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Modelling a spatial planning process based on trust and opinion

Bachelor thesis Spatial Planning

Richelle Raaphorst 940412677100 LUP80812

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Table of Contents

ABSTRACT 2 INTRODUCTION 3 THEORETICAL BACKGROUND 4 METHODOLOGY 5 THE MODEL 6 MODEL ELEMENTS 8 EQUATIONS 9 THE EXPERIMENT 12 RESULTS 12 DISCUSSION 15 WORKS CITED 17 APPENDIX I 19

Abstract

Trust and opinion are important aspects of spatial planning negotiations, as they influence how stakeholders behave. In present day society processes like individualisation cause people to express their opinion more open and to act for their self-‐interest. These changes make the negotiations more and more complex. The behaviour of the involved agents results in a complex adaptive system, in which they act and react constantly. Understanding these dynamics can help coping with the complexity. This thesis focuses on the dynamics of trust in spatial planning, with opinion also taken into account. For the examination of complex adaptive systems, agent-‐based modelling is most useful – and therefore also applied here. A model was built that simulates a simple planning process of a spatial planner trying to place a plan. Experiments were done, and the outcome was used to inform spatial planners. It concludes with some insights in the processes, and discusses the usefulness of models in spatial planning.

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Introduction

Planning and regulating contemporary societies is becoming more and more

complex. Groups are becoming less coherent because of – among others – the decline of the church, the women emancipation, and technology and social media (Schnabel, 1999). The consequence is that everyone has his own opinion and with the individualising of society, people tend to speak up more. Today, we live in a participation society where everyone has a share in society and participation is an important aspect of planning. A system of which the behaviour emerges from the interplay of numerous, heterogeneous actors can be considered a complex adaptive system. Planning processes often involve many independent stakeholders that all have a different perspective, and they all want to be heard and to be treated fairly. Because of these diverging perspectives and because organisation of the networks is ambiguous and unpredictable, decision making in planning processes is difficult and more often based on opportunity than vision.

The unpredictability of network dynamics partly evolves from dynamics in trust. Trust determines the extent to which stakeholders take risks, resist to change, are open to negotiations, and form coalitions with planners and policymakers. The outcomes of these processes influence trust in its turn. Trust is strongly related to uncertainty, as it is a mechanism to cope with uncertainty. (Ramchurn, Huynh, & Jennings, 2004) In any complex adaptive system such as contemporary society it is almost impossible for an actor to have perfect knowledge about the situation and other actors’ attitude and strategies. This implies that the actors have to deal with significant amounts of uncertainty in the process. The complex behaviour resulting from trust dynamics results in difficulties for policy making. Understanding trust is necessary for coping with complexity in decision-‐making networks. Increasing trust can make cooperation smoother and cheaper, and increases robustness of the cooperation (Edelenbos & Klijn, 2007).

One specific field of policy making that struggles with the influences of trust is spatial planning. This is a field particularly sensitive to trust dynamics as it can have major impact on people. The everyday surroundings sometimes are strongly and irreversibly affected by the work of spatial planners, which makes the negotiations with stakeholders prone to distrust. For a negotiation with citizens to be successful, those citizens have to be able to trust the planner. Therefore, understanding how trust works, and can be build, is important for spatial planning.

Intertwined with trust, spatial planning also struggles with the opinions of their stakeholders. The dynamics of opinion is partly based on trust, in order for a common opinion to develop. Besides, trust is also partly based on opinion. People tend to trust others with the same opinion more. In spatial planning, when citizens have a certain opinion or expectation, this could be a basis for distrust. Especially the NIMBY effect is something that obstructs many spatial developments from happening, although this is a less complex behaviour. “The NIMBY (Not In My Backyard) effect may be defined as social rejection of facilities, infrastructure and services location, which are socially necessary but have a negative connotation.” (Pol, et al., 2006) The effect is in most cases due to possible risk and nuisances associated with the proposed development. Reactions to negative plans can get serious. For example, arson has happened as response to an unwanted project. Citizens have even bought up places of destination to prevent development (Graaf, 2008). The opinion of the inhabitants on the subject and their possible reaction is surely something for a spatial planner to take into account. Because of the importance of trust and opinion in spatial planning negotiations, these subjects are the focus of this research.

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Theoretical background

In the research field of spatial planning, most studies are qualitative. Among others,

trust is a subject that has been much studied. Although little is known about the role of trust in planning and governance processes, there has been quite some research into the topic. For example, the research into trust dynamics by de Vries dealt with the development and importance of trust by doing case studies (de Vries, 2014). The results of the studies certainly give insights in trust dynamics, but it is highly case specific and cannot be generalised into frameworks or models. Another example of qualitative, case-‐study based research is Edelenbos & Klijn’s research into trust in complex decision-‐making networks (Edelenbos & Klijn, 2007). The background theory they used mainly focused on static aspects of trust, and made little notion of the development of it.

Also the research into opinion with regard to spatial planning is mainly of qualitative nature. For example, some research goes into the persuasive part of opinion, for example framing. De Boer examines how opinions about climate change are influenced through certain ways of risk communication (Boer, 2007). Other research discusses the NIMBY effect, where opposition partly depends on how close by a proposed development is (Hubbard, 2009; Dear, 1992; Pol et al., 2006). Pepermans & Loots approach the NIMBY effect not as a siting conflict, but as a framing conflict (Pepermans & Loots, 2013). Framing the project in a certain way can influence the resistance by stakeholders. In the research by Pepermans & Loots the focus is on wind farms, a proper example of a project that tends to provoke a strong NIMBY effect.

