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

The core of micro-simulation has been defined as “a means of modelling real life events by simulating the actions of the individual units that make up the system where the events occur” (Brown and Harding, 2002), and as “computer-simulation of a society in which the population is represented by a large sample of its individual members and their behaviours” (Spielauer, 2011). This has been broadened to encompass its role in policy so that “micro-simulation models are computer programs that simulate aggregate and distributional effects of a policy, by implementing the provisions of the policy on a representative sample of individuals and families, and then summing up the results across individual units using population weights” (Martini & Trivellato, 1997, p. 84).
Micro-simulation operates at the level of individual units, for example children, each possessing a set of associated attributes as a starting point. A set of rules, typically derived from statistical analyses, is then applied in a stochastic manner to each and every individual to simulate changes in state or behaviour. The primary strength of micro-simulation techniques is their use of actual individual-level data, which allows them to reproduce social reality and the intricacy of policy structures. These data can come from various sources, which micro-simulation is able to combine into a cohesive whole. The model can then be used to estimate the outcomes of “what if” scenarios (Brown & Harding, 2002, p. 4).
Spielauer (2011) notes that micro-simulation is certainly the preferred modelling choice in three situations: (1) if population heterogeneity matters and if there are too many possible combinations of considered characteristics to split the population into a manageable number of groups; (2) if behaviours are complex at the macro level but better understood at the micro level; and (3) if individual histories matter, that is, when processes possess memory (Spielauer, 2011, pp. 6-8).
Related terms: Simulation Model
Brown, L, Harding A. (2002). Social modeling and public policy: Application of microsimulation modeling in Australia. Journal of Artificial Societies and Social Simulation 5(4)6.
Martini A, Trivellato U. (1997). The role of survey data in microsimulation models for social policy analysis. Labour, 11(1), 83-112.
Spielauer M. (2011). What is social science microsimulation? Social Science Computer Review, 29(1), 9-20.
A model may be described as an abstract representation of reality constructed to fulfil a certain purpose for research or implementation activities. According to an early definition by Apostel (1960), any subject using a system A to obtain information about a system B, with A being neither directly nor indirectly interacting with B, is using A as a model for B.
According to Dietz (2006), there are three system categories: concrete, symbolic and conceptual systems. Their relationships are represented in the figure below. Referring to this figure:

- A concrete model of a concrete system is called an imitation (e.g. a scale model of an airplane or a ship or any other concrete thing).
- A conceptual model of a concrete system is called a conceptualization (e.g. the geometrical sphere as a model for celestial bodies; the Process Model as the conceptualization of the business processes in an enterprise).
- A concrete model of a conceptual system is called an implementation (e.g. the pyramids of Giza are an implementation of the geometric concept of pyramid; a business process as an implementation of the Process Model).
- A conceptual model of a conceptual system is called a conversion (e.g. the algebraic concept of a circle (x2 + y2 = r2) is a conversion of its geometric concept).
- A symbolic model of a conceptual system is called a formulation (e.g. the notion of the algebraic concept of a circle mentioned previously as a conversion model, is also a formulation model when referring to its notation).
- A conceptual model of a symbolic system is called an interpretation, which is actually the reverse of formulation (e.g. the deciphering of the Stone of Rosetta).
- A symbolic model of a symbolic system is called a transformation (e.g. from Morse to the Roman notation of letters). The term model can be equated to some graphical diagram. This is because in many fields (e.g. information systems, business processes management) most models used are graphical models. Models, however, do not necessarily have to be graphical (Op ’t Land et al, 2009). A model that is graphically displayed typically consists of three elements: (i) a collection of symbol structure types, (ii) a collection of operations that can be applied to any valid symbol structure, and (iii) a collection of inherent constraints that define the set of consistent symbol structure states, or valid changes of states (Mylopoulos and Borgida, 2009).
Apostel, L. (1960) Towards the formal study of models in the non-formal sciences. Synthese, 12(2-3), pp.125–161, pdf
Dietz, J.L.G. (2006) Enterprise Ontology: Theory and Methodology. Springer, Heidelberg.
Mylopoulos, J. & Borgida, A. (2009) A Sophisticate’s Guide to Information Modeling. In: Metamodeling for Method Engineering, Jeusfeld, M.A., Jarke, M. & Mylopoulos, J. (eds.), MIT Press, Cambridge, Massachusetts, pdf
Op ’t Land, M., Proper, E., Waage, M., Cloo, J. & Steghuis, C. (2009) Enterprise Architecture: Creating Value by Informed Governance. Springer, Heidelberg.
Modelling is an activity aiming to make a domain of the real world easier to understand, define, quantify, visualise, and simulate. It requires identifying aspects of the domain and then developing and/or using different models for different purposes, e.g. conceptual models are used to understand, operational models to operationalise, mathematical models to quantify and graphical models to visualise the domain [Wikipedia].
Modelling is an essential part of any scientific activity, and many scientific disciplines have their own ideas about specific types of modelling (Cartwright 1983, Hacking 1983). For example, in the Information Systems (IS) discipline, the term modelling describes the elicitation and the representation of the general knowledge that any information system operating in a domain needs to know (Olive 2007, Rolland 2007).
In the policy modeling and simulation field the term modelling refers to structuring and programming a simulation model capable to produce artificial data about the structures and behaviours of a policy system (Gilbert and Doran, 1994), aiming at the prediction of policy impacts, development of new governance models and collaborative solving of complex policy problems.
Related term: Model
Cartwright, N. 1983. How the Laws of Physics Lie. Oxford University Press
Hacking, I. 1983. Representing and Intervening. Introductory Topics in the Philosophy of Natural Science. Cambridge University Press
Olive, A. 2007. Conceptual Modeling of Information Systems, Springer Verlag Berlin.
Rolland, C. 2007. Capturing system intentionality with maps. Conceptual modelling in Information Systems engineering. p.141-158.
Gilbert, N. and J. Doran (eds.), 1994: Simulating Societies. The computer simulation of social phenomena. London: Routledge.
Organizational Networks have been widely recognized by both scholars and practitioners as an important form of multi-organisational governance. By network functioning we refer to the process by which certain network conditions lead to various network-level outcomes. There are some definitions about the network are available, according to Raab and Kenis networks are “consciously created groups of three or more autonomous but interdependent organisations that strive to achieve a common goal and jointly produce an output” (Raab & Kenis, 2009).
