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Centre for Geo-Information

Thesis Report GIRS-2015-24

The meaning of resident’s opinions for spatial decision making

An agent-based simulation of continuous opinion dynamics under consideration of social and spatial influence

Jan Neumann

April 1

2th 2

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The meaning of resident’s opinion for spatial decision making

An agent-based simulation of continuous opinion dynamics under consideration of social

and spatial influence

Jan Neumann

Registration number 810306-599-050

Supervisor:

Dr. Ir. Arend Ligtenberg

A thesis submitted in partial fulfilment of the degree of Master of Science

at Wageningen University and Research Centre,

The Netherlands.

April 12th

2015

Wageningen, The Netherlands

Thesis code number: GRS-80436

Thesis Report: GIRS-2015-24

Wageningen University and Research Centre

Laboratory of Geo-Information Science and Remote Sensing

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Acknowledgements

Almost eight years ago I made after all the decision against my old occupation and for a start

from scratch by second-chance education. Now, I have almost reached the goal. After a

bachelor in spatial planning this research topic was an interesting interface between geo-

information science and my previous studies.

I appreciated the challenging process and the associated responsibility against myself.

However, in some places the frustration grew big and I want to give thanks to my supervisor

Arend Ligtenberg for giving me back my night-time peace while struggling with NetLogo

table structures. Further, he always had a friendly ear and supplied me with new thought-

provoking impulses during our meeting. Thank you for that, Arend! Also, very special thanks

to my fellow students for social activities, coffee breaks, and dice games during lunch.

In addition I would like to thank both, the German government of Willy Brandt that

established the BAföG in the 1970’s as well as my old colleagues, friends, and employers

Marian and Basti. Without them, I would have barely been able to finance my studies during

the last years.

Further, I want to thank my parents and my sister for support, open ears, and the advice

during the last years. This is not taken for granted and I really appreciate it. Last but definitely

not least, thank you Milena for distraction, good advice, and yourself by my side.

VI

Table of content

ACKNOWLEDGEMENTS .................................................................................................................................. V

LIST OF FIGURES ...........................................................................................................................................VII

LIST OF TABLES ........................................................................................................................................... VIII

SUMMARY ......................................................................................................................................................... IX

1 INTRODUCTION.......................................................................................................................................... 1

1.1 PROBLEM DEFINITION ......................................................................................................................... 2 1.2 RESEARCH OBJECTIVE AND RESEARCH QUESTION ............................................................................... 3 1.3 STRUCTURE ......................................................................................................................................... 4

2 THEORETICAL BACKGROUND .............................................................................................................. 5

2.1 SPATIAL PLANNING ........................................................................................................................... 5 2.2 COMPLEX ADAPTIVE SYSTEMS .......................................................................................................... 8 2.3 MULTI-AGENT SYSTEMS (MAS) AND AGENT-BASED MODELS (ABM) ............................................. 9 2.4 OPINION FORMATION ...................................................................................................................... 12 2.5 OPINION DYNAMICS ......................................................................................................................... 16 2.6 CONCLUSION ................................................................................................................................... 21

3 CONCEPTUAL APPROACH .....................................................................................................................23

3.1 A SUITABLE MODEL ......................................................................................................................... 23 3.2 CONCEPTUAL MODEL ...................................................................................................................... 25 3.3 DESCRIPTION OF THE MODEL .......................................................................................................... 27

4 RESULTS ......................................................................................................................................................36

4.1 CASE STUDY .................................................................................................................................... 36 4.2 SCENARIOS ...................................................................................................................................... 39 4.3 OPINION DISTRIBUTION.................................................................................................................... 46

5 DISCUSSION AND CONCLUSIONS ........................................................................................................50

5.1 DISCUSSION ..................................................................................................................................... 50 5.2 CONCLUSION ................................................................................................................................... 54

REFERENCES .....................................................................................................................................................57

APPENDIX ...........................................................................................................................................................62

VII

List of figures

Figure 1: Theoretical background………………………………………...................... 6

Figure 2: Intentional model of actor based decision making (based on Ligtenberg)… 24

Figure 3: Conceptual approach……………………………………………………….. 26

Figure 4: ODD approach according to Grimm et al. (2006) ………………………….. 28

Figure 5: Iteration procedure per agent adapted from the model of Ligtenberg and Bregt

(2014)…………………………………………………………………………………… 30

Figure 6: Representation of actor communication. With increasing spatial distance

interaction decreases between locations. With increasing social distance

interaction decreases between agents……………………………………….. 33

Figure 7: Case study area in the Northern part of Wijchen/ the Netherlands………… 36

Figure 8: The case-study area as displayed in Google Earth………………………….. 36

Figure 9: Simulation results of scenario a……………………………………………... 40

Figure 10: Simulation results of scenario b……………………………………………. 42

Figure 11: Simulation results of scenario c………………………………………….… 41

Figure 12: Distribution of all opinions after 20 interactions (ticks)…………………... 46

Figure 13: Opinion distribution after different amount of interaction………………… 49

VIII

List of tables

Table 1: Comparison of continuous OD model approaches…………………………… 25

Table 2: Entities, variables, and scales of the simulation model………………………. 29

Table 3: Parameters, variables, and default values for the scenario case study on Dutch 45

Table 4: Scenario a - values of average opinion under various thresholds…………….. 41

Table 5: Scenario a - values of average opinion under various thresholds…………….. 41

Table 6: Scenario b - values of average opinion under various thresholds…………….. 43

Table 7: Scenario b - values of average opinion under various thresholds…………….. 43

Table 8: Scenario c - values of average opinion under various thresholds…………….. 45

Table 9: Scenario c - values of average opinion under various thresholds…………….. 45

Table 10: Statistical outcome for all scenarios after 20 rounds of interaction…………. 47

Table 11: Statistical outcome after different rounds of interaction…………………….. 48

IX

Summary

This research deals with the implementation of the dynamics of opinions in a planning related

agent-based model environment. The problem of current planning models is the ignoring of

opinions as influencing factor on decision. Controversially however, current existing models

for the simulation of opinion dynamics are ignoring a spatial component which is important

for spatial planning models. Adding both, a spatial and social component to an dynamic

model environment seemed a sensible approach to overcome this problem and to simulate

opinions dynamics and learning capacity of agents. Therefore, it is investigated how the

current role of opinions in multi-actor spatial planning could be simulated within an agent-

based model environment and what this would add to our current understanding of dynamics

in spatial planning. Literature review, conceptual model, ODD-protocol, and the

implementation of the model on a certain case are methods that were used to answer the

questions of this research. The results of the case study show, that multi-actor spatial

planning’s models should consider a spatial component since it is an important influencing

factor on the opinion of actors which are involved in decision making processes. The model

could be used to highlight locations that are more or less conflicting according to actor’s

opinions. Further, the model results showed adaptive-behaviour of actor’s opinions. The

validation of the model results, however, remains problematic. A limited amount of

components, the restriction to only several rules, and the absence of “reals” argumentation

capacity provides model results that are far from a realistic outcome and therefore, hard to

validate

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1 Introduction

“The real decision making process involves a lot of people, and the whole structure is

redolent with feedback. At every decisive moment, of which there will be great many

within the total decision, we range ahead and back and sideways. We gauge the effect of

this sub-decision on everything we have tentatively decided already, and on the sub-

decisions left to take. This is why I think the decision tree is an artefact, and of little use

to us. You cannot isolate these nodes either in time or in logical connectivity, and anyone

who has ever taken a complicated decision knows this.” (Beer, 1975)

Spatial planning deals with changing organization of the spatial environment in order to

satisfy the claims of a society (Van der Valk, 2002). The spatial development of the

environment requires spatial interaction (Klepinger, 2007) and takes “study of psychology,

logistics, economics, and sociology of individual and group decision-making processes”

(Ligtenberg, Wachowicz, Bregt, Beulens, & Kettenis, 2004). Within the last few decades,

spatial planning has shifted from top-down to a rather bottom-up approach. Nowadays, local

governments are no longer the primary actors and sole decision makers. They rather play one

role among others or assume the role as an observer (Wegener, 2004). Their shifting role

requires the ability to anticipate likely future plans of relevant private actors. Therefore, it is

necessary that local governments understand interests and motivations of their actions

(Wegener, 2004). About an increased amount of actors within the spatial environment,

competition for the same resource occurs more frequently (Ligtenberg et al., 2004) and

planners need to deal with increased complexity (Ligtenberg et al., 2004) to understand

dynamic spatial processes.

Since computational power has been rigorously increased in the last decades, computers

nowadays play the major role in “acting as a simulator for physical processes” (Holland,

1992). Humans form networks in their spatial environment by a social interaction. Agent-

based Models (ABMs) provide the opportunity to reproduce systems with a network structure

(Billari, 2006) and to forecast and explore future scenarios (Axelrod, 1997). However,

Ligmann-Zielinska and Jankowski (2007) mention that “using ABMs for spatially explicit

modelling of real world policy scenarios” has been limited to few activities. ABMs make use

of a multi-actor modelling approach. Since complex and dynamic spatial processes are non-

linear in their outcome, ABMs allow the simulation of alternatives, by assigning new values

to decision variables (Axelrod, 1997). An actor or a group of actors in the real world refers to

an agent within an ABM.