Studied significantly less are the quantitative aspects of trust in spatial planning processes. Qualitative research prevents application in new situations, while quantitative models can be adapted to simulate specific situations. Developments are easier to depict with a quantitative model. However, the qualitative research is helpful in the development of quantitative models.

Quantitative representation of trust dynamics has been done by several researchers, although they discuss diverging topics. Nooteboom discussed a list of topics already investigated, and proposed a model that relates trust to profit generation (Nooteboom, 2012). However, here trust is approached as a mean rather than a goal. Another example of research on trust already done with use of agent-‐based simulations is by Kim. Here it was used to simulate the effects of trust on supply networks (Kim, 2009). As with Nooteboom’s research, trust is seen as a mean, or in this case an effect on a separate goal. Hassani-‐Mahmooei & Parris investigated into the dynamic modelling of trust, and connected this to rent-‐seeking (Hassani-Mahmooei & Parris, 2014). Taken it the other way around, Gans et al. elaborated on the topic of distrust (Gans). They proposed a trust-‐confidence-‐distrust model of agent network dynamics. A more general model was made by Za et al. They took into account the dynamic perspective of trust, and recognized that the amount of trust can be updated while interactions go on (Za, 2015). Their model is focussed on dependence networks and ‘provides a tool for studying emergent properties/phenomena within social networks.’ Doloswala modelled the influence of lying on the behaviour of peer groups (Doloswala, 2014). She investigated how lying will influence the spatial distribution of the agents. However, no quantitative trust models have been developed or applied in the domain of spatial planning.

Many quantitative research on the topic of opinions is in the form of opinion dynamics. These are models that study in an abstract way how opinions of groups of people evolve (Kou, Zhao, Peng, & Shi, 2012; Allahverdyan & Galstyan, 2014; Iniguez, Kertesz, Kaski, & Barrio, 2001; Biswas, Sinha, & Sen, 2013; Weisbuch, Deffuant, Amblard, & Nadal, 2003). Less research has been done on opinion dynamics in the field of spatial planning. Ligtenberg & Bregt made a model that simulates the opinion dynamics for a hypothetical spatial allocation problem (Ligtenberg & Bregt, 2014). Lober & Green did research on attitudes towards facilities and made a model of the NIMBY effect that

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occurs. Next to this, little research has been done on the quantification of opinion in spatial planning processes and the NIMBY effect.

Although the subjects of trust and opinion have had sufficient attention from a qualitative point of view, it has had little expression in numbers and models. Whilst the networks can be very complex in reality, research into other topics show that it is possible to make models out of complex decision-‐making networks. Here is where this research fills the gap and tries to quantify the dynamics of trust and role of opinion in social networks within which spatial planning operates. The objective of this research is to develop a simulation model of a spatial planning process including trust dynamics and opinion. In an explorative manner, ways to simulate such a process are examined until a working model is created. This model is then used to simulate trust and opinion during a planning process and to explore how such a model can be used to inform spatial planners.

Methodology

A complex adaptive system such as the trust dynamics within a spatial planning

process consists of the interplay of numerous, heterogeneous actors that are constantly interacting. “A key property of complex systems is that no single component controls the system behaviour.” (Siegried, 2014) The behaviour of the system emerges from the interplay of the actors, where the whole is bigger that just the sum of its components. “Complexity theory shows that even if we were to have a complete understanding of the factors affecting individual action, this would still not be sufficient to predict group or institutional behaviour.”

Capturing the dynamics of complex adaptive systems requires a specific methodology. Models are typically applied to capture the dynamics of a system, being a simplification of a system or some other structure. A particular type of modelling is simulation (Gilbert & Troitzsch, 2005). “Simulations have ‘inputs’ entered by the researcher and ‘outputs’ which are observed as the simulation runs.” (Gilbert & Troitzsch, 2005) It can be used to develop a theory, as it is more precise and formal than textual material. Furthermore, it could also be used for theory testing, by simulating a certain development according to the proposed theory, and compare simulated with observed outcomes. Simulations can be repeated many times, and the average score of many simulations is useful in drawing conclusions. “Simulation allows the researcher to conduct experiments in a way that is normally impossible in social science.” Qualitative, textual theories can be formalized in such a way that it can be programmed into a computer. Moreover, simulations “can also usefully be applied to theories involving spatial location.” (Gilbert & Troitzsch, 2005)

There are multiple ways of simulating, which can be used for various purposes and the use also changed over time. Formerly, classical models were often used. Classical models serve to test understanding of how the known, aggregated system behaviour can be reduced to specific sub processes and variables. It assumes full knowledge of the dynamics and the results. A classical model tries to take apart the components of the system and assigns values or formulas to those components. It is based on whole system equations, which are typically applicable where universal laws apply, such as the law of conservation of mass.

