732 research outputs found
Objective Measurement of Sleep by Smartphone Application: Comparison with Actigraphy and Relation to Cognition, Mood, and Self-Reported Sleep
Over the past six decades, polysomnography, actigraphy, and most recently smartphone technology have created a trifecta of options for measuring sleep. It remains to be seen whether smartphone applications are comparable to actigraphy in objectively monitoring sleep. The present study had 29 healthy adult participants fill out a sleep diary and use the Sleep Time app (Azumio, Inc.) to monitor their sleep for one week. A subset of 19 participants also wore an actigraphy bracelet. Self-report questionnaires characterized sleep habits and psychological profiles of participants, while cognitive assessments were implemented to examine potential correlations between total sleep time (TST) and/or sleep efficiency and executive functioning. The smartphone app overestimated TST when compared to actigraphy, yielding a significant difference, t(18) = -6.64, p = .01, r2 = .71. Moreover, a statistical trend indicated that the app also overestimated sleep efficiency, t(18) = -2.06, p = .06, r2 = .12 There were no correlations between self-reported sleep quality and performance on cognitive tasks or total number of caffeinated beverages consumed in this sample. Overall, results show that this smartphone app is not accurate in monitoring TST or sleep efficiency when compared to actigraphy. Future research is needed to investigate the utility of smartphone applications in monitoring sleep in clinical populations and across other smartphone apps and phone models
Altruism can proliferate through group/kin selection despite high random gene flow
The ways in which natural selection can allow the proliferation of
cooperative behavior have long been seen as a central problem in evolutionary
biology. Most of the literature has focused on interactions between pairs of
individuals and on linear public goods games. This emphasis led to the
conclusion that even modest levels of migration would pose a serious problem to
the spread of altruism in group structured populations. Here we challenge this
conclusion, by analyzing evolution in a framework which allows for complex
group interactions and random migration among groups. We conclude that
contingent forms of strong altruism can spread when rare under realistic group
sizes and levels of migration. Our analysis combines group-centric and
gene-centric perspectives, allows for arbitrary strength of selection, and
leads to extensions of Hamilton's rule for the spread of altruistic alleles,
applicable under broad conditions.Comment: 5 pages, 2 figures. Supplementary material with 50 pages and 26
figure
Evolutionary Games with Affine Fitness Functions: Applications to Cancer
We analyze the dynamics of evolutionary games in which fitness is defined as
an affine function of the expected payoff and a constant contribution. The
resulting inhomogeneous replicator equation has an homogeneous equivalent with
modified payoffs. The affine terms also influence the stochastic dynamics of a
two-strategy Moran model of a finite population. We then apply the affine
fitness function in a model for tumor-normal cell interactions to determine
which are the most successful tumor strategies. In order to analyze the
dynamics of concurrent strategies within a tumor population, we extend the
model to a three-strategy game involving distinct tumor cell types as well as
normal cells. In this model, interaction with normal cells, in combination with
an increased constant fitness, is the most effective way of establishing a
population of tumor cells in normal tissue.Comment: The final publication is available at http://www.springerlink.com,
http://dx.doi.org/10.1007/s13235-011-0029-
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Biology and evolutionary games
This chapter surveys some evolutionary games used in biological sciences. These include the Hawk-Dove game, the Prisoner’s Dilemma, Rock–Paper–Scissors, the war of attrition, the Habitat Selection game, predatorprey games, and signalling games
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Asymmetric Games in Monomorphic and Polymorphic Populations
Evolutionary game theory is an increasingly important way to model the evolution of biological populations. Many early models were in the form of matrix games, or bi-matrix games in asymmetric situations when individuals occupy distinct roles within the contest, where rewards are accrued through independent contests against random members of the population. More recent models have not had the simple linear properties of matrix games, and more general analysis has been required. In this paper we carry out a general analysis of asymmetric games, comparing monomorphic and polymorphic populations. We are particularly interested in situations where the strategies that individuals play influence which role that they occupy, for example in a more realistic variant of the classical Owner-Intruder game. We both prove general results and consider specific examples to illustrate the difficulties of these more complex games
Change and Aging Senescence as an adaptation
Understanding why we age is a long-lived open problem in evolutionary
biology. Aging is prejudicial to the individual and evolutionary forces should
prevent it, but many species show signs of senescence as individuals age. Here,
I will propose a model for aging based on assumptions that are compatible with
evolutionary theory: i) competition is between individuals; ii) there is some
degree of locality, so quite often competition will between parents and their
progeny; iii) optimal conditions are not stationary, mutation helps each
