Please use this identifier to cite or link to this item:

Title: Neuroeconomics and agent-based computational economics
Authors: Chen, Shu-Heng
Contributors: 經濟系
Keywords: Agent-Based Computational Economics (ACE);Dopaminergic Reward Prediction Hypothesis;Dual System Conjecture;Hyperbolic Discounting;Individual Learning;Methodological Individualism;Multi-Agent System;Neuroeconomics;Modularity
Date: 2014-01
Issue Date: 2014-11-24 15:01:40 (UTC+8)
Abstract: Recently, the relation between neuroeconomics and agent-based computational economics (ACE) has become an issue concerning the agent-based economics community. Neuroeconomics can interest agent-based economists when they are inquiring for the foundation or the principle of the software-agent design. It has been shown in many studies that the design of software agents is non-trivial and can determine what will emerge from the bottom. Therefore, it has been quested for rather a period regarding whether anyone can sensibly design these software agents, including both the choice of software agent models, such as reinforcement learning, and the parameter setting associated with the chosen model, such as risk attitude. In this paper, the author will start a formal inquiry by focusing on examining the models and parameters used to build software agents.
Relation: International Journal of Applied Behavioral Economics, 3(2), 15-34
Data Type: article
Appears in Collections:[經濟學系] 期刊論文

Files in This Item:

File Description SizeFormat

All items in 學術集成 are protected by copyright, with all rights reserved.

社群 sharing