However, researchers have acknowledged the difficulties in using these models, and a switch has taken place to other, more suited models. Social processes are known not to obey the universal laws on which the classical models are based. However, they used to be applied for example in the field of economics, which is a social science. Traditional economic models are based on laws concerning equilibriums between supply and demand, and assume rational behaviour of economic agents. “Established economic

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theory is based on the rational actor paradigm which assumes that individual actors know their preferences, […] and best possible decision, based on complete information about their environment and the supposed consequences.” (Billari, Fent, Prskawetz, & Scheffran, 2006) However, in reality rational behaviour has its limits in complex and uncertain environments, and the recent credit crisis has demonstrated that equilibriums between supply and demand are nothing more than a theoretical construct. “One of the conditions that restrains rationality is the social environment itself, in particular the unpredictable behaviour of other agents.” (Billari, Fent, Prskawetz, & Scheffran, 2006) Complex adaptive systems are not suitable for a reductionist approach like the classical models, as their aggregated system behaviour shows irregular, unexpected behaviour, and emerges form the interplay of all individual subcomponents. Recently, also in economics a transition has taken place from rational actor models to other approaches, for example agent-‐based modelling.

The transition from the out-‐dated classical models towards approaches like agent-‐based modelling signals an understanding that social processes cannot be captured in such reductionist models. In other fields of social sciences, irrational behaviour and uncertainties have been taken more seriously much earlier. As far as social scientists used models, they incorporated these elements. One way of simulating that is most useful for complex adaptive systems is agent-‐based modelling (ABM). These models “consist of a number of ‘agents’ which interact both with each other and with their environment, and can make decisions and change their actions as a result of this interaction.” (Matthews, Gilbert, Roach, Polhill, & Gotts, 2007) ABM is appropriate for simulating a social system as it is able to handle the uncertainties and irrational behaviour usually found in these systems. Besides, ABM is also extremely suitable for complex systems, as it models the individual actors that constitute the behaviour of the group. The behaviour of single agents is simple but adaptive, together leading to the complexity of aggregate behaviour (Gilbert & Terna, 2000). For these reasons, ABM is used to model the dynamics of trust and opinion.

The model

To make an ABM specific programs are available. The program used for this model is

Netlogo, a multi-‐agent programmable modelling environment, free to download online (Wilensky). Due to the complicated code design of an agent-‐based model, it is difficult to describe it in an understandable way that makes it possible to duplicate. Grimm et al. have developed the ODD Protocol, which gives guidance for describing ABMs in a clear and organized way (Grimm et al., 2006). The protocol’s purpose is to help always structuring the information about an ABM in the same order. However, the here-‐presented model is quite simple, so ODD was not necessary to use. In Appendix I the complete code is visible. In this report, the model will be described loosely based on ODD, but shorter and more simple. Some assumptions were made, based on literature. These are explained after the model description.

Image an area of 5 by 5 kilometres with n inhabitants. The inhabitants only interact within this area, so no influence from the outside is present. Each inhabitants has a certain spot where it ‘lives’, randomly assigned to all of them at the beginning of the model. Several cores are present in the area with an urban area surrounding the cores. Leftover space is rural area. The inhabitants are only placed within the urban areas. They all have a certain degree of trust towards the government, which varies from inhabitant to inhabitant. This trust is composed of a) an inherent inclination to have trust to begin with, and b) a variable part that depends on the planner’s actions.

Then, the government proposes a plan to place an object somewhere that is subject to typical NIMBY responses, say a windmill. The inhabitants have an opinion about this

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plan – which is composed of a) a certain attitude towards windmills and b) an opinion that is based on how close by their home it is. It is assumed that windmills have a negative impact on their immediate surroundings, so inhabitants’ opinions tend to be more negative the closer located the windmills is.

Inhabitants can protest against the windmill. Whether they do that or not depends on how much trust they have in the planner, and what their opinion about the windmill is. If they have a lot of trust, there is a bigger chance they will protest. If their opinion is very negative, they will also protest. The combination of trust and opinion eventually should be above a certain threshold in order to protest. But, one protestor does not lead to a rejection of the windmill – there will have to be enough inhabitants protesting. This amount is also determined by a threshold, but this time at the side of the planner. If the planner listens to the protests and changes the plan, the trust of the inhabitants increases. But, if the plan remains unchanged and the windmill is implemented, the trust declines. The feedback loop that happens here is pictured in figure 1. The numbers in the figure are the equations explained later on.

When a windmill is placed because there wasn’t enough protest, it influences the opinion of the inhabitants. The more windmills and the closer the windmills, the more negative their opinion becomes. So in time, if more windmills are placed, there will be more protest and no more new ones will be placed.

During the process of placement of windmills, dynamics of trust occurs. The goal of a planner is to win trust of the inhabitants, but also to successfully place windmills. If too many plans are realized, trust will decline significantly. Trust will increase if no plans will be realized, but that is of course not desired by the government. A balance should be met between the placement of plans and the building of trust. This model will be used to search for this balance, and the prerequisites for it.

Figure 1 Feedback loop the model runs through every tick. The associated equations are between brachets.

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Model elements

In the model multiple parameters and variables are present, which will be

concisely explained in table 1. At first, the parameters are listed, which are set throughout a run of the model. After that, the variables are described. Variables are grouped together dependent on how they vary. Some change only over the course of time. Others stay the same during a run, but are different from agent to agent. Lastly, some vary both through time and between agents. A variable that is different for every agent is marked with an i. When a variable changes over time, it is marked with a t. Variables that concern placed windmills are marked with a j. In the explanation of the equations, some will be explained in more detail.