Brass et al. (2004) define a network in a very general way as “a set of nodes and the set of ties representing some relationship, or lack of relationship, between the nodes.” They point out that the content of the relationships between nodes is “limited only by a researcher’s imagination” (p. 795). Brass provide an overarching look at organizational network research at the interpersonal, inter unit, and inter organizational levels of analysis (Brass et al., 2004). They take a very broad approach to studying the phenomenon of social networks, focusing in particular on the antecedents and the consequences of networks at each of these levels.
Brass, D. J., Galaskiewicz, J., Greve, H. R., & Tsai, W. (2004). Taking stock of networks and organizations: A multilevel perspective. Academy of Management Journal, Vol. 47(No. 6), pp. 795-817.
Raab, J., & Kenis, P. (2009). Heading Toward a Society of Networks. Journal of Management Inquiry, 18(3), 198-210. doi: 10.1177/1056492609337493
 Network Governance School (NWG)
The network governance school has primarily been concerned with a set of macro-level examinations of the changing role of the state and state-society relationships (Chhotray & Stoker, 2009; Pierre & Peters, 2000; Rhodes, 2007; Sorenson & Torfing, 2007). Marinetto has argued that networks have become the dominant mode of governance and that the powers of states have been diminished upwards by international organisations, downwards by the marketisation of the public sector and sideways by the creation of arm’s length agencies (Marinetto, 2003). However, Jessop claims that although states may have become less hierarchical over time, this trend does not necessarily ‘exclude a continuing and central political role for nation states’ in establishing the ground rules and contexts within which governance occurs (Jessop, 2007). This phenomenon implies that the activities of self-regulating networks may be negotiated under hierarchical conditions; under these conditions, the state may explicitly or implicitly threaten to impose certain binding rules or laws on private actors to change the behaviours of these private actors (Hamza, 2013).
Networks thus emerge as the key space for policy interaction. In these networks, interaction does not take a hierarchic (vertical) form, but emerges horizontally or organically.
The network governance (NWG) school and the policy network analysis (PNA) school mainly focus on network governance, these two schools share a common interest in networks; however different to the NWG school, the PNA school has been more concerned with micro-level examinations about the relationships among policy-making outcomes, the structure of a network and the inclusion or exclusion of certain individuals or groups from the network in question (Fawcett & Daugbjerg, 2012).
Related terms: Network, Network theory, Networked Governance, Policy Governance, Policy Network Analysis (PNA), Public Governance

- References:
Chhotray, V. & Stoker, G., 2009. Governance Theory and Practice: A Cross Disciplinary Approach. Houndmills: Palgrave Macmillan.
Fawcett, P. & Daugbjerg, C., 2012. Explaining Governance Outcomes: Epistemology, Network Governance and Policy Network Analysis. Political Studies Review, 10(2), p.195–208.
Jessop, B., 2007. State Power: A Strategic-Relational Approach. Cambridge: Polity
Hamza, K., 2013. The Impact of Social Media and Network Governance on State Stability in Time of Turbulences: Egypt After 2011 Revolution. PhD Thesis. Brussels: Vrije Universiteit Brussel Institute for European Studies.
Pierre, J. & Peters, B., 2000. Governance, politics and the state. New York: Martin's Press.
Marinetto, M., 2003. Governing beyond the Centre: A Critique of the Anglo-Governance School. Public Administration, 51(3), pp.592-608.
Rhodes, R.A.W., 2007. Understanding Governance: Ten Years On. Organization Studies, 28(8), pp.1243-64.
Sorenson, E. & Torfing, J., 2007. Theories of democratic network governance. Basingstoke: Palgrave.
 Network theory
Network theory is an area of applied mathematics and part of graph theory. Network theory is explored by many scholars such as (Granovetter, 1973, Burt, 2009)  and (Borgatti & Halgin, 2011) and have gained renewed attention for analysing social networks on the Internet. Network theory uses graphs as a representation of symmetric or asymmetric relationships between actors. Network theory offers a structured way of conceptualising and measuring ties between actors and their impact (Borgatti & Halgin, 2011). Ties may spring from individual group members or from the group as a whole. Network theory looks at the network variables, such as how many ties are in place or the centrality of an actor (Brass, 2002).
References :
Borgatti, S. P., & Halgin, D. S. (2011), On network theory. Organization Science, 22(5), p. 1168-1181.
Brass, D. J. (2002), Social networks in organizations: Antecedents and consequences. Unpublished manuscript.
Burt, R. S. (2009), Structural holes: The social structure of competition: Harvard University Press.
Granovetter, M. S. (1973), The strength of weak ties. American journal of sociology, p. 1360-1380.
 Networked Governance
Networked governance is a common term in governance studies. Such networks comprise a wide variety of state actors or non-state actors with common interests in a specific political environment. (Political) Networks vary considerably regarding their degree of coherence or capability to alliance for specific issue. Networks may facilitate coordination of state and non-state actors interests and resources; in that respect sometimes these networks can enhance efficiency in the implementation of public policy. The state always has a form of interests exchange with key non-state actors in specific networks. But sometimes these networks may become sufficiently concerted and cohesive to resist or even challenge the state powers; and become self-regulatory structures within their political environment (Marsh & Smith, 2000; Rhodes, 1996; Rhodes 2000).
The relationship between the networks and the state could be described as one of mutual dependence. From the point of view of the state, networks can provide considerable expertise and interest representation and hence are potentially valuable components in the policy process. However, networks are held together by common interests that may challenge the interests of the state.
Networked governance is characterized by 1) interdependence between organizations, which sees governance as broader than government. 2) a reduction in the role of the formal institutions and agencies of the state and a greater role for non-state institutions. 3) continuing interactions between network members, caused by the need to exchange resources and negotiate shared purposes or interests, and 4) a significant degree of autonomy from the state, since these networks are not accountable to the state but they are more toward self-organizing (Rhodes, 1997; cf. Dijk & Winters-van Beek, 2009).
There have been a lot of studies focusing on networks governance in recent decades, and two main schools in this subject were identified, which are (1) The Network Governance School (NWG), and (2) The Policy Network Analysis School (PNA). Whereas NWG has been engaged in a set of macro level focus about the changing nature of state–society relations, PNA has been more concerned with a set of micro level focus about the relationship between policy making outcomes, the structure of a network and the inclusion or exclusion of certain individuals or groups from within that network (Fawcett & Daugbjerg, 2012).
