Basics of qualitative research: Grounded theory procedures and techniques

Preface 1. Decision-Theoretic Foundations 1.1 Game Theory, Rationality, and Intelligence 1.2 Basic Concepts of Decision Theory 1.3 Axioms 1.4 The Expected-Utility Maximization Theorem 1.5 Equivalent Representations 1.6 Bayesian Conditional-Probability Systems 1.7 Limitations of the Bayesian Model 1.8 Domination 1.9 Proofs of the Domination Theorems Exercises 2. Basic Models 2.1 Games in Extensive Form 2.2 Strategic Form and the Normal Representation 2.3 Equivalence of Strategic-Form Games 2.4 Reduced Normal Representations 2.5 Elimination of Dominated Strategies 2.6 Multiagent Representations 2.7 Common Knowledge 2.8 Bayesian Games 2.9 Modeling Games with Incomplete Information Exercises 3. Equilibria of Strategic-Form Games 3.1 Domination and Ratonalizability 3.2 Nash Equilibrium 3.3 Computing Nash Equilibria 3.4 Significance of Nash Equilibria 3.5 The Focal-Point Effect 3.6 The Decision-Analytic Approach to Games 3.7 Evolution. Resistance. and Risk Dominance 3.8 Two-Person Zero-Sum Games 3.9 Bayesian Equilibria 3.10 Purification of Randomized Strategies in Equilibria 3.11 Auctions 3.12 Proof of Existence of Equilibrium 3.13 Infinite Strategy Sets Exercises 4. Sequential Equilibria of Extensive-Form Games 4.1 Mixed Strategies and Behavioral Strategies 4.2 Equilibria in Behavioral Strategies 4.3 Sequential Rationality at Information States with Positive Probability 4.4 Consistent Beliefs and Sequential Rationality at All Information States 4.5 Computing Sequential Equilibria 4.6 Subgame-Perfect Equilibria 4.7 Games with Perfect Information 4.8 Adding Chance Events with Small Probability 4.9 Forward Induction 4.10 Voting and Binary Agendas 4.11 Technical Proofs Exercises 5. Refinements of Equilibrium in Strategic Form 5.1 Introduction 5.2 Perfect Equilibria 5.3 Existence of Perfect and Sequential Equilibria 5.4 Proper Equilibria 5.5 Persistent Equilibria 5.6 Stable Sets 01 Equilibria 5.7 Generic Properties 5.8 Conclusions Exercises 6. Games with Communication 6.1 Contracts and Correlated Strategies 6.2 Correlated Equilibria 6.3 Bayesian Games with Communication 6.4 Bayesian Collective-Choice Problems and Bayesian Bargaining Problems 6.5 Trading Problems with Linear Utility 6.6 General Participation Constraints for Bayesian Games with Contracts 6.7 Sender-Receiver Games 6.8 Acceptable and Predominant Correlated Equilibria 6.9 Communication in Extensive-Form and Multistage Games Exercises Bibliographic Note 7. Repeated Games 7.1 The Repeated Prisoners Dilemma 7.2 A General Model of Repeated Garnet 7.3 Stationary Equilibria of Repeated Games with Complete State Information and Discounting 7.4 Repeated Games with Standard Information: Examples 7.5 General Feasibility Theorems for Standard Repeated Games 7.6 Finitely Repeated Games and the Role of Initial Doubt 7.7 Imperfect Observability of Moves 7.8 Repeated Wines in Large Decentralized Groups 7.9 Repeated Games with Incomplete Information 7.10 Continuous Time 7.11 Evolutionary Simulation of Repeated Games Exercises 8. Bargaining and Cooperation in Two-Person Games 8.1 Noncooperative Foundations of Cooperative Game Theory 8.2 Two-Person Bargaining Problems and the Nash Bargaining Solution 8.3 Interpersonal Comparisons of Weighted Utility 8.4 Transferable Utility 8.5 Rational Threats 8.6 Other Bargaining Solutions 8.7 An Alternating-Offer Bargaining Game 8.8 An Alternating-Offer Game with Incomplete Information 8.9 A Discrete Alternating-Offer Game 8.10 Renegotiation Exercises 9. Coalitions in Cooperative Games 9.1 Introduction to Coalitional Analysis 9.2 Characteristic Functions with Transferable Utility 9.3 The Core 9.4 The Shapkey Value 9.5 Values with Cooperation Structures 9.6 Other Solution Concepts 9.7 Colational Games with Nontransferable Utility 9.8 Cores without Transferable Utility 9.9 Values without Transferable Utility Exercises Bibliographic Note 10. Cooperation under Uncertainty 10.1 Introduction 10.2 Concepts of Efficiency 10.3 An Example 10.4 Ex Post Inefficiency and Subsequent Oilers 10.5 Computing Incentive-Efficient Mechanisms 10.6 Inscrutability and Durability 10.7 Mechanism Selection by an Informed Principal 10.8 Neutral Bargaining Solutions 10.9 Dynamic Matching Processes with Incomplete Information Exercises Bibliography Index

