Tesi sul tema "Competing populations"

Pulsatilla vernalis is one of several endangered plant species that benefit from wildfires and small scale disturbance events that repel competing vegetation and create open patches in the vegetation cover. Previous studies argue that Pulsatilla vernalis is decreasing in numbers due to vegetation changes associated with the decrease in wildfires, forest grazing and changes in forest management. In this study, 17 populations of P. vernalis were inventoried in order to examine if soil and/or vegetation structures affect the population structure of P. vernalis (i.e. population size, presence of flowering individuals, density of juveniles) and if performed conservation attempts in the populations have been positive for P. vernalis. This was done by counting the number of vegetative, flowering and juvenile individuals and examining soil and vegetation structure in the populations. The population sizes were then compared with estimates of population sizes from earlier inventories of P. vernalis at the same localities. The study also presents specific recommendations for an improved conservation management of P. vernalis. The results of this study show that mechanical conservation management had a positive effect on the population size and that open areas in the vegetation cover provided better conditions for viable populations of P. vernalis. To prevent the trend of decreasing population sizes of P. vernalis, conservation measures must be carried out to repel competing vegetation and to restore or maintain open patches in existing populations.

Rapid population growth increases the competing water demand for agriculture and municipalities. This situation urges the necessity of using integrated water management to increase water supply and find possible symbiotic urban-agriculture relationships. Many studies have been done to simulate the integrated use of surface water, groundwater and reclaimed water for different water users. However, few studies use simulation/optimization (S-O) models for water resources to explicitly represent detailed interactions between the different resources as well as the relationship between users and resources. This research study uses an S-O model to show the symbiotic relationship between urban and agricultural water use. This model fully links the nonlinear flows of groundwater from multiple aquifer layers, surface waters, reclaimed water, and delayed returns of non-consumed water for municipal and agricultural uses. Using specific aquifer and stream properties, and related assumptions, the optimization result shows there is a symbiotic relationship between urban and agricultural water use. The unconsumed water returns to the hydrologic system, for both surface water and groundwater increase agricultural net return by 8.6 %, and urban population by 0.4%. This particular problem uses ModelMuse to create simulation input files, and SOMOS-Map to create the optimization input files to run the simulation/optimization problem in SOMOS. In addition to presenting an S-O model, we also provide practical information on how to create the model. The results of the study and the explanation on how to apply the method may be helpful information for engineers and water managers.

America's school-age population is experiencing a demographic shift. In 1972, students of color represented 22% of the school-age population; in 2005, minority students accounted for 33% of public school enrollment (Statistics, 2007 Villegas, 2002). This study sought to explore how these changing demographics affected University Town Community Schools, the district's interventions, and teachers' perceptions to those interventions. This study also explored teachers' feelings of efficacy when teaching minority students. Using a qualitative study among third-, fourth-, fifth-, and sixth-grade elementary school teachers, a random sample of 9 teachers from schools comprising a minority population of at least 40% were interviewed. Data analysis involved the use of themes that emerged from the interview data, observations, and quotations from participants. The findings indicated that the district acted on a school-by-school basis, with no specific actions to target any one racial group. Meanwhile, teachers were inconsistent when discussing race, behavior, and learning. Teachers felt comfortable assigning behaviors based on race and culture, but were hesitant to assign learning strengths and weaknesses based on race or culture.