Related terms: Network Governance School (NWG), Policy Network Analysis School (PNA), Governance, Hierarchic Governance, Network, Network Theory, Social Network Analysis
Dijk, J. & Winters-van Beek, A., 2009. The Perspective of Network Government: The Struggle Between Hierarchies, Markets and Networks as Modes of Governance in Contemporary Government. Innovation and the Public Sector, 14, pp.235-55.
Fawcett, P. & Daugbjerg, C., 2012. Explaining Governance Outcomes: Epistemology, Network Governance and Policy Network Analysis. POLITICAL STUDIES REVIEW, 10, p.195–207.
Marsh, D. & Smith, M., 2000. Understanding policy networks: towards a dialectical approach. Political Studies, 48(1), p.4–21.
Rhodes, R.A.W., 1996. The new governance: Governing without government. Political Studies, 44(4), pp.652-67.
Rhodes, R.A.W., 1997. Understanding governance: Policy networks, governance, reflexivity and accountability. Buckingham: Open University Press.
Rhodes, R.A.W., 2000. Governance and public administration. Oxford: Oxford University Press. In Debating governance: Authority, steering and democracy.
 New Public Management (NPM)
The New Public Management (NPM) approach to public service production and delivery runs counter to the old ('traditional') bureaucratic approaches that were born with the emergence of the modern states systems across most of the Western world. It rejects the idea of a specific culture for public organizations and typically argues that such organizations should be managed in the same way as any private sector organization (Osborne & Gaebler, 1992; Hood, 1995; Page, 2005; Riccucci, 2001, see also Dunleavy, Margetts, Bastow and Tinkler, 2006)). Discussions around NPM have been driven by two principle features that emerged in the 1980s during the era of politicians such as Margaret Thatcher and Ronald Reagan. First, NPM advocates a different role for elected officials when compared to traditional systems of government. Politicians are left primarily with a goal-setting role: anything relating to service production and delivery in the public sector should be conducted in a 'market'; the logic being that such an arrangement would ensure increased efficiency and lower costs to the government, and ultimately the taxpayer. Second, New Public Management advocates less input control but emphasises the evaluation of impact and thus performance. This, in turn, requires organisational models that prioritise 'management' in an economic sense rather than in terms of societal needs (Schedler, & Proeller, 2000).
Although this view had many advocates across many (particularly Anglo-Saxon) countries in the world, it was also criticised for its view of public administrations as places where democracy could be 'managed' through accountability mechanisms set up to render civil services accountable to the public, and to effectively 'de-politicise' the process of service delivery. This dominant view that was in practice in many public administrations across the world has now been complemented by discussions on Public Value Management.
Related terms: Public Governance, Policy Analysis, Public Policy
Behn, Robert D. 1998. “The New Public Management Paradigm and the Search for Democratic Accountability.” International Public Management Journal 1 (2): 131–164.
Dunleavy, Patrick, Helen Margetts, Simon Bastow, and Jane Tinkler. 2006. “New Public Management Is Dead-Long Live Digital-Era Governance.” Journal of Public Administration Research and Theory 16 (3): 467–494. doi:10.1093/jopart/mui057.
Hood, Christopher. 1995. “The ‘New Public Management’ in the 1980s: Variations on a Theme.” Accounting, Organizations and Society 20 (2-3) (February): 93–109. doi:10.1016/0361-36820361-3682 (93)E0001-W.
Osborne, D. & Gaebler, T., 1992. Reinventing Government: How the entrepreneurial spirit is transforming the public sector. Wokingham: Addison-Wesley.
Page, Stephen. 2005. “What's New About the New Public Management? Administrative Change in the Human Services.” Public Administration Review 65 (6): 713–727.
Riccucci, Norma. 2001. “The "Old" Public Management Versus the ‘New’ Public Management: Where Does Public Administration Fit in?.” Public Administration Review 61 (2): 172–175.
Schedler, K. & Proeller, I., 2000. New Public Management. Stuttgart: UTB.

Call Send SMS Add to Skype You'll need Skype CreditFree via Skype
 Normative Model
A normative model explains what is going on and what will happen in optimal conditions for a research object. This is a type of perspective model. Normative models allow to describe current roles and functions, understand the biases and develop new knowledge about further models (Baron, 2004).
For example, Vroom created a normative model of decision-making to demonstrate the effectiveness of a decision-making process (Vroom, 2007).
Victor H. Vroom (2007), Normative Models of Decision Making and Leadership.  In S. Rogelberg (Ed.), Encyclopedia of industrial and organizational psychology. (pp. 522-524). Thousand Oaks, CA: SAGE Publications, Inc.
Baron, J. (2004). Normative models of judgment and decision making. In D. J. Koehler & N. Harvey (Eds.), Blackwell Handbook of Judgment and Decision Making, pp. 19-36, London: Blackwell,2004
 Open Data
The defines that "Open data and content can be freely used, modified, and shared by anyone for any purpose". This means that any user can copy data, reuse it, or analyze and re-process it for his or her own purpose, without restrictions from copyright, patents or other mechanisms of control (cf. Auer et al 2007) and without financial or technical barriers. The goals of the open data movement are similar to those of other "Open" movements such as open source, open hardware, open content, and open access.
Murray-Rust et al argue that data related to published science should be explicitly placed in the public domain (Murray-Rust,  et al 2010).
Related terms: Open government data
Auer, S. R., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z. (2007), DBpedia: A Nucleus for a Web of Open Data, The Semantic Web, Lecture Notes in Computer Science 4825. p. 722.
Murray-Rust, P., Neylon, C., Pollock, R., Wilbanks, J. (2010). "Panton Principles, Principles for open data in science", 19 Feb 2010., Project of the Open Knowledge foundation, The Open Definition
 Open Government
The term "open government" refers to a new concept of public governance at central and local levels, based on models, tools and technologies that enable the government to be more "open", "transparent" and accountable to their constituency, especially in regards to designing, formulating and implementing public policies (OECD, 2003).