As computational decision making is limited to pre-written codes, and a priori knowledge

(Ligtenberg et al., 2004), it is necessary to understand how both, opinions and decisions of

social actors, are formed and changed. This is important to understand and deal with the

complexity of those social processes. Opinions and beliefs are driving forces in shaping social

interactions. Social interaction leads in an society to social learning and the formation of

different opinions (Acemoglu & Ozdaglar, 2011). Opinion forming is a dynamic process,

which also is true for spatial decision making. For ‘continuous opinions’, different models

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(Sznajd, Deffuant, Hegselmann-Krause) have been developed (Martins, 2008). It needs to be

considered that the assigning of dynamic opinions to distinct variables entails in a model shift

from linear to non-linear behaviour (Hegselmann & Krause, 2002). However, this leads to a

lack of validity in the model results.

Modelling of complex and dynamic systems should be approached in a simple and systematic

way (Hegselmann & Krause, 2002) and by reducing the complexity of the model (Ligtenberg

et al., 2004). The assumptions that “people construct their own realities” and that “experience

and behaviour is influenced by social relationships” (Smith & Guthrie, 1921), shows, that

sociological factors in opinion forming are highly complex. Here, the ABMs should be able to

represent this complexity in a simple way and not too complicated way. To do so, it is

important to gain adequate knowledge about decision making processes and to apply this

knowledge for the implementation of ABMs for complex spatial dynamics.

1.1 Problem definition

ABMs for the simulation of relevant issues in spatial planning received progressively more

attention during the last years. Decision making processes are often organized through

different interest groups (Faludi, 1973), which are vicarious for different agents within an

ABM. Yet, little is known about beliefs and preferences in the decision making of certain

agents (Ligtenberg et al., 2004).

One important domain of decision making processes refers to the field of opinion formation.

Opinion formation refers to the scientific field of social-psychology. An opinion describes the

“degree of preference” an agents has towards particular items or occurrences (Kaur, Kumar,

Bhondekar, & Kapur, 2013). It needs to be considered that opinion formation is an

evolutionary process and that it is influenced by “real or imagined presence of others" within

social networks (Nowak, Szamrej, & Latané, 1990). These dynamics and the formation of

opinions have received exceptional high attention in the study of social physics (Kaur et al.,

2013).

Continuous models for opinion dynamics consider that existing opinions of agents can be

changed by the influence of others (Stauffer, 2005). However, these models follow the

assumption of bounded confidence, which assumes a similar opinion among agents before

they interact with each other (Hegselmann & Krause, 2002; Sznajd-Weron & Sznajd, 2000).

This implies that opinions can only be simulated towards the boundaries that are given within

the source code of the model. Here, the interaction between agents is determined by present

threshold values. (Kaur et al., 2013).

Due to the constrain of pre-determined boundaries in these models, agents are limited to

evolve collective intelligence by social influence (Kaur et al., 2013). The issue that opinions

take continuous values in an interval, rather than binary states (‘pro’ or ‘contra’) has been

solved by an convergence parameter in the model of Deffuant, Neau, Amblard, and Weisbuch

(2000). Another issue is the circumstance that opinion formation “resembles a process where

consensus is reached by continuous influence from each other”(Kaur et al., 2013). However,

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theories about understanding opinion dynamics in social networks do not yet provide a

consensus among researchers (Mäs, Flache, & Helbing, 2010).

Ligtenberg and Bregt (2014) describe that “for most spatial problems the opinion space can

be considered multi-dimensional as the opinion dynamics not only depend on time but also its

location within a 2 dimensional geographic space”. However, for spatial planning purposes

the use of current models on opinion dynamics remains problematic, since they do not take

the spatial influence of an opinion into account. Most spatial ABMs do not represent the

diffusion and distribution of opinions (Ligtenberg & Bregt, 2014). It needs to be considered

that heterogeneity of an opinion is not only depending on the agent population, but also on

space and therefore requires an additional dimension for location.

Therefore, modelling opinions of complex spatial dynamics faces limitations when it comes to

validation (Engelen & White, 2008). Particularly, the validation of socio-spatial systems, to

which ABM based spatial planning belongs to (Ligtenberg, Beulens, Kettenis, Bregt, &

Wachowicz, 2009), is difficult since they often do not have a reference situation (Windrum,

Fagiolo, & Moneta, 2007).

This rests upon the case, that, firstly, only limited actor communication and opinions can be

included within a model and, secondly, that beliefs and preferences are only available in rule

form (Ligtenberg et al., 2004). As described earlier, equations about preferences and opinions

are based on a priori knowledge. Therefore, it is desirable to gain more knowledge about

crucial points in opinion formation. Here, the focus should lay on influencing factors that play

a role in spatial decision making (NIMBY, size, location, duration, costs et cetera). This might

help in better reasoning about certain agent decisions and to increase the validation of spatial

planning based ABMs.

It needs to be considered that models are only simplifications of reality. Often models are not

applicable to the real world. They rather help us to learn from their outcomes instead of

predicting the future. This enables us in learning from the results in order to develop better

theory and methods. Spatial models are often considered as being data intense, cross-

disciplinary as well as both dynamic and complex (Couclelis, 2002)

1.2 Research objective and research question

Based on this knowledge gap, my thesis focusses on opinion dynamics that play a role for the

implementation of ABMs of complex dynamics in spatial decision making. The overall

objective of this research is (i) to gain more knowledge on opinion formation processes, (ii) to

transform this knowledge into a set of equations, and (iii) to implement these set of equations

within a computer based modelling environment. For the modelling process an existing model

approach was used and extended by a constructed set of equations. Four research questions

(RQ) were formulated to achieve the objectives during my thesis research.

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RQ 1: What is the current role of opinions in multi-actor spatial planning?

RQ 2: How can opinion formation and dynamics are formalized into a logical set of

equations?

RQ 3: How can these set of equations be simulated in an agent-based model?

RQ4: What does this approach add to the current understanding of the land use/ planning

dynamics?

1.3 Structure

The structure of this thesis report answers the research questions in consecutive order. The

first research question is answered in chapter 2 by a literature review to highlight the state-of-

the-art and to avoid overlap during my research. Therefore, the five different subjects spatial

planning, complex adaptive systems, agent-based models, opinion formation and opinion

dynamics are investigated and described according to their usability according to the research

objectives.

The second and third research questions are answered in the methodology part of chapter 3.

First, different models of opinion dynamics are compared and the most suitable to the purpose

is chosen. After that the conceptual model describes aspects and approaches that were

considered in order to bring real world circumstances into a model environment.

Subsequently, all model steps are described in detail by using the approach of the ODD-

Protocol by Grimm (2010) and a formalised set of equations. Eventually, decisions and

formalised equations from the conceptual approach are implemented in in a simulation model

for a Dutch planning case for a specific four digit postcode area.

The results of the simulation model are used to answer the last research question. To answer

the question a comparison is done, in how far the results are corresponding to the findings of

the first research question. All research questions are summarized and critically assessed in

the subsequent discussion, which ends in a conclusion and recommendations for further

research.

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2 Theoretical background

Five different subjects were of relevance for this study. To ensure that the research had no

repeating overlap with existing knowledge the state-of-the-art was investigated. Figure 1

gives an overview over the theoretical background and demonstrates the five investigated

subjects spatial planning (SP), complex adaptive systems (CAS), agent-based models (ABM),

opinion formation (OF), and opinion dynamics (OD) under the appropriate section number in

this report.

Fig. 1: Theoretical background

2.1 Spatial Planning

Spatial planning is described as a highly complex activity (Geertman, 2006) for solving

existing and anticipating future societal problems (Vonk, 2006). To satisfy the claims of a

society (Van der Valk, 2002), relations among social actors need to be based on a societal set

of rules, norms and values (Kleefmann, 1984). According to Kleefmann (1984), common

societal acting assumes that spatial and societal sub-systems are necessarily woven together.

From the 1950’s on the role of spatial planning has shifted via different stages from a

‘rationality tradition’ of blueprint planning to ‘participatory tradition’ of participatory and

communicative planning (Geertman, 2006). This shift is related to distrust against experts and

regulators, since they were not be assumed to be able to mitigate unknowable and unavoidable

social risks (Laurian, 2009) due to planning decisions. This shift from authority-based spatial

planning to rather participatory approach (Ligtenberg et al., 2004) assumes that short term

solutions are no longer satisfactory as it comes to decisions of high collective interests (Vonk,

2006).

