SIMEX variance component tests in generalized linear mixed measurement error models

In the analysis of clustered data with covariates measured with error, a problem of common interest is to test for correlation within clusters and heterogeneity across clusters. We examined this problem in the framework of generalized linear mixed measurement error models. We propose using the simulation extrapolation (SIMEX) method to construct a score test for the null hypothesis that all variance components are zero. A key feature of this SIMEX score test is that no assumptions need to be made regarding the distributions of the random effects and the unobserved covariates. We illustrate this test by analyzing Framingham heart disease data and evaluate its performance by simulation. We also propose individual SIMEX score tests for testing the variance components separately. Both tests can be easily implemented using existing statistical software.     

Simulating Irrational Human Behavior to Prevent Resource Depletion

In a situation with a limited common resource, cooperation between individuals sharing the resource is essential. However, people often act upon self-interest in irrational ways that threaten the long-term survival of the whole group. A lack of sustainable or environmentally responsible behavior is often observed. In this study, we examine how the maximization of benefits principle works in a wider social interactive context of personality preferences in order to gain a more realistic insight into the evolution of cooperation. We used time perspective (TP), a concept reflecting individual differences in orientation towards past, present, or future, and relevant for making sustainable choices. We developed a personality-driven agent-based model that explores the role of personality in the outcomes of social dilemmas and includes multiple facets of diversity: (1) The agents have different behavior strategies: individual differences derived by applying cluster analysis to survey data from 22 countries (N = 10,940) and resulting in 7 cross-cultural profiles of TP; (2) The non-uniform distribution of the types of agents across countries; (3) The diverse interactions between the agents; and (4) diverse responses to those interactions in a well-mixed population. As one of the results, we introduced an index of overall cooperation for each of the 22 countries, which was validated against cultural, economic, and sustainability indicators (HDI, dimensions of national culture, and Environment Performance Index). It was associated with higher human development, higher individualism, lower power distance, and better environmental performance. The findings illustrate how individual differences in TP can be simulated to predict the ways people in different countries solve the personal vs. common gain dilemma in the global limited-resource situation. This interdisciplinary approach to social simulation can be adopted to explain the possible causes of global environmental issues and to predict their possible outcomes.     

Imitative Learning as a Connector of Collective Brains

The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation – imitative learning – that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent – the best performing agent in its influence network. There is a trade-off between the number of agents and the imitation probability , and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group.     

When Punishment Pays

Explaining cooperation in groups remains a key problem because reciprocity breaks down between more than two. Punishing individuals who contribute little provides a potential answer but changes the dilemma to why pay the costs of punishing which, like cooperation itself, provides a public good. Nevertheless, people are observed to punish others in behavioural economic games, posing a problem for existing theory which highlights the difficulty in explaining the spread and persistence of punishment. Here, I consider the apparent mismatch between theory and evidence and show by means of instructive analysis and simulation how much of the experimental evidence for punishment comes from scenarios in which punishers may expect to obtain a net benefit from punishing free-riders. In repeated games within groups, punishment works by imposing costs on defectors so that it pays them to switch to cooperating. Both punishers and non-punishers then benefit from the resulting increase in cooperation, hence investing in punishment can constitute a social dilemma. However, I show the conditions in which the benefits of increased cooperation are so great that they more than offset the costs of punishing, thereby removing the temptation to free-ride on others'' investments and making punishment explicable in terms of direct self-interest. Crucially, this is because of the leveraging effect imposed in typical studies whereby people can pay a small cost to inflict a heavy loss on a punished individual. In contrast to previous models suggesting punishment is disadvantaged when rare, I show it can invade until it comes into a producer-scrounger equilibrium with non-punishers. I conclude that adding punishment to an iterated public goods game can solve the problem of achieving cooperation by removing the social dilemma.     