Table 1 Parameters and variables present in the model Parameters Size world 50 by 50 patches = 5 by 5 kilometres Size patch 100 by 100 metres Town centres Several cores are present, around which the urban areas are

created They are placed randomly at the beginning of each run

Urban areas Patches with value istown = 1 Here the inhabitants are placed They are placed in a random radius around the town centres

Rural areas Patches with value istown = 0 This is leftover space

Inhabitant ‘Turtle’ (Netlogo term for agent) that represents an inhabitant with a level of trust and an opinion

Protest threshold In order for an inhabitant to protest, ‘sum trust and opinion’ has to be above a certain threshold The threshold can be changed to simulate its effects

Discard threshold Whether or not the spatial planner responds to protests depends on the threshold set The threshold measures if enough inhabitants protest against the windmill to discard it If more inhabitants protest than the amount set by the threshold, the windmill is not accepted

Increase trust If an inhabitant protests successfully and the windmill is discarded, their trust in the planner will increase

Decrease trust If an inhabitant protests unsuccessfully and the windmill is placed, their trust in the planner will decrease

Variables Plant Patch with value isplan = 1

This is the windmill proposed by the spatial planner It is placed randomly every tick See equation 6

Trust propensityi Base level of trust each inhabitant gets assigned at the beginning of a run The level is determined randomly with a normal distribution, mean of 0.5 and standard deviation 0.2

Opinion starti Base level of opinion each inhabitant gets assigned at the beginning of a run The level is determined randomly with a normal distribution, mean of 0.5 and standard deviation 0.2

Trust in planneri,t Level of trust an inhabitant has towards the spatial planner

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The level is determined randomly with a normal distribution, mean of 0.5 and standard deviation 0.2 Fluctuations take place because of the trust dynamics, see equation 7

Trust totali,t Total level of trust The level is determined with trust propensity and trust planner, see equation 1

Opinion plani,t Opinion dependent on the distance of the inhabitant to the proposed and placed windmills The level is determined by equation 3

Opinion totali,t Total opinion The level is determined with opinion plan and opinion start, see equation 2

Sum trust and opinioni,t Total value based on trust total and opinion total, see equation 4

Protesti,t Depending on whether the protest threshold is exceeded or not, a value of 0 (no protest) or 1 (protest) is assigned to each inhabitant, see equation 5

Distance to plani,t The distance of an inhabitant to the proposed plan, used in equations 3.1 and 3.2

Average distance to windmillsi,t

The average distance of an inhabitant to all the placed windmills, see equation 3.4. Used in equation 3.2

Windmillj,t If a proposed windmill is accepted, a flag is placed to indicate its location, see equation 6

Distance to windmilli,j,t The distance of an inhabitant to a placed windmill, used in equations 3.2 and 3.4

Total windmillst Summation of the amount of windmills, see equation 3.3 Amount protestorst Summation of the amount of protesting inhabitants, see

equation 5 Notice that there is no spatial planner present in the model. Only its actions and

choices are incorporated.

Equations

Several equations are present in the model, as mentioned in the model elements. The processes these equations describe are based on several assumptions about their operation, partly taken from literature.

𝑇𝑟𝑢𝑠𝑡 𝑡𝑜𝑡𝑎𝑙!,! = 𝑠!" ∗ 𝑡𝑟𝑢𝑠𝑡 𝑝𝑟𝑜𝑝! + ( 1 − 𝑠!" ∗ 𝑡𝑟𝑢𝑠𝑡 𝑖𝑛 𝑝𝑙𝑎𝑛𝑛𝑒𝑟!,!) [1] With str = share trust prop. This is a variable which can be set between 0 and 1, and determines how important the trust propensity is relative to the trust in planner.

The division of a base level trust and a fluctuating level in equation 1 is based on

researchers’ descriptions of trust. Mayer, Davis & Schoorman propose an abstract model of trust, that represents the important factors of trust and their relationships (Mayer, Davis, & Schoorman, 1995). Here, a person has a certain degree of propensity to trust, which they call “the general willingness to trust others.” This could be seen as a base-‐level trust. The propensity to trust is a characteristic ascribed to the person who has to trust someone.

Jones & George see trust as “a psychological construct, the experience of which is the outcome of the interaction of people’s values, attitudes, and moods and emotions.” The relatively stable and enduring characteristics of individuals – their values – are seen as

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the basis of trust (comparable to Mayer, Davis & Schoorman’s propensity to trust). Besides this stable manner of experiencing trust, attitude adds a more specific way of trusting based on knowledge, beliefs, and feelings about the other. During the evolution of a relationship, attitudes structure the experience of trust. In the model, this is the trust towards the planner, as this is influenced by choices made by the planner. These dynamics are further explained in equation 7.

𝑂𝑝𝑖𝑛𝑖𝑜𝑛 𝑡𝑜𝑡𝑎𝑙!,! = (𝑠! ∗ 𝑜𝑝𝑖𝑛𝑖𝑜𝑛 𝑠𝑡𝑎𝑟𝑡!) + ( 1 − 𝑠! ∗ 𝑜𝑝𝑖𝑛𝑖𝑜𝑛 𝑝𝑙𝑎𝑛!,!) [2] With so = share opinion start. This is a variable which can be set between 0 and 1, and determines how important the opinion start is relative to the opinion plan.

Two equations are incorporated in the model to determine the opinions of the

inhabitants towards the plan(s). First, the opinion towards the proposed windmill is calculated by a distance decay function. Then, after realization of a windmill a part is added to the equation that contains a calculation of the opinion towards the already placed windmills.