Ce travail a pour but de développer des outils statistiques pour l'étude du déclin cognitif général ou précédant le diagnostic de démence, à partir de données de cohorte en tenant compte du risque compétitif de décès et de la censure par intervalle. Le temps de démence est censuré par intervalle dans les études de cohortes car le diagnostic de démence ne peut être établi qu'à l'occasion des visites qui peuvent être espacées de plusieurs années. Ceci induit une sous-estimation du risque de démence à cause du risque compétitif de décès : les sujets déments sont à fort risque de mourir, et peuvent donc décéder avant la visite de diagnostic. Dans la première partie, nous proposons un modèle conjoint à classes latentes pour données longitudinales corrélées à un événement censuré par intervalle, en compétition avec le décès. Appliqué à la cohorte Paquid, ce modèle permet d'identifier des profils de déclin cognitif associés à des risques différents de démence et de décès. En utilisant cette méthodologie, nous comparons ensuite des modèles pronostiques dynamiques pour la démence, traitant la censure par intervalle, basés sur des mesures répétées de marqueurs cognitifs. Dans la seconde partie, nous conduisons une étude comparative afin de clarifier l'interprétation des estimateurs du maximum de vraisemblance des modèles mixtes et conjoints et estimateurs par équations d'estimation généralisées (GEE), couramment utilisés dans le contexte de données longitudinales incomplètes et tronquées par le décès. Les estimateurs de maximum de vraisemblance ciblent le changement individuel chez les individus vivants. Les estimateurs GEE avec matrice de corrélation de travail indépendante, pondérés par l'inverse de la probabilité d'être observé sachant que le sujet est vivant, ciblent la trajectoire moyennée sur la population des survivants à chaque âge. Ces résultats justifient l'utilisation des modèles conjoints dans l'étude de la démence, qui sont des outils prometteurs pour mieux comprendre l'histoire naturelle de la maladie
The purpose of this work is to develop statistical tools to study the general or the prediagnosis cognitive decline, while accounting for the selection by death and interval censoring. In cohort studies, the time-to-dementia-onset is interval-censored as the dementia status is assessed intermittently. This issue can lead to an under-estimation of the risk of dementia, due to the competing risk of death: subjects with dementia are at high risk to die and can thus die prior to the diagnosis visit. First, we propose a joint latent class illness-death model for longitudinal data correlated to an interval-censored time-to-event, competing with the time-to-death. This model is applied on the Paquid cohort to identify profiles of pre-dementia cognitive declines associated with different risks of dementia and death. Using this methodology, we compare dynamic prognostic models for dementia based on repeated measures of cognitive markers, accounting for interval censoring. Secondly, we conduct a simulation study to clarify the interpretation of maximum likelihood estimators of joint and mixed models as well as GEE estimators, frequently used to handle incomplete longitudinal data truncated by death. Maximum likelihood estimators target the individual change among the subjects currently alive. GEE estimators with independent working correlation matrix, weighted by the inverse probability to be observed given that the subject is alive, target the population-averaged change among the dynamic population of survivors. These results justify the use of joint models in dementia studies, which are promising statistical tools to better understand the natural history of dementia

碩士
淡江大學
化學工程學系
84
The fate of two microbial populations competing purely and simply for a common substrate in nonideal CSTRs, which are subject to time-invariant external influences, are examined. The nonideal reactor is modeled as two ideal CSTRs with interchange. The results for varying degrees of nonideality, i. e., different values of α and β, are presented in the form of operating diagrams in the D(dimensionless dilute rate)-zf (dimensionless feed substrate concentration) plane. It is found that up to four steady states may exist, but only one of them is stable, and that whenever it exists, the coexistence steady state isalways stable. For zf >1, the coexistence steady state exists in at least one, and at most three ranges of dilution rate. Effects of α and β on the domain of existence of the coexistence steady state are discussed.

Chan Hoi-Yeung = 網絡與競爭系統的物理 : 個體為本模型中的網絡效應 / 陳凱揚.
Thesis submitted in: September 2005.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (leaves 191-197).
Text in English; abstracts in English and Chinese.
Chan Hoi-Yeung = Wang luo yu jing zheng xi tong de wu li : ge ti wei ben mo xing zhong de wang luo xiao ying / Chen Kaiyang.
