In these models, agents are typically described by state-determined automata: that is, they function by reaction to input and present state using some iterative mapping in a state space. Such ABM can be used, for instance, to simulate massively parallel computing systems, a research interest of several members of our team. However, it has become clear in recent years, that the modeling of some phenomena, particularly, ecological and social phenomena, requires agents whose behavior is not simply dictated by local, state-determined interaction.
In a society empowered by language and hyperlinked by information channels, which in turn impacts planetary ecology, agents have access and rely on accumulated knowledge which escapes local constraints via communication and is stored in media beyond the agent itself and its state. Indeed, many if not most researchers in Artificial Intelligence AI , Cognitive Science and Psychology, have come to pursue the idea that intelligence is not solely an autonomous characteristic of agents, but heavily depends on social, linguistic, and organizational knowledge which exists beyond individual agents.
Such agents are often known as situated [Clark, ] or semiotic [Rocha, ] agents. It has also been shown that agent simulations which rely on shared social knowledge, can model social choice more effectively [Richards, et al, ]. Because most of our research projects deal with modeling social networks, we are interested in studying how agents trade and employ knowledge in their decision making. We are particularly interested in studying the structure of social networks that arise out of agents who exchange knowledge, as well as the dynamics of trends observed in such multi-agent systems.
We also use existing networks of documets e. We have developed methods to predict trends and identify latent associations between agents, documents, or keyterms. Latent associations are associations say between agents that have not occurred, but which are strongly implied by indirect network connections, and thus have a high chance of occurring in the future.
This methodology has been used in a recommendation system for the MyLibray Portal at Los Alamos and to study terrorist networks in Homeland Defense projects. Our team pursues other related research exploring metaphors from Nature, particularly in the areas of adaptive computation and optimization. The key notion of complex systems, that of many simple processes, under selective pressures, synergistically interacting to produce desirable global behavior, can be applied successfully to different problems.
In the area of optimization, we are developing heuristic algorithms inspired by non-equilibrium physical processes. The development of non-equilibrium optimization methods is likely to lead to the next generation of general-purpose algorithms - intended, like simulated annealing, for broad application. We expect that this analysis will lead us to new insights into the role criticality plays in combinatorial optimization, as well as to a deeper and more applied understanding of computational complexity. Similarly, we are developing biologically motivated designs for Adaptive Knowledge Management.
Distributed designs that draw from immune system metaphors and other aspects of biological systems can largely improve existing information retrieval and knowledge management in networked information resources. We have developed a recommendation system for LANL's Research Library that allows different databases to learn new and adapt existing keywords to the categories recognized by different communities, using algorithms inspired by biological and cultural evolution.
For more details on our research, please refer to our projects , competencies , and research list on this web site. Cariani, Peter .
Introduction to the Modeling and Analysis of Complex Systems - Open SUNY Textbooks
Langton, C. Taylor, J. Farmer, and S. Rasmussen eds.
Addison-Wesley, pp. Clark, Andy . MIT Press.
Langton ed. Pattee, Howard H. Richards, D. McKay, and W.
Richards . Rocha, Luis M. In Press. Rosen, Robert . Haken, A.
Karlqvist, and U. Svedin eds. Springer-Verlag, pp. The book has been organized in three parts: i fundamentals, ii systems with small number of variables, and iii systems with large number of variables. Each part is divided in chapters. This provides clarity in organization.
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This should be noted that the chapters are not arranged in order of the degree of difficulty. Easier chapters on "Modeling" precede less easier chapters on "Analysis" on a particular topic. This is not a negative or positive.
Social-Behavioral Modeling for Complex Systems
This is how the book is structured. The book has no interface problems except some issues with formatting such as continuation of a small table on the following page page I believe science is culture-neutral. Additionally, I see no examples which would give an impression of cultural bias. A very good and interesting book for modelers. Artificial neural networks and fuzzy sets are versatile modeling tools which are involved in our day-to-day systems. I believe, a brief and formal discussion of computational complexity of various systems would have nicely added to this book.
Complex systems are systems made of a large number of microscopic components interacting with each other in nontrivial ways. Many real-world systems can be understood as complex systems, where critically important information resides in the relationships between the parts and not necessarily within the parts themselves.
Conditions of Use
This textbook offers an accessible yet technically-oriented introduction to the modeling and analysis of complex systems. The topics covered include: fundamentals of modeling, basics of dynamical systems, discrete-time models, continuous-time models, bifurcations, chaos, cellular automata, continuous field models, static networks, dynamic networks, and agent-based models. Most of these topics are discussed in two chapters, one focusing on computational modeling and the other on mathematical analysis.
This unique approach provides a comprehensive view of related concepts and techniques, and allows readers and instructors to flexibly choose relevant materials based on their objectives and needs. Python sample codes are provided for each modeling example. Hiroki Sayama , D.