Recent literature describes a shift in spatial planning from a linear to continuous and

multifaceted process (Ligtenberg & Bregt, 2014). In the course of time, planning processes

have become more diverse (Wegener, 2001). Since nowadays more actors are participating in

the planning process and following their own motives and interests, it is crucial to understand

both, what and why something is going on as well as to predict probable futures (Wegener,

2001). Geertman (2006), described three main reasons for growing complexity due to

increased participation in planning processes, namely:

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An increased number of fields of policy that needs to be taken into account and

integrated

A growing amount of actors with divergent interest and agendas

The involvement of participants takes place at a much earlier stage in the planning

process

Therefore, it is important to understand that actors are guided by different intentions, different

strategies and different perception of space (Samsura, van der Krabben, & Van Deemen,

2010). Complexity due to participatory planning causes that, modern spatial planning strives

to “satisfies the needs of the community of actors as a whole” (Ligtenberg & Bregt, 2014).

Current spatial planning models tend to favour a methodological approach, depending on their

purpose. te Brömmelstroet and Bertolini (2008), for instance describe a rather quantitative

information approach in transport planning, while land-use planners make preferably use of

both quantitative spatial information as well as social sciences and the approach is

predominantly based on communication and deliberative rationality (Forester, 1999).

Wegener (2001), defines a model as a simplified representation of an object for purposes of

description, explanation, forecasting or planning, which state might be of single, bi-space

(spatial model), or tri-space (space-time model). He divides spatial planning models into five

different groups:

Economic modelling: Includes models of urban land as well as housing and

determines the optimum locations for placing an object. The model approach is based

on location theory

Geographical modelling: Includes migration models based on notions of distance and

dissimilarity. Couples with probabilistic models of population dynamics, spatial

interaction and location models.

Sociological modelling: Includes models of spatial segregation based ‘social ecology’

and models of urban ‘action spaces’.

Transport engineering modelling: Includes travel and good transport models based on

entropy or random utility theory.

Integrated modelling: Combines two or more above-specialised models

Regarding the increased complexity, planning processes require models of inclusive

strategies, that are capable in bringing together multiple goals of multiple disciplines (Zapatha

& Hopkins, 2007). Therefore, context and user requirements as well as the adaption of

planners need to be considered for the implementation of a model that suits the purpose

(Geertman, 2006; Vonk, 2006)

Ligtenberg (2006), described spatial planning as decision making process among different

actors. This multi-actor decision making is based on the perception of the spatial environment

as it is formed by emotions, senses, brains and communication (Ligtenberg, 2006). This

makes spatial planning a complex process (Samsura et al., 2010). Geertman (2006), describes

participation and collaboration as crucial elements of current spatial planning, that stresses

partnerships among all participants as well as the multidimensionality of problems and

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solutions. This so called ‘communicative planning’ is based on the theory of Habermas (1987)

about communicative action, in which he stresses a dialogical and public discourse. These

deliberative processes are essential to build up trust (Senecah, 2004), which is described as a

social construct that characterizes human relations (Seligman, 2000). Rousseau, Sitkin, Burt,

and Camerer (1998), define trust as:

“A psychological state of mind comprising the intention to accept vulnerability based

upon positive expectations of the intentions or behaviour of another”

Building up trust can be seen as an important issue for planning in order to deal with

phenomena like the ‘Not In My Back Yard’ (NIMBY) syndrome, as it comes to siting

decisions about unwanted facilities and/or objects (Dear, 1992). Since planning combines

both, public and private interests, trust is an essential element in order to encourage

participation for spatial decision making (Laurian, 2009). Further, trust is seen as prerequisite

for cooperation and the interaction between opinions of people among different groups of

interest (Brockner, 1996). Planning processes that are based on trust are legitimate, since

decisions are compromises made in the name of the common good (Tilly, 2005), even though

disagreements in values, interest and power relations can never be fully dissolved (Laurian,

2009). In order to reach the most legitimate results, Laurian (2009) refers to the necessity of

ongoing “communication, learning and understanding of issues as well as attention to views,

values and goals of others” for deliberative planning processes.

Since planning processes involve several stakeholders, spatial decision making is often

confronted with conflicts, which can be distinguished into conflict of interests and conflict of

values or belief (Aubert, 1963). To solve this conflicts it is intended to minimize losses for all

sites (Aubert, 1963) by compromising. Therefore, deliberative processes like dialogue,

consensus building et cetera are important for spatial decision making in order to improve the

quality of decision and to legitimate them (Laurian, 2009).

Consensus building is valuable for decision making processes since it helps in finding new

feasible strategies for complex planning tasks by producing new relationships, new practices,

and new ideas (Innes & Booher, 1999). This enables planners to deal better with issues of

“uncertainty, loss of meaning, and rapid changes in contemporary society” (Innes & Booher,

1999).

The increased meaning of computers led to the innovation of “Planning Support Systems”

(PSS) for spatial decision making. However, te Brömmelstroet and Bertolini (2008), argued

that current models were failing in representing large parts of complexity since they were

often not easy understandable to planners. Three main weaknesses of current PSS were

identified by Vonk (2006):

Planners usage remains limited to rather simple and un-complex tasks

Limited usage by executives in decision making

Little use of PSS made by citizens and professional stakeholders

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As described spatial planning faces complex processes in a complex system. Decision making

in these processes is based on interdependencies of its actors. Therefore, Ligtenberg (2006)

describes spatial planning as a complex adaptive system (CAS).

2.2 Complex Adaptive Systems

Processes in CAS imply that much can emerge from little (Holland, 1992). These processes

are without a central control and are rather driven by many, interacting parts (Holland,

1992).This means that “simple elements are governed by a few simple rules and operate

through trial and error with interaction and feedback” (Innes & Booher, 1999). A

precondition for CAS is an unstable environment, which is not entirely chaotic. This results in

still productive patterns that can be found (Innes & Booher, 1999). Manipulation or

suppression of individual views in CAS weakens the intelligence of the system, since CAS

depend on autonomous acting of its individuals. (Innes & Booher, 1999). Holland (2006)

argues that in a CAS, multiple processes are taking place simultaneously (parallelism),

processes are depending on each other (conditionality) , components are coupled differently at

different level (modularity), and that there are changes over time (adaption and evolution).

Ligtenberg (2006) describes processes in spatial planning as CAS since they are both,

complex as well as interdependent. CAS specific features of CAS as they are described in

Grus, Crompvoets, and Bregt (2010) can be recognised for decision making processes in

spatial planning. For instance, spatial planning processes are embedded in mutual interaction

between different components (Rotmans, 2005) that take place simultaneously (Cilliers,

2005). This interaction faces continuous change for both, the physical environment as well as

the social reality of actors. Small actions in the planning process may have a major effect in

the future (Grus et al., 2010). CAS specific interaction with the environment and sensitivity to

external influences (Rotmans, 2005) are also applicable in spatial planning processes.

Described unpredictability of CASs about the final system behaviour as well as the fractal

building (Grus et al., 2010) is also appropriate in spatial decision making.

Planning processes are interconnected by many influencing factors, which can be related to

the properties of conditionality and modularity as described in Holland (2006). Here,

conditionality focusses on the interaction between actors, while modularity is related to

different scales in time, space and organization. These depending processes on different levels

are related to issues of “policy, available information, political context and actors

characteristics” (Geertman, 2006).

Adaption and evolution is the tendency to use the own output to adjust its input and processes

via a feedback-loop mechanism, as it is described in Grus et al. (2010). This implies that the

system is able to change its own rules (Holland, 1992).

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2.3 Multi-Agent Systems (MAS) and Agent-based Models (ABM)

Complexity in spatial planning is mainly caused by actors, spatial environment, actor-based

processes and autonomous processes (Ligtenberg, Bregt, & Van Lammeren, 2001). Processes

of spatial planning often show unexpected behaviour caused by complexity. This complexity

is related to space as a limited resource in which conflicts among actors occur more often,

since their desires and expectations anticipate the spatial environment to fulfil multiple

functions (Van der Valk, 2002).

Through the shift towards a participatory planning approach current spatial planning requires

models that describe multi-actor processes (Van der Valk, 2002). Here, an artificial

environment enables planners to “cope with the increased complexity of reality” by

developing and testing policy (Ligtenberg et al., 2004).This multi-actor planning as it is

described by Ligtenberg et al. (2004) enables actors to observe their spatial environment and

to communicate as well as negotiate their preferences. These preferences lead to decisions

that are finally implemented in the spatial system. This supports understanding and helps

planners to deal better with conflicting interests among multiple- actors for the same

resources. However, conventional or statistical modelling techniques, show weaknesses in

modelling these processes, since (Ligtenberg, 2006):

Social spatial systems do not behave according physical laws

Land-use planning does not provide well-defined description of the interactions

Causalities occur and vaporize “on-the-fly”

Dynamics behaviour is to a large extent rooted in individual behaviour (Phipps &

Langlois, 1997)

In complex land-use systems, reducibility is not a rule but rather an exception (Itami,

1994)

In contrast, agent-based computing is proposed as an alternative to solve heterogeneous,

complex, and distributed problems (Bruno, 2010). A set of elements (agents), based on a set

of appropriated rules is interacting within a given environment in this so called agent-based

models (ABM) (Bruno, 2010). ABMs are related to the concept of multi-agent systems

(MAS) which include the actor factor of decision making into the conceptual and

methodological approach of dynamic spatial models (Parker, Manson, Janssen, Hoffmann, &

Deadman, 2003). In contrast to individual agents, a system of interacting individual agents

leads to consensus building and therefore to learning capacity, intelligence and the ability of

adaptation and innovation (Innes & Booher, 1999). However, Niazi and Hussain (2011)

argue, that ABMs rather searches for explanatory insight of collective behaviour than in

engineering problems and the design of agents. MAS can help in understanding the effects of

different planning styles and actors relations changes due to different decision making

(Ligtenberg et al., 2004). According to Bousquet and Le Page (2004), MAS are defined as a

system based on a spatial environment, set of situated objects, agents, relation between

objects, operations, operators.