Abstract --- p.i
Acknowledgments --- p.v
Contents --- p.vii
Chapter 1 --- Overview --- p.1
Chapter I --- Networks --- p.3
Chapter 2 --- Networks in nature --- p.4
Chapter 2.1 --- Introduction --- p.4
Chapter 2.2 --- Terminology of the networks studies --- p.6
Chapter 2.2.1 --- Nodes --- p.6
Chapter 2.2.2 --- Links --- p.6
Chapter 2.2.3 --- Adjacency matrix --- p.9
Chapter 2.2.4 --- Connectivity --- p.10
Chapter 2.2.5 --- Clustering coefficient --- p.11
Chapter 2.2.6 --- Shortest path --- p.11
Chapter 2.2.7 --- Connectivity correlation --- p.12
Chapter 2.3 --- Topology in the real-world networks --- p.13
Chapter 2.3.1 --- The Internet --- p.13
Chapter 2.3.2 --- The WWW --- p.15
Chapter 2.3.3 --- Collaboration networks --- p.15
Chapter 2.3.4 --- Food webs --- p.16
Chapter 2.3.5 --- Power grids --- p.17
Chapter 2.4 --- Discussion --- p.17
Chapter 3 --- Review on Network Models --- p.19
Chapter 3.1 --- Introduction --- p.19
Chapter 3.2 --- Graph Theory --- p.20
Chapter 3.2.1 --- Classical random graph --- p.20
Chapter 3.3 --- Evolving networks --- p.23
Chapter 3.3.1 --- Random growing network --- p.23
Chapter 3.3.2 --- Fitness growing network --- p.25
Chapter 3.3.3 --- Barabasi-Albert model --- p.27
Chapter 3.3.4 --- Fitness model --- p.31
Chapter 3.4 --- Lattice --- p.33
Chapter 3.4.1 --- Regular hypercubic lattices (Periodic) --- p.33
Chapter 3.4.2 --- Regular hypercubic lattices (Free boundary conditions) . --- p.35
Chapter 3.5 --- Discussion --- p.35
Chapter 4 --- Network Properties --- p.38
Chapter 4.1 --- More derivations on existing models --- p.38
Chapter 4.1.1 --- Classical random graphs --- p.38
Chapter 4.1.2 --- Barabasi-Albert model --- p.40
Chapter 4.1.3 --- Fitness Model --- p.42
Chapter 4.1.4 --- Regular hypercubic lattices (Periodic) --- p.45
Chapter 4.2 --- New model --- p.48
Chapter 4.2.1 --- Fitness-BA hybrid model --- p.48
Chapter 4.3 --- Link removal --- p.55
Chapter 4.3.1 --- Introduction --- p.55
Chapter 4.3.2 --- Formalism in connectivity --- p.55
Chapter 4.3.3 --- Pruned BA Model --- p.56
Chapter 4.4 --- Link addition --- p.58
Chapter 4.4.1 --- Introduction --- p.58
Chapter 4.4.2 --- Regular hypercubic lattices (Periodic) --- p.58
Chapter 4.5 --- Discussion --- p.60
Chapter II --- Games --- p.62
Chapter 5 --- Review on Agent-based models of competing population --- p.63
Chapter 5.1 --- Introduction --- p.63
Chapter 5.2 --- The El Farol Bar attendance problem --- p.65
Chapter 5.2.1 --- Model --- p.65
Chapter 5.2.2 --- Strategies --- p.66
Chapter 5.2.3 --- Discussion --- p.66
Chapter 5.3 --- Minority game --- p.67
Chapter 5.3.1 --- Model --- p.67
Chapter 5.3.2 --- Strategies --- p.68
Chapter 5.3.3 --- Attendance --- p.69
Chapter 5.3.4 --- History and quasi-Eulerian state --- p.69
Chapter 5.3.5 --- Success rate and Hamming distance --- p.71
Chapter 5.3.6 --- Volatility --- p.73
Chapter 5.3.7 --- Crowd-anticrowd theory --- p.75
Chapter 5.3.8 --- Discussion --- p.76
Chapter 6 --- B-A-R model : Dynamics --- p.78
Chapter 6.1 --- Model --- p.78
Chapter 6.2 --- Results: Plateaux and periodicity --- p.81
Chapter 6.3 --- A microscopic view: Agents' decisions and strategy performance --- p.86
Chapter 6.4 --- A macroscopic view: Bit-string patterns --- p.92
Chapter 6.4.1 --- The history space --- p.92
