Contains definitions of terms used in eGovPoliNet partly based on DCMI Metadata Terms.

The word community was derived from the Latin communitas, a broad term for fellowship or organised society. According to Merriam-Webster Dictionary (2013) a community is a unified body of individuals, e.g. the people with common interests living in a particular area or a group of people with a common characteristic or interest living together within a large society.
Community usually refers to a social unit that shares common values (Smith, 2013). Specifically in biology a community is a group of interacting living organisms sharing a populated environment.
Tönnies (2005) distinguishes two types of human association in sociology - community and society. He argues that community is perceived to be a tighter and more cohesive social entity (presence of a "unity of will"). Perfect expression of community is family and kinship. Society, on the other hand, is a group in which the individuals who make up that group are motivated to take part in the group purely by self-interest. As Tönnies proposed, in the real world no group was either pure community or pure society.
Community building is a field of practices directed toward the creation or enhancement of community among individuals within a regional area or with a common interest.
References: (2013), Community. Retrieved April 17, 2013
Smith, M. K. (2001), ‘Community’ in the encyclopedia of informal education, Retrieved April 17, 2013
Tönnies, F. (2005), Gemeinschaft und Gesellschaft, Darmstadt: Wissenschaftliche Buchgesellschaft, 8th edition (reprint).
 Complex Adaptive System (CAS)
Government officials and other decision makers increasingly encounter a daunting class of problems that involve systems composed of very large numbers of diverse interacting parts (Shalizi, 2006). These systems are prone to surprising, large-scale, seemingly uncontrollable behaviors. These traits are the hallmarks of what scientists call complex systems. A complex system is composed of many parts that interact with and adapt to each other and, in so doing, affect their own individual environments. The combined system-level behavior arises from the interactions of parts that are, in turn, influenced by the overall state of the system. Global patterns emerge from the autonomous but interdependent mutual adjustments of the components (Jacobson et al., 2011).
According to John Holland CAS is a special category of complex systems dealing with living systems that have the capacity to change, learn from experience and sometimes forecast (Holland, 1999). The control of a CAS tends to be highly dispersed and decentralized. If there is to be any coherent behavior in the system, it will have to arise from competition and cooperation among the agents themselves. The overall behavior of the system is the result of a huge number of decisions made every moment by many individual agents. (Holland, 1992, p. 17). Typical phenomena in complex adaptive systems are the emergence of macro-level structures due to interactions at the micro-level (self-organisation). These macro-structures in turn determine the behavioural freedom at the micro-level (downward causation).
Related terms: Complex System
Holland, J. H. (1992), Complex Adaptive Systems, Daedalus, Vol. 121(No. 1), pp. 17-30.
Holland, John H. (1999), Emergence: from chaos to order, Reading, Mass: Perseus Books.
Jacobson, M., Kapur, M., So, H.-J., & Lee, J. (2011). The ontologies of complexity and learning about complex systems. Instructional Science, Vol. 39(No. 5), pp. 763-783. doi: 10.1007/s11251-010-9147-0
Shalizi, C. R. (2006). Methods and Techniques of Complex Systems Science: An Overview Complex Systems Science in Biomedicine. In T. S. Deisboeck & J. Y. Kresh (Eds.), (pp. 33-114): Springer US.
 Conceptual Model
In science, there is the need to formally describe some aspects of the physical and social world around us for purposes of understanding and communication. Such descriptions, often referred to as conceptual schemata, require the adoption of a formal notation, namely a conceptual model (Mylopoulos, 2012). By assuming that any given domain consists of objects, relationships, and concepts, we commit ourselves to a specific way of viewing domains, the conceptual model namely. The set of concepts used in a particular domain constitutes a conceptualisation of that domain. The specification of this conceptualisation is sometimes called an ontology of the domain (Olive, 2007).