Two key components for MAS are described: Cellular Automata (CA) and ABM. Bio-

geophysical and ecological aspects in MAS are represented via CA and are part of the agent’s

10

environment. Social aspects important for human decision making, such as values and beliefs,

is represented in ABM. This allows the simulation of different spatial perceptions of complex

agent-agent and agent-environment interactions (Parker et al., 2003).

The priority of this research lays on decision making processes based on human opinions.

Actors involved in this study are different groups of interest or organizations participating in a

specific planning process. Therefore, the focus lays on ABM based modelling approach.

ABM allow to implement, explore, experiment, and analyse the features of an environment

based on different values and decisions (Axelrod, 1997). Further, ABMs follow a concept that

is inherently based on bottom-up approach and allows the representation of individual actor

properties, like desires, beliefs, and preferences and to simulate processes that are related to

them (Ligtenberg, 2006). In addition, ABM provide potential to its users by agents learning

ability about the environment and other agents(Gilbert & Troitzsch, 2005). To develop an

ABM four main building blocks have to be described in detail(Billari, 2006):

The object of simulation

The agents population

The adaptive capability of each agent category

The interaction paradigm among agents

ABM are described as method to simulate the interaction of heterogeneous and autonomous

agents in a common environment for the realization of both self or common interest

(Ligmann-Zielinska & Jankowski, 2007). A typical ABM contains of three main elements,

namely a set of agents, a set of agents relationships, and the agents environment (Macal &

North, 2010).

Within ABMs the active entities of the system are represented by a set of agents, a subset of

the objects (Bousquet & Le Page, 2004). These agents are defined by certain characteristics:

Within a complex dynamic environment, agents try to fulfil a set of goals (goal

oriented)

An agent has control about the own internal state, which allows to operate without

direct intervention by other agents or humans (autonomy)

Agents interact with other agents and/or humans (social ability) (Green, Hurst,

Nangle, & Cunningham, 1997).

Agents perceive and answer to signals from their environment in a precise way (re-

activeness)

In order to satisfy their own objectives, agents take initiatives (pro-activeness)

(Wooldridge & Jennings, 1995)

An agent can only exist as long as the environment is not changed. (Green et al.,

1997).

In addition Macal and North (2010) describe the state of an agents as essential characteristic

which is linked to time and environment. Further, adaptive, goal-directed and heterogeneous

behaviour were identified as useful characteristics of an agent (Macal & North, 2010).

11

Agents are connected to an environment by a set of rules and represent the main drivers of

changes (Ligtenberg, van Lammeren, Bregt, & Beulens, 2010). Agents can either show

reactive or deliberative behaviour. Deliberative behaviour assumes logic based knowledge

representations based on well-formed formulae, syntax and semantics (Singh, Rao, &

Georgeff, 1999). Reactive behaviour is built on agents linked to their environment, emerging

intelligence due to interaction, no “a-priori specification”, no high-level symbolic

representations and only partial representation of the environment (Weiss, 1999).

Agents within an ABM are always connected with other agents by a set of relationships. Two

main issues are required for the developing of an ABM. It is essential to consider, which

agent is, or could be, connected to which other agents and what the dynamics of these

interaction are (Macal & North, 2010). Since ABMs are decentralized systems, it is necessary

that local information are available (Macal & North, 2010),. Agents typically interact with a

subset of agents that are located in their close spatial environment (neighbours). Topology

immobilizes which agents transfer information and therefore it defines how agents are

connected among each other (Macal & North, 2010). It describes the spatial organization of

an agent and limits its movement and perception to the neighbourhood. Many spatial temporal

models, like ABMs, follow in the assumption of the first law of Geography by Tobler (1970):

“Everything is related to everything else, but near things are more related than distant

things.”

The environment of an agent can fulfil two main purposes. It can either be used for

information on the agent spatial location in relation to other agents or to allocate a large set of

geographic information, for instance in a GIS (Macal & North, 2010).

To design an ABM in an appropriate way, a series of questions should be asked (Macal &

North, 2010):

What problem should be solved by the model and what questions should be answered

by the model?

Who are the decision makers? What should the agent’s be? What are the entities that

have behaviour?

What is the agent’s environment and how do they interact within the environment?

What agent’s behaviour is of interest and what decision do they make?

How do agents interact with each other and with their environment?

Where might the data come from, especially for agent behaviour?

How might the model be validated, especially agent’s behaviour?

ABM based spatial planning explores new spatial future solutions and is rather driven by

emotions than by reason. Further, it involved intense negotiation processes which cannot be

repeated (Ligtenberg et al., 2009). These characteristics make it difficult to validate ABM

approached results. Human interaction outcomes like opinions, trust, or learning are

considered as unaccountable and abstract. This makes it hard to measure and therefore

12

difficult to validate the outcomes of these interactions (Parker et al., 2003). Ligtenberg et al.

(2010) explain this difficulty with the number of possible interaction between agents and their

environment and the limitation of models in representing these interactions.

However, ABMs are valuable techniques for spatial planning. Instead of predicting or

explaining future scenarios, it rather tries to explore the complex behaviour of a social-spatial

system (Ligtenberg et al., 2010). ABMs are helpful to improve our understanding about

human communication, negotiation and decision making processes (Ligtenberg et al., 2010)

related to desires, beliefs and preferences . By this the use of ABM is suitable for the purpose

of this research.

2.4 Opinion Formation

Collective intelligence is evolved by tendencies of social influence (integrative) and effects of

individualisation (disintegrative) and is essential in opinion formation (Kaur et al. 2013). The

concept of collective intelligence implies that “a large collection of people are smarter than an

elite few at solving problems” (Surowiecki, 2005). In CASs flow of information is

fundamental to explain opinion formation in networks that are formed by a collective of

individuals (Suo & Chen, 2008). Social interaction and communication are essentials in the

formation of public opinion (Powell, 1951). Individuals have preferences towards particular

phenomena. An opinion is defined as the degree of preference towards the phenomena (Kaur

et al., 2013). Individual behaviour is influenced by the environment where people live in (Suo

& Chen, 2008). A paradigm of a social network is related to the “exchange of norms, values,

ideas, and other social and cultural resources channelled through a network” (White,

Boorman, & Breiger, 1976). Cultural norms, interaction and mass media exert direct or

indirect influence on opinion formation (Kaur et al., 2013). Two fundamental axioms of social

psychology are that people construct their own realities and that all behaviour and experience

is influenced by social relationships (Smith and Guthrie 1921).

Computational models are of growing interest in cognitive science for the simulation of social

processes and group phenomena, which are caused by the interaction of social units, in order

to understand human behaviour (Gilbert & Troitzsch, 2005; Goldstone & Janssen, 2005).

Individual decisions are rather based on opinions that are formed in the context of social

interaction than purely on own values and preferences (Rilling & Sanfey, 2011). However, it

is criticised that the field of opinion formation in socio-physics suffers from lacking

connection to “real-world” societal behaviour, since it rather focusses on models instead on

social phenomena (Sobkowicz, 2009). Related to the orientation of social values in opinion

formation distinctions can be made between pro-social, individualistic and competitive

orientation. Pro-socials enhance collective outcomes (Van Lange, 1999) and are required in

spatial planning processes. Greater morality and fairness are tendencies with which pro-social

cooperation can be described (Beggan, Messick, & Allison, 1988). Katz (1992) states that

opinions lead to actions and argues:

13

“Opinions are really formed through the day-to day exchange of comments and

observations which goes on among people….By the very process of talking to one

another, the vague dispositions which people have are crystallized, step by step, into

specific attitudes, acts or votes.”

For their model about social structures of opinion formation Wu and Huberman (2004)

assume the following:

By two or three opinions the choice of individuals becomes asynchronous.

