INDEX

2-person nonnal fonn game, 128

a priori knowledge, 308, 331 adaptation of preferences, 123 adaptive, 277, 281 agent, 305 agent-based computational economics

(ACE),3 altruism, 224 artificial intelligence, 46, 48 artificial life, 3 as-if assumption, 322 associative learning, 340 Austrian school, 3

backpropagation, 177,199,205,206, 208,215

backward looking, 277 bankruptcy prediction, 180 basins of attraction, 255 Bayesian learning, 283, 298 beliefs, 247 bi-matrix game, 128 biological evolution, 80 bounded rationality, 182, 246, 305,

311,330 bucket brigade algorithm, 272, 281

cellular automaton, 46-48, 51, 67 centrality, 320, 322, 323 classification, see neural networks classification of learning, 334 classifier system, 271, 277, 280, 281 co-evolution, 105 cognitive capacity, 342 cognitive learning, 334, 338, 343 cognitive models, 338, 343

cognitive processes, 335 common platfonn, 3 complex dynamics, 255 comprehensibility, 120 computer assisted design, 101 computer simulation, 45, 47, 48, 50-

52,59,60 contact bifurcation, 255 contagious, 226 context-dependency, 306, 328, 330,

999 cooperation, 224, 336, 347 correct model assumption, 323 creative, 330 creativity, 307 crossover, 78 CS, 271, 272, 274 curse of dimensionality, 179 cycling, 117

data validation, 117 decision-making, 199,217 deduction, 306 delta rule, 176, 207 descriptive veracity, 306, 307 Dosi, G., 3 dynamic environments, 109 dynamic programming, 261, 264, 269,

278,281

economic evolution, 80 economic-engineering, 101 economomic meaning of genetic rep-

resentation, 81 economomic meaning of mutation op­

erator,86

388

economomic meaning of recombina­tion operator, 85

economomic meaning of selection op-erator,84

efficient market hypotheses, 181 EI Farol bar, 306, 321, 324 electricity, 104 emergence, 329,330 emergent hierarchical organization

(EHO),3 empirical risk minimization (ERM),

171 environmental model, 291 equilibrium, 261-263, 269, 271 equilibrium selection, 114 evolution, 124, 127,305,311 evolutionary economics, 3 evolutionary processes, 333 expectation, 261,263,270 expectational stability, 244 explanation, 120 explanatory model, 66 exploitation, 347 exploitative, 306, 311 exponentially fading memory, 253

feed-forward neural networks, 174 Fehr,105 fermi, 205,207 fictitious play, 109 fitness, 211 fitness function, 76, 198,209 Flip bifurcation, 255 forward looking, 277

GA, 271, 273 games, 333 garbage-can model, 60, 61, 67 generalization, 172 genetic algorithm, 73,105, 197,198,

200,202,271,273,280 . genetic programming, 305,307-310 genetic representation, 74

genotype, 198,200,201,212 Green, 105

Harbord, 105 Hayek,102 heterogeneity, 329, 330, 340 hidden layer, 204, 205, 207, 208, 213,

214 hierarchy of learning, 339 hypothesis space, 171

identification, 341 inconsistency, 306 indecisiveness, 113 indirect evolutionary approach, 124,

127 input function, 204, 205 interaction neighbourhood, 230 Islam, 283, 300

kernel trick, 186 knowledge

prior knowledge, 174

language of representation, 311, 313, 327,331

latent utility, 188, 190 lattice, 233 learning, 88, see 1. algorithms, see

preference 1., 206, 207, 209, 216,217,306,308,320, 333

learning algorithms, see support vec-tor network 1.

for multi-layer perceptrons, 177 for perceptrons, 176 for RBF networks, 178

learning in games, 333 learning machine, 169 learning neighbourhood, 230 learning process, 305 least mean squares, 249 local interactions, 222, 227

local stability conditions, 251 lock-in, 316, 317, 320 loss, 171 Lotka-Volterra model, 65, 66

market, 261, 262, 270, 274, 275, 278, 280

master equation, 56, 57 mathematical modeling, 45, 52 measurement, 60-62 mechanism design, 101 memory, 253 mental model, 305, 311 microanalytic simulation model, 46,

61 minimum state variable (MSV) cri-

terion, 244 mixed strategy, 128 model selection, 173 model specification, 51,52 modelling, 305, 307 motivation, 346 motive, 347 multi-agent modeling, 51 multilayer perceptron, 176 multilevel model, 48, 51, 57 multilevel stochastic process, 49 mutation, 138, 197, 198,200-203,