A concept has an extension and an intension. The extension of a concept is the set of its possible instances, while the intension is the property shared by all its instances. Concepts allow us to classify the things that we perceive as exemplars of the concepts that we have. In other words, what we observe depends on the concepts that we employ in the observation. Classification is the operation that associates an object with a concept. The inverse operation, instantiation, gives an instance of a concept. The set of objects that constitutes an instance of a concept at a given time is known collectively as the population of the concept at that time (Olive, 2007).
A conceptual model brings several advantages including: the documentation and the re-use of the domain knowledge, the reach of consensus among domain experts and IT stakeholders and the provision of a firm basis for the design and development of ISs within the domain (Wand and Weber, 2002).
To the best of our knowledge, limited work on conceptual models for the policy modelling domain has been proposed so far (until June 2013). The Consistent Conceptual Description CCD (Wimmer and Scherer, 2011) presents the vocabulary to describe policy contexts, and it describes how CCD supports the semi-automatic transformation of conceptual models within a policy context to generate formal policy models. Another preliminary conceptual model has been proposed by Wyner et al (2011) in the form of an ontology.
Mylopoulos, J. (2012) Conceptual Modelling and Telos
Olivé, A. (2007) Conceptual Modeling of Information Systems. Springer, Heidelberg.
Wand and Weber (2002) Research Commentary: Information Systems and Conceptual Modeling - A Research Agenda. Information Systems Research, 13(4), pp. 363-376.
Wimmer M. and Scherer S. (2011). Conceptual Models Supporting Formal Policy Modelling: Metamodel and Approach. Modelling Policy-making (MPM 2011).
Wyner A., Atkinson K. and Bench-Capon T. (2011). Semantic Models and Ontologies for Modelling Policy-Making. Modelling Policy Making (MPM 2011).
 Conceptual Modelling
Conceptual modelling is the process of abstracting a model from a real or proposed system (Robinson 2008, p. 3). Mylopoulos (1992) defines conceptual modelling as an activity of formally describing some aspects of the physical and social world around us for purposes of understanding and communication. The outcome of the conceptual modelling process is a conceptual model. Conceptual modelling is an iterative and repetitive process, with the conceptual model being continuously revised throughout the modelling process. However, the main issue in conceptual modelling is to abstract an appropriate simplification level of reality (Pidd, 2003).
Conceptual modelling is a complex process because we do not have measurable criteria for evaluating the value of its outcome - a conceptual model (Pritsker 1987). Therefore, during the process of conceptual modelling, a set of system requirements would be useful to consider. The requirements could provide a basis against which to determine whether  the obtained conceptual model is appropriate. Robinson (2008, p. 19) argues four main requirements, which should be fulfilled when measuring the outcome of conceptual modelling:

- validity (a conceptual model can be developed into a simulation model with sufficient accuracy),
- credibility (similar like validity, but from the viewpoint of a client),
- utility (developed model will be useful for the decision making),
- feasibility (conceptual model will be developed into a [simulation] model with respect to available time, resources and data). In Policy Making, conceptual modelling is carried out by policy analysts who extensively analyse available documents in order to get an accurate overview of the policy domain, i.e. to develop a conceptual model of it. They also collaborate with the stakeholders and the policy modellers to discuss model elements.
Related terms: Model, Modelling, Tool
Mylopoulos J, (1992). Conceptual modeling and Telos, Chapter 2 in Loucopoulos, Peri; Zicari, Roberto: Conceptual Modeling, Databases, and CASE : An Integrated View of Information Systems Development, New York.
Pidd, M. (2003). Tools for Thinking: Modelling in Management Science, 2nd ed. Wiley, Chichester, UK.
Pritsker, A.A.B. (1987). Model Evolution II: An FMS Design Problem. Proceedings of the 1987 Winter Simulation Conference (Thesen, A., Grant, H. and Kelton, W.D., eds.). IEEE, Piscataway, NJ, pp. 567-574.
Robinson, S., 2008. Conceptual modelling for simulation part I: definition and requirements. Journal of the Operational Research Society, 59 (3), pp. 278 - 290.