Arbitrary initial condition leads to time evolution of the set of opinions

The effect on a small number of individual with high social rank (connections) can

have on opinion formation might be larger than the effect by individuals with low rank

Asymmetries in information exchange. Some individuals are often influenced by

other’s opinion while they are unable to change the counterparts views

Group pattern formation, contagion, and cooperation are used by recent ABMs for

manipulating, predicting, and improving collective behaviour. That puts ABM based

approach in contrast to traditional assumptions of cognitive science, which sees the individual

as crucial unit of cognition (Goldstone & Janssen, 2005). According to Jager (2000) the

formation of individual opinions are seen as diverse and complex, and are affected by the

following attributes:

The preference for a particular distribution of outcomes for oneself or others (value

orientation)

Decisions are made to meet various needs, like, subsistence, protection, affection,

understanding, participation, leisure, creation, identity and freedom (needs)

People have physical, financial, and social resources that are governable to meet their

needs (ability)

Extraversion and agreeableness are personality factors affecting the behaviours of

individual decision making (personality)

Free and voluntary participation in discussion of public issues are the concept of an

deliberative democracy (Kim, Wyatt, & Katz, 1999). Habermas (1996) describes deliberative

democracy as an discursive system, which allows people to “share information about public

affairs, talk politics, form opinions, and participate in political processes”. People are

combined by their common citizenship. Individual opinions that are exchanged by interaction

are poor of influence. But they might provide powerful conviction if they are woven together

in the sense of communal will and public purpose (Barber, 2003). Social interaction and

deliberative processes confront humans with new information as well as the opinions of others

and leads to opinion change (Gerber, Bächtiger, Fiket, Steenbergen, & Steiner,

2014).Therefore deliberative processes should not be ignored for opinion forming processes.

Gerber et al. (2014) argue that the “give-and-take” of good arguments should lead to a change

in opinion, even though it stands in contrast to the assumption in cognitive science.

14

Decisions that are not accepted by the people are hard to implement in spatial planning.

Modern societies have become hard to regulate, by the shift to participatory and

communicative planning approach. Therefore, the public is mobilized by legislators to take

over responsibility of decisions (Eder, 2006). Terms like “public space” or “public sphere”

(Eder, 2006) are related to “public opinion” and are based on the concept of “Öffentlichkeit”

by Habermas (1991) in which decision making takes place. The public sphere is a domain of

social life in which public opinion can be formed and which is open to all citizens. Besides

the interaction with other people, access to media is a required medium for the formation of

public opinion (Habermas, 1991). In common surveys on public opinion, the important

deliberative component of opinion formation is missing (J. S. Fishkin, 1991). The public

sphere contains of autonomous individuals that are linked via cultural forms and provide the

basis of discourse (Eder, 2006). This discourse creates the social world individuals live in

(Eder, 2006).

Inconsistency of opinions, ignorance against political substance and incapability of valuable

contribution to public debates is addressed to the majority of citizens (Converse, 1962) and is

identified as issue and reason, why research on opinion formation and opinion change

processes remains problematic. It is assumed that the consistency of opinions differs among

different societal group. Jennings (1992) inferences opinions of political elites were more

consistent due to more political discussion. Alvarez and Brehm (2002) relate opinion

formation and opinion change to issues of framing.

Framing shapes thoughts and opinions of people against certain issues. Framing is related to

the selection and presentation of information (Gerber et al., 2014) and takes place in media

and discourses (Alvarez & Brehm, 2002). This implies that a lack in information hampers

people from opinion formation for political matters (Ackerman & Fishkin, 2002). It is

expected that framing due to deliberative processes limits the opportunity of political

manipulation (Druckman, 2001). Deliberation assumes openness to reflect on own

preferences in the light of better arguments (Barabas, 2004). Equally to all other kinds of

communication, deliberation assumes framing as unavoidable (Price & Neijens, 1998) in

order to form preferences due to political process (Chambers, 2003). The theory on

communicative acting by Habermas (1987) refers to five different action typologies of

communication:

Instrumental acting (IA): Aim is the manipulation of the outer world of objects

Strategic acting (SA): Not oriented on objects, but rather on other actors (e.g. game

theory)

Dramaturgic acting (DA): Aim of self-presentation

Norm-adjusted acting (NA): Based on natural validity of norms

Communicative acting (CA): Not success- or goal oriented; actors are not assumed to

be isolated; actors are willing to precipitate together real communication

According to Habermas (1987) deliberation is related to communicative acting. The purpose

of conversation is according to Habermas (1987) “communicative acting for mutual

understanding”. The concept of deliberative public sphere is a concept of argumentative

15

consideration. It assumes the common deliberation and communication about public affairs

based on the theory of (Habermas, 1991). This assumes ideal consulting and decision making

and is coupled to the following conditions (Schmidt, 2010):

An argumentative pattern for the exchange of information and reasons

Equal chances of access and participation for deliberative processes

The absence of external and internal constrains in deliberative processes (ideal

language situation)

The axiom that deliberative processes can be continued unlimited, or be resumed after

disruptions

The principle that debates can be extended to all materials which have to be regulated

in everyone’s interest

The chance to deliberate about necessities , pre-political attitudes and preferences

Setting the direction of the discourse towards constitution and fundamental rights

Deliberation and decision-making under participation of as much as possible

Recent literature describes an associated intensity of opinions with the intensity of political

participation (Goidel, Freeman, Procopio, & Zewe, 2008). This implies that people that are

more active in political participation and deliberative processes have a better understanding of

current issues and are therefore less polarizing and more goal-oriented towards consensus

building (Goidel et al., 2008). Further, deliberation attracts more participants that value the

role of discussion and which are more ideological moderate (Goidel et al., 2008). If we

assume that increased deliberation leads to less polarization and more consensus building, we

might gain insight what the “voice of the people” would look like under fully informed

conditions (Sturgis, Roberts, & Allum, 2005).

It is assumed that the exposure to divergent viewpoint increases the chance that people

participate in deliberation processes (Landemore & Mercier, 2012). The deliberative

exchange of arguments ceases in the reflection on own and others arguments. Either opinion

are changed due to better arguments of others, or own beliefs are perceived as fully valid and

initial opinions are established (Knight & Johnson, 2011). This implies that opinion are only

changed if the arguments of others are perceived as superior compared with own arguments

(Gerber et al., 2014).

The strength of an argument is related to robust reasoning and /or justification rationality

(Cohen, 1989). Justification rationality creates a basis of logical coherence. As a result,

arguments become accessible to rational critique and it is more likely to persuade others

(Gerber et al., 2014). In deliberation reasoning follows the purpose to convince each other of

the better argument or to honestly falsify the claims of opponents (Landemore & Mercier,

2012). Deliberative persuasion due to robust reasoning are normatively desirable conditions

for opinion change (Gerber et al., 2014). Deliberative persuasion assumes diverse pro-and

contra-positions and the linkage of these positions to well-justified premises (Gerber et al.,

2014). J. Fishkin (2009) argues that proper deliberation requires arguments for different sides

of an issue. This needs to be considered by participants in order to achieve “argumentative

16

balance”. Independence in the evaluation of arguments is an important assumption to ensure

the validity of opinion change in deliberative processes (Myers, 2012).

Social simulation (computational sociology) intends the simulation of simple activities that

show emergence of complex behaviour and is therefore an excellent technique for modelling

and understanding social processes (Salgado & Gilbert, 2013) to which opinion formation

belongs to. It allows to analyse relationships between phenomena on individual and social

level (Hedström, 2005). Through identifying a social mechanism certain social phenomena

like opinion forming can be explained (Hedström & Ylikoski, 2010). Individual interactions

form social patterns whose emergence can be investigated by social simulation (Salgado &

Gilbert, 2013).This enables in understanding how components are influenced by emerging

orders that arise from multitude of individual components (Salgado & Gilbert, 2013).Yet,

social communication is not an important issue of social simulation, which confines the

emergence and evolution of symbolic communication to computational linguistics and pre-

written code (Salgado & Gilbert, 2013). However, it is described that communication

constitutes the social realm where opinion formation takes place (Salgado & Gilbert, 2013).

Emergence assumes the coexistence of several self-organized levels in nature and society that

cannot be explained from its constituent units and where “ the whole is more than the sum of

its parts” (Salgado & Gilbert, 2013).

The social realm for opinion formation is shaped by both individual as well as collective

entities. While individual entities refer to actors, individual actions, desires, beliefs etc.,

collective entities address institutions, norms, and structures (Salgado & Gilbert, 2013).

According to Durkheim (2014) not the nature of individuals is important. Rather it is

recommended to consider the nature of society. Communication was identified as an

important issue for opinion formation. The process of emerging opinions is sensitive to

communication language and shows different results when the communication language is

changed (Sawyer, 2005).

For opinion formation it is assumed that thoughts or any psychic state are transformed into

communication language and that someone understands (ego) what is uttered by someone else

(alter) (Salgado & Gilbert, 2013) in order to create a reference situation for alters

communicative responsibility. Other individuals either understand or misunderstand this

communicative language. Individuality of human consciousness, extension of communication

beyond direct participants, and the improbability of success are three identified obstacles of

communication that have to be overcome (Luhmann, 1995).

