Handbook of Computational Economics: Agent-Based Computational Economics, Vol. 2

Introduction to the Series......Page 6
Contents of the Handbook......Page 8
Purpose......Page 12
Organization......Page 13
Reference......Page 16
Contents of Volume 2......Page 18
Agent-Based Computational Economics: A Constructive Approach to Economic Theory......Page 34
Keywords......Page 35
Introduction......Page 36
ACE study of economic systems......Page 39
From Walrasian equilibrium to ACE trading......Page 48
Walrasian bliss in a hash-and-beans economy......Page 49
Plucking out the Walrasian Auctioneer......Page 50
The ACE Trading World: Outline......Page 51
Defining ``equilibrium'' for the ACE Trading World......Page 53
Constructive understanding......Page 55
The essential primacy of survival......Page 56
Strategic rivalry and market power......Page 57
Behavioral uncertainty and learning......Page 58
The role of conventions and organizations......Page 62
Interactions among attributes, institutions, and behaviors......Page 64
Concluding remarks......Page 66
Activity flow for hash firms in period T......Page 69
Profit allocation method for hash firm j......Page 70
Representation of hash firm j's supply offers......Page 71
Hash firm j's learning problem......Page 72
The VRE learning algorithm for hash firm j......Page 73
Consumer price discovery process in period T......Page 75
A typical price-discovery round for an arbitrary consumer k......Page 76
Classification of variables......Page 79
References......Page 80
Computationally Intensive Analyses in Economics......Page 84
Keywords......Page 85
Computational tools......Page 86
Weaknesses of standard models......Page 87
Criticisms of computationally intensive research......Page 89
Systematic approaches to computationally intensive research......Page 90
Search for counterexamples......Page 91
Sampling methods......Page 92
Regression methods......Page 93
Synergies with conventional theory......Page 94
References......Page 95
Agent Learning Representation: Advice on Modelling Economic Learning......Page 98
Keywords......Page 99
History of modelling learning......Page 100
Psychological research on learning......Page 101
Increasing variety of learning models......Page 102
Potential alternative classifications......Page 103
Proposed classification......Page 104
Two ways of learning......Page 105
Modelling non-conscious learning......Page 106
Existing models......Page 107
Bush-Mosteller model......Page 109
Modelling routine-based learning......Page 110
Experimentation......Page 111
Melioration and experience collection......Page 112
Imitation......Page 114
Satisficing......Page 116
Replicator dynamics and selection-mutation equation......Page 117
Evolutionary algorithms......Page 118
Combined models: EWA and VID model......Page 120
Modelling belief learning......Page 121
Psychological findings about cognitive learning......Page 122
Fictitious play......Page 124
Bayesian learning......Page 125
Least-squares learning......Page 126
Genetic programming......Page 127
Classifier systems......Page 128
Rule learning......Page 129
Stochastic belief learning......Page 130
Conclusions and recommendations......Page 131
Non-conscious versus belief learning......Page 132
Choosing a learning model......Page 135
Aims in choosing a learning model......Page 136
Experimental evidence......Page 137
Adequateness of the details of the model......Page 138
Simplicity of the model......Page 139
Individual and population learning......Page 140
Recommendations for non-conscious learning......Page 141
General models......Page 142
Separate modelling......Page 143
Recommendations for belief learning......Page 144
References......Page 145
Agent-Based Models and Human Subject Experiments......Page 152
Keywords......Page 153
Introduction......Page 154
The double auction environment......Page 158
Gode and Sunder's zero-intelligence traders......Page 160
Reaction and response......Page 164
Other applications of the ZI methodology......Page 167
ZI agents in general equilibrium......Page 169
Summary......Page 174
Reinforcement learning......Page 175
Belief-based learning......Page 182
Comparisons of reinforcement and belief-based learning......Page 187
Summary......Page 188
Evolutionary algorithms as models of agent behavior......Page 189
Replicator dynamics......Page 190
Genetic algorithms......Page 192
Comparisons between genetic algorithm and reinforcement learning......Page 201
Classifier systems......Page 202
Genetic programming......Page 204
Conclusions and directions for the future......Page 206
References......Page 208
Economic Activity on Fixed Networks......Page 216
Keywords......Page 217
Introduction......Page 218
Some notable networks......Page 219
The star......Page 220
The tree......Page 222
Small-worlds......Page 223
Coordination and cooperation in networks......Page 224
