Santa Fe Institute

Cities, Scaling and Sustainability

Organizers: Luis Bettencourt, Geoffrey West

The 2010-2011 focus area on Cities, Scaling and Sustainability will attempt to create an interdisciplinary quantitative synthesis of organizational and dynamical aspects of human social organizations, with an emphasis on cities. Different disciplinary perspectives will be integrated in terms of the search for similar dependences of urban indicators on population size - scaling analysis - and other variables that characterize the system as a whole. A particularly important focus of this research area  is to develop theoretical insights about cities that can inform quantitative analyses of their long term sustainability in terms of the interplay between innovation, resource appropriation and consumption and the make up o their social and economic activity. This focus area will bring together urban planners, economists, sociologists, social psychologists, anthropologists and complex system theorists with the aim of generating an integrated and quantitative understanding of cities. Outstanding areas of research include the identification of general scaling patterns in urban infrastructure and dynamics around the world, the quantification of resource distribution networks in cities and their interplay with the city's socioeconomic fabric, issues of temporal acceleration and spatial density and the long term dynamics of urban systems.  The focus area organizes a number of working groups and workshops that will take place over the 2010-2011 period. These pages will showcase publications, discussions, and videos of the participants in this area.

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Emergence in Decision Making and Cognitive Systems

Organizers: David Krakauer

The 2010-2011 focus area on Emergence in Decision Making and Cognitive Systems aims to explore a full range of cognitive phenomena, from the molecular basis of neural learning, through to distributed cognition among social agents. This focus area aims to bring together neuroscientists, psychologists, economists, computer scientists, game theorists and evolutionary biologists, all seeking to understand intelligent behavior in systems rich in representation and behavioral adaptability. Outstanding areas of research include: hierarchies of representation; control of distributed adaptive elements, the construction of unitary states of comprehension, and the function of multiple reward networks. The focus area organizes a number of working groups and workshops that will take place over the 2010-2011 period. These pages will showcase publications, discussions, and videos of the participants in this area.

Multiple Scales of Conflict

Organizers: Jessica Flack

There are two primary challenges faced by all complex, adaptive systems.  One is an uncertain and noisy environment. The other –the topic of this 2010-2011 focus area– is conflict. Conflict arises when the interests of system components –whether genes, cells, individuals, or states– are not fully aligned. Some have gone so far as to argue that lack of alignment, or “frustration,” in many body systems is the defining feature of complex systems. In the long 3.5 billion year history of life on earth organisms and aggregates have devised manifold strategies in order to survive and prosper in the face of conflict. The solutions that organisms have built for managing conflict are thought to have played a central role in facilitating the major transitions from simple aggregates to more integrated, social organisms and cultures. 

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Risk, Markets, and Innovation

Organizers: J. Doyne Farmer

SFI’s focus area in Risk, Markets, and Innovation uses both empirical, analytical, and theoretical methods to explore the origins of financial risk. Using unique, real-world data sets and modeling approaches that simulate, probabilistically, the individual and aggregated behaviors of agents acting in a financial market, SFI scientists develop and test new, quantitative theoretical frameworks that describe the complex dynamics at play in markets and whole economies. These theoretical frameworks are a departure from mainstream economic theory; they do not assume the self-correcting or bounding influence of an equilibrium, nor do they assume that agents act rationally. By incorporating these ideas and methods, as well as the transdisciplinary insights of economists, physicists, and biologists, SFI researchers are gaining improved understandings of such phenomena as the leverage cycle, liquidity, volatility, noise, non-rational decision-making, the size and trading frequencies of funds, the ecology and evolution of market strategies, and the proper method of discounting the future. 

Behavioral Dynamics

Social institutions emerge, survive, and die in concert with distinctive human behaviors — cooperation with peers, hostility towards outsiders, and adaptive learning, for example. Traditional models used by economists and sociologists often overlook the important roles these behaviors play, relying instead on artificial, idealized contexts with little semblance to real-world human interaction. SFI’s behavioral dynamics research draws from physics and many other disciplines to study complex human behavior. It incorporates mathematical modeling and empirical case studies to address problems of potentially great contemporary practical relevance, such as economic crises, conflict, and wealth inequality.

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Evolution & Emergence

How did life emerge from early earth’s primitive geochemistry? What can computerized approximations of life tell us about the evolution of real-life processes such as metabolism, reproduction, and mutation? Can understanding the principles that underlie how ecosystems form, become stable, and change help us measure and respond to ecological threat? What norms have emerged that make human societies more stable? Will a better understanding of the eternal dance of viral mutation and human immune response teach us to beat HIV and other pathogens? SFI’s research of complex living systems incorporates theoretical inquiry, modeling, and empirical study.

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Information & Computation

Computation has been a central theme of SFI research since its inception, including seminal contributions in evolutionary and adaptive computation, relationships between physics and computation, models of distributed and collective agent-based computation, and applications of biological insights to engineered computational systems.

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Physics of Complexity

Fundamental physics is core area of research at SFI. It spans the principles of quantum and statistical mechanics, information theory, nonlinear dynamics and chaos, and discrete systems. These fields have provided techniques and approaches to problem solving that are useful across the sciences, and served as points of departure for the recognition of new principles. For instance, the application of self-organization to dynamical critical states arose from the study of granular systems, and agent-based simulation introduced a process-based generalization of Monte Carlo methods. Current and future SFI research in physics occupies four main areas: statistical physics with emphases on self-organized states and non-conventional statistics; foundations of quantum mechanics and quantum information and control; network structure and dynamics with a wide variety of applications; and scaling in social and biological systems. Significant progress has been made in understanding phenomena as varied as criticality in rainfall, modularity in complex networks, and metabolic scaling with body mass. Future directions in the physics of complex systems include universality in dissipative systems, quantum simulation and the feedback control of decoherence, and the structure of optimal distribution networks. The wide-ranging sciences brought together at SFI utilize more than merely existing methods and models from physics. Many dynamical properties in chemical, biological and engineered systems present new paradigms for organization that will expand the conceptual scope of physics. 

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Robustness & Innovation

Thomas Malthus’s concern over the differential between the growth of populations and the growth of the resources to support them underlies both Charles Darwin’s theory of natural selection, and much of traditional economics. But Malthus was wrong, at least over the long term.  Contrary to the predictions of the logistic growth model of Pearl and Reed in 1920, the population of the US did not top out at 197 million and has just reached 300 million. Economists have extensively addressed the issue of creation of wealth, most recently through the development of endogenous growth theory, and a clear conclusion of this work is the pivotal role played by innovations in ideas, physical technology and social institutions. Similarly in natural systems, Malthus was undoubtedly correct over the short term, but over the long-term, evolutionary innovations have proven sufficient to steadily expand the planet’s carrying capacity. Innovation is consequently of substantial theoretical and practical concern. Research at SFI on innovation focuses on evolutionary processes in biological, technological, and market systems.

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