2.5 Opinion dynamics

The study of opinion dynamics (OD) has become a popular field in socio-physics whose

interest is in the capability to predict human behaviour (Galam, 2012). In today’s democratic

societies it is essential that crucial institutional decisions consider corresponding public

opinion, in order to ensure the ability to implement certain regulations and projects (Galam,

2012). Notwithstanding, individuals often refuse proposals for certain projects or reforms,

even though they are interested in change (Galam, 2012). OD are pursued to deeper

17

confluence “hard” (e.g. physics, computer science, and mathematics) and “soft” (e.g. social

psychology, communication studies, and sociology) disciplines (Xia, Wang, & Xuan, 2011).

Similar to the first law of geography of Tobler (1970), even people are more likely to be

influenced by someone close by than by someone far away. Individual attitudes and beliefs

are randomly distributed in the begin of opinion spreading processes (Xia et al., 2011).

Models for opinion dynamics describe the opinion change due to time and influence of other

actors (Ligtenberg & Bregt, 2014). They are governed by four basic approaches namely,

social structure, opinion space, social influence, and updating rule (Kaur et al., 2013).

According to the theory of Latane (1981) social forces like strength, immediacy, and number

of sources are operating in a social structure and result in social impact. Social structure refers

to the interaction between and among individuals, as well as the frequency and way of

interaction (Kaur et al., 2013). Models of OD can be divided in two groups of opinion space:

Models of discrete and models of continuous OD. Social influence refers to the consensus in

which individual act to others expectations and beliefs (Kaur et al., 2013). The updating rule

governs the dynamics of opinion change of individuals due to interaction with others (Kaur et

al., 2013).

In discrete models opinions are defined in binary terms like “yes”, “no” or “accept”, “reject

decisions. The advantage of discrete opinions is that they can precisely find out whether two

opinions agree or not without computational based precision for numerical cut-offs , This

allows the simulation of a much larger number of opportunists (Fortunato, 2005). Ising

models and Sznajd models are examples for models of discrete OD (Stauffer, 2002).

Continuous opinion space is considered for models of continuous dynamics. For continuous

models applies that opinions on the best option are not necessarily binary, even though

someone faces a binary decision. To solve a specific problem, agents can choose among

certain alternatives. The assumption that one alternative is better than another is expressed

with the probability p. Under consideration of the consequences for a certain decision, the

alternative with highest p or 1-p is chosen (Martins, 2008). These models follow the concept

of “bounded confidence “ (BC) (Ligtenberg & Bregt, 2014). Deffuant-Weisburg (DW) and

Hegselmann-Krause (HK) are examples for models of continuous OD (Deffuant et al., 2000;

Hegselmann & Krause, 2005) where DW model exchanges information between two agents

and HK model assumes “global knowledge of each agent about the opinion of the other

agents” (Ligtenberg & Bregt, 2014). Further, Stauffer (2005) distinguishes between three

different types of OD models: Missionaries (e.g. Sznajd), Opportunists (e.g. HK), and

Negotiators (e.g. DW)

The Sznajd model is a model of discrete OD. The basic concept implies that all other

neighbours are influenced by two randomly drawn neighbours that agree to each other

(Martins, 2008). It is related to the concept of Ising spin models with the difference that

information flow outward. Opinions are represented by binary and discrete values (Xia et al.,

2011). Decisions are made in a closed community (Sánchez, 2004). It is ascribed to the model

type of missionaries which means that all neighbours are convinced within the confidence

bound of the mission. According to the idea of the model it is easier to persuade ones opinion

18

by two or more people (Xia et al., 2011). Following local rules of an Ising spins chain are

considered for the model of Sznajd-Weron and Sznajd (2000):

If SiSi+1 = 1, then Si-1 and Si+2 take the direction of the pair (I, i+1)

(R1)

if SiSi+1 = -1, then Si-1 takes the direction of Si+1 take and Si+2 the direction of Si

(R2)

This rule implies that interaction takes place when one’s agent opinion is influenced by an

agreeing neighbour-pair (R1) and other variations. The environmental structure of the Sznajd

model contains of regular lattice in of either one- or two dimension or networks (Xia et al.,

2011).However, for the purpose of spatial planning discrete opinion dynamic models, like the

Sznajd model, are perceived as inappropriate since it only allows opinion representation in

binary and discrete values (Xia et al., 2011) which always leads to consensus (Stauffer, 2005).

Due to the influence of complex processes and multiple actors (Ligtenberg, 2006) the

representation of OD for spatial planning purposes should not be restricted to a model that

only allows simple “yes” or “no” decisions and that owns no memory of their past opinions

(Martins, 2008).

Continuous models are ascribed to models of BC. BC implies a fundamental communication

rule, that “an agent’s opinion would not be influenced by another agent if the difference of the

two agent’s opinions is larger than a given threshold or ‘bound’ of confidence” (Xia et al.,

2011). This means that:

Agent X only interact with agents whose opinions is in the interval [X-Ɛ, X+Ɛ],

where Ɛ is the given confidence bound,

and that opinions at time t+1 are only updated by agents if the opinion difference at t1

is within a certain distance of each other(Ligtenberg & Bregt, 2014).

In BC models the representation of opinions is based on continuous values, real vectors. In

few researches even discrete values were adopted (Xia et al., 2011). Local rules of interaction

are based on the assumption that opinion exchange takes place in BC. The averaging of

opinions is either done in pairs (DW) or in groups (HK). Other variations are related to for

instance heterogeneous bounds of confidence. The environmental structure of BC models is

based on regular lattice and networks (Xia et al., 2011)

Negotiators in the DW model select one discussion partner in their neighbourhood per time

step. Negotiators in the DW model attempt to compromise on a square lattice which leads to

unsymmetrical opinion shifts (Deffuant et al., 2000). If the opinion difference of two

interacting agents is within a given threshold the opinions (O) of both sides are getting closer

to the average of their original opinions difference (Xia et al., 2011). This means for agent i

and j in rule form that xi(t), xj(t) is O at time t.

if |xi(t)-xj(t)| < Ɛ, then at time t+1:

Oi is: xi(t+1) = xi(t)+µ [xj(t)-xi(t)]

Oj is: xj(t+1) = xj(t)+µ [xj(t)-xj(t)]

19

where µ refers to a convergence parameter between 0.0 and 0.5 Due to a defined tolerance

threshold (D) in the DW model, interaction between agents with differences in opinion larger

than the threshold is impeded (Weisbuch, 2004). For large thresholds (D>0.5) it can be

assumed that all agents share the same opinion and complete consensus is reached and if this

threshold exceeds 0.5 the obtained consensus is independent from underlying social topology

(Fortunato, Latora, & Marchiori, 2004). In the model the D determines number and size of

clusters while µ affects the convergence speed of the system. If the threshold is smaller than

0.5 (D <0.5), different opinion clusters may live through during the evolution (Xia et al.,

2011).

The principle of the HK model is similar to the one of DW. The difference is the opinion

update of an agent-based on the average opinion of all neighbouring agents satisfying the

condition of BC (Xia et al., 2011). HK refers to a model that is non-linear and where opinions

defined model states change the model structure (Hegselmann & Krause, 2002). This implies

that agents are influenced in their forming opinions due to the consideration of other agent’s

opinions. Opportunists of HK ask per time step for the opinion of all neighbours. They take

all of them into account and calculate the arithmetic average weights of all opinions. The total

sum of all weights is 1 (Xia et al., 2011). Fewer number of final opinion clusters occur if the

bound of confidence Ɛ increases. Two main approaches were mentioned in HK to explore the

BC model with simulations, a systematic way and the KISS-principle (Keep it simple, stupid!)

(Hegselmann & Krause, 2002).

To bring the assumption into equations and rule form, we follow the description in

Hegselmann and Krause (2002). T is described as a discrete number of time (T=1,2,3…). For

a fixed agent i where 1≤i ≤n, the agents opinion at time t is denoted by xi(t) as a fixed number

and the opinion profile at time t is represented by a vector x(t)=(xi(t),…,(xn(t)) in n-

dimensional space. The weight for any other agent (e.g. j) is denoted by aij., where

ai1+a12+…+ain = 1, and aij ≥ 0 for all I,j. The averaging of opinion formation by agent i is

described by:

Xi (t+1) = ai1 x1 (t)+ ai2 x2 (t) +…+ ain xn (t)

Since weights may change with time or opinion aij = aij (t, x (t)) can be a function of t and/or

of the whole profile x(t). By obtaining a stochastic matrix with n rows and n columns we

weights can be collected within a matrix. The notation for this matrix refers to the general

form of the model (GM) and is written as:

X (t+1) = A (t, x(t)) x(t) for tƐT (GM)

The main problem that is identified in HK is the inability to answer the question on agent-

based approached consensus by computational simulation and mathematical analysis.