211,212,214,218 mutation operator, 80

Nash equilibrium, 129 Nelson, R., 3, 101 neural networks, see feed-forward

N.N., see backpropagation, see RBF n., 197-199,213

for classification of economic agents, 180

for modelling bounded rational agents, 182

for time series prediction, 181 neurons, 199, 203, 204, 207 Newbery, 105

389

non perfect foresight, 255

operator, 77 optimal growth, 283, 290, 298 optimal policy, 263, 266, 268, 275 ordinary differential equation, 248 output function, 204--207

parallelism, 202 path-dependency, 330 payoff dominance, 129 payoff-monotone dynamics, 137 perceptron, 175, see multylayer p. perfect informationlogical

omniscience, 306 phenotype, 200, 201, 212 population, 197, 198, 200-203, 211,

214,215,218 prcedural rationality, 305 prediction, 49, 54, 58, 60-62, 66, 323 preference formation model, 53, 58 preference learning, 187 preferences, 123 prisoner's dilemma, 102, 224, 335 private learning, 111 punishment, 226

qualitative simulation, 46, 48

random,263,265,272,273,275,277, 280,281

random matching, 226 rational expectations equilibria, 246 RBF network, 177 recombination operator, 78 recurrent nets, 205, 206 recursive ordinary least square , 248 regularization, 179 reliability, 316 repeated interaction, 336, 339 replication, 131, l37 replicator dynamics, 131 reproduction, 228

390

risk dominance, 129 risk functionals, 171 risk minimization, see empirical

R.M., see structural R.M. routine, 341 RPD, 198-200, 209,211-213,335

satisficing, 330 scenario modelling, 109 Schelling, T., 3 schema theorem, 273 Schumpeter, J. A., 3 Schumpeterian tradition, 3 SDML,312 selection operator, 77, 86 self referential linear stochastic

(SRLS) models, 247 serendipidy, 330 shattering, 173 Silverberg, G., 3 simon, 305 simulation, 3,236,305 simulation tool, 46, 51, 53, 61 single-mindedness, 113 social interactions, 221 social learning, 111 steepest descent gradient, 249 stochastic approach, 348 stochastic programming, 283 strategic interactions, 221 structural change, 317-319, 330 structural interactions, 222 structural risk minimization (SRM),

173 stylized facts, 3 sugerscape, 3 support vector network learning, 183,

187 support vectors, 185 symbol system, 45-47,50,51 synergetics, 54 system dynamics, 46, 47, 52, 61

thought experiment, 53 time series prediction, 181 tractability, 120 tree crossover, 310

unbeatable, 231 unit, 204, 205, 207, 208 utility function, 130, 132, 135, 313

VC-dimension, 173 Verspagen, B., 3

wealth distribution, 262, 263, 266, 268, 274,276

weight decay, 179 Winter, S., 3, 101

Advances in Computational Economics

1. A. Nagurney: Network Economics. A Variational Inequality Approach. 1993 ISBN 0-7923-9293-0

2. A.K. Duraiappah: Global Warming and Economic Development. A Holistic Approach to International Policy Co-operation and Co-ordination. 1993

ISBN 0-7923-2149-9 3. D.A. Belsley (ed.): Computational Techniques for Econometrics and Economic

Analysis. 1993 ISBN 0-7923-2356-4 4. W.W. Cooper and A.B. Whinston (eds.): New Directions in Computational Eco-

nomics.1994 ISBN 0-7923-2539-7 5. M. Gilli (ed.): Computational Economic Systems. Models, Methods & Econometrics.

1996 ISBN 0-7923-3869-3 6. H. Amman, B. Rustem, A. Whinston (eds.): Computational Approaches to Economic

Problems. 1997 ISBN 0-7923-4397-2 7. G. Pauletto: Computational Solutions of Large-Scale Macroeconometric

Models. 1997 ISBN 0-7923-4656-4 8. R.D. Herbert: Observers and Macroeconomic Systems. Computation of Policy Tra-

jectories with Separate Model Based Control. 1998 ISBN 0-7923-8239-0 9. D. Ho and T. Schneeweis (eds.): Applications in Finance, Investments, and Banking.

1999 ISBN 0-7923-8294-3 10. A. Nagumey: Network Economics: A Variational Inequality Approach. Revised

second edition. 1999 ISBN 0-7923-8350-8 11. T. Brenner: Computational Techniques for Modelling Learning in Economics. 1999

ISBN 0-7923-8503-9

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