The middle way

Laughlin, R. B.; Pines, David; Schmalian, Joerg; Stojković, Branko P.; Wolynes, Peter G.

doi:10.1073/pnas.97.1.32

Cited by 339 publications

(194 citation statements)

References 36 publications

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“…As stressed by Laughlin et al (2000) and Laughlin (2000), even when the underlying microscopic properties and relationships for a dynamic system are not fully understood, the hope would be to find higher-level organizational principles that reliably associate collections of related microscopic states to collections of related macroscopic states, thus allowing some form of quantitative analysis to proceed.…”

Section: Emergence In Economic Systemsmentioning

confidence: 99%

Co-learning patterns as emergent market phenomena: An electricity market illustration

Tesfatsion

2012

Journal of Economic Behavior & Organization

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The definition of emergence remains problematic, particularly for systems with purposeful human interactions. This study explores the practical import of this concept within a specific market context: namely, a double-auction market for wholesale electric power that operates over a transmission grid with spatially located buyers and sellers. Each profit-seeking seller is a learning agent that attempts to adjust its daily supply offers to its best advantage. The sellers are co-learners in the sense that their supply offer adjustments are in response to past market outcomes that reflect the past supply offer choices of all sellers. Attention is focused on the emergence of co-learning patterns, that is, global market patterns that arise and persist over time as a result of seller co-learning. Examples of co-learning patterns include correlated seller supply offer behaviors and correlated seller net earnings outcomes. Heat maps are used to display and interpret co-learning pattern findings. One key finding is that co-learning strongly matters in this auction market environment. Sellers that behave as Gode-Sunder budget-constrained zerointelligence agents, randomly selecting their supply offers subject only to a break-even constraint, tend to realize substantially lower net earnings than sellers that tacitly co-learn to correlate their supply offers for market power advantages.

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Section: Emergence In Economic Systemsmentioning

confidence: 99%

Co-learning patterns as emergent market phenomena: An electricity market illustration

Tesfatsion

2012

Journal of Economic Behavior & Organization

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“…The biochemically interesting emergent dynamics from a CME, thus, is not the deterministic differential equations, but rather a stochastic jump process within a set of discrete states defined by the deterministic attractors. This is distinctly a mesoscopic [24] driven system phenomenon: When the volume is too large, the time of escaping an attractor is practically infinite. Thus, the complex dynamics disappears.…”

Section: ∂P (Xt) ∂Tmentioning

confidence: 99%

Thermodynamic Limit of a Nonequilibrium Steady State: Maxwell-Type Construction for a Bistable Biochemical System

Qian

2009

Phys. Rev. Lett.

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“…An example of this problem is the self-organization of biosystems that evolve from basic molecules. This challenging subject is studied by using a variety of theoretical methods (2)(3)(4). The free-energy landscape model is nowadays the most used to describe such phenomena and especially the aging of the protein folding mechanism (1,5,6), i.e., the way in which proteins fold to their native state and then unfold (protein denaturation) (6,7).…”

mentioning

confidence: 99%

Energy landscape in protein folding and unfolding

Mallamace

Corsaro

Mallamace

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

102

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We use 1 H NMR to probe the energy landscape in the protein folding and unfolding process. Using the scheme ⇄ reversible unfolded (intermediate) → irreversible unfolded (denatured) state, we study the thermal denaturation of hydrated lysozyme that occurs when the temperature is increased. Using thermal cycles in the range 295 < T < 365 K and following different trajectories along the protein energy surface, we observe that the hydrophilic (the amide NH) and hydrophobic (methyl CH 3 and methine CH) peptide groups evolve and exhibit different behaviors. We also discuss the role of water and hydrogen bonding in the protein configurational stability.protein folding | proton NMR | energy landscape | hydration water A n intriguing problem of statistical physics concerns the evolutionary pathways that molecular systems follow as they form mesoscale structures and exhibit new functional behaviors (1). An example of this problem is the self-organization of biosystems that evolve from basic molecules. This challenging subject is studied by using a variety of theoretical methods (2-4). The free-energy landscape model is nowadays the most used to describe such phenomena and especially the aging of the protein folding mechanism (1, 5, 6), i.e., the way in which proteins fold to their native state and then unfold (protein denaturation) (6, 7). The model is based on the idea that in complex materials and systems there are many thermodynamical configurations in which the free-energy surface exhibits a number of local minima separated by barriers, i.e., as the system explores its phase space the trajectory of its evolution is an alternating sequence of local energy minima and saddle points (transition states), which are associated with the positions of all of the system particles. A trajectory thus specifies the path of the system as it evolves by moving across its energy landscape.A peptide is a linear chain of amino acids, and globular proteins are polypeptide chains that fold into their native conformation. During the folding process a polypeptide undergoes many conformational changes and there is a significant decrease in the system configurational entropy as the native state is approached. To understand folding we focus on how proteins search conformational space. The process is accompanied by many microscopic reactions, the nature of which is determined by the specifics of the energy surface. Thus, the characteristics of the energy surface of a polypeptide chain are the key to a quantitative understanding of folding. Although the degrees of freedom of a polypeptide chain allow an enormously large number of possible configurations, "constraints" on the energy decrease these configurations visited in the folding reaction to a limited number (8). Understanding the free-energy surface ("landscape") enables us to understand the folding process. A balance between the potential energy and the configurational entropy leads to a freeenergy barrier that generates the two-state folding behavior usually observed in small proteins. The...

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The middle way

Cited by 339 publications

References 36 publications

Co-learning patterns as emergent market phenomena: An electricity market illustration

Co-learning patterns as emergent market phenomena: An electricity market illustration

Thermodynamic Limit of a Nonequilibrium Steady State: Maxwell-Type Construction for a Bistable Biochemical System

Energy landscape in protein folding and unfolding

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