Shared Vision Planning as Policy Analysis: Opportunities for Shared Learning and Methodological Innovation

1

Shared Vision Planning as Policy Analysis:

Opportunities for Shared Learning and Methodological Innovation

W.E.Walker1, I. S. Mayer

2, E.R. Hagen

3

1Delft University of Technology, Professor of Policy Analysis, Faculty of Technology, Policy and

Management, Department of Policy Analysis, PO Box 5015 - 2600 GA Delft, Jaffalaan 5 - 2628 BX

Delft, The Netherlands, w: + 31 (0) 15 2785122, f: + 31 (0) 15 2786439, m: + 31 (0) 652334642, e-mail:

[email protected]

2Delft University of Technology, Associate Professor of Public Administration & Gaming, Director of

CPS - the TU-Delft/TPM Centre for Serious Gaming, Faculty Technology, Policy and Management

Department of Policy, Organization & Gaming, PO Box 5015 - 2600 GA Delft, Jaffalaan 5 - 2628 BX

Delft, The Netherlands, w: + 31 (0) 15 2787185, f: + 31 (0) 15 2786439, m: + 31 (0) 630145948, e-mail:

[email protected]

3President, Potamoi LLC, Parkweg 9a, 2585 JG Den Haag, Skype: +1 301 979 9395, m: +31 (0) 2069

8567, e-mail: [email protected].

ABSTRACT

Shared Vision Planning (SVP) is a collaborative approach to water (resource) management that combines

three practices: (1) traditional water resources planning; (2) structured participation of stakeholders; (3)

(collaborative) computer modeling and simulation. The authors argue that there are ample opportunities

for learning and innovation in SVP when we look at it as a form of Policy Analysis (PA) in a multi-actor

context. SVP faces three classic PA dilemmas: (1) the role of experts and scientific knowledge in

policymaking; (2) The design and management of participatory and interactive planning processes; and

(3) the (ab)use of computer models and simulations in (multi actor) policymaking. In dealing with these

dilemmas, SVP can benefit from looking at the richness of PA methodology, such as for stakeholder

analysis and process management. And it can innovate by incorporating some of the rapid developments

now taking place in the field of (serious) gaming and simulation (S&G) for policy analysis. In return, the

principles, methods, and case studies of SVP can significantly enhance how we perform PA for multi-

actor water (resource) management.

2

Key words: Shared Vision Planning (SVP), Policy Analysis (PA), Collaborative Modeling, Interactive

Policymaking, Participatory Policymaking, Simulation, Gaming, Policy exercises, Serious Gaming.

1. INTRODUCTION

Within a few decades, Water Management (WM) has risen markedly on the societal, political, and

scientific agendas, and it is expected that WM will gain in importance and urgency in the near future.

Policy issues in water management such as ‘flood protection’ and ‘flood control’, ‘river basin

management’, ‘resource management’ and ‘spatial planning’ have distinct characteristics that often make

them intractable, messy, and controversial. In this paper, we will argue that this has significant

consequences for the appropriate methods and tools for policy support.

The outline of the paper is as follows. First we give a general characterization of policy problems in

Water Management. We argue that this has consequences for tools and methods for policy support. We

then give a general introduction of Shared Vision Planning (SVP), since it aims to cope with some of the

problems. Then we address some of the inherent problems and dilemmas faced by SVP by looking at it as

a kind of PA in a multi-actor context. Then we introduce the notion of ‘gaming-simulation’ as one of the

interesting policy analytic methods that relate directly to SVP and where planning, stakeholder

participation, and modeling are highly integrated.

2. CHARACTERISTICS OF POLICY PROBLEMS IN WATER MANAGEMENT

What are the general characteristics of policy problems in water management that make them messy,

intractable, and often lead to conflicts and controversies? First of all, policy problems in WM are subject

to a great many vigorous ecological, economic, cultural, societal, and political trends, such as population

growth, urbanization, economic development and land use, climate change, and geopolitics. Second, they

are usually ‘multi-level’, which implies that problems, solutions, and governance structures at a local,

regional, … to a global level are strongly interrelated. Third, water management policy issues are highly

interconnected among each other as well as with issues in other policy areas (at the same or different

governance level), such as spatial planning, infrastructures (transportation, utilities), etc. Fourth, WM

policy problems can usually be framed from different perspectives – e.g., from a disciplinary perspective

(an engineering, economic, or cultural framing of the policy problem, or from the point of view of

different policy belief systems. Belief systems have their roots in ‘deep core’ values and divergent

viewpoints on ‘knowledge’ – e.g. about the sources or reliability of data, or the underlying cause-effect

3

relations. Hence, different belief systems can give rise to conflicts between opposing policy communities

that advocate different solutions, such as ‘giving more space to the river’ or ‘controlling the river by

raising the height of the levees’. Such conflicts can result in policy stalemates, which may be resolved by

analysis of the underlying belief system and a reframing of the policy problem. Fifth, issues in WM are

highly socio-technical in nature. This implies that the technological-physical and societal-political

dimensions of the policy problem are highly interwoven and are difficult to separate. ‘Hard’ scientific

data, models, engineering, and technology are closely tied to all kinds of interrelated, interdependent, and

interacting stakeholders – scientists, engineers, environmentalists, citizen groups, policymakers, policy

advocates, etc – who form a (policy) network and operate in a policy arena. Sixth, the WM domain is

‘driven’ by a high level of expertise and professionalism – a technocracy build up for many decades, even

centuries, in well-established, even dominant engineering institutions, such as the Army Corps of

Engineers in the U.S., or in renowned knowledge institutes and companies, such as Deltares in the

Netherlands. Much of the knowledge on the hydrological, geophysical, and even economic aspects of

water management has been incorporated in sophisticated computer models and simulations. These are

used to support engineering and planning, e.g. through processes of data-analysis (water levels, flows),

forecasting (prediction, scenario generation), evaluation of options (cost-benefit, impact, or risk analysis),

etc. The dependence on the institutional technocracy, hard data, and computer models has its downsides.

