Muutke küpsiste eelistusi

E-raamat: Agent-Based Computational Economics: How the idea originated and where it is going

(National Chengchi University, Taiwan)
Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 61,10 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book aims to answer two questions that are fundamental to the study of agent-based economic models: what is agent-based computational economics and why do we need agent-based economic modelling of economy? This book provides a review of the development of agent-based computational economics (ACE) from a perspective on how artificial economic agents are designed under the influences of complex sciences, experimental economics, artificial intelligence, evolutionary biology, psychology, anthropology and neuroscience.

This book begins with a historical review of ACE by tracing its origins. From a modelling viewpoint, ACE brings truly decentralized procedures into market analysis, from a single market to the whole economy. This book also reviews how experimental economics and artificial intelligence have shaped the development of ACE. For the former, the book discusses how ACE models can be used to analyse the economic consequences of cognitive capacity, personality and cultural inheritance. For the latter, the book covers the various tools used to construct artificial adaptive agents, including reinforcement learning, fuzzy decision rules, neural networks, and evolutionary computation.

This book will be of interest to graduate students researching computational economics, experimental economics, behavioural economics, and research methodology.

Arvustused

"Chens book is a meticulous scholarly study that should be of particular interest to economists with a good analytical background, a broad acquaintance with economic research, a strong interest in empirically grounded economic modeling, and a willingness to consider alternative viewpoints with an open mind."

