99 research outputs found
Biology helps you to win a game
We present a game of interacting agents which mimics the complex dynamics
found in many natural and social systems. These agents modify their strategies
periodically, depending on their performances using genetic crossover
mechanisms, inspired by biology. We study the performances of the agents under
different conditions, and how they adapt themselves. In addition the dynamics
of the game is investigated.Comment: 4 pages including 6 figures. Uses REVTeX4. Submitted for Conference
Proceedings of the "Unconventional Applications of Statistical Physics",
Kolkat
Searching good strategies in adaptive minority games
In this paper we introduce adaptation mechanism based on genetic algorithms
in minority games. If agents find their performances too low, they modify their
strategies in hope to improve their performances and become more successful.
One aim of this study is to find out what happens at the system as well as at
the individual agent level. We observe that adaptation remarkably tightens the
competition among the agents, and tries to pull the collective system into a
state where the aggregate utility is the largest. We first make a brief
comparative study of the different adaptation mechanisms and then present in
more detail parametric studies. These different adaptation mechanisms broaden
the scope of the applications of minority games to the study of complex
systems.Comment: 8 pages including 9 figures. Uses REVTeX
Human behavior in Prisoner's Dilemma experiments suppresses network reciprocity
During the last few years, much research has been devoted to strategic
interactions on complex networks. In this context, the Prisoner's Dilemma has
become a paradigmatic model, and it has been established that imitative
evolutionary dynamics lead to very different outcomes depending on the details
of the network. We here report that when one takes into account the real
behavior of people observed in the experiments, both at the mean-field level
and on utterly different networks the observed level of cooperation is the
same. We thus show that when human subjects interact in an heterogeneous mix
including cooperators, defectors and moody conditional cooperators, the
structure of the population does not promote or inhibit cooperation with
respect to a well mixed population.Comment: 5 Pages including 4 figures. Submitted for publicatio
If players are sparse social dilemmas are too: Importance of percolation for evolution of cooperation
Spatial reciprocity is a well known tour de force of cooperation promotion. A
thorough understanding of the effects of different population densities is
therefore crucial. Here we study the evolution of cooperation in social
dilemmas on different interaction graphs with a certain fraction of vacant
nodes. We find that sparsity may favor the resolution of social dilemmas,
especially if the population density is close to the percolation threshold of
the underlying graph. Regardless of the type of the governing social dilemma as
well as particularities of the interaction graph, we show that under pairwise
imitation the percolation threshold is a universal indicator of how dense the
occupancy ought to be for cooperation to be optimally promoted. We also
demonstrate that myopic updating, due to the lack of efficient spread of
information via imitation, renders the reported mechanism dysfunctional, which
in turn further strengthens its foundations.Comment: 6 two-column pages, 5 figures; accepted for publication in Scientific
Reports [related work available at http://arxiv.org/abs/1205.0541
Altered Metabolic Signature in Pre-Diabetic NOD Mice
Altered metabolism proceeding seroconversion in children progressing to Type 1 diabetes has previously been demonstrated. We tested the hypothesis that non-obese diabetic (NOD) mice show a similarly altered metabolic profile compared to C57BL/6 mice. Blood samples from NOD and C57BL/6 female mice was collected at 0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13 and 15 weeks and the metabolite content was analyzed using GC-MS. Based on the data of 89 identified metabolites OPLS-DA analysis was employed to determine the most discriminative metabolites. In silico analysis of potential involved metabolic enzymes was performed using the dbSNP data base. Already at 0 weeks NOD mice displayed a unique metabolic signature compared to C57BL/6. A shift in the metabolism was observed for both strains the first weeks of life, a pattern that stabilized after 5 weeks of age. Multivariate analysis revealed the most discriminative metabolites, which included inosine and glutamic acid. In silico analysis of the genes in the involved metabolic pathways revealed several SNPs in either regulatory or coding regions, some in previously defined insulin dependent diabetes (Idd) regions. Our result shows that NOD mice display an altered metabolic profile that is partly resembling the previously observation made in children progressing to Type 1 diabetes. The level of glutamic acid was one of the most discriminative metabolites in addition to several metabolites in the TCA cycle and nucleic acid components. The in silico analysis indicated that the genes responsible for this reside within previously defined Idd regions
Normalization in MALDI-TOF imaging datasets of proteins: practical considerations