One of them is that many of the computer models and simulations often remain black boxes to

policymakers or other stakeholders. On the other hand, water management involves many subjective,

social-cultural and political aspects that cannot be quantified or put into simulation models. Therefore,

other ways than computer models are needed to understand and incorporate knowledge into the planning

process. This is most often accomplished through some kind of interactive stakeholder-learning process.

The last characteristic of water management in countries like the U.K., the U.S., and the Netherlands

concerns the high level of institutionalization. On the one hand, water management has largely been

institutionalized in all kinds of legal and formal planning procedures that define the steps of political

decisionmaking, as well as the authorities, responsibilities, rights, and duties of the various stakeholders.

On the other hand, and due to the characteristics described above, decisionmaking in water management

takes place in a political reality characterized by stalemates and deadlocks, strategic behavior, fits and

starts, conflicts and fights, uncertainties, and mistakes. In other words, planning very seldom is a neat,

linear, rational process, but takes place in messy rounds and iterations, in which stakeholders enter and

leave the arena at will.

4

If such is the general nature of WM, we have to (re)consider carefully the procedures and methods

through which we support planning and decisionmaking. In this paper, we argue that Shared Vision

Planning (SVP) as adopted by the U.S. Army Corps of Engineers Institute for Water Resources (IWR),

takes into consideration some of the fundamental characteristics described above. We also argue that the

methodology and dilemmas underlying SVP can be better understood by considering SVP as a specific

kind of Policy Analysis (PA), namely the kind that operates in a multi-actor context. Furthermore, we

argue that SVP can innovate by incorporating some of the rapid developments now taking place in the

field of (serious) gaming and simulation (S&G) for policy analysis.

3. SHARED VISION PLANNING (SVP)

Shared Vision Planning facilitates a common understanding of a natural resource system and provides a

consensus-based forum for stakeholders to identify tradeoffs and new management options. SVP has been

applied to numerous case studies over the last 20 years.1 SVP is a collaborative approach to formulating

WM solutions that combines three disparate practices:

1) Traditional water resources planning;

2) Structured public participation;

3) Collaborative computer modeling.

SVP takes the formal, legislative water management planning procedures as a starting point (i.e., practice

1). At the same time it tries to open up the formal planning process by arranging and facilitating

stakeholder/public involvement and participation (i.e. practice 2). The strategy for involving stakeholders

in water resources planning has its roots in an approach used by the Vietcong to organize during the

Vietnam War, called "Circles of Influence" (U.S. Army Corps of Engineers, 1995). This approach

balances the desire for broad participation with the need for planning efficiency. It is more effective,

efficient, and broadly representative than other traditional stakeholder participation processes (Palmer et

al., in preparation). Manuals and guides for arranging stakeholder participation in SVP are currently

under construction.

1 SVP was taught at the University of Washington as part of the undergraduate and Master’s degree programs in the

department of Civil Engineering in Water Resources Planning and Management and is currently taught at the

University of Massachusetts in Amherst as a part of the water resources planning and management curriculum. The

U.S. Army Corps of Engineers Institute for Water Resources promotes Shared Vision Planning and maintains a

Website providing more information about SVP. (See www.svp.iwr.usace.army.mil/)

5

At the same time, SVP makes this stakeholder participation process more informative (intelligent) by

using collaborative modeling (i.e. practice 3). Collaborative modeling involves the development and use

of the Shared Vision Planning Model (SVPM). A SVPM captures a shared vision or understanding of the

problem, mutually defined by the stakeholders, connecting changes in management alternatives with the

stakeholders’ interests. The SVPM is designed to be easily understood and used by non-technical study

participants. The SVP practitioner may not be an expert modeler, but given his/her background in civil

engineering, the SVP study lead will usually be the person responsible for developing the ‘dashboard’ or

user interface that incorporates outputs from multiple, and sometimes complex models. Such models can

be object-oriented simulation models, spreadsheet models, or other types of models. The SVP model

incorporates all relevant information and models from a variety of sources and data into a single model.

The personality and skill-set of the SVP study lead are important. Personal charm and enthusiasm can

help, as can natural abilities at engineering coupled with strong communication skills, in both writing and

speaking. Training in system engineering is helpful for incorporating the modeling elements. A flair for

creative problem solving and a liberal arts background does not hurt. While these characteristics are

difficult to find in a single person, they are important characteristics of a successful SVP study lead.

4. UNDERSTANDING SHARED VISION PLANNING AS POLICY ANALYSIS

Based upon the above brief characterization of SVP, we argue that SVP can be understood as a specific

type of (public) policy analysis, i.e. a type of policy analysis that takes into account the multi-actor

context, and therefore relies upon interaction, participation, and learning. We believe that this implies that

the SVP – in its principles, methods, and tools – can benefit from a better understanding of some of the

intrinsic dilemmas involved in PA, mainly: (1) The role of experts and scientific knowledge in

policymaking; (2) The design and management of participatory and interactive planning processes; and

(3) The (ab)use of computer models and simulations in (multi-actor) policymaking.