While the early requests for open government as e.g. demanded in the OECD study of 2003 remained rather unheared, in 2009 the US government reinvigorated the demands for more collaboration, participation and transparency through the Obama Administration (cf. Dawes and Helbig 2010). Since then, the Open Government concept is steadily changing the previous concept of e-government to encompass more strongly the good governance principles (cf. Wimmer 2011). With the guiding principles of transparency, participation, openness and collaboration, Open Government has at first led to the set-up of open government data portals (e.g.,, where  maps, demographics, election results, values ​​of pollution and traffic, economic data and much more government data has been made accessible openly.
In 2011, the Open Government Partnership  - OGP ( has been launched.  It is a multilateral initiative (a platform) that aims to achieve concrete commitments from the member countries for the implementation of actions to openness, transparency and civic engagement, in order to be more responsive to citizens. Since then, OGP membership is steadily growing.
Related terms: Open Government Data, Good Governance
Dawes. S.S., and Helbig, N. (2010). Information Strategies for Open Government:
Challenges and Prospects for Deriving Public Value from Government Transparency. In M.A. Wimmer et al. (Eds.): Electronic Government, EGOV 2010, LNCS 6228, pp. 50–60
OECD. Open Government: Fostering Dialogue with Civil Society. OECD Study, 2003
Maria A. Wimmer. Open Government in Policy Development: From Collaborative Scenario Texts to Formal Policy Models. In Natarajan, R. and Ojo, A. (eds.): ICDCIT 2011. Lecture Notes in Computer Science (# 6536), Springer-Verlag: Berlin Heidelberg, 2011, pp. 76–91
 Open Government Data
According to the Open Knowledge Foundation, Open Government Data (OGD) refers to data produced or commissioned by government or government controlled entities. OGD must follow the principles of "open" as defined in the Open Definition (see related term), i.e. OGD can be freely used, reused and redistributed by anyone (Open Knowledge Foundations).
Along the demands for more openness, transparency and participation (cf. Open Government principles as explained in the related term), open government data has received high interest in the last half decade, and nearly every country, state and city has its open government data portal (such as,, etc.)
Related terms: Open Government, Open Data
Open Knowledge Foundation, Open Government Data.
A policy is a course or principle of action adopted or proposed by an organization or individual (Oxford Dictionaries). A policy is defined as a relatively stable, purposive course of action followed by an actor or set of actors in dealing with a problem or matter of concern (Anderson, 2003). A policy is a guiding principle used to set direction in an organisation and it can be a course of action to guide and influence decisions. It should be used as a guide to decision making under a given set of circumstances within the framework of objectives, goals and management philosophies as determined by senior management (Anderson, 2005)
Related terms: Policy Analysis, Policy Governance, Policy Informatics, Policy Lifecycle, Policy Model, Policy Modelling, Public Governance, Public Policy
Oxford Dictionaries, Available here
Anderson, J. E. (2003). Public policymaking: An introduction. Boston: Houghton Mifflin Company, pp. 1 – 34
Anderson, C. What's the Difference Between Policies and Procedures?, Bizmanualz, April 4, 2005.
 Policy Analysis
Policy analysis provides a tool with which to analyse the construction, formulation, implementation, evaluation, and reconstruction of policies and their frameworks. Policy analysis in a decision-making process in which conflicting interests are at stake may be difficult if such an analysis is made during a decision-making process in a ‘network’ (or ‘policy network’) (de Bruijn & ten Heuvelhof, 2002). The parties involved have conflicting interests and are interdependent; no party is able to impose its will hierarchically upon the other parties. Conflicting interests are a strong incentive for the parties involved to criticize the outcome of the analysis if it fails to meet their interests. The many empirical studies in this area give a picture of unpredictable and, sometimes chaotic decision-making environment (de Bruijn & ten Heuvelhof, 2000). This is because the stakeholders in a network hold different views about the nature and the seriousness of a problem, about the aims pursued, about the authority of the information available and about the need to make a decision.
De Bruijn, J. & ten Heuvelhof, F., 2000. Networks and Decision Making. Lemma, Utrecht.
De Bruijn, H. & ten Heuvelhof, E., 2002. Policy analysis and decision making in a network: how to improve the quality of analysis and the impact on decision making. Impact Assessment and Project Appraisal, 20(4), p.232–242.
 Policy Governance
Policy Governance is an integrated set of concepts and principles that, when consistently applied, allows governing boards to realise owner-accountable organisations (Carver, 2011). It is a model of governance created by Carver and often referred to as “Carver governance” or the “Carver model”. The model designed "to empower boards of directors to fulfill their obligation of accountability for their organisations and to enable them to focus on the larger issues, to delegate with clarity, to control management's job, to rigorously evaluate the accomplishment of the organisation" (Carver, 2013). Ten Policy Governance principles (International Policy Governance Assosiation) form a complete governance system, which enables boards to provide strategic leadership in creating the future for their organisations . Policy Governance is designed to ensure accountability of the Board to the owners or shareholders and of the CEO to the Board (The Governance Coach, 2013).
Related terms: Governance, Policy
Carver, John, and Miriam Carver. 2011. Reinventing your board: A step-by-step guide to implementing policy governance. Vol. 18. Wiley., The Policy Governance® Model
International Policy Governance Assosiation. 2013. Principles of Policy Governance
The Governance Coach. 2013. What is Policy Governance?
 Policy Informatics
Policy informatics is the "transdisciplinary study of how computation and communication technology leverages information to better understand and address complex public policy and administration problems and realise innovations in governance processes and institutions" (Center for Policy Informatics). This approach seeks to strengthen the connections among scholars and between scholars and practitioners who share an interest in how policy relevant information and data are used to formulate, implement, and evaluate public policies (Kamensky, 2012). Policy informatics also encompasses exploration of the implications of new analytical tools and data sources for conducting policy relevant research. The core intellectual focus is to advance research and practice that can enhance our understanding of complex policy and managerial problems.
The latest innovations in information and communications technology and information collection and dissemination capacity are changing the ways in which analysis can support public policy decisions. Policy informatics emphasises theories and research concerning decision-making, complexity theory, and visualisation of quantitative and qualitative information, collective intelligence, behavioural economics, and persuasive technologies. For example, availability of large quantities of data, often on whole populations, promoted by open data and social media raises new questions about how analyses are conducted (Helbig et al., 2012). Data visualisation tools expand ability to display and disseminate complex temporal and spatial information. Together, these innovations bring ample opportunities and challenges for developing new theories on complex dynamic social systems and new approaches that might be suitable for analysing how policies affect them (Johnson and Kim, 2011).