Chapter 6.4.2 --- Bit-string statistics of different states --- p.94
Chapter 6.5 --- The (max = 1 states --- p.97
Chapter 6.5.1 --- Values of wm3iX --- p.97
Chapter 6.5.2 --- "Strategy ranking evolvement: ni, (w)" --- p.101
Chapter 6.5.3 --- Substates . --- p.105
Chapter 7 --- B-A-R model : Formalism --- p.108
Chapter 7.1 --- Resource level at transitions of Cmax = 0 state --- p.108
Chapter 7.2 --- Resource levels at transitions of Cmax 二 1 states --- p.109
Chapter 7.2.1 --- Method --- p.109
Chapter 7.2.2 --- Lmin for upper substate --- p.110
Chapter 7.2.3 --- Lmin for lower substate --- p.113
Chapter 7.3 --- Discussion --- p.116
Chapter 8 --- B-A-R model : Statistics --- p.121
Chapter 8.1 --- Problem --- p.121
Chapter 8.2 --- Bit-string statistics --- p.122
Chapter 8.2.1 --- Allowed transitions --- p.122
Chapter 8.2.2 --- Grouping the history space --- p.122
Chapter 8.2.3 --- "Grouping the states, Cmax" --- p.127
Chapter 8.2.4 --- "Labelling each state, /(C)" --- p.129
Chapter 8.3 --- Discussion --- p.130
Chapter III --- Networked games --- p.131
Chapter 9 --- Networked minority game --- p.132
Chapter 9.1 --- Model --- p.132
Chapter 9.2 --- Preliminary results: Agents' success rates --- p.133
Chapter 9.3 --- Ranking the strategies --- p.135
Chapter 9.3.1 --- Ranking pattern --- p.136
Chapter 9.3.2 --- Fraction of strategies in each rank --- p.140
Chapter 9.4 --- Number of agents using a best strategy belonging to rank r --- p.141
Chapter 9.4.1 --- Unconnected population --- p.141
Chapter 9.4.2 --- Networked population . --- p.142
Chapter 9.5 --- Application: Mean success rate --- p.143
Chapter 9.6 --- Mean success rate of agents with degree k --- p.147
Chapter 9.7 --- Application in other networks --- p.149
Chapter 9.8 --- Discussion --- p.151
Chapter 10 --- Interacting agents: Networked B-A-R model --- p.154
Chapter 10.1 --- Model --- p.154
Chapter 10.2 --- The quasi-Eulerian state (wmax = 1/2 state) --- p.155
Chapter 10.3 --- The emergent states --- p.159
Chapter 10.3.1 --- General results --- p.159
Chapter 10.3.2 --- The Cmax = 0 state --- p.160
Chapter 10.3.3 --- The Cmax = 1 state --- p.161
Chapter 10.4 --- Discussion --- p.162
Chapter IV --- Conclusion --- p.164
Chapter 11 --- Conclusion --- p.165
Chapter V --- Appendices --- p.172
Chapter A --- List of symbols --- p.173
Chapter A.1 --- Networks --- p.173
Chapter A.2 --- Games --- p.174
Chapter A.3 --- Networked games --- p.176
Chapter B --- Distance distribution in classical random graphs --- p.177
Chapter B.1 --- Method --- p.177
Chapter B.2 --- Distance distribution --- p.177
Chapter B.3 --- Behaviour at small L --- p.178
Chapter B.4 --- Behaviour at large L --- p.179
Chapter C --- Co-ordination number in infinite hypercubic lattice --- p.181
Chapter C.1 --- Method --- p.181
Chapter C.1.1 --- ID lattice --- p.181
Chapter C.1.2 --- 2D square lattice --- p.182
Chapter C.1.3 --- Higher dimension hypercubic lattices --- p.183
Chapter C.2 --- Coefficients --- p.185
Chapter D --- Connectivity distribution in fitness-BA hybrid model --- p.187
Chapter D.1 --- Mean field approach --- p.187
Chapter D.2 --- Connectivity distribution --- p.188
Chapter D.3 --- Power-law exponent --- p.190
Bibliography --- p.191

Chan King Pak Keven = 競爭性系統個體模型中的策略動態、決策及整體表現 / 陳景柏.