Influenced by related disciplines like Operation Research (OR) and Systems Analysis (SA), PA emerged

after the World War II as an applied and action oriented discipline that uses analytical methods derived

from the social sciences to support public policymaking and public policymakers in non-defense policy

domains (cf. Brewer & deLeon, 1983; Hogwood & Gunn, 1984; Dunn, 1981 & 1994; Walker & Fisher,

1994; Parson, 1996; Radin, 2002; Mayer et al., 2004). Policy Analysis refers to the analysis ‘for’ public

policymaking; in other words, to the activities, methodology, and tools that are used to give aid and

advice in a context of public policymaking (Brewer and DeLeon 1983; Dunn 1994). Policy analysis is

very often (but not always) commissioned by, and conducted for, clients – most commonly public

6

policymakers or policy stakeholders. Much like an ‘art’ or ‘craft’, significant parts of the PA discipline

can be taught to and learned by professionals working in, for, or with the public sector.

PA encompasses a broad and versatile field of applied research in which a multitude of perspectives and

methods have developed (Mayer et al., 2004; Walker, 2000). There are various ‘schools’ in policy

analysis who disagree about the proper role of policy analysis in policymaking and on methodology. And

there has been an evolution of what the clients of policy analysis believe they need and prefer in terms of

knowledge demands and methodologies. A flourishing industry of think tanks, with leading corporations

such as RAND, has been innovating methods for public policy analysis, among others a broad and diverse

range of Modeling, Simulation, and Gaming methods (MSG) (Williams & Palmatier, 1992; Abelson,

2004).

Looking back at the evolution of SVP, it appears that it has evolved from roots similar to those of PA, in

engineering, OR, and SA. SVP, however, has evolved with a focus on water management/conflicts as

modified by planning principles from the Harvard Water Program.2 While not explicitly conceived or

developed within the PA context, Shared Vision Planning (SVP) as practiced in the United States and

supported by the U.S. Army Corps of Engineers Institute for Water Resources, shares many of the

attributes of PA; for example: 3

- Emphasis on the production and incorporation of policy relevant information – hard and soft – into

complex policymaking, planning, and management processes;

- Emphasis and focus on analytical tools and methods;

- Client oriented;

- Being an art and craft;

- Disciplinary and professional characteristics, such as in educational programs, conferences

2 Another parallel between the two disciplines is the connection through Cornell University. Dr. Richard Palmer, one

of the early developers and practitioners of SVP, obtained his doctorate at Cornell University. One of his thesis

advisors was Dr. Chuck ReVelle. Dr. Chuck Revelle and Dr. Warren Walker, who has since practiced PA for over

40 years, had the same thesis advisor at Cornell University – Professor Walter Lynn. 3 Other examples of PA can be found in a variety of publications, including Drake, Keeney, and Morse (1972),

House (1982), Mood (1983), and Pollock, Rothkopf, and Barnett (1994). More recently, RAND Europe has used the

approach in a range of studies, including:

- an examination policies for improving the Dutch river dikes (Walker, et al., 1994),

- an examination of options for reducing the negative impacts of road freight transport in the Netherlands

(Hillestad, et al., 1996),

- an examination of policy options for improving maritime safety in the North Sea (Walker, et al., 1998),

- an examination of infrastructure options for the Netherlands’ civil aviation system (RAND Europe, 1997).

7

5. POLICY ANALYSIS IN A MULTI-ACTOR CONTEXT 4

In other publications (Mayer, 2009, Mayer, forthcoming), we have argued that the interpretation of what

policy analysis (PA) is and how it should be conducted very much depends on one’s view towards public

policymaking in general: whether it is neat and rational or chaotic and messy. If policymaking is viewed

to be neat and rational – or at the least, that it should be – we can very much rely on the methods and

rational-analytic tools derived from science to support policymakers. Advisors to policymakers should

use the best available scientific knowledge and methods derived from the engineering, social, legal,

technical, and economic sciences in order to provide good answers to complex societal problems. For an

interventionist discipline like policy analysis, it implies that ‘political rationality’ must be accommodated

to ‘scientific’ or ‘technical’ rationality. In fact, over the years the discipline of policy analysis has

provided us with many rational-analytic tools – the PA toolbox – by which we can reduce uncertainty or

find ‘optimal’ solutions to policy problems. Typical examples are cost-benefit analysis, trend

extrapolation, and simulation (Dunn 1994). Most PA tools and methods that have their roots in the natural

sciences (mathematics, economics), positivist social sciences, and engineering are based upon

assumptions of rationality, linearity, and optimization. Hence, the historic dominance of such tools in a

domain like water (resource) management.

But, if we assume that planning and policymaking is inherently chaotic and messy, such tools have a

serious handicap: they are unable to cope with the un-predictive and frequently a-rational behavior

displayed by real people and organizations. Or, when they do try to incorporate human behavior, human

actors are reduced to factors such as variables or agents that can be put into a computer model. The thing

is that the latter ‘messy’ perspective on governance and public policymaking has found common ground.

Its models and theories – like bureaucratic politics, garbage can model, stream model and network theory

– are considered to be more in line with political reality (Bruijn and ten Heuvelhof, 2002; Bruijn et al

2002). Policy scientists increasingly have come to realize that government is not a unitary body that seeks

to optimize solutions to well-defined problems. Instead government, like society, is fragmented into many

loosely coupled agencies, departments, and individuals, who in many cases have their own interests in

mind (e.g. departmental budgets or personal careers). The many stakeholders that operate in the public

arena often have different and conflicting views on the causes and consequences of societal problems.