Therefore, the GM is treated by various specialisations. In the classical model (CM) of fixed

weights, where A is a fixes stochastic and x(t) is the column vector of opinions at time t:

20

x (t+1) = Ax (t) for Tɛt (CM)

Another variation of this model assumes the opinion formation under social influence where

agent i adheres to his initial opinion to a certain degree gi and that the agent is socially

influenced by susceptibility of 1-gi. When these assumptions are considered the CM becomes

either:

Xi (t+1) = gi xi (0) + (1-gi)(ai1x1(t)+…+ain xn (t))

,or written as matrix,

x (t+1) = Gx (0) + (I-G) Ax (t) for tƐT

Diffusion of opinions are considered as an important factor in the process of multi-actor

spatial planning processes but a behavioural approach is missing in most spatial ABMs

(Ligtenberg & Bregt, 2014). Heterogeneous stakeholders and actors with diverse and

changing goals and motivation make spatial planning a non-linear and dynamic process (van

Voorn, Ligtenberg, & ten Broeke, 2014). Four factors of spatial planning are mentioned by

that might influence OD significantly:

Reputation: “Higher” ranking agents are more dominant in interactions which leads to

biased opinion exchange

Peer pressure: Solitary agents changes opinion more often that a group with the same

opinion

Empathy: How an agents listens and is willing to accept other opinions

Isolation and spatial effects: Agents are mostly settled in a certain location with a

limited action radius. Therefore interaction takes more likely place with neighbouring

agents than with agents more far away.

Ligtenberg and Bregt (2014) recently presented a novel approach for the simulation of spatial

OD, based on the DW approach. The described approach deals with heterogeneity in spatial

opinions as well as the willingness of agents to adapt their opinions. The described approach

considers opinion forming as diffusion process what comprises learning, contagion, mimicry,

trust, reputation, etc. (Strang & Soule, 1998). Diffusion models that were modelled in a

traditional aggregated manner are unable in dealing with heterogeneity in population and

spatial environment, and therefore cannot explain social processes and social change

(Kiesling, Günther, Stummer, & Wakolbinger, 2012). In opposite ABMs system is built by

individual entities which allow the simulation of change under consideration of different

actors and a variety of spatial and temporal scales (Parker et al., 2003). Existing models of

21

OD, like WD and HK show limitations when it comes to answer economic or emotional

meaning of space in order to explain opinion diffusion of individual actors (Ligtenberg &

Bregt, 2014).

The proposed model for spatial OD by Ligtenberg and Bregt (2014) is characterized by three

aspects namely, the initial opinion of an agent about a location, the social influence, and the

spatial influence. Within the model it is focussed on OD under consideration that at individual

level decisions are made which results in initial opinion of each agent. These initial opinions

are based on the evaluation of spatial and non-spatial aspects which are considered as

important by agents in order to realize desires which are based on existing knowledge of the

agents. A number of rules are formulated to translate individual desires of agents. The

meaning of social influence often leads to clustering of individual actors in spatial planning

systems, since they are connected in the same location by similar interests (Ligtenberg &

Bregt, 2014).

2.6 Conclusion

In modern spatial planning short term solutions are no longer an adequate approach to deal

with collective interests. A growth in the amount of policies and actors with diverse interests

as well as a shift of participation towards an earlier stage in the process leads to increased

complexity of spatial planning processes and requires models of inclusive strategies. The

complexity occurs since desires and expectation on space, as limited resource, are various. In

order to simulate the interaction of heterogeneous and autonomous agents in a common

environment, the use of ABM seems to be a feasible method. However, validation of the

outcomes from human interactions is difficult since they are described as abstract and

unaccountable.

Most spatial ABMs are missing a behavioural approach of opinion diffusion as important

factor of multi-actor spatial planning. Spatial planning is a process of a CAS that changes

both physical environment and social reality of actors. Simultaneous and mutual interaction

between different components is embedded in spatial planning processes. The perception of

the spatial environment as well as intentions and strategies differ among participating actors

in planning processes. To encourage people to participate in spatial decision making, trust is

an essential element. Trust is considered as social construct that characterizes human relations

and is built by deliberative processes.

In current multi-actor spatial planning the role of opinions is narrowly included. This is less

referable to lacking awareness about the meaning of opinions for planning processes. Rather it

is related to limitations as it comes to explain opinion diffusion in a spatial context and to

model these complex systems in a computational simulation. Essential in opinion formation is

to evolve collective intelligence by integrative and disintegrative processes.

Influence is exerted by cultural norms, interaction and mass media which leads to individual

decision making based on opinions that are formed in the context of social interaction.

Individual opinions need to be woven together to become persuasive for certain approach in

22

multi-actor spatial planning. However, in current socio-physics, opinion formation suffers

from lacking connection to “real-world” societal behaviour. The problem is that current

approach in socio-physics rather focusses on models of OD instead of social phenomena.

Desirably, it should be considered that opinion forming due to deliberative processes is

confronted with different participative intensity of different actors. This leads to diverse

consensus building as well as variations in opinion formation among actors involved in the

planning process. The complexity of including opinion formation into models of OD becomes

obvious. Goals and motivations of individual actors are changing due to social-and spatial

influence as well as the initial opinions about certain locations.

Actual models are governed by the approaches of social structure, opinion space, social

influence, and updating rule and are either of discrete or continuous opinion space. Modelling

of discrete opinions does not fulfil the purpose of multi-actor spatial planning, due to discrete

and binary opinion representation. Continuous models of BC, however, are dealing with the

averaging of opinions, either in pairs (DW), or in groups (HK), and are more suitable to

represent the complexity of opinions in multi-actor spatial planning compared with models of

discrete OD. However, as it comes to model opinions for multi-actor spatial planning, current

models of OD are lacking in the inclusion of a spatial component.

23

3 Conceptual approach

3.1 A suitable model

It was assumed that opinions during the planning process were exchanged among different

actors. These opinions were formed by actor’s individual desires, beliefs, values, and

preferences on both, social and spatial aspects. Figure 2 below gives an overview on how

actors interact with their social and spatial environment. For the update of own opinions the

influence of interaction with other actors was taken into account.

Fig.2: Intentional model of actor based decision making

A suitable model to simulate OD for spatial planning purposes was linked to certain

requirements. Since discrete models were described as inappropriate for spatial planning

purposes, a suitable model was searched for among continuous OD models. Three different

model approaches (HK, DW, and Ligtenberg-Bregt (LB)) were compared in order to find the

best suitable approach for the simulation of OD in spatial planning processes. The LB

approach stands for an extended version of the DW model. All models followed the concept

of BC as a precondition for the communication among agents. This seemed reasonable for the

simulation of actor communication in a real world scenario of spatial planning. In addition,

the model had to make use of both, social and spatial components, since both exert influence

of the opinion of actors. Table 1 below gives an overview about social structure, social

influence as well as the identified strength and weaknesses of each of the three models.

The approach by Ligtenberg and Bregt was assessed as most suitable for the purpose of this

study. Firstly, because the social structure of LB assumes information exchange between pairs

of individual agents per time step. This seemed a realistic assumption for the majority of

opinion exchanges in planning processes since most opinion exchange takes place in daily

interaction among neighbouring actors and not in group discussion.

24

Tab. 1: Comparison of continuous OD model approaches

Hegselmann-Krause (HK) Deffuant-Weisbuch (DW) Ligtenberg-Bregt (LB)

Social

structure

Agent asks for the

opinion of all

neighbours per time step

Information exchange

between two agents per

time step

Information

exchange between

two agents per time

step

Social

influence

The calculated average

of weights of all

opinions determine the

degree of influence

Both sides are getting

closer to the average of

their original opinion

difference

Both sides are getting

closer to the average

of their original

opinion difference

Strength

Polarisation and

consensus can be

reached by both,

asymmetric and

symmetric confidence

Straight approach in

agent interaction

The influence on

opinion change by

individual agents can

be defined

The influence on

opinion change by

individual agents can

be defined

Takes spatial

influence of an

opinion into account

Generates plausible

spatial patterns of

opinions

Straight approach in

agent interaction

Weaknesses

In spatial planning

opinion exchange barely

takes place between all

agents at the same time

Does not take the spatial

influence of an opinion

into account

Global knowledge of

each agent about the

opinion of other agents

is not realistic in spatial

planning

Does not take the

spatial influence of an

opinion into account

Interacts randomly

with other agents and

focusses rather on

threshold value instead

of social connection

Only allows

representation of OD

at aggregate level

Group dynamic

processes as

cooperation and

negotiation cannot be

easily included in the

model

Secondly, paired interaction between agents allows the definition of individual influence per

agent. Thirdly, the consideration of spatial influence and economic or emotional meaning of

space for opinion diffusion in, gives each agent an initial opinion about space. This seemed

realistic, since spatial planning processes deal with issues of spatial decisions making and

actors are influenced in their initial opinion not only by their social environment but also from

25

their spatial environment. However, it had to be considered that LB only takes one issue of

spatial planning into account.

3.2 Conceptual Model

Fig. 3 gives an overview on the conceptual approach. All variables from a field of interest

were collected within a real world scenario. The variables were assessed regarding the utility

for further implementation within the simulation model.