Thesis submitted in: August 2005.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2006.
Includes bibliographical references (leaves vii-viii (4th gp.)).
Text in English; abstracts in English and Chinese.
Chan King Pak Keven = Jing zheng xing xi tong ge ti mo xing zhong de ce lüe dong tai, jue ce ji zheng ti biao xian / Chen Jingbo.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- Review on the Minority Game --- p.5
Chapter 2.1 --- Background --- p.5
Chapter 2.2 --- Model of MG --- p.6
Chapter 2.3 --- Features --- p.7
Chapter 2.3.1 --- Phase Transition --- p.7
Chapter 2.3.2 --- Inefficient and Efficient Phase --- p.8
Chapter 2.3.3 --- Anti-persistence --- p.9
Chapter 2.3.4 --- Data Collapse --- p.10
Chapter 2.4 --- Existing Theories --- p.10
Chapter 2.4.1 --- Reduced Strategy Space --- p.11
Chapter 2.4.2 --- The Crowd-Anticrowd Theory --- p.12
Chapter 2.5 --- Summary --- p.13
Chapter 3 --- Introduction to Strategy Ranking Theory --- p.15
Chapter 3.1 --- Strategy Ranking Theory for Mean Success Rate --- p.15
Chapter 3.1.1 --- Time evolution of Virtual Point Ranking --- p.15
Chapter 3.1.2 --- Winning Probability for m = 1 --- p.17
Chapter 3.2 --- Calculation of Mean Success Rate --- p.21
Chapter 3.3 --- "Size Dependence of weυen(K) (""Market Impact"" Effect)" --- p.23
Chapter 3.4 --- Size Dependence of wodd、K) (Uneven Distribution of Agents into Split Ranks) --- p.25
Chapter 4 --- Implementation of Strategy Ranking Theory --- p.30
Chapter 4.1 --- Feature of wodd(k) for higher m --- p.30
Chapter 4.2 --- Derivation of wodd(k) from Strategy Ranking Theory --- p.32
Chapter 4.3 --- Proof of Eq. (4.14) --- p.36
Chapter 4.4 --- Discussion on wodd(k) --- p.41
Chapter 4.4.1 --- Asymptotic Behavior of wodd(k) --- p.42
Chapter 4.4.2 --- Finite size correction of wodd(k) --- p.43
Chapter 5 --- Applications of Strategy Ranking Theory --- p.46
Chapter 5.1 --- Probability Density Function of Agents Making a Particular Choice --- p.46
Chapter 5.1.1 --- Odd time steps： k = 1 --- p.47
Chapter 5.1.2 --- Odd time steps： k = 2 --- p.48
Chapter 5.1.3 --- "Rodd,K" --- p.49
Chapter 5.1.4 --- Even time steps --- p.51
Chapter 5.1.5 --- Overall Attendance Distribution --- p.51
Chapter 5.2 --- The Variance of the Attendance --- p.52
Chapter 5.2.1 --- Asymptotic behavior of the variance --- p.54
Chapter 5.3 --- Anti-persistent Nature of Efficient Phase of MG --- p.55
Chapter 5.4 --- Summary --- p.58
Chapter 6 --- Strategy Ranking Theory and Crowd-Anticrowd Theory --- p.59
Chapter 6.1 --- Introduction --- p.59
Chapter 6.1.1 --- Strategy Ranking Theory --- p.60
Chapter 6.1.2 --- Crowd-Anticrowd Theory --- p.61
Chapter 6.2 --- Crowd-Anticrowd Theory with Ranking Patterns Characterized by k --- p.63
Chapter 6.3 --- Variance: Crowd-Anticrowd Theory --- p.65
Chapter 6.3.1 --- m = 1 --- p.65
Chapter 6.3.2 --- m = 2 --- p.66
Chapter 6.4 --- Variance： Modified Crowd-Anticrowd Theory for m̐ơح 1 --- p.66
Chapter 6.4.1 --- k = 0 --- p.67
Chapter 6.4.2 --- k = 1 --- p.67
Chapter 6.4.3 --- k = 2 --- p.67
Chapter 6.4.4 --- Sum over all k --- p.68
