Welcome to the wiki of the Voter Decision Support Community Group. This community group discusses voter decision support systems and related topics. This community group shall advance the theory and practice of decision-making software and decision support systems for use by citizens during voting-related and civic participation activities. This community group shall advance the theory and practice of voter-centric design, equipping and empowering citizens. This community group may draft suggestions and best practices and may coordinate with other groups to support pertinent standards.
Voter decision support utilizes computing technology to provide decision support to voters, for instance to equip them during voting preparation. Voting preparation can start before an election season and can be described as an ongoing activity for voters. Voters, for instance, may gather information with respect to the ongoing performance of elected officials. Approaches to empowering voters with computing technology should recognize that there may be a number of distinct voter preparation styles, information gathering styles, and information reviewing styles.
- 1 Decision support systems
- 2 Intelligent decision support systems
- 3 Benefits of voter decision support systems
- 4 Discussion topics
- 5 Relevant Web content
- 6 External hyperlinks
Decision support systems
A decision support system is a software system which supports individual or organizational decision-making activities.
The history of decision support systems traces back to the middle of the 1960's and the concepts and technologies are still evolving. Historically, there are five types of decision support systems to consider: communication-driven, data-driven, document-driven, knowledge-driven and model-driven. Communication-driven decision support systems provide users with the ability to communicate, collaborate and share knowledge with one another. Data-driven decision support systems provide users with the ability to access, manipulate and visualize data. Document-driven decision support systems provide users with the ability to search and retrieve documents and multimedia. Knowledge-driven decision support systems provide users with the ability to access and utilize structured knowledge, inference engines, reasoning systems, automated reasoning and expert systems. Model-driven decision support systems provide users with the ability to access and utilize statistical, financial, economic, optimization and simulation models. Voter decision support systems can draw upon a number of these approaches simultaneously.
Intelligent decision support systems
An intelligent decision support system is a decision support system which makes extensive use of artificial intelligence techniques.
Intelligent decision support systems have made use of expert systems. Expert systems are knowledge-based systems, software systems which reason and use knowledge bases to make decisions or solve complex problems. In addition to uses in intelligent decision support systems, expert systems have been utilized in the applications of: interpretation, prediction, diagnosis, design, planning, monitoring, debugging, repair, instruction and control.
Historically, there are six types of expert systems to consider: rule-based, frame-based, hybrid, model-based, ready-made and real-time expert systems. A rule-based expert system represents knowledge as a series of rules. A frame-based expert system represents knowledge as frames. A hybrid expert system represents knowledge in multiple ways simultaneously. A model-based expert system is structured around the use of one or more models. Ready-made expert systems are mass-produced; there are two types of ready-made expert systems, those for general use and those which are industry- or product-specific. A real-time expert system always produces a response in an allocated amount of time.
Machine learning is a useful technology for decision support systems. Machine learning is a field of artificial intelligence which uses statistical techniques to give computer systems the ability to “learn” (e.g. to progressively improve performance on a specific task) from data, without being explicitly programmed. Machine learning techniques utilized with decision support systems include: artificial neural networks, evolutionary algorithms, decision tree learning, support vector machines, case-based reasoning, Bayes learning and pattern recognition.
Intelligent decision support systems can support natural-language, conversational and multimodal user interfaces.
Benefits of voter decision support systems
Using voter decision support systems, voters can make more rational decisions with respect to their individual interests and the interests of their communities.
Depth and comparability of information search
Using voter decision support systems, voters can explore political information with more depth of search and more comparability of search.
See also: Measuring Voter Decision Strategies in Political Behavior and Public Opinion Research by Richard R. Lau, Mona S. Kleinberg and Tessa M. Ditonto.
Number and complexity of deliberation topics
Using voter decision support systems, voters can consider a larger number of more complex topics simultaneously.
Using voter decision support systems, voters can better hold their governments accountable, taking notes throughout the terms of elected officials, notes of use during elections.
Candidates should be expected to publish their plans in open, standard, machine-readable formats so that value-added intermediary services can make such information readily available and usable by voters. Suitable formats include Strategy Markup Language (StratML) Part 1, Strategic Plans (ISO 17469-1). Upon election, officials should provide the additional information necessary to transform their strategic plans into performance plans and reports by specifying stakeholder roles and performance indicators. Section 10 of the GPRA Modernization Act (GPRAMA) requires United States federal agencies to publish their performance plans and reports in machine-readable formats.
Using voter decision support systems, and thanks to technologies such as Solid, voters’ data privacy and political privacy can be preserved.
Search engine, recommender system and content discovery platform components can provide users with notifications as new content pertinent to their voting preparation becomes available.
Supporting the gathering and use of information
A recommender system or a recommendation system is a subclass of information filtering system which seeks to predict the "rating" or "preference" that a user would give to items. News recommender systems can provide users with news articles which are predicted to be of interest to them, for instance based upon factors including the voting districts which they reside in.
A news aggregator aggregates content, such as content from online newspapers, blogs, podcasts and video blogs, into one location for easy viewing.
A news organizer organizes content for later use, providing indexing, search and retrieval capabilities. A news organizer may organize: (a) all of the content that a user encounters, (b) all of the content of one or more categories, such as news, that a user encounters, or (c) certain content which a user selects to store for later use. Relevant are scenarios where users make use of news organizers to store news articles and other content for later uses including civic participation and voter preparation.
Computer-aided and automated analysis upon the content which voters encounter and/or store for later use, for instance news articles, can produce valuable data of use to voters. Such data can inform voters with regard to patterns in the information which they consume, patterns with regard to their information gathering behavior, and patterns with regard to their elections. Such data can equip voters in a number of ways, for instance enhancing the depth and comparability of their information searching.
Relevant Web content
Types of content pertinent to voter decision support systems include content from: government websites, the websites of political parties and candidates, candidates’ platforms, news articles, interviews, panel discussions, debates, debate coverage and analysis, fact-checking, editorials, letters to the editor, expert content (e.g. content from scientists, historians, economists, foreign policy experts) and encyclopedia articles.