Dynamic Pricing and Automated Resource Allocation for Complex Information Services

Reinforcement Learning and Combinatorial Auctions

By: Michael Schwind

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Paperback | 15 February 2007

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Many firms provide their customers with online information products which require limited resources such as server capacity. This book develops allocation mechanisms that aim to ensure an efficient resource allocation in modern IT-services. Recent methods of artificial intelligence, such as neural networks and reinforcement learning, and nature-oriented optimization methods, such as genetic algorithms and simulated annealing, are advanced and applied to allocation processes in distributed IT-infrastructures, e.g. grid systems. The author presents two methods, both of which using the users' willingness-to-pay to control the allocation process: The first approach uses a yield management method that tries to learn an optimal acceptance strategy for resource requests. The second method is a combinatorial auction able to deal with resource complementarities. The author finally generates a method to calculate dynamic resource prices, marking an important step towards the industrialization of grid systems.

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Non-Fiction Computing & I.T.Information Technology General Issue Computer Networking & Communications Computer Science Artificial Intelligence Business & Management Business Mathematics & Systems Economics Economic Theory & Philosophy Computer Hardware Mathematics

Optimisation Game Theory Engineering & Technology Technology in General Engineering in General

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
Table of Contents provided by Publisher. All Rights Reserved.

Dynamic Pricing and Automated Resource Allocation for Complex Information Services

Reinforcement Learning and Combinatorial Auctions

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