Facts are often disputed, knowledge is negotiated, and scientists often are stakeholders – hired guns – in

the policy arena (Jasanoff 1990). Furthermore, there are many societal actors that are largely unresponsive

4 This section is adapted from Mayer (2009).

8

towards deliberate government interventions e.g. by regulations, subsidies or taxes. And these

stakeholders deliberately attempt to influence the outcome of the political process to their own advantage

– e.g., by lobbying, by going to court, by hiring consultants, by presenting counter evidence, and most of

all by making strategic use of their resources (money, authorities, information) upon which government

bodies depend for the implementation of their policies. In other words, public policymaking takes place in

a dynamic arena where policy issues come and go and where stakeholders enter and leave as they will.

Thus, in many situations there are no ‘optimal’ or ‘best’ solutions; only politically negotiated, acceptable,

and feasible solutions. For an interventionist discipline like PA, this implies that technical and scientific

rationality must accommodate to political rationality. The analyst’s function, then, is to analyze and

structure the policy arena and to facilitate and support the deliberation and negotiation process. Hence,

new schools of PA – e.g. participative/tory, interactive, argumentative policy analysis – have emerged

that emphasize the production and use of policy relevant information in a multi-actor context. In their

principles, methods, and tools they focus on the participation, interaction, collaboration, discourse,

learning, and process management or meta-decisionmaking among stakeholders. ‘Simple’ PA tools and

techniques, such as (interactive) stakeholder analysis or network analysis, can give insights into the

underlying stakeholder or network structure – their resources and influences, the push and pull forces, the

coalitions. Such techniques could be a sophistication or elaboration of the methods used in SVP, such as

the circles of influence.

Theories and literature in the area of process management/meta-decisionmaking could provide insights

into the dilemmatic structure of stakeholder processes. According to this branch of literature, the design

and management of PA is a constant, dilemmatic trade-off between four different criteria in a stakeholder

process:

1. Openness: the process should be designed in such a way that stakeholders can join and can put their

important issues on the agenda. The rules of entry and exit for stakeholders, however, can and should

be defined at the start of the PA process.

2. Safety: the process should be designed in such a way that the core values of the stakeholders are

safeguarded. The rules that guarantee safety, for instance in terms of confidentiality or transparency

of information or relations with the media, can and should be defined at the start of the PA process.

3. Progress: the process should be designed in such a way that it shows sufficient progress and timely

results. An open stakeholder process is not the same as a slow, endless process.

9

4. Content: the process should be designed in such a way that it based upon adequate, best available

knowledge and intelligence. In other words, things like ‘negotiated nonsense’ in a stakeholder process

should be avoided.

Figure 1 shows an overview of the actors involved in a PA study.

Figure 1: Actors in Policy Analysis

6. DILEMMAS AND QUESTIONS IN MULTI-ACTOR PA AND SVP

PA involves a constant balancing to avoid ‘superfluous knowledge’ – i.e. scientific knowledge that is not

used politically – and ‘negotiated knowledge’ – i.e. political compromises that are not based upon facts

or knowledge (van de Riet, (2003). Given the role of policy analysis in a multi-actor context, there exists

a myriad of fundamental dilemmas and challenges for PA and SVP, such as:

- How to design a PA process so that it is at the same time: (1) open, (2) safe, (3) informed/intelligent,

and (4) shows progress?

- What are the underlying design principles of PA in a multi-actor context – e.g., for stakeholder

identification and selection, for structuring the process in phases, for creating safety for the

participants, for the incorporation of scientific knowledge into the process (e.g. knowledge that is

trusted by the stakeholders), etc?

10

- How to create institutions and arrangements that study and mediate the various tensions that occur in

the science-policy interface (e.g. in WM)?

- How, when, under what conditions, and to what extent are simulation-models used (in WM) and how

can we enhance the effectiveness of simulation models in multi-actor PA?

The different views on PA – reduced here to a simple dichotomy of a rational-linear mode of PA and a

political-interactive mode of PA – both have advantages and disadvantages. This is especially true in a

socio-technical domain like water management, where hard facts, evidence, science, and models have as

much credibility, usefulness, and relevance as the qualitative knowledge and insights of stakeholders. In

other words, the inherent dilemma in socio-technical policy domains such as water management is that

rational-scientific analysis and the social-political process are intrinsically connected – as two sides of the

same coin. This implies that multi-actor PA, including SVP, is in need of a next generation of methods

and tools that are able to analyze the technical-physical complexity and the socio-political complexity of

policy problems in an integrated fashion. A comparison of the tools in SVP with selected tools used in PA

can lead to a broad discussion of what are the deep challenges in both fields, and may stimulate

improvements to both. In the remaining part of this paper we argue that this next generation of methods

that are able to cope with socio-technical analysis of policy problems is derived from gaming-simulation

(G&S).

7. MODELING, SIMULATION, AND GAMING FOR POLICY ANALYSIS 5

Modeling, simulation, and gaming (MSG) increasingly is being used as a general reference term for a

varied cluster of related methodologies and techniques. MSG includes research and application

communities concerned with systems dynamics modeling, agent-based modeling, discrete event or

continuous simulation, decision support systems, geographical information systems, gaming-simulation

and, more recently, (serious) gaming and virtual reality. As such, they are part of the so-called decision-

sciences, intervention, or applied sciences, like operations research, systems analysis, policy analysis, etc.

(Mayer, 2009).

Simulation mainly refers to the manner in which the dynamic behavior of the reference system is

represented in such a way that it enables human experimentation and manipulation over time. Although

simulation is usually associated with running computer models, e.g. a continuous simulation of a system

5 This section is adapted from Zhou et al (2009).

11

dynamics model on climate change, it can very well be done in an analogue or social fashion. Human

actors for instance can move physical objects on a table-top city map; or they can be asked to behave like

certain objects or agents, moving around a room according to some pre-established rules (Brewer, 1986;

Duke & Geurts, 2004).