If no windmills are realized yet: 𝑜𝑝𝑖𝑛𝑖𝑜𝑛 𝑝𝑙𝑎𝑛!,! =

!!!!.!!"∗!"#$%&'( !" !"#$!,!!

[3.1]

If windmills already have been realized: 𝑜𝑝𝑖𝑛𝑖𝑜𝑛 𝑝𝑙𝑎𝑛!,! =

!!!!.!!"∗!"#$%&'( !" !"#$!,!!

+ !

!!!.!!"∗!!"#$%" !"#$%&'( !" !"#$%"&&'!,!

!!!

! [3.2]

With 𝑚 = 𝑡𝑜𝑡𝑎𝑙 𝑤𝑖𝑛𝑑𝑚𝑖𝑙𝑙𝑠! = 𝑤𝑖𝑛𝑑𝑚𝑖𝑙𝑙!,!!

!!! [3.3]

𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑡𝑜 𝑤𝑖𝑛𝑑𝑚𝑖𝑙𝑙𝑠!,! = !"#$%&'( !" !"#$%"&&!,!,!

!!!!! [3.4]

The decision to incorporate a basis level of opinion is based on pure logic that every person has an opinion about a situation. The opinion towards the plan is based on the NIMBY effect. P. Hubbard explains NIMBY as a reaction if people who are at risk of having a new development close by that brings negative externalities (Hubbard, 2009). Especially the externalities are of importance. With a distance-‐decay curve, the decline of nuisances with increasing distance can be displayed. In most cases the development is needed, but because of the externalities nobody wants it in their vicinity. It becomes more likely that people will oppose the plan the closer to their home it gets. Therefore, a distance-‐decay function is incorporated in the model in order to calculate the opinion towards the plan. It is assumed that the inhabitants do not oppose the plan anymore after 1 kilometre, or 10 patches (see figure 2). If the plan is next to an inhabitant, the opinion will be close to 1 – very negative. The further away it is placed, the less negative the opinion will be. After 1 kilometre, the opinion starts to become negligible. For the already placed windmills, the distance is calculated as the mean distance to all the placed ones. As the opinion should increase as more plans are placed, the mean is corrected for the amount of plans. 𝑆𝑢𝑚 𝑡𝑟𝑢𝑠𝑡 𝑎𝑛𝑑 𝑜𝑝𝑖𝑛𝑖𝑜𝑛!,! = 𝑠!" ∗ 𝑜𝑝𝑖𝑛𝑖𝑜𝑛 𝑡𝑜𝑡𝑎𝑙!,! + ( 1 − 𝑠!" ∗ 𝑡𝑟𝑢𝑠𝑡 𝑡𝑜𝑡𝑎𝑙!,!)

[4] With sto = share opinion total. This is a variable which can be set between 0 and 1, and determines how important the opinion total is relative to the trust total.

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𝐴𝑚𝑜𝑢𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑠𝑡𝑜𝑟𝑠! = 𝑝𝑟𝑜𝑡𝑒𝑠𝑡!,!!

!!! [5] 𝑖𝑓 𝑠𝑢𝑚 𝑡𝑟𝑢𝑠𝑡 𝑎𝑛𝑑 𝑜𝑝𝑖𝑛𝑖𝑜𝑛!,! > 𝑝𝑟𝑜𝑡𝑒𝑠𝑡 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑, 𝑡ℎ𝑒𝑛 𝑝𝑟𝑜𝑡𝑒𝑠𝑡!,! = 1 [5.1] 𝑖𝑓 𝑠𝑢𝑚 𝑡𝑟𝑢𝑠𝑡 𝑎𝑛𝑑 𝑜𝑝𝑖𝑛𝑖𝑜𝑛!,! < 𝑝𝑟𝑜𝑡𝑒𝑠𝑡 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑, 𝑡ℎ𝑒𝑛 𝑝𝑟𝑜𝑡𝑒𝑠𝑡!,! = 0 [5.2]

𝐼𝑓 𝑎𝑚𝑜𝑢𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑠𝑡𝑜𝑟𝑠 < 𝑑𝑖𝑠𝑐𝑎𝑟𝑑 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑, 𝑡ℎ𝑒𝑛 𝑝𝑙𝑎𝑛! → 𝑤𝑖𝑛𝑑𝑚𝑖𝑙𝑙!,!!! [6]

First, for every inhabitant it is decided whether he will protest or not. The sum trust

and opinion (equation 4) should be above the protest threshold in order to protest (equation 5). The protest threshold is the boundary above which the inhabitant is motivated enough to try and do something against the plan. Then, if the summation of all the protesting inhabitants is above the discard threshold, the plan does not continue. Otherwise, the proposed plan is accepted and a windmill is placed (equation 6). The discard threshold is decided by the spatial planner and determines what part of the population should protest in order to be significant.