Centre for Policy Informatics, Arizona State University
Kamensky, J. (2012). Policy Informatics is Bridging the Gap Between Researchers and Politicians, Government Executive
Helbig, N., Nakashima, M. and Dawes, S., (2012), Understanding the Value and Limits of Government Information in Policy Informatics: A Preliminary Exploration. In Proceedings of the 13th Annual International Conference on Digital Government Research. pp 291-293. College Park, MD. ACM Digital Library.
Johnson, E. and Kim, Y. (2011). Introduction to the Special Issue on Policy Informatics, The Innovation Journal: The Public Sector Innovation Journal, Volume 16(1)
 Policy Lifecycle
The policy lifecycle is a description of a standard approach to understanding the way in which policies are made in a given political context. In democratic societies, a basic representation of the policy cycle (cf. e.g. Howlett and Ramesh 1995; OECD 2003, p. 34) is:

- Agenda setting, which refers to the process by which issues are raised and selected for attention by governments
- Policy formulation (or consultation), whcihrefers to the process by which policy options are formulated within government
- Policy decision making (legislation), which refers to the process by which governments adopt a particular course of action or non-action
- Policy implementation (the task of the executive): refers to the process by which governments put policies into effect
- Policy evaluation (or monitoring), which refers to processes by which the results of policies are monitored by both state and societal actors, the result of which may be a re-conceptualisation of policy problems and solution
This cycle enables to identify five broad areas that divide up the process of policymaking. Each one requires a slightly different constituency, and logically imply a different approach to how and why they should be involved in the process. For example, at the agenda-setting and consultative stages of the policy cycle, representativity (of the general populace) is not in question, as policy makers need to talk to those who are directly engaged in the given policy area. Policy decision making (and legislation) is in  representative democracies a field that has traditionally been left to elected representatives, although the Swiss tradition of direct democracy provides some insight into how this might evolve in a more interactive societal framework (cf. e.g. Rossel and Finger 2007). Additionally, some attempts at online rule-making in local contexts have been carried out to great success in the United States (Carlitz and Gunn, 2002). The monitoring phase is an interesting sphere where potentials lie for greater participation from involved stakeholders as well.
R. Carlitz and R. Gunn (2002). 'Online rulemaking: a step toward E-governance' Government Information Quarterly 19(4): 389-405.
M. Howlett and M. Ramesh (1995). Studying Public Policy: Policy Cycles and Policy Subsystems, Oxford University press.
OECD. Open Government: Fostering Dialogue with Civil Society. OECD Study, 2003
P. Rossel and M. Finger (2007). Conceptualising e-governance. In: Proceedings of the 1st international conference on Theory and practice of electronic governance (ICEGOV 2007), ACM, New York, pp. 399-407
 Policy Model
Policy models can be used mathematically or non-mathematically for different purposes, such as to investigate analyses and predict and understand the conditions under which a specific phenomenon or event may occur (Clarke & Primo, 2007). Most political models in the modelling literature address voting, election forecasting and government formation. Morton (2004) notes that a model’s developmental cycle progresses from non-formal to formal and finally to empirical. Non-formal models have been used to rationally evaluate the construction of new theories (Hamza, 2013).
Political models are classified into five categories: foundational, structural, generative, explicative and predictive (Clarke & Primo, 2007) where a model can occupy one (or more) of the categories. The most common type is the prediction model (Hamza, 2013).
Different policy models exist, such as a bargaining model (Diermeier et al., 2003), a model of parliamentary electoral competition (Quinn & Martin, 2002), formal model of the politics of delegation in a separation of powers system (Volden, 2002; Hamza, 2013), the spatial model and the bargaining model of collective choice (Banks & Duggan, 2000).
Several simulation modelling trials have been performed. However, most of these models were developed by computer science or statistics researchers (Johnson, 1999). The main objective of these models is to simplify the complexity of the decision-making process in a political environment for politicians (Hamza, 2013).
Banks, J., & Duggan, J. (2000). A bargaining model of collective choice. American Political Science Review, 94(1), 73–88.
Clarke, K., & Primo, D. (2007). Modernizing Political Science: A Model-Based Approach. Perspectives on Politics, 5(4), 741-754.
Diermeier, D., Eraslan, H., & Merlo, A. (2003). A Structural Model of Governmment Formation. Econometrica, 71(1), 27-70.
Hamza, K. (2013). The Impact of Social Media and Network Governance on State Stability in Times of Turbulence: Egypt After 2011 Revolution. Institute for European Studies. Brussels: Vrije Universiteit.
Johnson, P. (1999). Simulation Modeling in Political Science. American Behavioral Scientist, 42(10), 1509-1530.
Morton, R. (2004). Methods and Models: A Guide to the Empirical Analysis of Formal Models in Political Science. New York: Cambridge University Press.
Quinn, K., & Martin, A. (2002). An Integrated Computational Model of Multiparty Electoral Competition. Statistical Science, 17(4), 405–419.
Volden, C. (2002). A Formal Model of the Politics of Delegation in a Separation of Powers System. American Journal of Political Science, 46(1), 111-133.
 Policy Modelling
Policy modelling means to identify areas that need intervention, to specify the desired state of the target system, to find the regulating mechanisms, policy formation and implementation, and to control and evaluate the robustness of interventions (John, 1998). The methodological difficulty is to bridge the gap between policy practice, often expressed in qualitative and narrative terms, and the scientific realm of formal models. Furthermore, policy making in complex social systems is not a clear-cut cause-effect process but is characterised by contingency and uncertainty (Kingdon, 1984). To take into account technological, social, economic, political, cultural, ecological and other relevant parameters, policy modelling has to be enhanced and supported by new ICT-oriented research initiatives. Recent developments move in the direction of advanced ICT tools for policy modelling, prediction of policy impacts, development of new governance models and collaborative solving of complex policy problems (Loveridge, 2007). These tools enable the modelling of policy initiatives taking into account relevant parameters and performing social simulations to forecast potential impacts of proposed policy measures.