When one or more human actors become part of the simulation, it is not uncommon to call it a social

simulation or an interactive simulation. But when there is also a set of rules for human interaction

involved, and actors have certain goals within the simulation, it becomes a simulation-game or simply a

game. When the interaction between human actors, e.g. the decisionmakers, stakeholders, and experts,

takes place during the design and development process of a MSG, it is commonly referred to as a

participative, collaborative, or group modeling process. When the social interaction primarily takes place

during the run of the simulation model – in other words, the human actors interact with the simulation

model – it is usually referred to as an interactive simulation, a simulation-game, or a (serious) game. The

notion of a serious game is usually applied to the use of computer game technology or computer game

concepts for non-entertainment purposes, among others for policymaking.

The relation between MSG and public policymaking became problematic during the late 1960s and early

1970s (for an overview, see Mayer, 2009). Various authors and streams of thought have pointed to the

non-effectiveness, even irrelevance and manipulation, of models and simulations for policymaking

(Brewer, 1975). An important bottleneck, for instance, is the lead time in the production of simulations

and models. Another one is the lack of transparency of models and simulations – the so-called black box

– for policymakers and stakeholders. Others have pointed out that computer simulations are

fundamentally limited or ineffective to aid policymaking on ‘long term problems’ characterized by ‘deep

uncertainties’, such as with global climate change (Toth, 1989 & 1995; Funtowicz & Ravetz, 1994;

Saloranta, 2001; Bankes, 2002). Such problems require the adoption of ‘post normal science’ strategy in

MSG which raises the requirement of involving stakeholders in order to interacting knowledge from all

disciplines that needed to deal the unclear and complex problem (Funtowicz & Ravetz, 1994).

Interestingly, the integration of models and simulations with sophisticated stakeholder processes (with

emphasis on interaction, participation, and learning) has been presented as a next generation of policy

analytic methods. One of the strong methods in which models and simulations are intrinsically combined

with stakeholder interaction is Simulation-Games, and more recently Serious Games. MSG is at the

methodological heart of both PA and SVP. Shared Vision Planning Models (SVPM) are computer models

of water systems built, reviewed, and tested collaboratively with all stakeholders. The models are usually

12

simulation models. The models represent the water infrastructure and operation as well as the most

important effects of that system on society and the environment. SVPMs employ user-friendly, generic,

graphical simulation software (STELLA, Extend, Goldsim, Powersim, etc.) to connect specialized water

models with the human decisionmaking processes. Spreadsheets have also been used as the basis for the

simulation software. The choice of tool is driven by the process. Often, output from complex models will

be simplified using simple relationships and incorporated into an SVPM (this is called a meta-model). An

SVPM combines various sub-models within a common interface, incorporating all relevant information.

Various scenarios and alternatives can be modeled, and model outputs are presented in terms of the

metrics or performance measures that have been collaboratively defined. The SVPM is designed to be

vigorously exercised by the study participants, so that concerns and problems about the model or planning

process can be communicated back to the analyst and addressed in the model design or planning process.

The SVPM model can be characterized as (derived from Palmer et al., in preparation):

- Interactive – The model is designed to be used by stakeholders to envision alternatives and to

investigate their implications.

- Transparent and credible – The model is designed with the preferences and input of the stakeholder

groups, in agreement on the facts. Underlying mathematical relationships are visible to stakeholders,

data easily accessible, and the model well documented.

- Integrated and centralized – All information and workings of the system are incorporated into a

single model.

- Dynamic and adaptive – The resultant plan and model are easily updated as participants refine

understanding of their objectives and interests through the planning process.

- Flexible – the model must be sufficiently flexible to accurately reflect the unique planning objectives,

measures of system performance, and unusual alternatives.

- Easy to use – The model must be easy to use for the stakeholders. This often requires organizing the

model interface by either planning objectives or planning impacts.

- Fast – The model must execute quickly, so that users can explore many alternatives and scenarios in

their evaluation.

- Stakeholder involvement and interests – The stakeholders are involved in the development of the

model, all the impacts of interest to the stakeholders are included in the model, and results are

structured in a way that is clear to the decisionmakers.

- Simple - The model must balance relevance and applicability, between level of detail and simplicity,

so that only central features are maintained to understand the trade-offs among the alternatives.

13

Models and tools built for a PA process should support the substantive (that is, the content) as well as the

political (that is, the context). It is instructive to compare the characteristics of the PA model with that of

the SVP model. The methods and approaches of a PA model have a number of characteristics (Geurts and

Joldersma, 2001; Mayer et al., 2005):

- Integrative – they should consider different aspects and levels of design and decisionmaking in a

holistic and systematic way.

- Dynamic – they should be able to show the `performance' of various alternatives in relation to

preferences and `behavior' of stakeholders.

- Interactive – they should be able to support the negotiation process among stakeholders.

- Transparent – they should produce results that are clear and understandable to all stakeholders (that

is, they should not be a `black box').

- Flexible and reusable – they should be usable for, or adaptable to, a range of (similar) situations.

- Fast and easy to use – the time required to apply them should be relatively short, and non-experts, for

example, residents and politicians, should be able to use them.

- Communicative and educational – they should be able to convey meaning and insight to stakeholders

about problem structure, alternatives, and different perspectives.

- Authoritative – they should meet analytical standards (of, for example, validity) and political

standards (for example, safeguarding core values and timeliness) in order to increase the likelihood

that the outcomes are used.