Evolution of cooperation driven by reputation-based migration

How cooperation emerges and is stabilized has been a puzzling problem to biologists and sociologists since Darwin. One of the possible answers to this problem lies in the mobility patterns. These mobility patterns in previous works are either random-like or driven by payoff-related properties such as fitness, aspiration, or expectation. Here we address another force which drives us to move from place to place: reputation. To this end, we propose a reputation-based model to explore the effect of migration on cooperation in the contest of the prisoner's dilemma. In this model, individuals earn their reputation scores through previous cooperative behaviors. An individual tends to migrate to a new place if he has a neighborhood of low reputation. We show that cooperation is promoted for relatively large population density and not very large temptation to defect. A higher mobility sensitivity to reputation is always better for cooperation. A longer reputation memory favors cooperation, provided that the corresponding mobility sensitivity to reputation is strong enough. The microscopic perception of the effect of this mechanism is also given. Our results may shed some light on the role played by migration in the emergence and persistence of cooperation.     

Strict or Graduated Punishment? Effect of Punishment Strictness on the Evolution of Cooperation in Continuous Public Goods Games

Whether costly punishment encourages cooperation is one of the principal questions in studies on the evolution of cooperation and social sciences. In society, punishment helps deter people from flouting rules in institutions. Specifically, graduated punishment is a design principle for long-enduring common-pool resource institutions. In this study, we investigate whether graduated punishment can promote a higher cooperation level when each individual plays the public goods game and has the opportunity to punish others whose cooperation levels fall below the punisher’s threshold. We then examine how spatial structure affects evolutionary dynamics when each individual dies inversely proportional to the game score resulting from the social interaction and another player is randomly chosen from the population to produce offspring to fill the empty site created after a player’s death. Our evolutionary simulation outcomes demonstrate that stricter punishment promotes increased cooperation more than graduated punishment in a spatially structured population, whereas graduated punishment increases cooperation more than strict punishment when players interact with randomly chosen opponents from the population. The mathematical analysis also supports the results.     

Experimental evolution of multicellularity using microbial pseudo-organisms

In a major evolutionary transition to a new level of organization, internal conflicts must be controlled before the transition can truly be successful. One such transition is that from single cells to multicellularity. Conflicts among cells in multicellular organisms can be greatly reduced if they consist of genetically identical clones. However, mutations to cheaters that experience one round of within-individual selection could still be a problem, particularly for certain life cycles. We propose an experimental evolution method to investigate this issue, using micro-organisms to construct multicellular pseudo-organisms, which can be evolved under different artificial life cycles. These experiments can be used to test the importance of various life cycle features in maintaining cooperation. They include structured reproduction, in which small propagule size reduces within-individual genetic variation. They also include structured growth, which increases local relatedness within individual bodies. Our method provides a novel way to test how different life cycles favour cooperation, even for life cycles that do not exist.     

Managing performance and power consumption tradeoff for multiple heterogeneous servers in cloud computing

There are typically multiple heterogeneous servers providing various services in cloud computing. High power consumption of these servers increases the cost of running a data center. Thus, there is a problem of reducing the power cost with tolerable performance degradation. In this paper, we optimize the performance and power consumption tradeoff for multiple heterogeneous servers. We consider the following problems: (1) optimal job scheduling with fixed service rates; (2) joint optimal service speed scaling and job scheduling. For problem (1), we present the Karush-Kuhn-Tucker (KKT) conditions and provide a closed-form solution. For problem (2), both continuous speed scaling and discrete speed scaling are considered. In discrete speed scaling, the feasible service rates are discrete and bounded. We formulate the problem as an MINLP problem and propose a distributed algorithm by online value iteration, which has lower complexity than a centralized algorithm. Our approach provides an analytical way to manage the tradeoff between performance and power consumption. The simulation results show the gain of using speed scaling, and also prove the effectiveness and efficiency of the proposed algorithms.     

Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models

Predictive performance is important to many applications of species distribution models (SDMs). The SDM ‘ensemble’ approach, which combines predictions across different modelling methods, is believed to improve predictive performance, and is used in many recent SDM studies. Here, we aim to compare the predictive performance of ensemble species distribution models to that of individual models, using a large presence–absence dataset of eucalypt tree species. To test model performance, we divided our dataset into calibration and evaluation folds using two spatial blocking strategies (checkerboard-pattern and latitudinal slicing). We calibrated and cross-validated all models within the calibration folds, using both repeated random division of data (a common approach) and spatial blocking. Ensembles were built using the software package ‘biomod2’, with standard (‘untuned’) settings. Boosted regression tree (BRT) models were also fitted to the same data, tuned according to published procedures. We then used evaluation folds to compare ensembles against both their component untuned individual models, and against the BRTs. We used area under the receiver-operating characteristic curve (AUC) and log-likelihood for assessing model performance. In all our tests, ensemble models performed well, but not consistently better than their component untuned individual models or tuned BRTs across all tests. Moreover, choosing untuned individual models with best cross-validation performance also yielded good external performance, with blocked cross-validation proving better suited for this choice, in this study, than repeated random cross-validation. The latitudinal slice test was only possible for four species; this showed some individual models, and particularly the tuned one, performing better than ensembles. This study shows no particular benefit to using ensembles over individual tuned models. It also suggests that further robust testing of performance is required for situations where models are used to predict to distant places or environments.     

Combining multiple markers for classification using ROC

In biomedical studies, it is of great interest to develop methodologies for combining multiple markers for the purpose of disease classification. The receiving operating characteristic (ROC) technique has been widely used, where classification performance can be measured with the area under the ROC curve (AUC). In this article, we study a ROC-based method for effectively combining multiple markers for disease classification. We propose a sigmoid AUC (SAUC) estimator that maximizes the sigmoid approximation of the empirical AUC. The SAUC estimator is computationally affordable, n(1/2)-consistent and achieves the same asymptotic efficiency as the AUC estimator. Inference based on the weighted bootstrap is investigated. We also propose Monte Carlo methods to assess the overall prediction performance and the relative importance of individual markers. Finite sample performance is evaluated using simulation studies and two public data sets.     

Social semantics: altruism, cooperation, mutualism, strong reciprocity and group selection

From an evolutionary perspective, social behaviours are those which have fitness consequences for both the individual that performs the behaviour, and another individual. Over the last 43 years, a huge theoretical and empirical literature has developed on this topic. However, progress is often hindered by poor communication between scientists, with different people using the same term to mean different things, or different terms to mean the same thing. This can obscure what is biologically important, and what is not. The potential for such semantic confusion is greatest with interdisciplinary research. Our aim here is to address issues of semantic confusion that have arisen with research on the problem of cooperation. In particular, we: (i) discuss confusion over the terms kin selection, mutualism, mutual benefit, cooperation, altruism, reciprocal altruism, weak altruism, altruistic punishment, strong reciprocity, group selection and direct fitness; (ii) emphasize the need to distinguish between proximate (mechanism) and ultimate (survival value) explanations of behaviours. We draw examples from all areas, but especially recent work on humans and microbes.     

Variable investment, the Continuous Prisoner's Dilemma, and the origin of cooperation.

Cooperation is fundamental to many biological systems. A common metaphor for studying the evolution of cooperation is the Prisoner's Dilemma, a game with two strategies: cooperate or defect. However, cooperation is rare all or nothing, and its evolution probably involves the gradual extension of initially modest degrees of assistance. The inability of the Prisoner's Dilemma to capture this basic aspect limits its use for understanding the evolutionary origins of cooperation. Here we consider a framework for cooperation based on the concept of investment: an act which is costly, but which benefits other individuals, where the cost and benefit depend on the level of investment made. In the resulting Continuous Prisoner's Dilemma the essential problem of cooperation remains: in the absence of any additional structure non-zero levels of investment cannot evolve. However, if investments are considered in a spatially structured context, selfish individuals who make arbitrarily low investments can be invaded by higher-investing mutants. This results in the mean level of investment evolving to significant levels, where it is maintained indefinitely. This approach provides a natural solution to the fundamental problem of how cooperation gradually increases from a non-cooperative state.     