Leigh Tesfatsion, Iowa State University, Journal of Economic Literature

List of figures xiii
List of tables xvi
Preface xvii
Part I: Ideas and structures of the book 1(28)
1 Economics in an interdisciplinary context
3(8)
1.1 The interdisciplinary framework
5(1)
1.2 Organization of the book
6(5)
2 Agent-based modeling in the social sciences
11(18)
2.1 What is it?
11(4)
2.2 Why?
15(3)
2.3 Agent-based modeling in different disciplines
18(6)
2.4 The ten that make it new
24(5)
Part II: Origins of ACE 29(116)
3 The markets origin
31(57)
3.1 Agent-based modeling of the tatonnement process
34(4)
3.2 Agent-based modeling of the non-tatonnement process
38(22)
3.3 Auctions
60(11)
3.4 Macroeconomics
71(12)
3.5 Interacting heterogeneous agents
83(5)
4 Cellular automata
88(25)
4.1 Segregation
89(3)
4.2 Game of Life
92(2)
4.3 Elementary cellular automata
94(5)
4.4 Opinion dynamics and market sentiment
99(9)
4.5 Other physics-oriented agent-based models
108(1)
4.6 Further explorations
109(4)
5 Economic tournament origin
113(8)
5.1 Novelty-discovering agents
113(1)
5.2 Tournament-based economic analysis
114(2)
5.3 Automated open tournaments
116(3)
5.4 Concluding remarks
119(2)
6 Agent-based modeling of economic experiments
121(24)
6.1 Agent-based modeling of cobweb experiments
122(6)
6.2 Agent-based modeling of inflation experiments
128(8)
6.3 Agent-based modeling of foreign exchange experiments
136(6)
6.4 Concluding remarks
142(3)
Part III: Designing artificial economic agents 145(48)
7 Calibrated artificial agents
147(9)
7.1 Challenges proposed
147(2)
7.2 N-armed bandit problem
149(1)
7.3 Reinforcement learning: Arthur's version
149(2)
7.4 Early experiments on learning and choice-making
151(4)
7.5 Concluding remark
155(1)
8 Zero-intelligence agents in the DA markets
156(9)
8.1 Principles beyond calibration
156(1)
8.2 Double auction markets and experiments
157(1)
8.3 Gode-Sunder model
157(2)
8.4 Near zero-intelligence agents
159(1)
8.5 Simplicity, intelligence, and randomness
160(3)
8.6 Concluding remarks
163(2)
9 Autonomous agents in the DA markets
165(28)
9.1 Programmed agents and autonomous agents
165(2)
9.2 Santa Fe double auction markets
167(9)
9.3 Andrews-Prager model
176(4)
9.4 AIE-DA tournaments
180(6)
9.5 U-Mart
186(1)
9.6 Wisdom of crowds
186(5)
9.7 Further explorations
191(2)
Part IV: Computational intelligence 193(58)
10 Reinforcement learning
195(12)
10.1 The three-parameter Roth-Ever model
196(2)
10.2 Generalized reinforcement learning
198(4)
10.3 Level-k reasoning and sophisticated EWA
202(3)
10.4 Regime-switching agents
205(2)
11 Fuzzy logic and rough sets
207(4)
11.1 Fuzzy logic
207(2)
11.2 Rough sets
209(2)
12 Artificial neural networks
211(26)
12.1 Multilayer perceptron neural networks
211(2)
12.2 Radial basis network
213(1)
12.3 Recurrent neural networks
214(2)
12.4 Auto-associative neural networks
216(3)
12.5 Support vector machines
219(2)
12.6 Self-organizing maps and k-means
221(3)
12.7 K nearest neighbors
224(1)
12.8 Instance-based learning
225(2)
12.9 Finite state automata
227(4)
12.10 Decision trees
231(2)
12.11 Further study
233(4)
13 Evolutionary computation
237(14)
13.1 Tools for evolutionary economics
237(2)
13.2 Evolutionary strategies
239(2)
13.3 Evolutionary programming
241(1)
13.4 Genetic programming and genetic algorithms
242(9)
Part V: Agent-based financial markets 251(64)
14 Artificial financial markets with programmed agents
253(22)
14.1 Few-type design
254(4)
14.2 Many-type designs
258(2)
14.3 Illustrations
260(1)
14.4 A mesoscopic approach to complex dynamics
261(5)
14.5 Market fraction hypothesis
266(9)
15 Artificial financial markets with autonomous agents
275(10)
15.1 Genetic algorithms
275(2)
15.2 Classifier system
277(1)
15.3 Genetic programming and autonomous agents
277(1)
15.4 Artificial stock markets
278(7)
16 Empirically based agent-based models
285(30)
16.1 Financial stylized facts
285(7)
16.2 Presenting agent-based economics with econometrics
292(5)
16.3 Building ACE with econometrics
297(1)
16.4 How to estimate?
297(4)
16.5 What to estimate?
301(5)
16.6 Forecasts with agent-based financial models
306(1)
16.7 ACE as a foundation of econometrics
307(4)
16.8 Concluding remarks
311(4)
Part VI: Cognitive and psychological agent-based modeling 315(60)
17 Economic significance of personal traits
317(16)
17.1 Intelligence, income, and prosperity
317(1)
17.2 Intelligence in experimental economics
317(4)
17.3 Intelligence and risk preference
321(1)
17.4 Intelligence and time preference
322(1)
17.5 Personality and earning capacity
323(1)
17.6 Personality in experimental economics
323(2)
17.7 Culture in experimental economics
325(8)
18 Neuroeconomic agents
333(12)
18.1 Neuroeconomics: an ACE viewpoint
333(1)
18.2 Preference
334(3)
18.3 Value and choice
337(2)
18.4 Risk
339(2)
18.5 Learning
341(1)
18.6 Software agents with neurocognitive dual system
342(1)
18.7 Agent-based or brain-based?
343(2)
19 Cognitive agents
345(14)
19.1 Introduction: heterogeneity and hierarchy
345(2)
19.2 Heterogeneity
347(4)
19.3 Cognitive hierarchy
351(6)
19.4 Concluding remarks
357(1)
19.5 Further study
357(2)
20 Culturally sensitive agents
359(4)
20.1 Evolutionary models of cultures
360(1)
20.2 Culturally based behavioral rules
360(1)
20.3 Culturally based preference
361(2)
21 Agent-based lottery market
363(12)
21.1 Behavioral institutional economics
363(1)
21.2 Lottery market designs
364(5)
21.3 Lottery tax rates
369(2)
21.4 Simulation
371(1)
21.5 Agent-based modeling and policy design
372(1)
21.6 Further exploration
373(2)
Part VII: Networks 375(22)
22 Graphs and social networks
377(20)
22.1 Origin in sociology
378(2)
22.2 Origin in mathematics: graphs
380(1)
22.3 Network formation: sociophysical models
380(6)
22.4 Networks in economics
386(2)
22.5 Network formation: agent-based models
388(6)
22.6 Further reading
394(3)
Part VIII: Economics of changes 397(54)
23 Agent-based modular economy
401(38)
23.1 Twin assumptions of the modular economy
402(8)
23.2 Preference and utility function
410(8)
23.3 Firms' behavior
418(5)
23.4 Markets
423(4)
23.5 ModularEcon: a demonstration
427(3)
23.6 Innovation
430(5)
23.7 Further explorations
435(4)
24 Epilogue
439(12)
24.1 The Simon hierarchy
440(6)
24.2 The Chomsky hierarchy
446(4)
24.3 Challenges ahead
450(1)
Bibliography 451(49)
Index 500
Shu-Heng Chen is Distinguished Professor at the Department of Economics at National Chengchi University, Taiwan.