8. (SERIOUS) GAMING-SIMULATION FOR PA AND SVP

There are many ways to imbed models in a multi-actor planning or policymaking process. PA has a

multitude of tools and procedures that can be combined in various ways, usually through some form of

collaborative model building. Another, more integrated way is (serious) gaming simulation. Simulation-

games can generally be defined as experi(m)ent(i)al, rule-based, but open environments where players

learn by taking actions and experiencing the effects through feedback mechanisms in and around the

game. The underlying idea is that the individual and social learning in the game can be transferred to the

outside world, but that the actions in the game have no undesirable or immediate impact on reality (see

also, Mayer, 2009). Both users and developers of such systems can benefit from these experiences before

they are applied to real world problems with real stakeholders (Mayer and de Jong, 2004). For reasons

elaborated above, gaming-simulation lies at the foundations of SVP (see text box below).

14

In the late 1970s, the Washington D.C., metropolitan area faced the prospect of serious water shortages. Planners

realized that droughts might be better managed by collaborative operation of the independent water suppliers and

their reservoirs, but needed to convince the relevant policymakers of the results of their analysis. A team of planners

from Johns Hopkins developed a collaboratively built model with the input of the policymakers, and ran a simple

simulation to test their theories. The simulation model they designed could be used in a drought simulation or game,

thereby simulating the process by which policy is made. Models, stakeholders, and expert knowledge were brought

together in a SVP gaming exercise. Policymakers may have different objectives, some of which may be poorly

understand or defined. The Hopkins team recognized that, as such, decision-making can be described as perhaps the

most important system variable (Palmer et al., 1980). Palmer and his team knew that typical simulation models of

the time failed to provide a forum in which decisionmakers could articulate the reasons behind specific actions, so

they designed their model in such a way that it could be used interactively by the actual decisionmakers as it was

being executed, so as to simulate the actual decisions being made during a drought in Washington. In this way, the

Hopkins team, in close collaboration with the Interstate Commission on the Potomac River Basin (ICPRB), hoped to

force the active participants in an exercise to incorporate non-quantitative factors into the simulation process,

thereby increasing awareness and understanding of the motivations behind decisions. The team created a virtual

drought exercise, i.e., a realistic simulation of a drought using the collaborative model to simulate that experience

without the risk associated with real droughts. Placed in the roles of regional decisionmakers (often not

corresponding to their actual roles, in order to stimulate understanding of the issues and concerns of the other parties

to the conflict), the participants were asked to use the model to manage their water supply system during the worst

simulated droughts. The organizers of the Drought Exercise provided different “hats” with names on them, and the

managers were asked to wear different hats, that is, to operate someone else’s system during a drought. To aid

decisions, graphical displays of the condition of the system were available throughout the simulation. The managers

could see the recommendation provided by the Potomac River Simulation Model, but were free to over-ride the

recommendations with their preferred operating policies. At the conclusion of each weekly timestep, the model

would pause and solicit new instructions from the participants. Upon completion of each iteration (drought

management period) of the model, a summary showed the effectiveness of decisions made during that period,

providing an immediate feedback of information to be used in the evaluation of previous management decisions. At

the end of the simulation, a complete record of the exercise was available for further analysis. The managers gained

a new understanding of the system, and a new sympathy for the challenges and positions of the other utilities. The

exercise not only demonstrated how they could improve the reliability of their individual systems by joint operation,

it convinced them to do it. (Hagen, in press.).

15

9. USING GAME TECHNOLOGY FOR SVP: SERIOUS GAMING

A recent development in the field of gaming-simulation is the emergence of serious gaming. Serious

gaming involves the use of concepts and technologies derived from computer entertainment games for

non-entertainment purposes, such as for learning and policymaking. It literally and figuratively brings

together a large international community of computer scientists, game designers, think tank consultants,

decisionmakers, and public policymakers, and is becoming a major global industry.

There are several reasons, why entertainment games and serious gaming are important for decisionmaking

and computer supported planning. The first reason is related to a distinct sociological phenomenon: the

coming of age of the net or game generation. The present and future generation of students of planning,

modeling, and design will have grown up with computer games that are incredibly sophisticated in user

interaction. Because the game-generation students will soon become decisionmakers, managers, and

modelers, computer games will have indirect but marked consequences for decisionmaking and

organization. They will expect the same sort of sophistication and interaction from the professional

models, simulations, and serious games as their favorite entertainment games at home. 3D virtual

representations have been turned into playgrounds for planning, community involvement, and serious

gaming. It is expected that the use of game technology and concepts will revolutionize the possibilities of

stakeholder interaction, collaboration, and visioning. An example of how new computer game technology

is starting to have an impact on urban decisionmaking is the Decision Theater at Arizona State University.

In this 3D immersive environment, multiple stakeholders can conduct policy analytic activities, for

instance on the state’s water management problems (Arizona State University 2007).

Since 2002, the faculty of Technology, Policy and Management (TPM) of Delft University of Technology

(TUD) has been experimenting and implementing the use of (serious) gaming-simulation for policy

analysis in a multi-actor context. The TUD researchers have used advanced forms of gaming, among

others for port planning, spatial planning, water management, sustainable urban renewal, electricity

networks, and rail network planning. For example, the serious game SIMPORT MV2 is a multi-player

computer supported simulation game (a serious game) that mimics the real processes involved in

planning, equipping, and exploiting the Maasvlakte 2 (MV2) in the Port of Rotterdam. The Second

Maasvlakte is an existing and ambitious port expansion project to be situated on newly reclaimed land

adjacent to the existing Rotterdam port area. The simulation game was primarily intended for staff of the

Port of Rotterdam. Another example is CONSTRUCT.IT – a state-of-the-art, game-based learning

environment that allows players to experience and debrief some of the economical, cultural, social, and

16

spatial complexities involved in large-scale urban projects. The game can be played in multiplayer mode

in a local area network (LAN) of computers, or in single player mode on a single computer.

