| Introduction | p. 1 |
| Pricing of Distributed Information Services | p. 1 |
| Motivation | p. 1 |
| Structure of the Thesis | p. 3 |
| Methodology, Definitions and Scenario | p. 5 |
| Methodology | p. 5 |
| Definition of Information Services and Information Production | p. 5 |
| Definition of Price Controlled Resource Allocation | p. 7 |
| Scenario for Price Controlled Resource Allocation for Information Services and Information Production | p. 7 |
| Classic Economists and Paradigms in Pricing and Resource Allocation | p. 11 |
| Léon Walras and the "Equilibrium Tâtonnement Process" | p. 13 |
| Schumpeter's Theory of Economic Development and "Evolutionary Economics" | p. 14 |
| Hayek's Economic Competition and the "Theory of Spontaneous Order" | p. 16 |
| Arrow's General Equilibrium Theory and "Neo-Walrasian Economics" | p. 18 |
| Tesfatsion's "Agent based Computational Economics" | p. 23 |
| Dynamic Pricing and Automated Resource Allocation | p. 27 |
| Dynamic Pricing | p. 28 |
| Negotiation-based Pricing | p. 30 |
| Auctions | p. 30 |
| Reverse Pricing | p. 31 |
| Dynamic Price Discrimination | p. 32 |
| Yield Management | p. 32 |
| Automated Resource Allocation | p. 34 |
| Properties of Resources | p. 34 |
| Properties of Allocation Mechanisms | p. 35 |
| Classification of Resource Allocation and Scheduling Problems | p. 37 |
| Economic Resource Allocation in Distributed Computer Systems | p. 40 |
| Allocation Games | p. 41 |
| Market-based Allocation | p. 45 |
| Multi-Agent Systems and Automated Resource Allocation | p. 58 |
| Distributed Artificial Intelligence and Multi-Agent Systems | p. 58 |
| Ontologies for the Coordination of Multi-Agent Systems | p. 61 |
| Dynamic Pricing versus Automated Resource Allocation | p. 63 |
| Yield Management Systems | p. 65 |
| Combinatorial Auction Mechanisms | p. 66 |
| Empirical Assessment of Dynamic Pricing Preference | p. 67 |
| Basic Data of the Study | p. 67 |
| Reference Groups Used in the Evaluation | p. 71 |
| Digital Business Group | p. 72 |
| Digital Yield Group | p. 72 |
| Dynamic Pricing Group | p. 74 |
| Detailed Findings on Dynamic Pricing Preference | p. 76 |
| Conventional vs. Internet Pricing Behavior | p. 77 |
| Automated Pricing Acceptance | p. 79 |
| Individual Pricing Acceptance | p. 80 |
| Empirical Analysis of Dynamic Pricing for ISIP Provision | p. 82 |
| Empirical Implications of Dynamic Pricing for ISIP Provision | p. 87 |
| Reinforcement Learning for Dynamic Pricing and Automated Resource Allocation | p. 89 |
| Basics of Reinforcement Learning | p. 89 |
| Markov-Processes in Decision Trees | p. 89 |
| Idea of Reinforcement Learning | p. 91 |
| Stochastic Dynamic Programming | p. 91 |
| Monte-Carlo Methods | p. 96 |
| Temporal-Difference Learning | p. 100 |
| Q-Learning | p. 102 |
| Basics of Yield Management | p. 103 |
| Classic Yield Management and Dynamic Pricing in Information Services and Information Production | p. 104 |
| Solving Yield Management Problems Using Stochastic Dynamic Programming | p. 106 |
| Dynamic Pricing, Scheduling, and Yield Management Using Reinforcement Learning | p. 110 |
| Dynamic Pricing and Reinforcement Learning | p. 110 |
| Scheduling and Reinforcement Learning | p. 113 |
| Yield Management and Reinforcement Learning | p. 116 |
| A Yield Maximizing Allocation System for a Single ISIP Resource | p. 117 |