Chapter 6.5 --- Variance: Modified Crowd-Ant icrowd Theory for m=2 --- p.68
Chapter 6.5.1 --- k = 3 --- p.69
Chapter 6.5.2 --- k = 4 --- p.70
Chapter 6.5.3 --- Sum over all k --- p.71
Chapter 6.6 --- "Strategy Ranking Theory Expressed in (Nkl-Nk,(l)" --- p.71
Chapter 6.7 --- Summary --- p.73
Chapter 7 --- Variance of the Attendance in MG: Data Collapse --- p.75
Chapter 7.1 --- Previous Studies --- p.75
Chapter 7.2 --- Attempt 1 --- p.76
Chapter 7.2.1 --- Understanding from the Existing Theories --- p.76
Chapter 7.2.2 --- Numerical Results --- p.79
Chapter 7.3 --- Attempt 2 --- p.80
Chapter 7.3.1 --- Modification Based on αc ß 1/2 --- p.81
Chapter 7.3.2 --- Numerical Results --- p.81
Chapter 7.4 --- Summary --- p.82
Chapter 8 --- Minority Game in Networked Population --- p.83
Chapter 8.1 --- Introduction --- p.83
Chapter 8.2 --- Model --- p.84
Chapter 8.3 --- Numerical Results --- p.85
Chapter 8.4 --- Classification of Predictors --- p.86
Chapter 8.4.1 --- Major Classification of Predictors - Hamming Distance D --- p.87
Chapter 8.4.2 --- "Minor Classification of Predictors - Dynamical Ranking (k,1)" --- p.88
Chapter 8.4.3 --- "Using the Classification (k,l, D)" --- p.89
Chapter 8.5 --- "Winning Probability of a Predictor (wk,l,d)" --- p.89
Chapter 8.5.1 --- "Odd Steps, k = 1" --- p.90
Chapter 8.5.2 --- "Odd Steps, k = 2" --- p.91
Chapter 8.6 --- Number of Predictors --- p.93
Chapter 8.7 --- Mean Success Rate of Non-networked MG： m = 1 --- p.93
Chapter 8.8 --- "Cluster Size of a Predictor (sk,l,D)" --- p.95
Chapter 8.9 --- Mean Success Rate of Networked MG --- p.97
Chapter 8.9.1 --- With wK(even)=0.5 --- p.97
Chapter 8.9.2 --- "Modification of wK(even) Using skl,D" --- p.98
Chapter 8.9.3 --- Modification of Using Modified wK(even) --- p.100
Chapter 8.10 --- Variance of the Attendance in Networked MG --- p.101
Chapter 8.11 --- Attendance Distribution --- p.103
Chapter 8.12 --- A Network-type Independent Approach --- p.104
Chapter 8.12.1 --- Degree Depending Success Rate --- p.104
Chapter 8.12.2 --- Evaluating w(k) --- p.107
Chapter 8.12.3 --- Application on Random Graph as Underlying Network --- p.108
Chapter 8.13 --- The Position of the Minimum Variance --- p.108
Chapter 8.14 --- Summary --- p.110
Chapter 9 --- Conclusion --- p.111
Bibliography --- p.115

Part I focuses on studying the extent of cooperation in networked entities, within the context of the Prisoner's Dilemma (PD) and the Snowdrift Game (SG). The iterated prisoner's dilemma (IPD) is studied in the full payoff space spanned by two parameters beta and gamma. A theoretical study on two-strategy IPD is presented. We then numerically study the IPD in the full payoff space, with four different initial configurations. It is found that including the Tit-for-tat-like (ETFT) and Always-defecting-like (EAllD) strategies as initial strategies can maximize the dominating area of generous strategies in the payoff space at equilibrium. The roles played by ETFT and EAllD are further studied on the diagonal and anti-diagonal lines of the payoff space.