10. CONCLUSION

This paper serves the purpose of introducing the SVP and PA communities. The authors hope that the

dual communities of SVP planners and PA practitioners at Delft may benefit from this introduction and

may begin to collaborate in more meaningful ways to further enhance both disciplines, since each has an

interest in and similar approaches for collaborative multi-objective planning for complex systems. There

are ample opportunities for learning and innovation in SVP when we look at it as a form of Policy

Analysis (PA) in a multi-actor context. SVP faces some of the same classic dilemmas as PA, regarding

(1) the role of experts and scientific knowledge in policymaking, (2) the design and management of

participatory and interactive planning processes, and (3) the (ab)use of computer models and simulations

in (multi-actor) policymaking. In dealing with these dilemmas, SVP can benefit from looking at the

richness of PA methodology, such as for stakeholder analysis and process management. And it can

innovate by incorporating some of the rapid developments now taking place in the field of (serious)

gaming and simulation (S&G) for policy analysis. In return, the principles, methods, and case studies of

SVP can significantly enhance how we perform PA for multi-actor water (resource) management.

11. REFERENCES

Bankes, S. (2002). “Tools and Techniques for Developing Policies for Complex and Uncertain Systems”,

Proceedings of the National Academy of Sciences, Vol. 99, Supplement 3, pp. 7263–7266.

Bekebrede, G., Mayer I. (2006). “Build your seaport in a game and learn about complex systems“, Journal

of Design Research 5(2): 273-298.

Bots, P., Daalen, C. van (2007).”Functional design of games to support natural resource management

policy development“, Simulation & Gaming, 38(4):512-532.

Brewer G. (1986). “Methods for synthesis: policy exercises“, In: Clark, W., Munn, R. (eds.) Sustainable

development of the biosphere. Cambridge: Cambridge University Press: 455-475.

Brewer, G. (1975). An analysts view of the uses and abused of modeling for decisionmaking. Santa

Monica, California: RAND Corporation. Report P-5395, www.rand.org.

Brewer, G., deLeon, P. (1983). The Foundations of Policy Analysis, Homewood, Il.: Dorsey.

Bruijn, H. de Porter, A. (2004). “The Education of a Technology Policy Analyst to Process

Management”, Technology Analysis & Strategic Management, Vol. 16, No. 2, June 2004, 261–274.

17

Bruijn, J. de, Ten Heuvelhof, E. (2000). Networks and Decision-Making, Utrecht: Lemma.

Bruijn, J. de, Ten Heuvelhof, E., In ’t Veld, R. (2002). Process Management. Why project management

fails in complex decision making processes. Dordrecht, (the Neth.): Kluwer.

deLeon, P. (1988). Advice and consent; the development of the policy sciences. New York: Russell Sage

Foundation.

Drake, A.W., R.L. Keeney, and P.M. Morse (eds.)(1992). Analysis of Public Systems; Cambridge, MA:

MIT Press.

Duke, R. (1974). Gaming: The future's language. New York, Sage Publications.

Duke, R., Geurts J. (2004). Policy Games for Strategic Management: Pathways into the Unknown.

Amsterdam, Dutch University Press.

Dunn, W. (1981/1994) Public policy analysis: an introduction, Englewood Cliffs: Prentice-Hall, (1st and

2nd ed.)

Fischer, F., Miller, G., Sidney, M. (eds.)(2007). Handbook of public policy analysis: theory, politics, and

methods. Boca Raton: CRC Press

Geurts, J. Duke, E., Vermeulen, P. (2007). “Policy gaming for strategy and change“, Long Range

Planning, 40:535-558

Geurts, J., Joldersma, C., Roelofs, E. (eds.) (1998). Gaming/simulation for policy development and

organizational change, Tilburg: Tilburg University Press

Hagen, E. (In press). “New Approaches in the Potomac River Basin and Beyond: Pioneering Work in

the Development of Shared Vision Planning“, In E. Bourget (Ed.), Washington D.C., U.S. Army

Corps of Engineers, Institute for Water Resources.

Hillestad, R.J., W.E. Walker, M.J. Carrillo, J.G. Bolten, P.G.J. Twaalfhoven, O.A.W.T. van de Riet

(1996). FORWARD – Freight Options for Road, Water, and Rail for the Dutch: Final Report. MR-

736-EAC/VW. Santa Monica: RAND.

Hogwood B., Gunn. L. (1984). Policy Analysis for the Real World, Oxford University Press

House, P.W. (1982). The Art of Public Policy Analysis, Sage Library of Social Research Vol. 135.

Beverly Hills, California: Sage Publications.

House, P., Shull, R. (1991). The Practice of Policy Analysis; Forty Years of Art and Technology,

Washington: Compass

Jasanoff, S. (1990). The Fifth Branch. Science Advisors as Policymakers. Cambridge, Massachusetts:

Harvard

Kuit, M. (2002) Strategic behavior and regulatory styles in the Netherlands energy industry. Delft:

Eburon (PhD thesis TU-Delft).

Kuit, M., Mayer, I., Jong, M. (2005). “The INFRASTRATEGO game: An Evaluation of Strategic

18

Behavior and Regulatory Models in a Liberalizing Electricity Market“, Simulation and Gaming, 36

(1): 58-74.

Mayer I, Veeneman W. (eds.) (2002). Games in a World of Infrastructures. Simulation-games for

Research, Learning and Intervention (Eburon, Delft).

