The Agent ID Model

Orlando Gomes

ISBN 10: 3031759982 / ISBN 13: 9783031759987

Published by Springer, Berlin, Springer, 2025

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a comprehensive model that simulates human behavior in society, meticulously selecting and analyzing fundamental determinants such as skills and productivity, connectivity and network formation, psychological biases, moral behavior, consumption preferences, institutional arrangements, and political choices. Each agent in the model is characterized by a unique profile, and the aggregate outcomes emerge from the interactions of these diverse agents. The model is codified and simulated, and its results are compared with empirical evidence. This work offers an elegant and stylized approach to understanding complex outcomes resulting from simple interaction rules, making a significant contribution to the literature on complexity and agent-based computational economics. The model developed here serves as a transdisciplinary framework, providing an integrated and holistic perspective on human behavior. 196 pp. Englisch.

Seller Inventory # 9783031759987

Bibliographic Details

• Title: The Agent ID Model
• Publisher: Springer, Berlin, Springer
• Publication Date: 2025
• Language: English
• ISBN 10: 3031759982
• ISBN 13: 9783031759987
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 35D millimeters 235 millimeters width by 155 millimeters height by 35 millimeters depth

About this title

Synopsis

This book presents a comprehensive model that simulates human behavior in society, meticulously selecting and analyzing fundamental determinants such as skills and productivity, connectivity and network formation, psychological biases, moral behavior, consumption preferences, institutional arrangements, and political choices. Each agent in the model is characterized by a unique profile, and the aggregate outcomes emerge from the interactions of these diverse agents. The model is codified and simulated, and its results are compared with empirical evidence. This work offers an elegant and stylized approach to understanding complex outcomes resulting from simple interaction rules, making a significant contribution to the literature on complexity and agent-based computational economics. The model developed here serves as a transdisciplinary framework, providing an integrated and holistic perspective on human behavior.