𝑇𝑟𝑢𝑠𝑡 𝑖𝑛 𝑝𝑙𝑎𝑛𝑛𝑒𝑟!,! = 𝑡𝑟𝑢𝑠𝑡 𝑖𝑛 𝑝𝑙𝑎𝑛𝑛𝑒𝑟!!!,! + ∆ 𝑡𝑟𝑢𝑠𝑡 [7]

If 𝑎𝑚𝑜𝑢𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑠𝑡𝑜𝑟𝑠! > 𝑑𝑖𝑠𝑐𝑎𝑟𝑑 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑, then [7.1] ∆ 𝑡𝑟𝑢𝑠𝑡 = 𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒 𝑡𝑟𝑢𝑠𝑡

If 𝑎𝑚𝑜𝑢𝑛𝑡 𝑝𝑟𝑜𝑡𝑒𝑠𝑡𝑜𝑟𝑠! ≤ 𝑑𝑖𝑠𝑐𝑎𝑟𝑑 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑, then [7.2] ∆ 𝑡𝑟𝑢𝑠𝑡 = 𝑑𝑒𝑐𝑟𝑒𝑎𝑠𝑒 𝑡𝑟𝑢𝑠𝑡

Lewis & Weigert mention in an overview of eighteen years of trust research a

complicated feedback process where in case of betrayal, the inclination to trust others declines (Lewis & Weigert, 2012). In the feedback loop, risk-‐taking behaviour affects trust expectations. Risk-‐taking behaviour, which they term behavioural trust, “not only results from trust expectations, it strengthens trusting expectations over time.” Thus, in case of betrayal, the inclination to trust others declines. Betrayal is in the model’s situation when inhabitants protest, but the plan still continues. The essence of the trust dynamics in the model is based on the old saying ‘trust is hard to gain, but easy to lose’. In other words, the decrease in trust in case of betrayal is higher than the increase in trust if the plan is discarded.

After the renewal of the trust level and the placement of a new plan, the loop is run through completely and starts over from the beginning.

Figure 2 Distance decay function used to calculate the opinion towards the plan

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The experiment

The model consists of a multitude of variables. They all have an important role in the

dynamics, and they are all interesting to vary and see what happens. But, as one use of the model is to propose insight to spatial planners, the experiments are done with the variables within the planners’ reach. Most variables are part of the behaviour of the inhabitants, so those are not an option. The most relevant left is the discard threshold – how many people have to protest before the planner decides to discard the proposed windmill. Next to that, the protest threshold is also varied because the planner has some influence on how easy it is to protest. As outcome the average level of trust and the amount of protestors are given. Netlogo has an application called Behaviour Space that make it possible to do experiments. The variables that have to be varied, and the variables of which the values are wanted as outcome are entered. These are listed in table 2. Netlogo then makes runs for each combination of the varied variables, and reports the values of the outcome variables. A run goes from the initialisation of the model, through the ‘go’ procedure, until the given time limit is reached. Time is set with ticks. Every tick represents one ‘go’ procedure – one round in which it is decided to continue the placement of the proposed windmill. In reality, this is roughly half a year. With 11 values for the protest threshold, and 31 values for the discard threshold, 341 runs are made per experiment. The same experiment is repeated over again 15 times, to account for the random variables. The outcome values can then be used to analyse the effects of varying the inputs – the choices the spatial planner can make. The chosen values are based on earlier, explorative experiments. These were done to see how the model would react to certain variables. Because the results are not interesting enough to show here, these experiments are also not described.

Table 2 Used variables in the experiment Fixed variables Initial number of towns 6 Number of actors 75 Share trust prop 0.5 Share trust start 0.5 Share opinion total 0.5 Input variables Protest threshold 0.5 – 0.6 increment 0.01 Discard threshold 20 – 50 increment 1 Outcome variables Mean Trust total of inhabitants Amount of inhabitants protesting

Results

As mentioned earlier, the model is used to search for a balance between the

placement of plans and the building of trust. Figure 3 shows how trust decreases as the amount of plans increases. The protest threshold is set to 0.51, this is partly an arbitrary choice and partly because this showed a clear intersection. On the x-‐axis the discard threshold is varied. The trust is expressed as the average value of trust of the inhabitants over 15 runs at the 25th tick, and the placement of plans is expressed as the percentage of times a windmill was placed at the 25th tick out of the 15 runs. The figure shows how changing the discard threshold influences the successful placement of plans and the trust level. An optimal discard threshold to choose by the spatial planner can be

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found with use of figure 3. This is elaborated upon in the discussion. Why both variable are measured at the 25th tick will become clear later, as some explanation is necessary first.

Figure 3 is composed out of the figures 4 and 5. In those figures the protest

threshold is also varied, next to the discard threshold. The average trust level at tick 25 for each protest threshold–discard threshold combination is visible in figure 4. As the one of the thresholds gets higher, the average level of trust declines. However, with a high discard threshold the trust actually increases with increasing protest threshold.

Figure 3 Development of the average trust level and the % plans realized as the discard threshold increases, with a protest threshold of 0.51 at 1:1 exchange

Figure 4 Mean trust level of all inhabitants, averaged over 15 experiments, at varying protest threshold and discard threshold

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The percentage of times a windmill was placed at the 25th tick is shown in figure 5.

Whether a windmill is placed or not is the result of a process shown in figure 6. Here the development of the amount of protestors is set out as a function of time, from data of one experiment. The different sets of values are the different discard thresholds. At a protest threshold of 0.5, the amount of protestors gradually increases until it reaches a steady level, for most discard thresholds. But, at some discard thresholds, a decline of protest is visible. Surprisingly, this decline always kicks in at tick 12, and the higher the protest threshold, the more discard threshold levels show this shape. Which discard thresholds show which behaviour depends partly on the random variables.

