美國(guó)世界信息系統(tǒng)學(xué)會(huì)主席、院士 Richard Watson 教授學(xué)術(shù)報(bào)告：A research agenda for self-organizing ecosystems: The case for Maritime Informatics 2016-06-20

題目1：A research agenda for self-organizing ecosystems: The case for Maritime Informatics

時(shí)間 ：6月21日上午9:30-10:40，地點(diǎn) 313會(huì)議室

主講：美國(guó)世界信息系統(tǒng)學(xué)會(huì)主席、院士Richard Watson 教授

摘要：Shipping, an important component in the operation of the world economy is a digital age laggard. Digitization can make it 

more efficient, sustainable, and safer. For IS researchers this domain is also relatively unexplored, and there are opportunities for

 IS scholars to provide theoretical advances in describing, modeling, and redesigning the maritime ecosystem. This article

 introduces a research agenda for maritime informatics. Building on complex adaptive systems (CAS) theory, capital creation, 

systems thinking, and episodic tight coupling, the maritime industry is conceived as a self-organizing ecosystem (SOE). 

In an SOE, autonomous organizations emerge to meet the various needs of a central, but not coordinating, entity. A systematic

 approach is applied to develop seven research questions about SOEs, based on three of their major characteristics identified

 by network analysis, namely (1) An SOE is an aggregation of agents whose diverse capabilities are identified by tags; (2) Actions

 by agents, both intentional and unintentional, change an ecosystem’s environment and can have non-linear effects on other agents,

 including the initiating agent; (3) As part of the capital creation process, agents episodically tightly couple to create physical and 

informational flows and capital. System dynamics and object orientation are applied to provide validity for, and to suggest methods

 for answering the research questions. These questions provide a grounding for maritime informatics research, as well as exploring 

SOEs generally.

題目2：The framework and value of big data analysis

時(shí)間：10:50-12:00，地點(diǎn) 324

摘要：This section will talk about big data analysis in many areas, including geography, finance, smart city, et al.