Introduction and definition

According to a cybernetic view of intelligent organisations knowledge supersedes 1. the facts, 2. data (statements about facts) and 3. meaningful information (what changes us), the last also defined as “the difference that makes the difference”. Knowledge most often defined as “whatever is known, the body of truth, information and principles acquired” by a subject on a certain topic. Therefore knowledge is always embodied in someone. It implies insight, which, in turn, enables orientation, and thus may be also use as a potential for action (when we are able to use information in a certain environment, then we start to learn, which is the process that helps developing and grounding knowledge). Two more concepts come after knowledge on the same scale, and are Understanding and Wisdom. Understanding is the ability to transform knowledge into effective action, i.e. in-depth knowledge, involving both deep insights into patterns of relationships that generate the behaviour of a system and the possibility to convey knowledge to others, whereby wisdom is a higher quality of knowledge and understanding the ethical and aesthetic dimensions.

The research challenge is related to the elicitation of information which, in turn, during the overall model building and use processes will help decision makers to learn how a certain system works and ultimately to gain insights (knowledge) and understanding (apply the extracted knowledge from those processes) in order to successfully implement a desired policy. It is important to note that other research fields (in particular, ICT disciplines) tend to misuse the word “knowledge” and invert it with ”information”.

Why it matters in governance

Proper information acquisition and knowledge development are the key aspect in all research fields, so this research challenge has a horizontal importance for research in general. According to the general need for policy assessment and evaluation, there are some specific issues stemming from this research challenge, which are strongly related to governance:

1. Public data use and thus public information elicitation (by citizens)
2. Citizens’ behavioural data which are gradually becoming essential for any policy assessment process
3. Interoperability of public IT systems
4. Creation of a common understanding on a certain system’s behaviour (by means of learning) in order to develop a shared vision on the problems that a certain policy might want to overcome

Current Practice and Inspiring cases

In current practice, information is drawn from data stored in different types of media (mainly DBMS/ERPs). Web 2.0 has further transformed the way we create data and elicit information from data. Data availability ceased to pose problems as a result of:

1. The Internet growth and its uptake
2. User Generated Content in Social Networks
3. Cooperation of IT systems from different organisations thanks to the Service-Oriented Architectures (even among old legacy systems), which resulted also in private data availability
4. Public Administration Transparency and Public Data use/reuse

Available Tools

A review of the available tools is ongoing.

Key challenges and gaps

The knowledge is still mostly created and passed on by formal methods of teaching, even though the advents of the e-Learning field allow for an increased possibility to perform Distance Learning on the Web. But, since knowledge is developed and grounded by the learning process through action in the environment, the learning in real life comes from committing mistakes. In the field of real life governance, it entails implementing a wrong policy and observing the positive and negative consequences that this policy generates (for example due to a system’s “policy resistance”). At present, thanks to the increasing data availability, information elicitation process is much easier, either by tacitly bringing users (data generators) to provide data in a guided way (according to a pre-set framework for data input) or with a help of a specific process (e.g.: consultations in e-Participation tools).

Current research

According to current research, the main focus is put on the Knowledge Management field or also (more properly, as in our case) to the Knowledge Elicitation field. The latter basically encompasses the following steps:

1. Data retrieval and extraction
2. Data analysis and interpretation (which usually produces information)
3. Data/information adaptation and integration (this is particularly the case where information needs to be used in a model)

Future research

There is still a large field to be explored – the methods of extraction of meaningful information from unstructured sources of data, e.g. when analysing free texts, which applies to all sources of User-Generated Content (forums, wikis, social networks, etc.), where the semantic dimension is essential to derive meaningful information rather than just quantitatively analysing the syntax of text. In general, a lot of data is generated by citizens and particularly by their behaviour online, so that the available aggregated data sets contains information on what a citizen does, what s/he likes, how s/he behaves in certain environments, and so on. This data is considered very valuable both for private and public organisations (even though under privacy restrictions which have to be properly addressed).

Also, according to the knowledge creation and development of understanding (regarding a specific system), there is some research currently carried out on how to improve the learning process via the use of e-Learning systems. Yet, what is still missing is the availability of micro-worlds, i.e. complex virtual environments where reality is somehow reproduced and where a decision maker is trained in order to implement his/her strategies and hypothesis and perform what-if analysis without the need to necessarily learn from mistakes in real life.

Future research will thus have to focus on the following issues:

1. Information elicitation by analysing and interpreting data, also taking into account the semantic point of view.
2. Creation of proper micro-worlds (or ILEs, Interactive Learning Environments), where the acquired information on a certain system is used (by means of actions), and knowledge is developed by observation of the outcomes of the actions. Also, ILEs will have to be integrated into LMS (Learning Management Systems) in order to extend the potential of distance learning practices, eventually also in a cooperative way (mass learning).
3. Interoperability of data sources in order to integrate/aggregate different types of data and be able to automatically infer information from more meaningful datasets.
4. In view of the “Internet of Things”, the provision of “portable” models/tools for citizens in order to gather valuable data based on citizens’’ real behaviours. Moreover, these models and tools would enable citizens to check the results of their actions by analysing in real-time the response of the model to the information they are contributing to generate, and thus evaluating the eventual benefits they are receiving from their virtuous behaviour or harm they are creating either to their environment or to themselves (e-Cognocracy).