Coordination......Page 225
Cooperation......Page 227
The star......Page 229
The ring......Page 230
The grid......Page 233
The tree......Page 234
Small-world networks......Page 236
Power networks......Page 237
Exchange in networks......Page 238
Conclusions......Page 245
References......Page 246
ACE Models of Endogenous Interactions......Page 250
Keywords......Page 251
Introduction......Page 252
Schelling (1971): residential pattern......Page 255
Epstein and Axtell (1996): resource gradient......Page 257
Arthur (1994): predictors......Page 259
Vriend (1995): advertising/patronage......Page 262
Ashlock et al. (1996): (threshold) expected payoff......Page 264
Riolo (1997): arbitrary tags......Page 266
Hanaki et al. (2004): trust......Page 270
Kirman and Vriend (2001): expected payoff/familiarity......Page 272
Chang and Harrington (2005): past success rate......Page 275
Jackson and Rogers (2004): directed random search......Page 278
Concluding remarks......Page 279
For further reading......Page 280
References......Page 281
Social Dynamics: Theory and Applications......Page 284
Keywords......Page 285
Adaptive dynamics......Page 286
Stochastic stability......Page 289
Technology adoption......Page 291
Characterizing the stochastically stable states......Page 293
Efficiency versus stochastic stability......Page 295
Application to Schelling's segregation model......Page 297
Local interaction models......Page 300
Contractual norms......Page 303
Conclusion......Page 308
References......Page 310
Heterogeneous Agent Models in Economics and Finance......Page 312
Keywords......Page 313
Introduction......Page 314
An early example......Page 319
Survey data on expectations......Page 321
An exchange rate model......Page 325
Noise traders and behavioral finance......Page 327
Rational versus noise traders......Page 328
Imitation of beliefs......Page 329
Informed arbitrage versus positive feedback trading......Page 331
An early disequilibrium model with speculators......Page 334
Market maker models......Page 336
A chaotic exchange rate model......Page 341
An exchange rate model with local interactions......Page 345
Social interactions......Page 349
Heterogeneity and important stylized facts......Page 354
Contagion behavior of chartists......Page 355
Switching between chartists and fundamentalists......Page 356
Price formation......Page 357
Dynamical behavior and time series properties......Page 358
Examples......Page 361
Rational versus naive expectations......Page 362
An asset pricing model with heterogeneous beliefs......Page 368
The model......Page 369
Evolutionary selection of strategies......Page 370
Few-type examples......Page 371
Fundamentalists versus opposite biases......Page 372
Fundamentalists versus trend and bias......Page 373
Many trader types......Page 375
Concluding remarks and future perspective......Page 378
References......Page 382
Agent-based Computational Finance......Page 390
Keywords......Page 391
Introduction......Page 392
Price determination......Page 397
Evolution and learning......Page 399
Information representation......Page 400
Artificial financial markets......Page 401
Few-type models......Page 402
Model dynamics under learning......Page 403
Emergence and many-type models......Page 407
Calibration......Page 414
Memory and return autocorrelations......Page 415
Volatility......Page 416
Macro fundamentals......Page 417
Estimation and validation......Page 419
Other markets......Page 421
Cautions and criticisms......Page 425
Conclusions......Page 428
References......Page 430
Agent-based Models of Innovation and Technological Change......Page 438
Keywords......Page 439
Introduction......Page 440
General characteristics of the evolutionary approach......Page 446
The analysis of Nelson and Winter (1982)......Page 448
Knowledge accumulation, knowledge structure and spillovers......Page 451
Dealing with substantive uncertainty: design of innovations, search in the technology landscape and prediction of market response......Page 454
The importance of the heterogeneity of innovation strategies......Page 458
Micro-founded models of economic growth......Page 459
Industry studies and `history-friendly' models......Page 462
Discussion......Page 466
Outlook......Page 468
References......Page 470
Agent-Based Models of Organizations......Page 476
Abstract......Page 477
Keywords......Page 478
Introduction......Page 479
Roadmap and a guide for neoclassical economists......Page 481
How to model an organization......Page 482
Agents......Page 483
Organizations......Page 484
Environments......Page 486
Implementation of an agent-based model of an organization......Page 487
How does agent-based computational economics differ from neoclassical economics?......Page 488
Search and learning......Page 491
Modelling search......Page 492
NK model......Page 493
