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 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
References:
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.
References:
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.
References:
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
References:
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)
References:
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

 Social Network Analysis (SNA)

Social Network Analysis (SNA) is focussed on the structure of relationships among actors (Hanneman & Riddle,2005). SNA maps and measures formal and informal relationships to understand what facilitates or impedes the knowledge flows that bind interacting actors, departments, organisations or other entities.
SNA is a method with increasing application in the social sciences and has been applied in areas as diverse as psychology, health, business organisation, and electronic communications. More recently, interest has grown in analysis of social media to understand social networks (Scott 1988). Visualising the interorganisational network by social network analysis enable the government and policy makers to describe and analyse interactions among actors. There is good tool support such as NodeXL (Hansen, Shneiderman & Smith, 2011).
Related terms: Social Network, Social Media
References:
Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. from http://faculty.ucr.edu/~hanneman/nettext/Introduction_to_Social_Network_Methods.pdf
Hansen, D. L., Shneiderman, B., & Smith, M. A. (2011). Analyzing socila media network swith NodeXL. Insigths from a connected world. Amsterdam: Elsevier.
Scott, J. (1988), Social Network Analysis, Sociology (22:1) February 1, 1988, pp 109-127.

 Stakeholder

Stakeholders can be defined in the simplest terms as individuals or groups who affect or are affected by a policy, following Freeman’s (1984) classic definition of stakeholder as "any group or individual who can affect or is affected by the achievement of the organization's objectives."  In the public sector, “organisation” is understood to include a wide variety of political bodies, government institutions and other entities involved in the policy making process. Stakeholders can be both internal to the government (e.g., the government organisations responsible for policy implementation) or external to it (e.g., the industries, communities, or individuals to be affected by government actions or rules). Most private sector definitions mention similar stakeholder categories such as companies and their employees or external entities such as suppliers, customers, governments or creditors. In the public sector, the definition of stakeholder often emphasises categories of citizens defined by demographic characteristics, life stages, interest groups, or organisational boundaries (Bingham, Nabatchi, and O’Leary 2005; Ackerman 2004; Yetano, Royo, and Acerete 2010). Stakeholders can be involved at any point in the policy cycle from framing issues to evaluating results.
Various structured approaches exist to identify, select and prioritise relevant stakeholders (Bryson, 2004). These techniques focus attention on the interrelations of groups or organisations with respect to their interests in, or impacts on policies within a broader political, economic and cultural context. These techniques also provide ways for analysts to understand stakeholder power, influence, needs, conflicts of interest, and changes in stakeholder types or interests over time. Selection of stakeholders often rests on two foundational considerations: information and power or influence. Certain stakeholders should be involved if they have information that cannot be obtained in other ways or if their participation is necessary for successful policy implementation (Thomas, 1995).
References:
Ackerman, John. 2004. “Co-Governance for Accountability: Beyond ‘Exit’ and ‘Voice.’” World Development 32 (3)
Bingham, Lisa Blomgren, Tina Nabatchi, and Rosemary O’Leary. 2005. “The New Governance: Practices and Processes for Stakeholder and Citizen Participation in the Work of Government.” Public Administration Review 65 (5): 547–558.
Bryson, John. (2004), "What To Do When Stakeholders Matter," Public Administration Review
Freeman, Edward. (1984), "Strategic Management: A Stakeholder Approach," Boston: Pitman
Thomas, J. C. (1995), Public Participation in Public Decisions, San Francisco CA: Jossey-Bass
Yetano, Ana, Sonia Royo, and Basilio Acerete. 2010. “What Is Driving the Increasing Presence of Citizen Participation Initiatives?” Environment and Planning C: Government and Policy 28 (5): 783 – 802.

 Stakeholder Engagement

Stakeholder engagement is a process which entails bringing in all parties involved in a policymaking process to ensure that they are represented in discussions in all elements of the policy cycle (Gains & Stoker, 2009). As representative democracy has evolved, particularly through the use of new Information and Communication Technologies, the approach to involving more stakeholders in policymaking has become apparent (Marinetto, 2003). It has emerged as a key challenge in the 21st Century in politically developed societies, as one of the central political innovations that is taking place (Newman, 2010). This is related to an increase in the desire for political institutions to show more openness and transparency in their activities, which is part of the process of building good governance (see, for example Sœbø et al, 2008). One primary example comes from the Directorate General CONNECT's (European Commission) Stakeholder Engagement Strategy/Approach.
Related terms: Governance, Policy Analysis, Public Policy, Public Value Management (PVM)
References:
Gains, F., & Stoker, G. (2009). Delivering “Public Value”: Implications for accountability and legitimacy. Parliamentary Affairs, 62(3), 438–455.
Marinetto, M. (2003). Who wants to be an active citizen? Sociology, 37(1), 103–120.
Newman, J. (2010). Remaking Governance: Peoples, Politics and the Public Sphere. Policy Press.
Sæbø, Ø., Rose, J., & Flak, L. S. (2008). The shape of eParticipation: Characterizing an emerging research area. Government Information Quarterly, 25(3), 400–428. doi:10.1016/j.giq.2007.04.007