Abstract: Agent-based models are inherently microstructures–with their attention to agent behavior in a field context–
and only aggregate up to systems with recognizable macroeconomic characteristics. One might ask why
the traditional Keynes-Kalecki or structuralist (KKS) model would bear any relationship to the multi-agent
modeling approach. This paper shows how KKS models might benefit from agent-based microfoundations,
without sacrificing traditional macroeconomic themes, such as aggregate demand, animal spirits and endogenous
money. Above all, the integration of the two approaches gives rise to the possibility that a KKS
system–stable over many consecutive time periods–might lurch into an uncontrollable downturn, from which
a recovery would require outside intervention. As a by-product of the integration of these two popular
approaches, there emerges a cogent analysis of the network structure necessary to bind real and financial
agents into a integrated whole. It is seen, contrary to much of the existing literature, that a highly connected
financial system does not necessarily lead to more crashes of the integrated system.
[edit database entry]
Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).