Gossip-Based Solutions for Discrete Rendezvous in Populations of Communicating Agents

The objective of the rendezvous problem is to construct a method that enables a population of agents to agree on a spatial (and possibly temporal) meeting location. We introduce the buffered gossip algorithm as a general solution to the rendezvous problem in a discrete domain with direct communication between decentralized agents. We compare the performance of the buffered gossip algorithm against the well known uniform gossip algorithm. We believe that a buffered solution is preferable to an unbuffered solution, such as the uniform gossip algorithm, because the use of a buffer allows an agent to use multiple information sources when determining its desired rendezvous point, and that access to multiple information sources may improve agent decision making by reinforcing or contradicting an initial choice. To show that the buffered gossip algorithm is an actual solution for the rendezvous problem, we construct a theoretical proof of convergence and derive the conditions under which the buffered gossip algorithm is guaranteed to produce a consensus on rendezvous location. We use these results to verify that the uniform gossip algorithm also solves the rendezvous problem. We then use a multi-agent simulation to conduct a series of simulation experiments to compare the performance between the buffered and uniform gossip algorithms. Our results suggest that the buffered gossip algorithm can solve the rendezvous problem faster than the uniform gossip algorithm; however, the relative performance between these two solutions depends on the specific constraints of the problem and the parameters of the buffered gossip algorithm.     

Will

[Specially designed] problems (or tasks) are now universally used to study and diagnose human intellectual capacities. The problem places the subject in a problem situation that is to be solved (and the assignment carried out) by thinking. Of course (as Dunker [2] once pointed out), this means that the problem situation (or task) must have at least some minimal intellectual content so far as the subject is concerned, a fact that must certainly be taken into account in planning tasks and analyzing experimental data, although we shall not here deal especially with this requirement. For our present purposes it is more important to point out that in experimental studies, problem tasks are assigned both singly and in sets. In the latter case, the relations among the individual tasks in an experimental series may be quite diverse. If these relations are such that the problem tasks constitute a certain class whose solution is governed by a common principle, which the subjects come to understand by successfully performing the individual tasks in a series, then this class of tasks (in the terminology used by I. Lenhart [3]) is a "multiple problem."     

The Optimal Solution of a Non-Convex State-Dependent LQR Problem and Its Applications

This paper studies a Non-convex State-dependent Linear Quadratic Regulator (NSLQR) problem, in which the control penalty weighting matrix in the performance index is state-dependent. A necessary and sufficient condition for the optimal solution is established with a rigorous proof by Euler-Lagrange Equation. It is found that the optimal solution of the NSLQR problem can be obtained by solving a Pseudo-Differential-Riccati-Equation (PDRE) simultaneously with the closed-loop system equation. A Comparison Theorem for the PDRE is given to facilitate solution methods for the PDRE. A linear time-variant system is employed as an example in simulation to verify the proposed optimal solution. As a non-trivial application, a goal pursuit process in psychology is modeled as a NSLQR problem and two typical goal pursuit behaviors found in human and animals are reproduced using different control weighting . It is found that these two behaviors save control energy and cause less stress over Conventional Control Behavior typified by the LQR control with a constant control weighting , in situations where only the goal discrepancy at the terminal time is of concern, such as in Marathon races and target hitting missions.     

gemini: software for testing the effects of genotyping errors and multitubes approach for individual identification

Improvements in the determination of individual genotypes from samples with low DNA quantity and quality are of prime importance in molecular ecology and conservation for reliable genetic individual identification (molecular tagging using microsatllites loci). Thus, errors (e.g. allelic dropout and false allele) appearing during samples genotyping must be monitored and eliminated as far as possible. The multitubes approach is a very effective but a costly and time‐consuming solution. In this paper, we present a simulation software that allows evaluation of the effect of genotyping errors on genetic identification of individuals and the effectiveness of a multitubes approach to correct these errors.     

Amoeba-based computing for traveling salesman problem: Long-term correlations between spatially separated individual cells of Physarum polycephalum

