Contents
xi
5.2.2 Assigning Conditions with Functions . . . . . . . . . . 113
5.2.3 Using Dynamic Arrays and Data Structures . . . . . . 113
5.2.4 Creating Local Dynamic Arrays . . . . . . . . . . . . . 114
5.3 Modeling Pedestrians in Crowds . . . . . . . . . . . . . . . . 114
5.3.1 Calculate Movement toward Other Agents . . . . . . . 116
5.3.2 Entering and Exiting Agents . . . . . . . . . . . . . . 118
6 Agents in Economic Markets and Games
121
6.1 Perfect Rationality versus Bounded Rationality . . . . . . . 125
6.2 Modeling Multiple Shopper Behaviors . . . . . . . . . . . . . 126
6.3 Learning Firms in a Cournot Model . . . . . . . . . . . . . . 129
6.3.1 Genetic Programming with Agents . . . . . . . . . . . 143
6.3.2 Filtering Messages in Advance . . . . . . . . . . . . . 150
6.3.3 Comparing Two Data Structures . . . . . . . . . . . . 151
6.4 A Virtual Mall Model: Labor and Goods Market Combined . 152
6.5 Programming Games . . . . . . . . . . . . . . . . . . . . . . 159
6.5.1 Nash Equilibrium . . . . . . . . . . . . . . . . . . . . . 160
6.5.2 Evolutionary Game Theory . . . . . . . . . . . . . . . 161
6.5.3 Evolutionary Stable State . . . . . . . . . . . . . . . . 162
6.5.4 Game Theory versus Evolutionary Game Theory . . . 162
6.5.5 Continuous Strategies . . . . . . . . . . . . . . . . . . 163
6.5.6 Red Queen and Equilibrium . . . . . . . . . . . . . . . 163
6.6 Learning in an Iterated Prisoner’s Dilemma Game . . . . . . 164
6.7 Multi-Agent Systems and Games . . . . . . . . . . . . . . . . 173
7 Agents in Biology
175
7.1 Example Models . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.1.1 Molecular Systems Models . . . . . . . . . . . . . . . . 176
7.1.2 Tissue and Organ Models . . . . . . . . . . . . . . . . 179
7.1.3 Ecological Models . . . . . . . . . . . . . . . . . . . . 182
7.1.4 Industrial Applications of Agent-Based Modeling with
FLAME . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.2 Modeling Epithelial Tissue . . . . . . . . . . . . . . . . . . . 184
7.2.1 Merging with Other Toolkits . . . . . . . . . . . . . . 185
7.3 Modeling Drosophila Embryo Development . . . . . . . . . . 187
7.3.1 Stochastic Modeling . . . . . . . . . . . . . . . . . . . 188
7.3.2 Converting to an Agent-Based Model . . . . . . . . . 188
7.3.3 Find Optimum Model Settings . . . . . . . . . . . . . 196
7.4 Output Files for Analysis . . . . . . . . . . . . . . . . . . . . 198
7.5 Modeling Pharaoh’s Ants (Monomorium pharaonis) . . . . . 202
7.6 Model Drug Delivery for Cancer Treatment . . . . . . . . . . 224
7.6.1 Using Multiple Outputs . . . . . . . . . . . . . . . . . 234
xi
5.2.2 Assigning Conditions with Functions . . . . . . . . . . 113
5.2.3 Using Dynamic Arrays and Data Structures . . . . . . 113
5.2.4 Creating Local Dynamic Arrays . . . . . . . . . . . . . 114
5.3 Modeling Pedestrians in Crowds . . . . . . . . . . . . . . . . 114
5.3.1 Calculate Movement toward Other Agents . . . . . . . 116
5.3.2 Entering and Exiting Agents . . . . . . . . . . . . . . 118
6 Agents in Economic Markets and Games
121
6.1 Perfect Rationality versus Bounded Rationality . . . . . . . 125
6.2 Modeling Multiple Shopper Behaviors . . . . . . . . . . . . . 126
6.3 Learning Firms in a Cournot Model . . . . . . . . . . . . . . 129
6.3.1 Genetic Programming with Agents . . . . . . . . . . . 143
6.3.2 Filtering Messages in Advance . . . . . . . . . . . . . 150
6.3.3 Comparing Two Data Structures . . . . . . . . . . . . 151
6.4 A Virtual Mall Model: Labor and Goods Market Combined . 152
6.5 Programming Games . . . . . . . . . . . . . . . . . . . . . . 159
6.5.1 Nash Equilibrium . . . . . . . . . . . . . . . . . . . . . 160
6.5.2 Evolutionary Game Theory . . . . . . . . . . . . . . . 161
6.5.3 Evolutionary Stable State . . . . . . . . . . . . . . . . 162
6.5.4 Game Theory versus Evolutionary Game Theory . . . 162
6.5.5 Continuous Strategies . . . . . . . . . . . . . . . . . . 163
6.5.6 Red Queen and Equilibrium . . . . . . . . . . . . . . . 163
6.6 Learning in an Iterated Prisoner’s Dilemma Game . . . . . . 164
6.7 Multi-Agent Systems and Games . . . . . . . . . . . . . . . . 173
7 Agents in Biology
175
7.1 Example Models . . . . . . . . . . . . . . . . . . . . . . . . . 176
7.1.1 Molecular Systems Models . . . . . . . . . . . . . . . . 176
7.1.2 Tissue and Organ Models . . . . . . . . . . . . . . . . 179
7.1.3 Ecological Models . . . . . . . . . . . . . . . . . . . . 182
7.1.4 Industrial Applications of Agent-Based Modeling with
FLAME . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.2 Modeling Epithelial Tissue . . . . . . . . . . . . . . . . . . . 184
7.2.1 Merging with Other Toolkits . . . . . . . . . . . . . . 185
7.3 Modeling Drosophila Embryo Development . . . . . . . . . . 187
7.3.1 Stochastic Modeling . . . . . . . . . . . . . . . . . . . 188
7.3.2 Converting to an Agent-Based Model . . . . . . . . . 188
7.3.3 Find Optimum Model Settings . . . . . . . . . . . . . 196
7.4 Output Files for Analysis . . . . . . . . . . . . . . . . . . . . 198
7.5 Modeling Pharaoh’s Ants (Monomorium pharaonis) . . . . . 202
7.6 Model Drug Delivery for Cancer Treatment . . . . . . . . . . 224
7.6.1 Using Multiple Outputs . . . . . . . . . . . . . . . . . 234