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

 Declarative Model
Declarative model is a model that expresses the logic of a computation without describing its control flow. The model attempts to minimize or eliminate side effects by describing what the program should accomplish in terms of the problem domain, rather than describing how to go about accomplishing it as a sequence of the programming language primitives. The declarative model is in contrast to imperative, because in imperative model algorithms are implemented in terms of explicit steps. Generally, the declarative model is mathematical representations of physical systems implemented in computer code that is declarative. The code of the model contains a number of equations, not imperative assignments, that declare or describe the behavioral relationships.
Two classes of declarative models are systems dynamics models and declarative agent-based models, as mentioned in Villa et al. (2006). The processes leading to outcomes and the outcomes themselves emerge from simulations with these models. As mentioned in Fahland et al. (2009) the principal difference between declarative agent-based models and systems dynamics models is that the agent-based models describe social interactions whilst the systems dynamics models do not. Agent-based models  in which the behaviour of agents is determined by if-then rules are inherently declarative.
Fahland, D., Lübke, D., Mendling, J., Reijers, H. Weber, B., Weidlich, M., Zugal, S.(2009). Declarative versus Imperative Process Modeling Languages: The Issue of Understandability. In: BPMDS 2009 and EMMSAD 2009, LNBIP 29, pp. 353-366, Springer-Verlag Berlin Heidelberg.
Villa, F., Donatelli, M., Rizzoli, A., Krause, P., Kralisch, S., van Evert, F. (2006). Declarative modelling for architecture independence and data/model integration: a case study. In: iEMSs 2006 Summit on Environmental Modelling and Software. [Online] (verified on November 7, 2013).
 Democratic Governance
Democratic Governance implies good governance from the human development perspective (UNDP, 2002). Democratic Governance concept refers to a governance process that is based on fundamental and universally accepted principles, including: participation, accountability, transparency, rule of law, separation of powers, access, subsidiarity, equality and freedom of the press (United Nations Economic and Social Council, 2006).
Related terms: Good Governance, Governance, Policy Governance
UNDP (2002), Human Development Report 2002. Deepening democracy in a fragmented world. New York Oxford University Press
United Nations Economic and Social Council. (2006). Definition of basic concepts and terminologies in governance and public administration. pdf
A discipline is characterised in scientific literature as a recognised approach to a specific issue. It enables in depth reflection and discussion to take place. Generally, disciplines are closed environments that develop common epistemologies and ontologies (Barrett, 2012).
Disciplines in academia are well-established spaces that are often institutionally defined (Departments, Faculties, etc.) (Chettiparamb, 2007). Teaching and research are often executed in single-disciplinary studies, as 'expertise' is determined by recognition from peers from the same discipline.
An understanding that different disciplines must work together has emerged. This can operate in different ways: multidisciplinary, interdisciplinary, or transdisciplinary activities (e.g. Choi and Pak, 2006). In multidisciplinary interactions, different disciplines work together to solve particular policy (or other) problems by making use of knowledge, methods and theories drawn from their own specific disciplines (Chua and Yang, 2008). Interdisciplinarity attempts to build up new approaches to solving 'real' problems by developing methods, theories and practices that cross two or more disciplines, often resulting in the fusing of two or more disciplines (Newell, 2001). Transdisciplinarity is a third ideal type of interaction: different disciplines are 'integrated,' thereby developing new understandings of the role of knowledge in solving given policy or social problems (Lawrence and Despres, 2004).
Policy modelling is one of the fields where discussion is vivid on the role of multiple disciplines, with many observers aiming to develop the domain as an opportunity for interdisciplinary approaches to flourish, given the specific requirements of the area (information science, public administration, political science, etc.) (See Chen, Gregg and Dawes, 2007).
Barrett, Brian D. 2012. “Is Interdisciplinarity Old News? a Disciplined Consideration of Interdisciplinarity.” British Journal of Sociology of Education 33 (1) (January): 97–114.
Chen, H, L Brandt, V Gregg, and Sharon S Dawes. 2007. Digital Government: E-Government Research, Case Studies, and Implementation.
Chettiparamb, A. 2007. “Interdisciplinarity: a Literature Review.” … Teaching and Learning Group.
Choi, Bernard C K, and Anita W P Pak. 2006. “Multidisciplinarity, Interdisciplinarity and Transdisciplinarity in Health Research, Services, Education and Policy: 1. Definitions, Objectives, and Evidence of Effectiveness..” Clinical & Investigative Medicine 29 (6) (December): 351–364.
Chua, Alton Y K, and Christopher C Yang. 2008. “The Shift Towards Multi-Disciplinarity in Information Science.” Journal of the American Society for Information Science and Technology 59 (13) (November): 2156–2170.
Lawrence, Roderick J, and Carole Despres. 2004. “Futures of Transdisciplinarity.” Futures 36 (4): 397–405.
Newell, William H. 2001. “A Theory of Interdisciplinary Studies.” Issues in Integrative Studies (19): 1–25.
 Dynamic Stochastic General Equilibrium Models
Theory-based macroeconomic forecasting and policy analysis has largely relied in recent years on (calibrated) Dynamic Stochastic General Equilibrium (DSGE) models. In particular, most policy relevant institutions, such as central banks or the International Monetary Fund (IMF) rely on DSGE models, for example the ‘Global Economy Model (GEM)’ used by the IMF (Bayoumi, 2004). The majority of the recent contributions in this area rely on New Keynesian monetary models.
DSGE models are based on the concept of rational representative agents whose behaviour is derived from their preferences and technologies by solving inter-temporal optimization problems. A number of different types of frictions and rigidities have been introduced in parts of the model such as the labour or the financial markets. Forecasting in this framework is typically based on Bayesian estimation of the model using time series of macroeconomic quantities such as output, consumption, investment, wages, inflation and interest rates (Smets and Wouters (2003), see Del Negro and Schorfheide (2012) for a recent survey).
Related terms: Economic Theories, Econometrics, Econometric Modelling, Forecasting, Macroeconomic Models, Macro-Simulation, Policy Modelling
Bayoumi, T. (2004), GEM: A New International Macroeconomic Model, Occasional Paper 239, International Monetary Fund
Del Negro, M. and F. Schorfheide (2012), “DSGE Model-Based Forecasting”, Staff Report No. 554, Federal Reserve Bank of New York.
Smets, F. and R. Wouters (2003), “An Estimated Dynamic Stochastic General Equilibrium Model”, Journal of the European Economic Association, 1, 1123-1175