| Infrastructure of the Yield Optimizing System | p. 117 |
| Properties of the Resource Requests | p. 119 |
| Reinforcement Learning Algorithm | p. 119 |
| Deterministic Scheduler Component | p. 121 |
| Benchmark Algorithm for the RL-YM Scheduler | p. 121 |
| Performance of the RL-YM Scheduler | p. 122 |
| A Yield Maximizing Allocation System for Multiple ISIP Resources | p. 124 |
| Artificial Neural Networks for Value-Function Representation | p. 124 |
| Using Evolutionary Techniques to Improve the Value-Function | p. 126 |
| Reinforcement Learning for Network Yield Management Processes | p. 132 |
| Combinatorial Auctions for Resource Allocation | p. 137 |
| Basics of Combinatorial Auctions | p. 138 |
| Variants of the Combinatorial Auction | p. 139 |
| Advantages of the Combinatorial Auction | p. 140 |
| Problems with the Combinatorial Auction | p. 140 |
| Formalization of the Combinatorial Auction Problem | p. 142 |
| Formalization of the Weighted Set Packing Problem | p. 144 |
| Complexity of the Combinatorial Auction Problem | p. 144 |
| Bidding Languages for Combinatorial Auctions | p. 145 |
| Incentive Compatibility for Combinatorial Auctions | p. 148 |
| Benchmarking Combinatorial Auctions | p. 150 |
| Solving the Combinatorial Auction Problem | p. 153 |
| Deterministic Procedures | p. 153 |
| Heuristic Approaches | p. 167 |
| Equilibrium Methods | p. 173 |
| Design of Combinatorial Auctions | p. 183 |
| Complexity Reduction in Combinatorial Auctions | p. 183 |
| Framework for Combinatorial Auction Design | p. 184 |
| Validation of Combinatorial Auction Design | p. 188 |
| Dynamic Pricing and Automated Resource Allocation Using Combinatorial Auctions | p. 191 |
| Solving the Combinatorial Auction Problem in ISIP Environments | p. 191 |
| Price-Controlled Resource Allocation Scenario and Benchmark Problems | p. 193 |
| Three Heuristics for the Combinatorial Auction Problem | p. 195 |
| Performance of the Three CAP Solution Heuristics | p. 200 |
| An Agent-Based Simulation Environment for Combinatorial Scheduling | p. 203 |
| The Combinatorial Scheduling Auction | p. 205 |
| The System's Budget Management Mechanism | p. 206 |
| The Combinatorial Auctioneer | p. 207 |
| The Task Agents' Bidding Model | p. 211 |
| Experimental Settings and Results | p. 212 |
| Benchmarking the Combinatorial Auctioneer | p. 212 |
| Comparing Open and Closed Economy Behavior | p. 215 |
| Comparing Bidding on Scarcity and Shadow Prices | p. 218 |
| Evaluating Resource Pricing Behavior | p. 219 |
| Testing Different Bidding Strategies | p. 223 |
| Searching for Utility Optimizing Strategies | p. 227 |
| Comparison of Reinforcement Learning and Combinatorial Auctions | p. 231 |
| Properties of Reinforcement Learning and Combinatorial Auctions for ISIP Provision | p. 231 |
| Economic Properties of Dynamic Pricing Using RL and CA | p. 231 |
| Technology Properties of Dynamic Pricing Using RL and CA | p. 233 |
| Main Results and Recommendations | p. 235 |
| Reinforcement Learning | p. 235 |
| Combinatorial Auctions | p. 237 |
| Outlook and Future Research | p. 239 |
| Appendix | p. 243 |
| Internet Survey Questionnaire | p. 243 |
| List of Symbols | p. 247 |
| List of Figures | p. 253 |
| List of Tables | p. 257 |
| Glossary | p. 259 |
| References | p. 263 |
| Index | p. 287 |
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