Mayer, I. & Jong, M. de (2004). “Combining GDSS and gaming for decision support”, Group Decision

and Negotiation, 13, 223- 241 ISSN 0926-2644.

Mayer, I. (1997). Debating Technologies. A Methodological Contribution to the Design and Evaluation of

Participatory Policy Analysis. Tilburg: Tilburg University Press (PhD thesis).

Mayer, I. (2007). “Evolution of policy analysis in the Netherlands“, In: Fischer, F., Miller, G., Sidney, M.

(eds.), Handbook of Public Policy Analysis, Taylor & Francis: 553-571

Mayer, I. (2008). “Gaming for policy analysis. Learning about complex multi-actor systems“, In: de

Caluwé, L., Hofstede, G.J., Peters, V. (eds.) Why do games work? In Search of the Active Substance.

Kluwer: pp 31-40.

Mayer, I. (2009). “The Gaming of Policy and the Politics of Gaming: A Review“, Simulation & Gaming

40(6) 825–862. First online September 2009 as doi: 10.1177/1046878109346456.

Mayer, I., Bekebrede, G., Bilsen, A., Zhou, Q. (2009). “Beyond SimCity: Urban gaming and Multi-actor

systems”, In: Stolk, E., te Brommelstroet, M., Model Town (eds.). Using Urban Simulation in New

Town Planning. Amsterdam: Sun / INTI. pp: 168 – 181. ISBN 978 90 8506 8044

Mayer, I., Bockstael-Blok, W. & Valentin, E. (2004). “A building block approach to simulation. An

evaluation using Containers Adrift”, Simulation and Gaming, 35 (1) pp 29-52.ISSN: 1046-8781.

Mayer, I., Carton, L., de Jong, M., Leijten, M. & Dammers, E., (2004). “Gaming the future of an urban

network”, Futures, 36 (3) 311-333, ISSN 0016-3287.

Mayer, I., P. Bots & E. van Daalen (2004). “Perspectives on policy analysis: a framework for

understanding and design”, International Journal of Technology, Policy and Management, 4 (2) pp

169-191, ISSN 1468-4322.

Mayer, I., van Bueren, E., Bots, P., Voort, H. van der, and Seidel, R. (2006). “Collaborative decision-

making for sustainable urban renewal projects: a simulation-gaming approach“, Environment and

Planning B., 31, pp 403-423.

Miser, H. and Quade, E. (1985). Handbook of Systems Analysis. Overview of Use, Procedures,

Applications and Practice, John Wiley and Sons Ltd, Chichester [England].

Mood, A.M., 1983. Introduction to Policy Analysis. New York: North-Holland.

Quade, E.S. 1989. Analysis for Public Decisions, Third Edition. New York: Elsevier Science Publishers.

Radin, B. (2002). Beyond Machiavelli – Policy Analysis Comes of Age, Washington D.C. Georgetown

University Press.

19

Sabatier, P., Jenkins-Smith, H. (eds.) (1993). Policy Change and Learning - An Advocacy Coalition

Approach, Boulder, Colorado USA.

Toth F. (1995a). “Policy exercises, the first ten years”, in: Crookall, D., Arai, K. (eds.) Simulation and

gaming across disciplines and cultures, pp 257-264, Sage, Thousand Oaks.

Toth F. (1995b). Simulation/gaming for long-term policy problems. In: Crookall, D., Arai, K. (eds) (eds.)

Simulation and gaming across disciplines and cultures. Thousand Oaks: Sage: 134-142

U.S. Army Corps of Engineers (1995). National Study of Water Management during Drought: The

Report to the U.S. Congress. Institute for Water Resources, U.S. Army Corps of Engineers.

Washington D.C. IWR Report 94-NDS-12. September, 1995.

van de Riet, O. (2003). Policy Analysis in Multi-Actor Policy Settings. Navigation Between Negotiated

Nonsense & Superfluous Knowledge. Delft Eburon (PhD thesis)

Vennix, J. (1996). Group Model Building. Chicester: Wiley.

Walker, W. (1995). The Use of Scenarios and Gaming in Crisis Management Planning and Training,

Santa Monica, California: RAND Corporation. Report P-7897, www.rand.org

Walker, W.E., and G.H. Fisher (1994). Public Policy Analysis: A Brief Definition, Santa Monica,

California: RAND Corporation. Report P-7856, www.rand.org.

Walker, W.E., A. Abrahamse, J. Bolten, J.P. Kahan, O. van de Riet, M. Kok,, and M. den Braber. 1994.

“A Policy Analysis of Dutch River Dike Improvements: Trading off Safety, Cost, and Environmental

Impacts”. Operations Research 42(5): 823-836.

Walker, W.E., M. Pöyhönen, J.H. de Jong, and C. van der Tak (1998). POLSSS — Policy for Sea

Shipping Safety: Cost-Effectiveness Analysis. RE-98.006.1. Leiden: RAND Europe.

Walker, W., 2000. “Policy Analysis: A Systematic Approach to Supporting Policymaking in the Public

Sector”, Journal of Multicriteria Decision Analysis, Volume 9, No. 1-3 (2000), pp. 11-27.

Zhou. Q., de Bruijn, H., ten Heuvelhof, E., Mayer, I. (2009). Room to Play: How the Planning Kit

Blokkendoos (PKB) Prevented a Deadlock in Water Management. In: Learn to Game – Game to

Learn, Proceedings of 40th Isaga conference 2009 (CD Rom), June 29 – July 3, 2009, Singapore.

ISBN 978-981-08-3769-3