| Market Mechanisms | |
| Zero-Intelligence Trading Without Resampling | p. 3 |
| Introduction | p. 3 |
| The Model | p. 4 |
| Results | p. 7 |
| Test 1: Does Resampling Matter? | p. 7 |
| Test 2: Which Protocol Performs Better Under Zero Intelligence? | p. 8 |
| Test 3: Does Learning Make a Difference? | p. 9 |
| Conclusions | p. 13 |
| References | p. 14 |
| Understanding the Price Dynamics of a Real Market Using Simulations: The Dutch Auction of the Pescara Wholesale Fish Market | p. 15 |
| Introduction | p. 15 |
| Market Description | p. 16 |
| Modeling the Buyers' Bidding Behavior | p. 17 |
| Simulations and Validation | p. 20 |
| Discussion and Conclusions | p. 23 |
| Appendix | p. 24 |
| The Bidding Threshold | p. 24 |
| Simulations Settings | p. 24 |
| References | p. 25 |
| Market Behavior Under Zero-Intelligence Trading and Price Awareness | p. 27 |
| Introduction | p. 27 |
| The Model | p. 28 |
| Behavioral Assumptions | p. 29 |
| Open and Closed Book Scenarios | p. 30 |
| Experimental Design | p. 30 |
| Results | p. 31 |
| Outcome Variables | p. 31 |
| Efficiency | p. 32 |
| Volume | p. 33 |
| Transaction Prices | p. 35 |
| Conclusions | p. 36 |
| References | p. 37 |
| Evolution and Decision Making | |
| Evolutionary Switching between Forecasting Heuristics: An Explanation of an Asset-Pricing Experiment | p. 41 |
| Introduction | p. 41 |
| Laboratory Experiment | p. 42 |
| Findings of the Experiment | p. 44 |
| Discussion | p. 44 |
| Evolutionary Model | p. 46 |
| Forecasting Heuristics | p. 47 |
| Evolutionary Switching | p. 48 |
| Model Initialization | p. 49 |
| Simulations of the Model | p. 50 |
| Conclusion | p. 52 |
| References | p. 52 |
| Prospect Theory Behavioral Assumptions in an Artificial Financial Economy | p. 55 |
| Introduction | p. 56 |
| The Model | p. 57 |
| Results and Discussion | p. 60 |
| Conclusions | p. 65 |
| References | p. 66 |
| Computing the Evolution of Walrasian Behaviour | p. 67 |
| Introduction | p. 67 |
| The Vega-Redondo Economy Model | p. 69 |
| The Behavioural Rules Set | p. 70 |
| Walrasian Equilibrium Revisited | p. 74 |
| Conclusions | p. 74 |
| References | p. 76 |
| Multidimensional Evolving Opinion for Sustainable Consumption Decision | p. 77 |
| Introduction | p. 77 |
| Multidimensional Opinion | p. 78 |
| Direct Opinion: An Opinion About the Characteristic | p. 79 |
| Indirect Opinion: An Opinion Resulting from Social Interaction | p. 80 |
| Consumers Classification | p. 81 |
| Computer Simulation and Results | p. 82 |
| Groups' Characteristics | p. 83 |
| Impact of Elasticity Values | p. 84 |
| Impact of Discussion Rate | p. 85 |
| Conclusion | p. 86 |
| References | p. 86 |
| Information Economics | |
| Local Interaction, Incomplete Information and Properties of Asset Prices | p. 91 |
| Introduction | p. 91 |
| The Economy | p. 94 |
| Simulation Results | p. 98 |
| Conclusion | p. 103 |
| References | p. 104 |
| Long-Term Orientation in Trade | p. 107 |
| Introduction | p. 108 |
| Long- vs. Short-Term Orientation | p. 108 |
| The Effect of LTO on Trade Processes | p. 110 |
| Representation in Agents | p. 112 |
| Experimental Verification | p. 115 |
| Conclusion | p. 117 |
| References | p. 118 |
| Agent-Based Experimental Economics in Signaling Games | p. 121 |
| Three Approaches to Study Signaling Games | p. 121 |
| Human-Subject Behaviour in a Signaling Game Experiment | p. 123 |
| Modelling Artificial Agents' Behaviour in Signalling Games | p. 124 |
| Parameters and Scenarios of the Simulation | p. 127 |
| Some Simulations Results | p. 127 |
| Conclusions | p. 128 |
| References | p. 129 |
| Methodological Issues | |
| Why do we need Ontology for Agent-Based Models? | p. 133 |
| Introduction | p. 133 |
| From Ontology in Philosophy and Computer Science to Ontological Design for ABM | p. 134 |
| From Individuals to Spatial Entities: What Entities Make Sense from the Ontological Standpoint? | p. 136 |
| Model vs. "Real" World and Ontological Test | p. 139 |
| Conclusion | p. 143 |
| References | p. 144 |
| Production and Finance in EURACE | p. 147 |
| Introduction | p. 148 |
| The EURACE Project | p. 148 |
| FLAME | p. 149 |
| The Real Sector | p. 150 |
| The Real-Financial Interaction | p. 150 |
| The Financial Management Module | p. 151 |
| General Assumptions | p. 151 |
| The Operating Cycle | p. 152 |
| Conclusion | p. 158 |
| References | p. 158 |
| Serious Games for Economists | p. 159 |
| Introduction | p. 159 |
| Individual-Based Methods | p. 161 |
| System Theories | p. 162 |
| Mathematical Biology and Game Theory | p. 163 |
| Simulation Methods | p. 164 |
| AI in Computer Games | p. 165 |
| Conclusions | p. 168 |
| References | p. 169 |
| Invited Speakers | |
| Computational Evolution | p. 175 |
| Introduction | p. 175 |
| Catastrophic Events in Macro Evolution | p. 177 |
| Variations of Micro Evolution | p. 181 |
| Evolution Strategy for Throwing | p. 183 |
| Other Examples for Micro Evolution | p. 186 |
| Bottom-Up Evolution by Digital Biochemistry | p. 187 |
| Summary and Outlook | p. 191 |
| References | p. 192 |
| Artificial Markets: Rationality and Organisation | p. 195 |
| Introduction | p. 195 |
| Relationships in Markets | p. 197 |
| The Marseille Fish Market (Saumaty) | p. 199 |
| A Simple Market Model | p. 201 |
| Trading Relationships Within the Market | p. 202 |
| A Little Formal Analysis | p. 203 |
| An Artificial Market Based on a Simpler Modelling Approach | p. 209 |
| Other Forms of Market Organisation | p. 216 |
| MERITAN a Market Based on Dutch Auctions | p. 217 |
| The Empirical Evidence | p. 219 |
| Price Dynamics | p. 219 |
| Loyalty Again | p. 221 |
| Comparison Between Auctions and the Decentralised Market in an Agent-Based Model | p. 223 |
| Common Features | p. 224 |
| The Auction Market | p. 225 |
| Profit Generated by the Rules | p. 227 |
| Simulations | p. 227 |
| Results with a Large Supply | p. 228 |
| Results with a Limited Supply | p. 231 |
| The Market when Both Sides Learn | p. 231 |
| Conclusion | p. 231 |
| References | p. 233 |
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