A particular challenge in policy modelling is the relationship between large-scale quantitative data and formal policy models (Sabatier, 2007). This is a twofold issue: First, providing knowledge discovery and data mining techniques for large and heterogeneous databases is an urgent requirement for policy analysis in general. Second, to connect these large databases to formal models constitutes a methodological gap in the existing state-of-the-art.
Related term: Policy model
John, P., 1998. Analysing Public Policy. London.
Kingdon, J. W., 1984. Agendas, Alternatives, and Public Policies. Little, Brown, Boston.
Loveridge, D., 2007. Foresight. The Art and Science of Anticipating the Future. Routledge, New York.
Sabatier, P. A., 2007. Theories of the Policy Process (2nd edition). Westview Press, Boulder, CO.
 Policy Network Analysis (PNA)
The Policy Network Analysis (PNA) school has developed a series of micro-level analyses. These analytical frameworks have been used to develop a series of hypotheses about how policy-making outcomes are influenced by the structure of a network and the interactions that occur within a network, including the inclusion and exclusion of certain interests in the policy-making process (Rhodes, 2006). PNA starts with the assumption that - to achieve particular goals - actors within policy networks must exchange resources with each other (Rhodes, 2006). The power-dependent relationships that emerge from this set of interactions define, which actors will become core members of a network; which actors will be positioned in this network with occasional, albeit typically limited, influence; and which actors will be completely excluded from the network (Rhodes, 2006; Hamza, 2013).
Both, network governance school and policy network analysis mainly focus on network governance, however, they look at it on distinct levels. PNA is more concerned with micro-level examinations about the relationships among policy-making outcomes, the structure of a network and the inclusion or exclusion of certain individuals or groups from the network in question (Fawcett & Daugbjerg, 2012). Network governance school has been engaged in a set of macro-level examinations of the changing nature of state-society relationships (Hay & Richards, 2000).
Related terms: Network, Network Governance School (NWG), Network Theory, Networked Governance, Policy Analysis, Policy Governance, Policy Model, Policy Modelling, Public Governance, Public Participation, Public Policy, Social Network, Social Network Analysis
Fawcett, P. & Daugbjerg, C., 2012. Explaining Governance Outcomes: Epistemology, Network Governance and Policy Network Analysis. Political Studies Review, 10(2), p.195–208.
Hamza, K., 2013. The Impact of Social Media and Network Governance on State Stability in Time of Turbulences: Egypt After 2011 Revolution. PhD Thesis. Brussels: Vrije Universiteit Brussel Institute for European Studies.
Hay, C. & Richards, D., 2000. The Tangled Webs of Westminster and Whitehall:The Discourse, Strategy and Practice of Networking within the British Core Executive. Public Administration, 78(1), p.167–76.
Rhodes, R.A.W., 2006. Policy network analysis. In M. Moran, M. Rein & R. Goodin, eds. The oxford handbook of public policy. New York: Oxford University Press. p.425–447.
Provenance refers to the origin or source of an information (Munroe et al., 2006). According to the Oxford English Dictionary, it may also refer to the history of the ownership or location of an object, especially when documented or authenticated. In policy development, provenance is used to provide evidence as to the views and background information for the creation of a public policy. In the OCOPOMO project, provenance is e.g. ensured through the establishment of traces and links between sources of information (the scenarios and background documents) provided by the stakeholders of a policy domain, and the simulation models developed by policy experts (Lotzmann and Wimmer, 2012). The links show the evolution of formal elements of a simulation model from the description of the real-world section (the scenarios and background documents, i.e. informal artefacts) which is subject of the model. Along the traces of a policy development process, two perspectives are important in policy modelling (see Lotzmann and Wimmer 2013):

- the perspective of the model developer, who is interested in traceability in order to understand or to keep track of the structure of the simulation model code,
- the perspective of the stakeholder not directly involved in model development, for whom provenance is essential in order to gain confidence in model results (and the simulation method as such) by unveiling the "black box" simulation model.  
Related terms: Traceability, Evidence
Munroe, S.; P. Groth; S. Jiang; S. Miles; V. Tan; J. Ibbotson; and L. Moreau. 2006. "Overview of the Provenance Specification Effort". University of Southampton Institutional Research Repository ePrints Soton
Ulf Lotzmann, Maria A Wimmer. Provenance and Traceability in Agent-based Policy Simulation. In: Proceedings of 26th European Simulation and Modelling Conference - ESM'2012, 2012
Ulf Lotzmann, Maria A. Wimmer. Traceability in evidence-based policy simulation. In: Webjørn Rekdalsbakken, Robin T. Bye, Houxiang Zhang (Editors) Proceedings of 27th European Conference on Modelling and Simulation (ECMS), 2013
 Public Governance
OECD defines public governance as "the formal and informal arrangements that determine how public decisions are made and how public actions are carried out, from the perspective of maintaining a country’s constitutional values in the face of changing problems, actors and environments" (OECD 2005). Similarly, UNDP argues the "exercise of economic, political and administrative authority to manage a country’s affairs at all levels". This understanding includes "mechanisms, processes and institutions through which citizens and groups articulate their interests, exercise their legal rights, meet their obligations and mediate their differences” (UNDP 1997). Governance in this context includes the "State, but transcends it by taking in the private sector and civil society. All three are critical for sustaining human development:

- State creates a conducive political and legal environment
- Private sector generates jobs and income
- Civil society facilitates political and social interaction - mobilising groups to participate in economic, social and political activities." (UNDP 1997)
Source of figure: UNDP
Good governance therewith promotes constructive interaction among all three actors. (UNDP 1997)
Related terms: Good Governance
OECD (2003). Promise and Problems of E-Democracy, Challenges of online citizen engagement, OECD study Available here
UNDP (1997). United Nations Development Programme, Governance for sustainable human development, UNDP policy document, New York, 1997.
 Public Participation
Conceptually similar to 'stakeholder engagement,' public participation is the mechanism by which citizens (and sometimes, also more generally 'residents') of a territory can be engaged in decisionmaking processes (OECD 2001; Brodie et al, 2010). Whereas stakeholder engagement may well refer to entities that are not only individuals, public participation often refers specifically to the way in which individuals can be brought into the processes surrounding policymaking (Barnes et al, 2007). This can be through various mechanisms and tools, notably voting, consultation, and support for monitoring of policies. Many public institutions across the world have been engaged in debates around how public participation is evolving due to various societal and technological transformations (e.g. European Commission, 2005; Shahin et al, 2009).
