Municipal Government and the Interactive Web

What opportunities do citizens have to interact with government online at the local level? This study uses content analysis of the websites of the 75 largest U.S. cities to identify the extent to which they integrate features that allow online information customization and online citizen participation. Completed from March-May 2009, the coding includes analysis of Web 2.0 applications and older web-based tools such as citizen surveys, online town meetings, and other features relevant to citizen engagement. The study finds that municipal governments have steadily developed their online capacity to provide information to local residents, but new media such as Facebook, Twitter, and YouTube remain underutilized. Local e-government has yet to evolve as a tool to advance deliberative democracy, but some opportunities for input have increased. An initial analysis indicates that cities with large African-American and Latino populations have less interactive websites, and that larger cities are likely to have more participatory opportunities online.

Municipal Government and the Interactive Web

What opportunities do citizens have to interact with government online at the local level? This study uses content analysis of the websites of the 75 largest U.S. cities to identify the extent to which they integrate features that allow online information customization and online citizen participation. Completed from March-May 2009, the coding includes analysis of Web 2.0 applications and older web-based tools such as citizen surveys, online town meetings, and other features relevant to citizen engagement. The study finds that municipal governments have steadily developed their online capacity to provide information to local residents, but new media such as Facebook, Twitter, and YouTube remain underutilized. Local e-government has yet to evolve as a tool to advance deliberative democracy, but some opportunities for input have increased. An initial analysis indicates that cities with large African-American and Latino populations have less interactive websites, and that larger cities are likely to have more participatory opportunities online.

Extracting and Customizing Information Using Multi-Agents

Rapidly evolving network and computer technology, coupled with the exponential growth of the services and information available on the Internet, has already brought us to the point where hundreds of millions of people should have fast, pervasive access to a phenomenal amount of information, through desktop machines at work, school and home, through televisions, phones, pagers, and car dashboards, from anywhere and everywhere. The challenge of complex environments is therefore obvious: software is expected to do more in more situations, there are a variety of users (Power/Naive, Techie/ Financial/Clerical, ...), there are a variety of systems (Windows/NT/Mac/Unix, Client/Server, Portable, Distributed Object Manager, Web, ...), there are a variety of interactions (Real-time, Data Bases, Other Players, ...), and there are a variety of resources and goals (time, space, bandwidth, cost, security, quality, ...). To cope with such environments, the promise of information customization systems is becoming highly attractive. In this chapter we discuss important problems in relationship to such systems and smooth the way for possible solutions. The main idea is to approach information customization using a multi-agent paradigm.

Combating Information Overload by Means of Information Customization Systems

The evolution of the Internet into the Global Information Infrastructure has led to an explosion in the amount of available information. The result is the “information overload” of the user, i.e., users have too much information to make a decision or remain informed about a topic. Information customization systems are supposed to be the answer for information overload. They allow users narrowcast what they are looking for and get information matching their needs. Information customization systems are also a bargain of consummate efficiency. The value proposition of such systems is reducing the time spent looking for information. We hold the view that information customization could be best done by combining various artificial intelligence technologies such as collaborative filtering, intelligent interfaces, agents, bots, web mining, and intermediaries. MASACAD, the system described in this chapter, is an example of an information customization system that combines many of the technologies already mentioned and others to approach information customization and combat information overload.

Extracting and Customizing Information Using Multi-Agents

Rapidly evolving network and computer technology, coupled with the exponential growth of the services and information available on the Internet, has already brought us to the point where hundreds of millions of people should have fast, pervasive access to a phenomenal amount of information, through desktop machines at work, school and home, through televisions, phones, pagers, and car dashboards, from anywhere and everywhere. The challenge of complex environments is therefore obvious: software is expected to do more in more situations, there are a variety of users (Power/Naive, Techie/ Financial/Clerical, ...), there are a variety of systems (Windows/NT/Mac/Unix, Client/Server, Portable, Distributed Object Manager, Web, ...), there are a variety of interactions (Real-time, Data Bases, Other Players, ...), and there are a variety of resources and goals (time, space, bandwidth, cost, security, quality, ...). To cope with such environments, the promise of information customization systems is becoming highly attractive. In this chapter we discuss important problems in relationship to such systems and smooth the way for possible solutions. The main idea is to approach information customization using a multi-agent paradigm.