Public Values Leadership

Instead of private gain or corporate profits, what if we set public values as the goal of leadership?Leadership means many things and takes many forms. But most studies of the topic give little attention to why people lead or to where they are leading us. In Public Values Leadership, Barry Bozeman and Michael M. Crow explore leadership that serves public values—that is to say, values that are focused on the collective good and fundamental rights rather than profit, organizational benefit, or personal gain. While nearly everyone agrees on core public values, there is less agreement on how to obtain them, especially during this era of increased social and political fragmentation. How does public values leadership differ from other types of organizational leadership, and what distinctive skills does it require? Drawing on their extensive experience as higher education leaders, Bozeman and Crow wrestle with the question of how to best attain universally agreed-upon public values like freedom, opportunity, health, and security. They present conversations and interviews with ten well-known leaders—people who have achieved public values objectives and who are willing to discuss their leadership styles in detail. They also offer a series of in-depth case studies of public values leadership and accomplishment. Public values leadership can only succeed if it includes a commitment to pragmatism, a deep skepticism about government versus market stereotypes, and a genuine belief in the fundamental importance of partnerships and alliances. Arguing for a "mutable leadership," they suggest that different people are leaders at different times and that ideas about natural leaders or all-purpose leaders are off the mark. Motivating readers, including students of public policy administration and practitioners in public and nonprofit organizations, to think systematically about their own values and how these can be translated into effective leadership, Public Values Leadership is highly personal and persuasive

Power Transmission Control using Distributed Max-Flow

Existing maximum flow algorithms use one processor for all calculations or one processor per vertex in a graph to calculate the maximum possible flow through a graph\u27s vertices. This is not suitable for practical implementation. We extend the max-flow work of Goldberg and Tarjan to a distributed algorithm to calculate maximum flow where the number of processors is less than the number of vertices in a graph. Our algorithm is applied to maximizing electrical flow within a power network where the power grid is modeled as a graph. Error detection measures are included to detect problems in a simulated power network. We show that our algorithm is successful in executing quickly enough to prevent catastrophic power outages

Metastability and anomalous fixation in evolutionary games on scale-free networks

We study the influence of complex graphs on the metastability and fixation properties of a set of evolutionary processes. In the framework of evolutionary game theory, where the fitness and selection are frequency-dependent and vary with the population composition, we analyze the dynamics of snowdrift games (characterized by a metastable coexistence state) on scale-free networks. Using an effective diffusion theory in the weak selection limit, we demonstrate how the scale-free structure affects the system's metastable state and leads to anomalous fixation. In particular, we analytically and numerically show that the probability and mean time of fixation are characterized by stretched exponential behaviors with exponents depending on the network's degree distribution.Comment: 5 pages, 4 figures, to appear in Physical Review Letter

Adaptive evolution of molecular phenotypes

Molecular phenotypes link genomic information with organismic functions, fitness, and evolution. Quantitative traits are complex phenotypes that depend on multiple genomic loci. In this paper, we study the adaptive evolution of a quantitative trait under time-dependent selection, which arises from environmental changes or through fitness interactions with other co-evolving phenotypes. We analyze a model of trait evolution under mutations and genetic drift in a single-peak fitness seascape. The fitness peak performs a constrained random walk in the trait amplitude, which determines the time-dependent trait optimum in a given population. We derive analytical expressions for the distribution of the time-dependent trait divergence between populations and of the trait diversity within populations. Based on this solution, we develop a method to infer adaptive evolution of quantitative traits. Specifically, we show that the ratio of the average trait divergence and the diversity is a universal function of evolutionary time, which predicts the stabilizing strength and the driving rate of the fitness seascape. From an information-theoretic point of view, this function measures the macro-evolutionary entropy in a population ensemble, which determines the predictability of the evolutionary process. Our solution also quantifies two key characteristics of adapting populations: the cumulative fitness flux, which measures the total amount of adaptation, and the adaptive load, which is the fitness cost due to a population's lag behind the fitness peak.Comment: Figures are not optimally displayed in Firefo

Corrigendum: Collective search by ants in microgravity

The problem of collective search is a tradeoff between searching thoroughly and covering as much area as possible. This tradeoff depends on the density of searchers. Solutions to the problem of collective search are currently of much interest in robotics and in the study of distributed algorithms, for example to design ways that without central control robots can use local information to perform search and rescue operations. Ant colonies operate without central control. Because they can perceive only local, mostly chemical and tactile cues, they must search collectively to find resources and to monitor the colony's environment. Examining how ants in diverse environments solve the problem of collective search can elucidate how evolution has led to diverse forms of collective behavior. An experiment on the International Space Station in January 2014 examined how ants (Tetramorium caespitum) perform collective search in microgravity. In the ISS experiment, the ants explored a small arena in which a barrier was lowered to increase the area and thus lower ant density. In microgravity, relative to ground controls, ants explored the area less thoroughly and took more convoluted paths. It appears that the difficulty of holding on to the surface interfered with the ants’ ability to search collectively. Ants frequently lost contact with the surface, but showed a remarkable ability to regain contact with the surface

Schwinger Boson Formulation and Solution of the Crow-Kimura and Eigen Models of Quasispecies Theory

We express the Crow-Kimura and Eigen models of quasispecies theory in a functional integral representation. We formulate the spin coherent state functional integrals using the Schwinger Boson method. In this formulation, we are able to deduce the long-time behavior of these models for arbitrary replication and degradation functions. We discuss the phase transitions that occur in these models as a function of mutation rate. We derive for these models the leading order corrections to the infinite genome length limit.Comment: 37 pages; 4 figures; to appear in J. Stat. Phy

Developing patient-friendly genetic and genomic test reports: formats to promote patient engagement and understanding

10.1186/s13073-014-0058-6Genome Medicine675

Finite population size effects in quasispecies models with single-peak fitness landscape

We consider finite population size effects for Crow-Kimura and Eigen quasispecies models with single-peak fitness landscape. We formulate accurately the iteration procedure for the finite population models, then derive the Hamilton-Jacobi equation (HJE) to describe the dynamic of the probability distribution. The steady-state solution of HJE gives the variance of the mean fitness. Our results are useful for understanding the population sizes of viruses in which the infinite population models can give reliable results for biological evolution problems

Large Fluctuations and Fixation in Evolutionary Games

We study large fluctuations in evolutionary games belonging to the coordination and anti-coordination classes. The dynamics of these games, modeling cooperation dilemmas, is characterized by a coexistence fixed point separating two absorbing states. We are particularly interested in the problem of fixation that refers to the possibility that a few mutants take over the entire population. Here, the fixation phenomenon is induced by large fluctuations and is investigated by a semi-classical WKB (Wentzel-Kramers-Brillouin) theory generalized to treat stochastic systems possessing multiple absorbing states. Importantly, this method allows us to analyze the combined influence of selection and random fluctuations on the evolutionary dynamics \textit{beyond} the weak selection limit often considered in previous works. We accurately compute, including pre-exponential factors, the probability distribution function in the long-lived coexistence state and the mean fixation time necessary for a few mutants to take over the entire population in anti-coordination games, and also the fixation probability in the coordination class. Our analytical results compare excellently with extensive numerical simulations. Furthermore, we demonstrate that our treatment is superior to the Fokker-Planck approximation when the selection intensity is finite.Comment: 17 pages, 10 figures, to appear in JSTA