Organisation for Economic Cooperation and Development (OECD) (2001), Citizens as Partners. (J. Caddy & C. Vergez, Eds.), pp. 1–253
Brodie, E., Cowling, E., Nissen, N., Ellis Paine, A., Jochum, V., and Warburton, D. (2010), Understanding participation, pp. 1–50
Barnes, M., Newman, J., and Sullivan, H. (2007), Power, Participation and Political Renewal: Case Studies in Public Participation, Policy Press.
Shahin, J., Soebech, O., & Millard, J. (2009), Participation in the European Project: How to mobilise citizens at the local, regional, national, and European levels (No. QG-31-09-153-EN-C). Committee of the Regions, pp. 1–185
Brussels: Committee of the Regions, doi:10.2863/14653 European Commission. (2005), The Commission’s contribution to the period of reflection and beyond: Plan-D for Democracy, Dialogue and Debate (COM(2005) 494 final), pp. 1–12, Brussels: European Commission.
 Public Policy
Public Policy is a "set of interrelated decisions taken by a political actor or group of actors concerning the selection of goals and the means of achieving them within a specified situation where those decisions should, in principle, be within the power of those actors” (Jenkins, 1978). Public policy can be considered as: (i) a process; (ii) series of decisions; (iii) limited by internal and external constraints of government’s capacity to implement the decisions; (iv)as goal-oriented behaviour.
A public policy is "a document drawn up by governmental actors to present both their vision of an issue calling for public action and, to some extent, the legal, technical, practical and operational aspects of this action" (Turgeon, 2011). Public policy refer to "the process through which elected representatives decide on a public action designed to deal with an issue considered by certain actors, whether governmental or non-governmental, to require some kind of intervention".
Public policies in modern political systems are designed to accomplish specified goals or produce definite results, although these are not always achieved (Anderson, 2003). Public policies emerge in response to policy demands, or those claims for action or inaction on some public issue made by other actors—private citizens, group representatives, or legislators and other public officials—upon government officials and agencies.
Related terms: Policy, Policy Analysis, Policy Governance, Policy Informatics, Policy Model, Policy Modelling.
Jenkins, William (1978). Policy Analysis: A Political and Organizational Perspective. London: Martin Robertson
Turgeon, J. and J.-F. Savard (2012).“Public Policy,”in L. Côté and J.-F. Savard (eds.), Encyclopedic Dictionary of Public Administration, [online],
Anderson, J. E. (2003). Public policymaking: An introduction. Boston: Houghton
 Public Value Management (PVM)
Public Value Management (PVM) has emerged from a critique of New Public Management (NPM) (Stoker, 2003). It shares with more traditional approaches the idea that the public sector differs from the private. It rejects NPM’s assumption that democratic governance resembles consumer choice in the market, and is sceptical of insights drawn directly from the private sector (Moore, 1995). Three main categories of values can be observed:

- Legal values comprise the belief in legislation as the guiding principle in governance structure. The rule of law is an important legal value. Governance structures must behave in accordance with the laws that have been democratically agreed. Similarly, citizens should be protected from abuse by (administrative) courts (Considine & Lewis, 1999; 2003).
- Economic values include a variety of values, such as effectiveness, efficiency, flexibility and customer orientation. Governance structures attempt to maximise output while minimising inputs. These economic values can be compared with the business values of private sector companies (Pollitt & Bouckaert, 2004).
- Democratic values include transparency, accountability, openness and social equity. Subsequently, a governance structure should not be a closed organisation but must be open to citizens criticism and respond to their wishes or needs. Citizens must be able to influence or participate in decision-making processes during the course of policymaking. The state should treat all citizens in an equal manner with respect to not only legal equity but also real equity in everyday life (Thompson et al., 1991).  
Considine, M. & Lewis, J., 1999. Governance at ground level: the frontline bureaucrat in the age of markets and networks. Public Administration Review, 59(6), pp.467-81.
Considine, M. & Lewis, J., 2003. Bureaucracy, network or enterprise? Comparing models of governance in Australia, Britain, the Netherlands and New Zealand. Public Administration Review, 63(2), pp.131-40.
Stoker, G., 2003. Public Value Management (PVM): A new resolution of the democracy/efficiency tradeoff. Manchester: Institute for Political and Economic Governance (IPEG),University of Manchester.
Thompson, G., Frances, J., Levacic, R. & Mitchell, K., 1991. Markets, hierarchies and network. The coordination of social life. London: Sage Publications.
Pollitt, C. & Bouckaert, G., 2004. Public Management Reform: A Comparative Analysis. 2nd ed. New York: Oxford University Press.
Moore, M., 1995. Creating Public Value: Strategic management in government. Cambridge: Harvard University Press.
 Scenario Building
In policy development, scenario building is considered a method for foresight (Fradfield et al, 2005; Geschka and Hammer, 1997; Mietzner and Reger, 2005). According to Geschka, it provides a "systematic, participatory, future intelligence gathering and medium-to-long-term vision building process aimed at present-day decisions and mobilising joint action" (Geschka, 1978). An example of such future vision scenario is e.g. developed in Kahn and Weiner in 1967 for the year 2000 (Kahn and Weiner, 1967).
Scenario building is inherently flexible in terms of design and construction. Scenarios help stimulate different internally consistent alternatives of a specific situation and its settings concerning a specific policy issue. Focus of scenarios in foresight exercises and policy planning is on the identification and description of impact factors as well as on cause and effect interdependencies.
Scenario building hardly grounds on literature review. It focuses on stakeholder involvement, instead (Wimmer et al., 2012). Scenarios are often built by groups of experts or stakeholders in workshops. Hence, scenarios support the communication among the participants thereby bringing down the level of conflict and facilitating cooperation. The participatory process can help build consensus as the different policy alternatives, and the consequences of those alternatives, are shared and discussed by all. With these assets, scenario building can contribute to achieve the good governance principles. Precondition for successful application of scenario technique to engage stakeholders is a well-designed process, which stimulates reflection and learning among all participants (Johnson et al., 2012).