Part II focuses mainly on studying the optical properties of grating within the Rigorous Coupled-Wave Analysis (RCWA) method. The surface plasmon (SP) dispersion relation in a system consisting of a thin metallic film sandwiched between a linear dielectric and nonlinear dielectric of arbitrary non- linearity is derived, based on a generalized "first integral" approach. The SP dispersion relation in a system consisting of a thin metallic film sandwiched in a symmetric nonlinear dielectric environment is then derived. The changes in SP dispersion relations on film thicknesses are discussed for both cases.
The optical properties of two samples of one-dimensional metallic reflection gratings are studied. The numerical results of the zeroth order reflectance are in good agreement with experimental data. The Wood's anomaly occurs when a particular diffraction order emerges or disappears, thus inducing a change in the efficiency of other diffraction orders. This phenomenon is studied by calculating and measuring the efficiencies of all allowed diffraction orders. Numerical results of the near field patterns show a coupling between the waveguide and SP modes. We also study the controllable enhanced trans- mission in a semiconductor grating. The dielectric constant of a semiconductor becomes a tensor in the presence of a static magnetic field parallel to the slit. Numerical results based on RCWA reveal that the zeroth order transmission peaks at normal incidence can be shifted to longer wavelengths and the peak values of transmission can largely be enhanced when a moderate magnetic field is applied. A single-mode theory incorporating anisotropy is developed. The analytic results are in quantitative agreement with RCWA, indicating that the tunability in the transmission stems from the waveguide mode.
The Snowdrift Game is regarded as an important alternative to PD in studying the emergence of cooperation in competing populations. The phase transitions in spatial snowdrift games are introduced. By studying the relative alignments of the payoffs of C and D nodes, the phase transitions are analytically explained. As an extension to the standard two-person SG, an N-person Snowdrift Game (NPSG) is proposed to include generic multi-person interactions. NPSG in a well-mixed population is studied analytically by using the replicator dynamics. A simulation algorithm is developed. We also study NPSG on lattices and find a suppressed cooperation frequency, when compared with the well-mixed case. For NPSG played on 1D chain, the problem can be solved analytically. We further extend our work to study co-evolving dynamics. We propose and study a model in which the connections are driven to evolve by the dynamics of SG. Compared with played on static network, the cooperation frequency is promoted. A semi-analytic theory is proposed, with the results qualitatively agree with simulation results.
The thesis consists of two independent parts. Part I focuses on evolutionary games in networked entities and Part II focuses on calculations on optical properties of metallic gratings.
Yin, Haiping = 競爭群體中合作的產生及具規則結構之金屬薄膜的光學性質 / 尹海平.
Adviser: Hui Pak Ming.
Source: Dissertation Abstracts International, Volume: 72-04, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 185-200).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
Yin, Haiping = Jing zheng qun ti zhong he zuo de chan sheng ji ju gui ze jie gou zhi jin shu bo mo de guang xue xing zhi / Yin Haiping.