species to keep competitive. When conditions change, a senescent species can
drive immortal competitors to extinction. This counter-intuitive result arises
from the pruning caused by the death of elder individuals. When there is change
and mutation, each generation is slightly better adapted to the new conditions,
but some older individuals survive by random chance. Senescence can eliminate
those from the genetic pool. Even though individual selection forces always win
over group selection ones, it is not exactly the individual that is selected,
but its lineage. While senescence damages the individuals and has an
evolutionary cost, it has a benefit of its own. It allows each lineage to adapt
faster to changing conditions. We age because the world changes.Comment: 19 pages, 4 figure
Population Dynamics Constrain the Cooperative Evolution of Cross-Feeding
Cross-feeding is the exchange of nutrients among species of microbes. It has two
potential evolutionary origins, one as an exchange of metabolic wastes or
byproducts among species, the other as a form of cooperation known as reciprocal
altruism. This paper explores the conditions favoring the origin of cooperative
cross-feeding between two species. There is an extensive literature on the
evolution of cooperation, and some of the requirements for the evolution of
cooperative cross-feeding follow from this prior work–specifically the
requirement that interactions be limited to small groups of individuals, such as
colonies in a spatially structured environment. Evolution of cooperative
cross-feeding by a species also requires that cross-feeding from the partner
species already exists, so that the cooperating mutant will automatically be
reciprocated for its actions. Beyond these considerations, some unintuitive
dynamical constraints apply. In particular, the benefit of cooperative
cross-feeding applies only in the range of intermediate cell densities. At low
density, resource concentrations are too low to offset the cost of cooperation.
At high density, resources shared by both species become limiting, and the two
species become competitors. These considerations suggest that the evolution of
cooperative cross-feeding in nature may be more challenging than for other types
of cooperation. However, the principles identified here may enable the
experimental evolution of cross-feeding, as born out by a recent study
Incipient Cognition Solves the Spatial Reciprocity Conundrum of Cooperation
Background: From the simplest living organisms to human societies, cooperation among individuals emerges as a paradox difficult to explain and describe mathematically, although very often observed in reality. Evolutionary game theory offers an excellent toolbar to investigate this issue. Spatial structure has been one of the first mechanisms promoting cooperation; however, alone it only opens a narrow window of viability. Methodology/Principal Findings: Here we equip individuals with incipient cognitive abilities, and investigate the evolution of cooperation in a spatial world where retaliation, forgiveness, treason and mutualism may coexist, as individuals engage in Prisoner’s Dilemma games. In the model, individuals are able to distinguish their partners and act towards them based on previous interactions. We show how the simplest level of cognition, alone, can lead to the emergence of cooperation. Conclusions/Significance: Despite the incipient nature of the individuals ’ cognitive abilities, cooperation emerges for unprecedented values of the temptation to cheat, being also robust to invasion by cheaters, errors in decision making an
Evolutionary Games
International audienceEvolutionary games constitute the most recent major mathematical tool for understanding, modelling and predicting evolution in biology and other fields. They complement other well establlished tools such as branching processes and the Lotka-Volterra [6] equations (e.g. for the predator - prey dynamics or for epidemics evolution). Evolutionary Games also brings novel features to game theory. First, it focuses on the dynam- ics of competition rather than restricting attention to the equilibrium. In particular, it tries to explain how an equilibrium emerges. Second, it brings new de nitions of stability, that are more adapted to the context of large populations. Finally, in contrast to standard game theory, players are not assumed to be \rational" or \knowledgeable" as to anticipate the other players' choices. The objective of this article, is to present founda- tions as well as recent advances in evolutionary games, highlight the novel concepts that they introduce with respect to game theory as formulated by John Nash, and describe through several examples their huge potential as tools for modeling interactions in complex systems
Calculating Evolutionary Dynamics in Structured Populations
Evolution is shaping the world around us. At the core of every evolutionary process is a population of reproducing individuals. The outcome of an evolutionary process depends on population structure. Here we provide a general formula for calculating evolutionary dynamics in a wide class of structured populations. This class includes the recently introduced “games in phenotype space” and “evolutionary set theory.” There can be local interactions for determining the relative fitness of individuals, but we require global updating, which means all individuals compete uniformly for reproduction. We study the competition of two strategies in the context of an evolutionary game and determine which strategy is favored in the limit of weak selection. We derive an intuitive formula for the structure coefficient, σ, and provide a method for efficient numerical calculation
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