Forecasting Model of Marine Transport Economies Based on Artificial Life Concept

预报应该开始为未来的环境准备各种图景，这里称之为“丰富图景”。下一步是对个人富裕图景的制度利益行为进行分析。基本上，在长期预测中，定性分析比定量分析更可取。依赖于过去的实际记录的外推预测，无法应对环境条件的剧烈变化。为了研究“可能的未来”，我们需要一个能够适应这些丰富图景的系统动力学模型。人工生命是对表现出自然生命系统行为特征的人造系统的研究。AL关注的是微/局部对象之间相互作用所产生的系统行为。因此，AL关注的是系统动力学。基于AL概念的系统动力学模型可以灵活地适应给定的环境，因为该模型只是描述局部对象在环境中如何反应的简单程序的大集合，提出了基于AL概念的交通网络增长预测模型。这个模型是一个人工世界，在这个世界里，当地的对象，即人、资源开发商、能源工厂、产品工厂、运输商、贸易商、银行和政府，为个人目的而行动。基于AL的模型为我们提供了交通规划中的各种场景。给出了东亚运输网络增长的应用实例，说明了该模型的适用性。

The forecasting should begin preparing a variety of pictures, herein called "rich pictures, " for the future environment. The next step is analyzing the system behavior of interest for individual rich picture. Basically, qualitative analysis is more preferable than quantitative analysis in long-term forecasts. The extrapolative forecasting, which depends on actual record of the past, does not cope with drastic changes in environmental conditions. In order to study the "future-as-it-can-be, " we desire a system dynamics model which can adapt to those rich pictures.Artificial Life (AL) is the study of man-made systems that exhibit behavior characteristics of natural living systems. The AL focuses on the system behavior which emerges out of the interactions among micro/local objects. Thus, the AL is concerned with system dynamics. The system dynamics model based on the AL concept can flexibly adapt itself to the given environment, since that model is merely a large aggregation of simple programs which specify how the local objects react in the environment.We propose a forecasting model based on the AL concept for transport network growth. This model is an artificial world where the local objects, i.e.people, resources developers, energy factories, products factories, transporters, traders, banks, and governments, act for individual purpose. The AL-based model presents us a variety of scenarios in transport planning. Application example of East-Asia transport network growth is provided to illustrate the applicability of the proposed model.