Modeling human reasoning about meta-information

Abstract

Information, as well as its qualifiers, or metainformation, forms the basis of human decisionmaking. Modeling human reasoning therefore requires the development of representations of both information and meta-information. However, while existing models and modeling approaches may include computational technologies that support meta-information analysis, they generally neglect its role in human reasoning. Herein, we describe the application of Bayesian Belief Networks to model how humans calculate, aggregate, and reason about metainformation when making decisions.

Influence of meta-information on decision-making: Lessons learned from four case studies

“…Meta-information has been defined as characteristics or qualifiers of information that affect a human's decision-making and behavior [2]. Classifications of types and sources of metainformation can be found in [3] and [2].…”

“…Classifications of types and sources of metainformation can be found in [3] and [2]. Examples of metainformation are uncertainty, ambiguity, reliability of source, relevance, characteristics of the information source, level of abstraction of information, etc.…”

“…This understanding normally involves managing complex information at various levels, not only domain related information but also qualifiers associated with it (meta-information). These qualifiers contextualize the information and can critically influence how a decision-maker processes, understands and acts on it [2].…”

“…As discussed by Guarino et al [2] and Pfautz et al [3], decision-makers reason using both information and metainformation, and how successful the decision-makers are on deciding upon effective courses of action depends on their skills and experience in processing and understanding this information. There are a number of different sources and types of meta-information that can have a large impact on an operator's decision-making.…”

“…There are a number of different sources and types of meta-information that can have a large impact on an operator's decision-making. These include, for example, characteristics of the information source, the uncertainty and ambiguity of the collected information, the information context, the reliability and credibility of the source content, as well as temporal qualifiers (see [2] for more examples). However, as stated by Pfautz et al [3] and Bisantz et al [4], it is not common that decision support systems (DSSs) incorporate such information qualifiers into the primary displays.…”