Economic model......Page 495
Modelling search by a single agent......Page 497
Organizational search with units solving similar problems......Page 498
Kollman et al. 2000......Page 499
Chang and Harrington (2000)......Page 500
Chang and Harrington (2003)......Page 502
Organizational search with units solving different problems......Page 503
Rivkin and Siggelkow (2003)......Page 504
Siggelkow and Levinthal (2003)......Page 506
Evolving an organizational structure......Page 508
Ethiraj and Levinthal (2002)......Page 509
What do we learn from a computational agent-based approach?......Page 511
Information processing......Page 513
Generic properties of information processing networks......Page 514
Miller (2001)......Page 515
Carley (1992)......Page 517
Barr and Saraceno (2002)......Page 519
Barr and Saraceno (2005)......Page 521
Carley and Svoboda (1996)......Page 522
Miller (2001)......Page 523
Effort and the commons problem in organizations......Page 525
Axtell (1999)......Page 526
Organizational norms......Page 528
March (1991)......Page 529
Growing an organization......Page 530
Epstein (2003)......Page 531
Critique of the past and directions for the future......Page 534
References......Page 537
Market Design Using Agent-Based Models......Page 542
Keywords......Page 543
Designer markets......Page 544
Simulation and analysis......Page 546
Evolutionary simulation techniques......Page 547
Learning......Page 549
From analysis to design......Page 552
Market design......Page 553
Design trade-offs......Page 555
Moving from closed-form equilibria......Page 556
Explicit use of agents......Page 557
Electricity market design......Page 559
Academic engineers......Page 560
Hämäläinen et al. (1997) model both sides of the market......Page 561
Talukdar (2002) models customers holding down the wholesale price......Page 562
Lane et al. (2000) use GAs for double auctions......Page 563
MacGill and Kaye (1999) simulate for system efficiency......Page 564
Curzon Price (1997) models electricity markets......Page 565
Nicolaisen et al. (2000) search for market power......Page 566
Nicolaisen et al. (2001) use reinforcement learning......Page 567
Bunn and Oliveira (2003) help design a new wholesale market......Page 570
Recent non-academic research centers......Page 571
``Evolutionary mechanism design'' at Liverpool......Page 573
Byde (2002) evolves a new form of sealed-bid single auction......Page 575
References......Page 577
Automated Markets and Trading Agents......Page 584
Keywords......Page 585
Introduction......Page 586
Marketplace design framework......Page 587
Marketplace systems......Page 588
Formal model......Page 590
Design and goods......Page 591
Designing agents......Page 592
Possibilities and impossibilities......Page 593
Connecting: discovery services......Page 594
Auction aggregation......Page 595
Dealing: negotiation mechanisms......Page 596
Allocating computational and communication network resources......Page 597
Scheduling......Page 600
Belief discovery and aggregation......Page 601
Problems with complementarities......Page 602
Combinatorial market design......Page 604
Automating market participants......Page 608
Program trading......Page 610
Agent strategies......Page 611
Continuous double auction strategies......Page 612
Simultaneous ascending auction strategies......Page 613
TAC travel-shopping rules......Page 616
TAC experience......Page 617
A computational reasoning methodology for analyzing mechanisms and strategies......Page 619
Generate candidate strategies......Page 621
Estimate the ``empirical game''......Page 622
Solve the empirical game......Page 623
Analyze the results......Page 624
References......Page 625
Computational Methods and Models of Politics......Page 636
Keywords......Page 637
Introduction......Page 638
Core questions motivating political scientists......Page 639
Different practices in comparison with economics......Page 640
Methodological diversity......Page 642
Adaptation......Page 644
Difference......Page 645
Externalities......Page 646
Path dependence......Page 647
Geography......Page 648
Networks......Page 649
Emergence......Page 650
Models of electoral competition......Page 651
Institutional comparisons......Page 656
Individuals or agents adapting in complex political environments......Page 658
The spread of collective identities or authority structures......Page 661
Conclusion......Page 663
References......Page 664
Governing Social-Ecological Systems......Page 668
Keywords......Page 669
Introduction......Page 671
A framework for social-ecological systems......Page 675
Social dilemmas......Page 678
Theoretical models......Page 679
Laboratory experiments related to the governance of social-ecological systems......Page 682
Agent-based models of laboratory experiments......Page 683
Applications to social-ecological systems......Page 686
What have we learned?......Page 688