Figure 5 Percentage of times a plan was accepted at tick 25 out of 15 experiments, at varying protest threshold and discard threshold

Figure 6 Amount of protestors for different sets of discard thresholds. Per figure, the protest threshold is set and the discard threshold is varied. Decrease trust = 0.05 and increase trust = 0.10

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This change at tick 12 only applies in cases where the increase in trust in case of discard is set at 0.05 and the decrease in trust in case of accepting a plan is 0.10. The fascinating thing is that if these numbers are altered, or even if the increase is set higher than the decrease, the bifurcation still occurs but the decline kicks in at a different tick. But, it is always at the same tick as long as the same values for the increase and decrease are chosen. See for example figure 7, where the decrease in trust was changed to 0.15. Now the bifurcation happens around tick 10 at both protest thresholds.

Figure 7 Amount of protestors for different sets of discard thresholds. The protest threshold is set and the discard threshold is varied. Decrease trust = 0.05 and increase trust = 0.15

Now it can also be explained why everything is measured at tick 25. At that time the

declining is mostly finished, or the amount of protestors is still stable. With ticks of approximately half a year, in reality this would be more than 12 years. If a longer run was chosen it would take up too much time to do all the runs. So, figure 5 not only shows the placement of plans, but also whether the protesting started to decline or not like in figure 6. If there is a steady level of protestors, no more plans will be placed. But in case of a declining development, in most cases the plan is accepted at the 25st tick. It shows that with a higher protest threshold or discard threshold, it is more likely that a plan will be accepted, thus that protest will decline.

Discussion

The bifurcation in the amount of protestors in figure 6 indicates that at some point

after the increase in protest, a turning point is reached. In some cases a steady level of protest continues. But other times, the amount of protestors suddenly decreases. The situation in which the protest stays at one level can be compared to a good planner-‐inhabitants relationship, as the trust of the inhabitants is still high. A bad planner-‐inhabitants relationship is when trust declines among the inhabitants and they do not even have the motivation to protest anymore. Figure 6 shows that there are more good relationships at lower protest thresholds, so if the planner wants the inhabitants to stay satisfied, he should make sure the protest threshold is not too high.

An explanation for the bifurcation can be that the randomly generated trust propensity was too low on average. It could be that if, by chance, too many inhabitants had a too low trust level, not enough protest took place. Therefore plans continued to be placed and the point where enough inhabitants protest to keep the protest steady is never reached. Trust declines because the plans keep being accepted, thus protest declines. Because whether the inhabitants protest or not depends on the protest threshold, a decline in protest occurs more often with a higher protest threshold.

This explanation shows that the success or failure of a planner-‐inhabitants relationship is partly based on a random factor – or luck in reality.

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The amount of times a plan is accepted, in figure 5, gives an expected image. It is quite logic that if the discard threshold is higher, it is less likely that a plan will be discarded. The same applies for the protest threshold – the higher it is, the less likely it becomes that there will be enough protest. One thing to notice is that the ‘border’ between 100% acceptance and 0% acceptance runs in a straight line through the image. Thus, 0.01 change protest threshold equals a change of 3 in discard threshold.

From the acceptance of plans (figure 5) and the average trust levels (figure 4), optimal choices for the spatial planner can be determined. This optimal choice, however, depends on the preferences of the planner. How much loss in trust is the planner willing to give up for more plans? As the amount of plans is an increasing function of the discard threshold, and the average trust level a decreasing function, the optimal level is at the intersection. However, at what discard threshold the intersecting takes place depends on the exchange rate between the two. For example, looking at figure 3, a 1:1 ratio is pictured with a protest threshold of 0.51. It shows that in this situation the best choice for the planner would be a discard threshold of 42. But, if the ratio is changed to 1:2, the optimal discard threshold becomes 45 (figure 8). This shows that there is no best option, but this wholly depends on the preferences of the planner. Besides, this all assumes the protest threshold is measurable. In reality, the planner can’t exactly know what this threshold is. The threshold is just a fictional number not present in real situations.

Looking at the usefulness in reality of the model, many comments can be made.

Firstly, the inhabitants are only placed inside the urban areas. The rural areas are considered desert, while in reality these spaces are inhabited by farmers etc. In the model, most plans are placed in rural areas as there are no inhabitants close by that will protest.

Second, the trust dynamics are very simple. The only thing actually happening is the increase or decrease depending on the action of the spatial planner. Surely there is more to incorporate, but that was not possible due to limits of this project. For example, now only the trust in the planner is taken into account. But the inhabitants also have some level of trust in their neighbours, and perhaps more people are inclined to protest if they do it together.

Third, in the code a form of opinion dynamics was written. But, in order to focus on the trust dynamics, this was turned off in the experiments. It could be interesting to see what would happen if the opinion dynamics was turned on again. Due to time limits, this was not carried through.

Figure 8 Development of the average trust level and the % plans realized as the discard threshold increases, with a protest threshold of 0.51 at 1:2 exchange

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Lastly, the model consisted of many variables that were not altered during the experiments. It may be that the values chosen while experimenting may have influenced the results, but in order to make sure these variables should also be altered. Again, due to constraints this was left out.

Some of the variables in the model could be measured in reality through surveys. For example, the share opinion total decides how important opinion is compared to trust in order to protest. In the model this share is the same for each inhabitant. With use of surveys, the importance of trust and opinion could be measured for many people. The outcomes can then be used to amplify the model.

Conclusions from this model may be not realistic, but they are useful. Although models are a simplification of reality and thus almost always leave out several important factors, they can give insight in a situation and provide aid in making decisions. In a real society the citizens could be harmed by interventions made by the spatial planner. When using a model, anything is possible without doing actual damage. No long negotiations are necessary, but the consequences can be monitored directly. Besides, the more time that can be put into the model in order to expand it, the more it approaches reality.

