UID:
almahu_9947367669802882
Format:
1 online resource (905 p.)
ISBN:
1-280-64156-8
,
9786610641567
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0-08-045987-0
Series Statement:
Handbooks in economics ; bk. 13
Content:
The explosive growth in computational power over the past several decades offers new tools and opportunities for economists. This handbook volume surveys recent research on Agent-based Computational Economics (ACE), the computational study of economic processes modeled as dynamic systems of interacting agents. Empirical referents for ""agents"" in ACE models can range from individuals or social groups with learning capabilities to physical world features with no cognitive function. Topics covered include: learning; empirical validation; network economics; social dynamics; financial markets; in
Note:
Description based upon print version of record.
,
Front cover; Title page; Copyright page; Introduction to the Series; Contents of the Handbook; Preface; Purpose; Organization; Acknowledgements; Reference; Contents of Volume 2; Part 1: ACE Research Reviews; 16 Agent-Based Computational Economics: A Constructive Approach to Economic Theory; Abstract; Keywords; Introduction; ACE study of economic systems; From Walrasian equilibrium to ACE trading; Walrasian bliss in a hash-and-beans economy; Plucking out the Walrasian Auctioneer; The ACE Trading World: Outline; Defining ""equilibrium"" for the ACE Trading World
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ACE modeling of procurement processesConstructive understanding; The essential primacy of survival; Strategic rivalry and market power; Behavioral uncertainty and learning; The role of conventions and organizations; Interactions among attributes, institutions, and behaviors; Concluding remarks; The ACE Trading World; The economy in the initial period; Activity flow for hash firms in period T; Profit allocation method for hash firm j; Learning for hash firms; Representation of hash firm j's supply offers; Hash firm j's learning problem; The VRE learning algorithm for hash firm j
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Activity flow and learning for bean firmsActivity flow for consumers in period T; Consumer price discovery process in period T; A typical price-discovery round for an arbitrary consumer k; Classification of variables; References; 17 Computationally Intensive Analyses in Economics; Abstract; Keywords; Introduction; Computational tools; Weaknesses of standard models; Criticisms of computationally intensive research; Systematic approaches to computationally intensive research; Search for counterexamples; Sampling methods; Regression methods; Replication and generalization
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Synergies with conventional theoryConclusion; References; 18 Agent Learning Representation: Advice on Modelling Economic Learning; Abstract; Keywords; Introduction; History of modelling learning; Psychological research on learning; Learning and optimisation; Increasing variety of learning models; Classification of learning models; Potential alternative classifications; Proposed classification; Two ways of learning; Further distinction of learning processes; Modelling non-conscious learning; Existing models; Bush-Mosteller model; Modelling routine-based learning; Experimentation
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Melioration and experience collectionImitation; Satisficing; Replicator dynamics and selection-mutation equation; Evolutionary algorithms; Combined models: EWA and VID model; Modelling belief learning; Psychological findings about cognitive learning; Fictitious play; Bayesian learning; Least-squares learning; Genetic programming; Classifier systems; Neural networks; Rule learning; Stochastic belief learning; Conclusions and recommendations; Situational characteristics and learning; Non-conscious versus belief learning; Routine-based versus belief learning; Choosing a learning model
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Aims in choosing a learning model
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English
Additional Edition:
ISBN 0-444-51253-5
Language:
English
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