Theoretical models......Page 689
Laboratory experiments......Page 696
Applications......Page 697
Topology of interactions......Page 699
Theoretical models......Page 700
Applications......Page 701
What have we learned?......Page 703
Discussion and conclusions......Page 704
References......Page 705
Computational Laboratories for Spatial Agent-Based Models......Page 714
Abstract......Page 715
Keywords......Page 716
Overview of chapter......Page 717
Challenges posed by spatial systems......Page 718
Addressing spatial system challenges with comp labs......Page 720
Richly structured spatial or organizational network landscapes......Page 721
A well-equipped comp lab for spatial agent-based modeling......Page 722
Generating, controlling, and observing many simulations for each model......Page 724
Advanced tools for effective search, control, optimization, and testing......Page 725
Hierarchies of Agent Classes......Page 726
Comp lab spatial and organizational landscapes......Page 729
Introduction to comp lab generation of synthetic landscapes......Page 730
Aspatial small-world and scale-free networks......Page 731
Spatial small-worlds, contraction factors, and modeling globalization......Page 733
Addition of new links......Page 734
A. Network landscapes (usually spatial, but may be aspatial)......Page 735
C. Simple models of agent travel patterns......Page 736
Locational games and spatio-temporal coordination problems......Page 737
Innovation and ecological emergence of new ideas, inventions, or diseases......Page 739
Exploration and analysis of spatial system behaviors......Page 740
Preliminary thought experiments......Page 741
Scaling agent-based simulation models......Page 742
Visualization and data collection......Page 743
Testing model components......Page 744
Exploring model behavior......Page 745
Inference and discovery of key exceptions......Page 747
Opportunities, challenges, and resources......Page 748
References......Page 749
Out-of-Equilibrium Economics and Agent-Based Modeling......Page 754
Keywords......Page 755
Beyond equilibrium......Page 756
Equilibrium indeterminacy and the selection process......Page 760
Expectational indeterminacy and inductive behavior......Page 762
Conclusion......Page 765
References......Page 766
Agent-based Modeling as a Bridge Between Disciplines......Page 768
Keywords......Page 769
Introduction......Page 770
ABM can address fundamental problems seen in many disciplines......Page 771
ABM facilitates interdisciplinary collaboration......Page 774
ABM provides a useful multidisciplinary tool when the math is intractable......Page 777
ABM can reveal unity across disciplines......Page 780
ABM can be a hard sell......Page 781
Convergence within the ABM community can enhance the interdisciplinary value of ABM......Page 785
References......Page 786
Remarks on the Foundations of Agent-Based Generative Social Science......Page 788
Keywords......Page 789
Features of agent-based models......Page 790
Recent expansion......Page 791
Generative sufficiency......Page 792
Equations exist......Page 793
Agent models deduce......Page 794
What about randomness?......Page 795
Social science as the satisfaction of normal forms......Page 796
Example 2. Spatial patterns......Page 797
Generative implies deductive, but not conversely: nonconstructive existence......Page 798
Incompleteness (attainability at all) and complexity (attainability on time scales of interest) in social science......Page 799
Generality is quantification over sets......Page 801
Truth and beauty......Page 802
Summary......Page 804
References......Page 805
Coordination Issues in Long-Run Growth......Page 808
Keywords......Page 809
Introduction......Page 810
The representative agent model and its limitations......Page 811
Externalities and unintended side effects......Page 814
Uncertainty and the classical stability hypothesis......Page 818
Looking ahead......Page 823
References......Page 825
Agent-Based Macro......Page 828
Keywords......Page 829
Two traditions......Page 831
The ambivalence of neoclassical economics......Page 832
Tapping into the older tradition......Page 833
Taking supply and demand seriously......Page 834
Keynes and all that......Page 837
Decline and fall......Page 838
Conclusion......Page 839
References......Page 840
Some Fun, Thirty-Five Years Ago......Page 842
Keywords......Page 843
References......Page 847
A Guide for Newcomers to Agent-Based Modeling in the Social Sciences......Page 850
Keywords......Page 851
Agent-based modeling and the social sciences......Page 852
Complexity and ABM......Page 854
Emergence of collective behavior......Page 855
Learning......Page 856
Norms......Page 857
Markets......Page 858
Institutional design......Page 859
Networks......Page 860
What to do next......Page 861
Author Index......Page 864
Subject Index......Page 890