Related terms: Stakeholder engagement, Method
Bradfield, R., Wright, G., Burt, G., Cairns, G., & Van Der Heijden, K. (2005). The origins and evolution of scenario techniques in long range business planning. Futures, 37(8), 795–812. doi:10.1016/j.futures.2005.01.003
Geschka, H. (1978). Delphi. In Bruckmann, G. (Ed.), Langfristige Prognosen: Möglichkeiten und Methoden der Langfristprognostik komplexer Systeme. Würzburg, Germany: Physica-Verlag.
Geschka, H., & Hammer, R. (1997). Die Szenario-Technik in der strategischen Unternehmensplanung. Strategische Unternehmensplanung. In Hahn, D., & Taylor, B. (Eds.), Strategische Unternehmensplanung. Strategische Unternehmensführung. Stand und Entwicklungstendenzen (pp. 464–489). Heidelberg, Germany
Johnson, K. A., Dana, G., Jordan, N. R., Draeger, K. J., Kapuscinski, A., Schmitt Olabisi, L. K., & Reich, P. B. (2012). Using participatory scenarios to stimulate social learning for collaborative sustainable development. Ecology and Society, 17(2), 9. doi:10.5751/ES-04780-170209
Kahn, H., & Weiner, A. J. (1967). The year 2000: A framework for speculations on the next thirty-three years. New York, NY: Macmillan.
Mietzner, D., & Reger, G. (2005). Advantages and disadvantages of scenario approaches for strategic foresight. International Journal of Technology Intelligence and Planning, 1(2), 220–239. doi:10.1504/IJTIP.2005.006516
Wimmer, M.A., Scherer, S., Moss, S. & Bicking, M. (2012) Method and Tools to Support Stakeholder Engagement in Policy Development. The OCOPOMO Project. In: International Journal of Electronic Government Research (IJEGR), 8 (3), pp. 98-119
 Semantic technologies
Semantic technologies provide tools to analyse data and to distinguish meaning from data. As example of these technologies, they include the Resource Description Framework (RDF), the Friend of a Friend ontology (FOAF), the Simple Knowledge Organization System (SKOS), and triple stores. Semantic technologies are linked to ontology, but do not define ontologies themselves. Examples of semantic technologies include Calais and Alchemy (Osimo, Smith, Verona, Szkuta, Shahin and Meyer, 2014).
Semantics emerged as a key phenomenon of the worldwide web as a result of the Semantic Web movement, launched around the turn of the Century by Time Berners-Lee et al (2001). This article expressed the future of technologies that would be able to extract meaning from structured data on the worldwide web (semantic data).
Semantic technologies refer to either Hard semantic technologies or Soft semantic technologies, as stated in (Tiropanis et al., 2009). Hard semantic technologies "provide ways to express meanings of resources  and their relationships in machine-processable formats, and ways to draw conclusions—to reason—based on these meanings" (Tiropanis et al. 2009).
On the other hand, Soft semantic technologies "provide ways to express the meanings of resources in formats that humans can interpret, or in formats that employ domain-specific information structures" (Tiropanis et al., 2009). For example, we can mention traditional tagging tools, topic maps, and domain-specific XML schemas.
Berners-Lee, T., Hendler, J. and Lassila, O. (2001). The Semantic Web. Scientific American.
Osimo, D. , Smith, F., Verona, M., Szkuta, K., Shahin, J. and Meyer, T. (2014). Feasibility study on using automated technologies to support policy-making. Brussels: European Commission.
Tiropanis, T., Davis, H., Millard, D., and Weal, M. (2009). Semantic Technologies for Learning anTeaching in the Web 2.0 Era. Society Online,
 Simulation Model
A simulation model is a “running model” that produces artificial data about the structures and behaviours of a target (e.g. a social system), where empirical target data and artificial model data are sufficiently similar to serve the purpose of the modeller. The advantage of a simulation model of the target is that it allows experimenting with structural and behavioural change (cf. Gilbert and Doran, 1994). Artificial data compared to empirical data is the output data of the model. If there is a sufficient evidence of isomorphism of artificial and empirical data, we talk about "validation" of the model. Behavioural change on the micro level of a simulated target system may lead to structural change of global phenomena on the system level.
Gilbert, N. and J. Doran (eds.), 1994: Simulating Societies. The computer simulation of social phenomena. London: Routledge.
 Social Media
At the heart of social media are social networks. Boyd & Ellison describe social networks as "web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their list of connections and those made by others within the system.” (Boyd & Ellison 2007, p.211). In addition, social media is extensively related to ‘Web 2.0’ (see relevant term definition in the glossary) and the rise of user-generated content, ie. users in all shapes and sizes are posting their own information to the social networks and social media in the form of text, images, etc. Social media is associated with all aspects of modern life, from politics to business, from friendships to family.
In the area of policy making and e-participation, social media can be a powerful tool in the hands of citizens to express ideas, report problems and create on-line communities. Also politicians and local and central government representatives are enabled to connect with citizens through social media.
Related terms: Social Network, Social Network Analysis (SNA), Web 2.0
Boyd, D. & Ellison, N. 2007. Social Network Sites: Definition, History, and Scholarship. Journal of Computer Mediated Communication, 13, 1, 210-230
 Social Network
Social networks express the ties between humans (and other organisms) that allow for interaction, and thus form a prerequisite for processes of social influence, e.g. sharing information or imposing norms. Ties between people cover multiple channels, e.g. physical meetings or on-line communication. Tie strength may differ over time (e.g., frequency of interaction). Social networks are emergent and self-organising complex systems originating from interaction (Barabási, 2002).
The pattern of ties connecting people can be studied using social network analysis, allowing for measuring typical network properties such as reciprocity in ties, centrality and connectivity of a person and clustering of (segments of) people (Newman et al, 2006).
Because empirical data on large social networks is hard to obtain, often stylized formalisations of social networks are used in agent based simulation models. These networks can be fixed, e.g. small world or scale free, or dynamic, e.g. based on similarity. Because the effects and evaluation of policies are often transmitted through social networks, it is important to consider potential network effects when developing policy.
Related terms: Social Media, Social Network Analysis (SNA)
Barabási, Albert-László (2002), Linked: The New Science of Networks, Perseus Books Group
Newman, Mark, Albert-László Barabási, and Duncan J. Watts. (2006), The Structure and Dynamics of Networks, Princeton, NJ: Princeton University Press