Another useful side of models is that they usually contain multiple theories. Qualitative work describes one theory thoroughly in a research. Models use these theories, summarize them, and relate them to each other. In this way, they can provide an overview of the many theories there are on a certain subject, like the development of trust.

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Appendix I

;;trust model ;;june 2015 ;;===================================================== globals [ max_distance amount_protest amount_windmills ] breed [towns] breed [inhabitants inhabitant] breed [windmills] patches-‐own [ landuse isplan istown ] inhabitants-‐own [ trust_prop trust_planner opinion_windmill trust_total opinion_start opinion_plan opinion_total distance_to_plan distance_to_windmill sum_trust_opinion protest ] ;=============================================================================== to init clear-‐all set-‐default-‐shape towns "house" set-‐default-‐shape inhabitants "face happy" make-‐towns make-‐townarea make-‐ruralarea make-‐plan make-‐inhabitants reset-‐ticks end ;;=====================================================

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to go ask inhabitants [ set protest 0 if sum_trust_opinion > protest_threshold [ set color scale-‐color red trust_total 0 1 set shape "face sad" set protest 1 ] if sum_trust_opinion < protest_threshold [ set color scale-‐color grey trust_total 0 1 set shape "face happy" ] ] set amount_protest count inhabitants with [protest = 1] ask inhabitants with [protest = 1] [ if amount_protest > discard_threshold [ set trust_planner trust_planner + increase_trust ] if amount_protest <= discard_threshold [ set trust_planner trust_planner -‐ decrease_trust ] ] if amount_protest <= discard_threshold [ ask patches with [isplan = 1][sprout-‐windmills 1 [set shape "flag" set size 3 set color red]] new-‐location ] set amount_windmills count windmills if amount_protest > discard_threshold [ new-‐location ] ; opinion-‐dynamics ask inhabitants [ set trust_total ( share_trust_prop * trust_prop ) + ( ( 1 -‐ share_trust_prop ) * trust_planner ) set opinion_total ( share_opinion_start * opinion_windmill ) + ( ( 1 -‐ share_opinion_start ) * opinion_plan ) set sum_trust_opinion ( share_opinion_total * opinion_total ) + ( ( 1 -‐ share_opinion_total ) * trust_total ) ] tick end ;;=====================================================

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to make-‐towns create-‐towns initial-‐number-‐towns [ set color white set size 1 setxy random-‐xcor random-‐ycor set istown 1 ] end to make-‐townarea ask towns [ ask patches in-‐radius ( 10 + random 10 ) [ set pcolor 35 set landuse 2 set isplan 0 ] ] end to make-‐ruralarea ask patches [ if landuse != 2 [ set pcolor green set landuse 1 set isplan 0 ] ] end to make-‐plan ask one-‐of patches [ if not any? windmills-‐here [set isplan 1 set pcolor yellow] ] end to make-‐inhabitants create-‐inhabitants nr_actors [ set size 1.5 move-‐to one-‐of patches with [landuse = 2] set protest 0 set trust_prop random-‐normal 0.5 0.2 set trust_planner random-‐normal 0.5 0.2 set trust_total ( share_trust_prop * trust_prop ) + ( ( 1 -‐ share_trust_prop ) * trust_planner ) set opinion_start random-‐normal 0.5 0.2 set distance_to_plan calc_distance_to_plan set opinion_plan 1 / ( 1 + 0.003 * (distance_to_plan ^ 3 ) ) set opinion_total ( share_opinion_start * opinion_start ) + ( ( 1 -‐ share_opinion_start ) * opinion_plan ) set sum_trust_opinion ( share_opinion_total * opinion_total ) + ( ( 1 -‐ share_opinion_total ) * trust_total ) set color 4 ] end ;;=====================================================

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to-‐report calc_distance_to_plan report distance one-‐of patches with [isplan = 1] end to-‐report calc_distance_windmill report mean [ distance myself ] of windmills end to new-‐location ask patches [ if landuse = 2 [ set pcolor 35 set isplan 0 ] if landuse = 1 [ set pcolor green set isplan 0 ] ] make-‐plan ask inhabitants [ set distance_to_plan calc_distance_to_plan set opinion_plan 1 / ( 1 + 0.003 * (distance_to_plan ^ 3 ) ) if amount_protest <= discard_threshold [ set distance_to_windmill calc_distance_windmill set opinion_windmill opinion_start + ( 1 / ( 1 + 0.003 * ( (distance_to_windmill / ( 1 + amount_windmills ) ) ^ 3 ) ) ) ] ] end ;;===================================================== ;to opinion-‐dynamics ; ask inhabitants [ ; let neighbours inhabitants in-‐radius distance_threshold ; let min_opinion opinion_start -‐ opinion_threshold ; let max_opinion opinion_start + opinion_threshold ; let similar_neighbours neighbours with [opinion_start > min_opinion and opinion_start < max_opinion] ; let opinion_receiver self ; ask similar_neighbours [ ; let update_opinion opinion_start ; ask opinion_receiver [ ; set opinion_start (opinion_start + update_opinion) / 2 ; ;; als twee agents een opinie bijna delen, zal het basisvertrouwen toenemen ; ] ; ] ; ] ;end