Dynamic optimisation problems are problems where the search space does not remain constant over time. Evolutionary algorithms aimed at static optimisation problems often fail to effectively optimise dynamic problems. The main reason for this is that the algorithms converge to a single optimum in the search space, and then lack the necessary diversity to locate new optima once the environment changes. Many approaches to adapting traditional evolutionary algorithms to dynamic environments are available in the literature, but differential evolution (DE) has been investigated as a base algorithm by only a few researchers. This thesis reports on adaptations of existing DE-based optimisation algorithms for dynamic environments. A novel approach, which evolves DE sub-populations based on performance in order to discover optima in an dynamic environment earlier, is proposed. It is shown that this approach reduces the average error in a wide range of benchmark instances. A second approach, which is shown to improve the location of individual optima in the search space, is combined with the first approach to form a new DE-based algorithm for dynamic optimisation problems. The algorithm is further adapted to dynamically spawn and remove sub-populations, which is shown to be an effective strategy on benchmark problems where the number of optima is unknown or fluctuates over time. Finally, approaches to self-adapting DE control parameters are incorporated into the newly created algorithms. Experimental evidence is presented to show that, apart from reducing the number of parameters to fine-tune, a benefit in terms of lower error values is found when employing self-adaptive control parameters.
Thesis (PhD)--University of Pretoria, 2012.
Computer Science
unrestricted

Yip Kin Fung.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.
Includes bibliographical references (leaves 101-104).
Abstracts in English and Chinese.
Chapter 1 --- Introduction --- p.1
Chapter 2 --- The Distribution of Fluctuations in Financial Data --- p.5
Chapter 2.1 --- Empirical Statistics --- p.5
Chapter 2.2 --- Data analyzed --- p.8
Chapter 2.3 --- Levy Distribution --- p.10
Chapter 2.4 --- Returns Distribution and Scaling Properties --- p.12
Chapter 2.5 --- Volatility Clustering --- p.19
Chapter 2.6 --- Conclusion --- p.21
Chapter 3 --- Models of Herd behaviour in Financial Markets --- p.22
Chapter 3.1 --- Cont and Bouchaud's model --- p.22
Chapter 3.2 --- The Model of Egiuluz and Zimmerman --- p.24
Chapter 3.3 --- EZ Model with Size-Dependent Dissociation Rates --- p.28
Chapter 3.4 --- Democratic and Dictatorship Self-Organized Model --- p.31
Chapter 3.5 --- Effect of Size-Dependent Fragmentation and Coagulation Prob- abilities --- p.33
Chapter 3.6 --- Extensions of EZ model --- p.35
Chapter 3.7 --- Conclusion --- p.39
Chapter 4 --- Review on the Minority Game(MG) --- p.42
Chapter 4.1 --- The Model and Results --- p.42
Chapter 4.2 --- Crowd-anticrowd Theory and Phase Transition --- p.46
Chapter 4.3 --- Market Efficiency --- p.48
Chapter 5 --- MG with biased strategy pool --- p.52
Chapter 5.1 --- The Model --- p.53
Chapter 5.2 --- Numerical Results and Discussion --- p.53
Chapter 5.3 --- Theory: MG with Biased Strategy Pool --- p.61
Chapter 5.4 --- Conclusion --- p.69
Chapter 6 --- MG with Randomly Participating Agents --- p.71
Chapter 6.1 --- The Model with One RPA --- p.71
Chapter 6.2 --- Results for q = 0.5 --- p.72
Chapter 6.3 --- Inefficiency and Success Rate --- p.76
Chapter 6.4 --- Results for q ≠ 0.5 --- p.82
Chapter 6.5 --- Many RPAs --- p.85
Chapter 6.6 --- Conclusion --- p.86
Chapter 7 --- A Model on Coupled Minority Games --- p.88
Chapter 7.1 --- The Model --- p.89
Chapter 7.2 --- Results and Discussion。 --- p.90
Chapter 7.3 --- Conclusion --- p.95
Chapter 8 --- Conclusion --- p.97
Bibliography --- p.101
Chapter A --- Solving Cluster Size distribution in EZ model --- p.105