Normalization is critically important for the proper interpretation of matrix-assisted laser desorption/ionization (MALDI) imaging datasets. The effects of the commonly used normalization techniques based on total ion count (TIC) or vector norm normalization are significant, and they are frequently beneficial. In certain cases, however, these normalization algorithms may produce misleading results and possibly lead to wrong conclusions, e.g. regarding to potential biomarker distributions. This is typical for tissues in which signals of prominent abundance are present in confined areas, such as insulin in the pancreas or β-amyloid peptides in the brain. In this work, we investigated whether normalization can be improved if dominant signals are excluded from the calculation. Because manual interaction with the data (e.g., defining the abundant signals) is not desired for routine analysis, we investigated two alternatives: normalization on the spectra noise level or on the median of signal intensities in the spectrum. Normalization on the median and the noise level was found to be significantly more robust against artifact generation compared to normalization on the TIC. Therefore, we propose to include these normalization methods in the standard “toolbox” of MALDI imaging for reliable results under conditions of automation
Influence of opinion dynamics on the evolution of games
Under certain circumstances such as lack of information or bounded
rationality, human players can take decisions on which strategy to choose in a
game on the basis of simple opinions. These opinions can be modified after each
round by observing own or others payoff results but can be also modified after
interchanging impressions with other players. In this way, the update of the
strategies can become a question that goes beyond simple evolutionary rules
based on fitness and become a social issue. In this work, we explore this
scenario by coupling a game with an opinion dynamics model. The opinion is
represented by a continuous variable that corresponds to the certainty of the
agents respect to which strategy is best. The opinions transform into actions
by making the selection of an strategy a stochastic event with a probability
regulated by the opinion. A certain regard for the previous round payoff is
included but the main update rules of the opinion are given by a model inspired
in social interchanges. We find that the dynamics fixed points of the coupled
model is different from those of the evolutionary game or the opinion models
alone. Furthermore, new features emerge such as the resilience of the fraction
of cooperators to the topology of the social interaction network or to the
presence of a small fraction of extremist players.Comment: 7 pages, 5 figure
Mesoscopic effects in an agent-based bargaining model in regular lattices
The effect of spatial structure has been proved very relevant in repeated games. In this work we propose an agent based
model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The
model extends the multiagent bargaining model by Axtell, Epstein and Young [1] modifying the assumption of global
interaction. Each agent is endowed with a memory and plays the best reply against the opponent’s most frequent demand.
We focus our analysis on the transient dynamics of the system, studying by computer simulation the set of states in which
the system spends a considerable fraction of the time. The results show that all the possible persistent regimes in the global
interaction model can also be observed in this spatial version. We also find that the mesoscopic properties of the interaction
networks that the spatial distribution induces in the model have a significant impact on the diffusion of strategies, and can
lead to new persistent regimes different from those found in previous research. In particular, community structure in the
intratype interaction networks may cause that communities reach different persistent regimes as a consequence of the
hindering diffusion effect of fluctuating agents at their borders.Spanish Ministry of Science and Innovation, references TIN2008-06464-C03-02 and CSD2010-00034 (CONSOLIDER-INGENIO 2010), and by the Junta de Castilla y Leon, references VA006A009, BU034A08 and GREX251-200
The dental calculus metabolome in modern and historic samples.
INTRODUCTION: Dental calculus is a mineralized microbial dental plaque biofilm that forms throughout life by precipitation of salivary calcium salts. Successive cycles of dental plaque growth and calcification make it an unusually well-preserved, long-term record of host-microbial interaction in the archaeological record. Recent studies have confirmed the survival of authentic ancient DNA and proteins within historic and prehistoric dental calculus, making it a promising substrate for investigating oral microbiome evolution via direct measurement and comparison of modern and ancient specimens. OBJECTIVE: We present the first comprehensive characterization of the human dental calculus metabolome using a multi-platform approach. METHODS: Ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) quantified 285 metabolites in modern and historic (200 years old) dental calculus, including metabolites of drug and dietary origin. A subset of historic samples was additionally analyzed by high-resolution gas chromatography-MS (GC-MS) and UPLC-MS/MS for further characterization of metabolites and lipids. Metabolite profiles of modern and historic calculus were compared to identify patterns of persistence and loss. RESULTS: Dipeptides, free amino acids, free nucleotides, and carbohydrates substantially decrease in abundance and ubiquity in archaeological samples, with some exceptions. Lipids generally persist, and saturated and mono-unsaturated medium and long chain fatty acids appear to be well-preserved, while metabolic derivatives related to oxidation and chemical degradation are found at higher levels in archaeological dental calculus than fresh samples. CONCLUSIONS: The results of this study indicate that certain metabolite classes have higher potential for recovery over long time scales and may serve as appropriate targets for oral microbiome evolutionary studies
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