sickness to health. Complex Adaptive Systems has proven to be a powerful framework for exploring these and other related phenomena. Our hypothesis is that by modeling differing complex systems we can use the known causes and mechanisms in one domain to gain insight into the controlling properties of similar effects in another domain. To that end, we have created a general Complex Adaptive Systems model so that it can be individually tailored and mapped to phenomena in various domains. Here we describe how this model applies to two domains: cancer/immune response and political dissent.

A Multi-level Complex Adaptive System Approach for Modeling of Schools

The amount of data available to build simulation models of schools is immense, but using these data effectively is difficult. Traditional methods of computer modeling of educational systems often either lack transparency in their implementation, are complex, and often do not natively simulate non-linear systems. In response, we advocate a Complex Adaptive Systems approach towards modeling and data mining. By simulating agent-level attributes rather than system-level attributes, the modeling is inherently transparent, easily adjustable, and facilitates analysis of the system due to the analogous nature of the simulated agents to real-world entities. We explore the design a CAS model of schools using multiple levels of data from varied data streams.

EMERGENT FEATURES IN A GENERAL FOOD WEB SIMULATION: LOTKA–VOLTERRA, GAUSE'S LAW, AND THE PARADOX OF ENRICHMENT

Computer simulations of complex food-webs are important tools for deepening our understanding of these systems. Yet most computer models assume, rather than generate, key system-level patterns, or use mathematical modeling approaches that make it difficult to fully account for nonlinear dynamics. In this paper, we present a computer simulation model that addresses these concerns by focusing on assumptions of agent attributes rather than agent outcomes. Our model utilizes the techniques of complex adaptive systems and agent-based modeling so that system level patterns of a marine ecosystem emerge from the interactions of thousands of individual computer agents. This methodology is validated by using this general simulation model to replicate fundamental properties of a marine ecosystem, including: (i) the predator–prey oscillations found in Lotka–Volterra; (ii) the stepped pattern of biomass accrual from resource enrichment; (iii) the Paradox of Enrichment; and (iv) Gauses Law.

Reports of the AAAI 2009 Fall Symposia

The Association for the Advancement of Artificial Intelligence was pleased to present the 2009 Fall Symposium Series, held Thursday through Saturday, November 5–7, at the Westin Arlington Gateway in Arlington, Virginia. The Symposium Series was preceded on Wednesday, November 4 by a one-day AI funding seminar. The titles of the seven symposia were as follows: (1) Biologically Inspired Cognitive Architectures, (2) Cognitive and Metacognitive Educational Systems, (3) Complex Adaptive Systems and the Threshold Effect: Views from the Natural and Social Sciences, (4) Manifold Learning and Its Applications, (5) Multirepresentational Architectures for Human-Level Intelligence, (6) The Uses of Computational Argumentation, and (7) Virtual Healthcare Interaction.

Reports of the AAAI 2011 Fall Symposia

The Association for the Advancement of Artificial Intelligence was pleased to present the 2011 Fall Symposium Series, held Friday through Sunday, November 4–6, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the seven symposia are as follows: (1) Advances in Cognitive Systems; (2) Building Representations of Common Ground with Intelligent Agents; (3) Complex Adaptive Systems: Energy, Information and Intelligence; (4) Multiagent Coordination under Uncertainty; (5) Open Government Knowledge: AI Opportunities and Challenges; (6) Question Generation; and (7) Robot-Human Teamwork in Dynamic Adverse Environment. The highlights of each symposium are presented in this report.

Reports of the AAAI 2010 Fall Symposia

The Association for the Advancement of Artificial Intelligence was pleased to present the 2010 Fall Symposium Series, held Thursday through Saturday, November 11-13, at the Westin Arlington Gateway in Arlington, Virginia. The titles of the eight symposia are as follows: (1) Cognitive and Metacognitive Educational Systems; (2) Commonsense Knowledge; (3) Complex Adaptive Systems: Resilience, Robustness, and Evolvability; (4) Computational Models of Narrative; (5) Dialog with Robots; (6) Manifold Learning and Its Applications; (7) Proactive Assistant Agents ; and (8) Quantum Informatics for Cognitive, Social, and Semantic Processes. The highlights of each symposium are presented in this report.