A single-celled, multi-nucleated amoeboid organism, a plasmodium of the true slime mold Physarum polycephalum, can perform sophisticated computing by exhibiting complex spatiotemporal oscillatory dynamics while deforming its amorphous body. We previously devised an “amoeba-based computer (ABC)” to quantitatively evaluate the optimization capability of the amoeboid organism in searching for a solution to the traveling salesman problem (TSP) under optical feedback control. In ABC, the organism changes its shape to find a high quality solution (a relatively shorter TSP route) by alternately expanding and contracting its pseudopod-like branches that exhibit local photoavoidance behavior. The quality of the solution serves as a measure of the optimality of which the organism maximizes its global body area (nutrient absorption) while minimizing the risk of being illuminated (exposure to aversive stimuli). ABC found a high quality solution for the 8-city TSP with a high probability. However, it remains unclear whether intracellular communication among the branches of the organism is essential for computing. In this study, we conducted a series of control experiments using two individual cells (two single-celled organisms) to perform parallel searches in the absence of intercellular communication. We found that ABC drastically lost its ability to find a solution when it used two independent individuals. However, interestingly, when two individuals were prepared by dividing one individual, they found a solution for a few tens of minutes. That is, the two divided individuals remained correlated even though they were spatially separated. These results suggest the presence of a long-term memory in the intrinsic dynamics of this organism and its significance in performing sophisticated computing.     

Heterogeneity stabilizes reciprocal altruism interactions

In considering the phenomena of reciprocal altruism few would dispute that there are differences in individual quality-in particular, that for some individuals, at least on occasion, the cost of doing favors will exceed the potential of future benefits. That is, at any given time, a typical population is heterogeneous with respect to the affordability of reciprocal altruism. However, methodological limitations of the traditional analytical framework-Single Type (symmetric) Evolutionary Game Theory-have restricted previous analytical efforts to addressing populations idealized in terms of their averages. Here we use the methods of Multitype Evolutionary Game Theory to analyse the role of individual differences in direct reciprocity interactions. Multitype analysis shows that non-idealized populations possess an ESS profile wherein individuals who cannot afford reciprocity (low-quality) defect, while individuals who derive net benefits from reciprocity (high-quality) cooperate. Furthermore, this cooperation is implemented via unmodified tit-for-tat (TfT) strategy. Hence, our results may help resolve a long-standing problem concerning the evolutionary stability of TfT in direct reciprocal altruism. Finally, this difference between idealized and real populations is not restricted to direct reciprocal cooperation. Previously (Lotem et al., 1999) we have demonstrated evolutionarily stable indirect reciprocal cooperation among high-quality individuals in heterogeneous populations.     

Morphometric estimation of growth of cell nuclei from plane section

In biological histology, cell size is measured from two-dimensional cell sections. The profile radii are recorded as a histogram. The estimation of real size distribution from the profile radius is Wicksell's corpuscle problem. Several approximate solutions of the integral equations have been discussed in the field of stereology. Cell size distribution can be used in the differential diagnosis between normal cells and malignant cells. In this study, we considered the estimation of cell growth rate from cell nuclei size distribution by methods analogous to those applied to life time analysis. The performance of estimates was investigated using a simulation study. Comparisons of performance between the estimates obtained for the profile radius and that for the radius obtained by estimated real size distribution are also made.     

Modeling,analysis and simulation of ant-based network routing protocols

Using the metaphor of swarm intelligence, ant-based routing protocols deploy control packets that behave like ants to discover and optimize routes between pairs of nodes. These ant-based routing protocols provide an elegant, scalable solution to the routing problem for both wired and mobile ad hoc networks. The routing problem is highly nonlinear because the control packets alter the local routing tables as they are routed through the network. We mathematically map the local rules by which the routing tables are altered to the dynamics of the entire networks. Using dynamical systems theory, we map local protocol rules to full network performance, which helps us understand the impact of protocol parameters on network performance. In this paper, we systematically derive and analyze global models for simple ant-based routing protocols using both pheromone deposition and evaporation. In particular, we develop a stochastic model by modeling the probability density of ants over the network. The model is validated by comparing equilibrium pheromone levels produced by the global analysis to results obtained from simulation studies. We use both a Matlab simulation with ideal communications and a QualNet simulation with realistic communication models. Using these analytic and computational methods, we map out a complete phase diagram of network behavior over a small multipath network. We show the existence of both stable and unstable (inaccessible) routing solutions having varying properties of efficiency and redundancy depending upon the routing parameters. Finally, we apply these techniques to a larger 50-node network and show that the design principles acquired from studying the small model network extend to larger networks.