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Wright’s shifting balance theory and factors affecting the probability of peak shifts

The Adaptive Landscape in Evolutionary Biology, , 74-86, 2012


Status: Published

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Abstract: Perhaps the most controversial feature of Sewall Wright’s body of literature is his “shifting balance theory”(SBT) of evolution metaphor. It should not be surprising that the SBT was not developed beyond the level of a metaphor. It is intrinsically a “complex systems” model, which is a discipline that has only recently developed (Bar-Yam 1997). Prior to the advent of modern computers most models, including models of evolution, were necessarily purely analytic. The general approach was to use the “mean field” approximation, that is to assume that the specific environment an individual experiences is equal to the average environment that all individuals experience. Examples of the mean field assumptions in population genetics include assumptions of random mating, the assumption that populations are unstructured and that interactions are random, and for some applications, Fisher’s concept of average effects (but see Frank Chapter 3). The mean field approximation has led to stunning advances in a large number of fields, and indeed, the field of classical quantitative genetics is primarily developed using the mean field approximation. However, with the advent of modern computers the study of complex systems has developed as it became possible to explore models that were not analytically tractable. The field of complexity theory holds promise for developing the SBT from the attractive metaphor that Wright first described; however, before this can be done the details of the model need to be developed adequately for formal modeling. It is apparent that when Wright developed his SBT and his concept of adaptive topographies he developed it more to illustrate his ideas about the complexity of evolution rather than to develop a formal model. I will discuss some of the …



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Joshua Bongard - Department of Computer Science, Associate Professor

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.


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    Sam Kriegman, Nick Cheney, and Josh Bongard. How morphological development can guide evolution. arXiv 2017. [arXiv]


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Chris Danforth -Department of Mathematics and Statistics, Flint Professor of Mathematical, Natural, and Technical Sciences

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.

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    Peter Sheridan Dodds , Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth. Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter. PLoS ONE 2011. [Journal Page].
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Laurent Hébert-Dufresne - Assistant Professor, Computer Science

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.

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Paul Hines - School of Engineering, Associate Professor

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.

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James Bagrow - Assistant Professor, Department of Mathematics and Statistics

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).

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