博弈论 : 矛盾冲突分析 = Game theory : analysis of conflict

Introduction to stochastic programming

Power Aware Routing in Mobile Ad Hoc Networks

The Grid 2: Blueprint for a New Computing Infrastructure

Unmanned Aerial Vehicles (UAVs, commonly known as drones) hold great potential to reduce operational costs and guarantee on-time delivery of packages. This paper aims to minimize the number of drones used in a depot, in which each package has its own customized release time, distance to the depot, and personalized deadline. For decision-makers, it is difficult to determine the optimal number of drones to ensure that all packages can be delivered before the corresponding deadline. We propose a mixed integer programming model formulate the problem. Due to the NP-hardness of the problem, a scheduling decision support model with a genetic algorithm (SDSMGA) is developed to address the problem. A fitness function that can determine the minimum number of drones required by a package delivery sequence is proposed. We develop a swap-based correction algorithm to correct unqualified individuals in SDSMGA. Experimental results show that compared with CPLEX for small instances, SDSMGA can obtain solutions of the same quality or sub-optimal solutions. Computational results among SDSMGA, Estimation of Distribution Algorithm (EDA), and Particle Swarm Optimization (PSO) indicate that SDSMGA can effectively and efficiently address the problem. As the number of packages increases, SDSMGA outperforms the other two algorithms. Sensitivity analysis shows that the smaller the dense factor, or the more extensive the service radius, the more drones are needed.

A scheduling decision support model for minimizing the number of drones with dynamic package arrivals and personalized deadlines

Most children who die from health-related conditions spend their brief lives encountering a wide variety of clinicians, from primary physicians and nurse practitioners to oncologists and neurologists. Many of these providers act as gate-keepers to hospice care through the referral process. One of the barriers to pediatric hospice and palliative care use is that pediatric clinicians generally lack an understanding of and experience with this service, which influences their decision to refer children to hospice.1 In fact, most physicians have limited or no training in core end-of-life competencies,2,3 less than 50% of residents are taught how to hold conversations about pediatric end of life,4 and over 25% of pediatricians do not even know whether local hospice services even exist.5 Although there have been significant strides to increase formal endof-life educational opportunities for clinicians, improved training in communications between clinicians and families about end of life, and increased understanding of clinicians’ hospice referral practices, very few children access hospice and palliative care at end of life.6

Perhaps it is time to try something completely different. What if we provided clinicians with a technological tool to assist them in understanding and experiencing pediatric hospice care, all in the palm of their hands? Along with formal education and training, information on pediatric hospice care could be accessible 24/7 when clinicians need it, whether in the exam room with children and their families, in the office while reviewing tests, or in the clinical conference with the care team. Although the evidence is in its infancy,7-10 the cutting-edge technology associated with smart phones and tablets (e.g., iPads) may have the potential to significantly influence health care. By providing a powerful platform, mobile applications (apps) may increase awareness of pediatric hospice care, assist clinicians’ discussions with children and families about it, and create a seamless referral to hospice that would ultimately improve access to pediatric hospice and palliative care.

/pdf/pediatric-hospice-and-palliative-care-designing-a-mobile-app-3qio2xz4ev.pdf

Pediatric hospice and palliative care: designing a mobile app for clinical practice.

Stochastic optimization of distributed generator location and sizing in an islanded utility microgrid during a large-scale grid disturbance

Operating room scheduling is often done in steps. First, surgeries are assigned to an operating room's time blocks. Assigned surgeries are then sequenced. Idle time is often reserved at the end of the time block in order to buffer against possible overtime. This research focuses on the next step of determining the amount of time reserved for each of the pre-sequenced surgeries so that surgical teams know their exact start times. In this way the buffer time is redistributed to each of the surgeries in order to minimize total overtime and idling costs. The problem is modeled as a special periodic review inventory model and a simulation-based response surface method is used to optimize surgery start times for a single operating room with stochastic operation durations represented by an infinite set of stochastic scenarios. This purposed method does not require extensive computational effort and is easy for practitioners to implement.

Optimizing surgery start times for a single operating room via simulation

Metamodels are abstractions of the simulation model that expose the system's input-output relationship through simple mathematical expression (Santos and Santos 2007). It provides an analytical way to study the behavior of a complex system. Multi-echelon supply chain are one of the complex systems. It is hard if not impossible to draw close-form analytical solutions due to the complexities of inventory system and the underlying uncertainty. In this paper, we will apply the methodology of simulation metamodel to a multi-echelon supply chain problem and make statistically analysis of the parameters. The model is validated using training experiment conditions.

/pdf/multi-echelon-supply-chain-simulation-using-metamodel-2vrdvc4vpy.pdf

Xueping Li

Papers

A scheduling decision support model for minimizing the number of drones with dynamic package arrivals and personalized deadlines

Pediatric hospice and palliative care: designing a mobile app for clinical practice.

Stochastic optimization of distributed generator location and sizing in an islanded utility microgrid during a large-scale grid disturbance

Optimizing surgery start times for a single operating room via simulation

Multi-echelon supply chain simulation using metamodel