Fig. 3: Conceptual approach

Opinions

Opinions in the real world are diverse and differ among actors to a large extent. For this study

the model approach of Ligtenberg and Bregt was chosen as a starting point which is based on

the OD model of DW. This model was chosen for three reasons. Firstly, it assumes

continuous exchange and influence of opinion between actors where differences in opinions

are within a certain threshold (BC). Secondly, interaction among individual agents takes place

among two agents per time step, instead between all actors. Thirdly, the model includes the

economic and emotional meaning of space to explain the spreading of actor’s opinions.

Compared with other OD models, this one was assumed as the most suitable for the purpose

to model multi-actor spatial planning processes.

Spatial planning

In the real world spatial planning is diverse and addresses several fields of interests.

Therefore, a relevant case in spatial planning was chosen that refers to a local allocation

26

problem. Finding a siting location for wind turbines that is least conflicting with the average

opinion of all residents within an area was the case that was chosen for the model. Site

allocation of wind turbines on local scale is a planning approach that enabled us to limit the

scope of the research as well as the amount of actors. This was relevant since the intentions,

strategies, and spatial perceptions are different among actors and planning decision are of

high collective interests.

Demographics

The initial opinions of actors were also influenced by non-spatial characteristics. In the real

world, demographics like age, gender, education level and residence assign actors to certain

social roles. As previously described the intensity of opinions is related to participation in

deliberative processes. Actors have different interests and standards for participation in

planning processes. Actors that make more use of media and which are capable to argue

decently, to reflect on own and others arguments, and thus confirm or shift their own

opinions, participate more often in deliberative processes. In this study it was assumed that

these skills were related to different level of education. Education level was used as a social

parameter that was represented by two values, resilience factor against opinion adaptation

plus a influence factor. That enabled us to build a hierarchy on opinions. Certain groups in the

model had weak, moderate or strong initial opinions about their spatial environment.

Spatial aspects

In the real world, actors are influenced in their opinions by several spatial aspects like for

instance location, topography, spatial distance, and size. The evaluations of these aspects were

equally to social aspects important as agents form initial opinions about a certain locations.

For this study, the spatial distance of individual actors to both, the possible site of wind

turbines as well as to other actors was essential. Actors had spatial opinions that were formed

by individual desires, beliefs, and preferences about their spatial environment. In- and

decreasing spatial distance between planned facilities to the actor location was one important

aspect for opinion change (e.g. NIMBY syndrome). In addition, spatial distance was an

important aspect since communication and influence among actors’ decreases by increasing

spatial distance between them.

Social connections

In the real world actors are connected in social community via social networks. Social

interaction takes place between two, more than two, or groups of actors and is essential for

opinion formation and opinion change in the social space. It was assumed that agents have

different threshold for cooperation and interaction with other agents. This threshold changes

over time and by location. In this study, social interaction took temporary place between pairs

of individual actors per time step in the model. This was assumed to be most realistic since

most planning processes do not ask all actors at once at the same moment in time. Rather,

27

negotiation, communication, and opinion exchange among actors takes place in many

consecutive steps.

Modelling tool

To model the OD in spatial decision making an ABM based model was implemented in

NetLogo software. An ABM approach was chosen as it was described as excellent technique

for modelling and understanding social processes like opinion formation over time due to

model iterations. NetLogo was chosen since it is described as suitable software to model

complex system evolving over time.

3.3 Description of the Model

The ABM model is described according to the principles of the ODD-protocol. This approach

was chosen since it was designed by academics to ensure the readability and completeness of

the model descriptions. The ODD-protocol follows a clear and simple structure and contains

of three different blocks that are subdivided into seven different elements (Fig.4).

Fig.4: ODD approach according to Grimm et al. (2006)

Purpose

The purpose of the model was to gain better understanding on how communication and the

exchange of opinions among actors leads to opinion diffusion, in particular for consensus

building on a spatial planning allocation problem. Therefore, it was investigated how model

outcomes were influenced under different conditions of spatial distance, social distance and

participation.

State variables and scales (Tab. 2)

The fundamental low-level entities of the model were individual actors. Actors were selected

by a four digit postcode for a certain location and by age. Further, actors were assigned to

groups based on their level of education. The selection was done via provided data by the

Dutch office for statistics (www.cbs.nl). Each agent has the following characteristics:

28

Tab. 2: Entities, variables, and scales of the simulation model

Entities/ variables/ scales Characteristics

Individual actors

defined by age, level of education, and

location.

Education level

low (vmbo, max. 3 years havo/ vwo)

moderate (havo and vwo)

high (wo and hbo)

Location

four digit postcode (state 2011)

Spatial distance

spatial distance between actor-actor and

actor-siting location

Social distance

communication only place

if social distance < threshold (D)

Actors with low education level

low resilience against opinion adaptation

highly influenced by others opinion

little influenced by spatial distance

Actors with moderate education

mod. resilience against opinion adaptation

mod. influenced by others opinion

mod. influenced by spatial distance

Actors with high education level

high resilience against opinion adaptation

little influenced by others opinion

highly influenced by spatial distance

Land-use

buildings

agriculture

forest

water

Time step

one tick in the model equals one interaction

Time horizon

200 ticks

Cell size

25x25 meter

Model extent

world consists of 107 rows and 163 columns

(2.675 x 4075 km)

29

Process overview and scheduling

The dynamics of opinions (Ok) in the model followed three main characterizations, namely, an

agent’s initial opinion about a location (Oi), the social influence (Qs), and the spatial influence

(Qg). According to these characteristics opinion dynamics in a social-spatial system were

expressed with:

Ok,(t+1) = f(Oi(k,g,t), Qs(k,g,t), Qg(k,g,t))

Fig. 5 below gives a general overview of the model approach. A summary of variables and

equations can be found in appendix. The model proceeds in ticks. Each tick represents one

time step and thereby the interaction of groups of two randomly paired agents. In total the

model runs for a total of 200 interactions. Within each time step the model processes eight to

nine different phases in seven to eight time steps. The initial opinion of an agent about a

location is calculated in the setup function and represents the starting situation of the model.

From here, social influence and spatial influence on own opinions are calculated

simultaneously during the following step.

Based on the factor of social and spatial influence the social distance threshold D is updated

in the next step of the model. First, a weighting factor for the adaptation of d is calculated.

Afterwards the weighting factor is used to update d. Next agents are randomly linked to

another agent and calculate their social distance for all individual location. However, that

more close agents are socially related that higher the frequency of linked connections. Social

distance is defined as the difference of opinion about a certain location. Agents update their

opinions if the social distance on a certain location is below the updated d.

Otherwise, the opinion update is omitted and the social distance for the next location is

compared until the opinions for all cells have been compared. Updated opinion are stored and

used as input for the next iteration step and the opinion exchange with a new linked agent. .

Fig 5: Iteration procedure per agent adapted from the approach by Ligtenberg and Bregt (2014)

30

Design concepts

Emergence: Opinion dynamics emerged due to the interaction of agents and the mutual social

and spatial influence. A coexistence of different self-organized agents was assumed whose

spatial decision making cannot be explained from their constituent opinions and where the

whole of opinions has a bigger influence than the sum of individual opinions.

Adaptation: Agents adapted their opinions by interaction with other agents. Default values for

spatial and social influence were assigned by a set of equation. According to the opinion

properties, agents influenced other agents or got influenced by them to a certain amount. Due

to different opinion properties, the adaptation of opinions among different agents took

different number on interactions.

Fitness and Prediction: The model did not explicitly calculate the fitness of an opinion.

Rather the model result highlights locations that assume both, areas with least expected

conflicts and areas that are absolutely not negotiable. The results are based on the average

opinion of all agents. Further it highlighted locations where the differences in opinions where

low or high.

Sensing: All individuals were assumed to know their location and level of education. By

knowing this they applied a specific rate of resilience against the adaptation of others

opinions. Further they were differently influenced by social and spatial characteristics.

Interaction: One type of interaction was modelled implicitly per model iteration, namely,

interaction between pairs of two randomly chosen agents. Opinion update between them took

place if social distance for a certain cell was below the updated d

Stochastic: All participation, opinion and influence parameter and values were interpreted as

continuous probabilities between [0…1], where 0 was the lowest and 1 was the highest

probability. These probabilities were drawn to include noise for opinions, and influence.

Noise could for instance be occurring due older farmers with a low education level, which

were assumed to have strong spatial opinions and a high participation rate.

Collectives: Individuals were grouped into collectives of three different social groups. The

classification was based on their education level. Individuals were grouped to either low

educated, moderate educated, or high educated.

Observation: For model testing the distribution and influence of agents opinions were

observed step by step. Starting with a small scale model with only two interacting agents, the

amount of influencing parameters, agents and the scale was increased stepwise. This should

help in understanding the influences of different model components and to approach in the

most accurate way. For analysis individual group sizes of different actors and their

distribution were recorded in order to analyse differences in the spatial pattern of different

model outcomes.

31

Environment: Agents were located in a grid based representation of the environment that was

formalised in an ordered collection of cells. The environment was represented with

C = (ci, j, ci+1, j, ci+1,j+1,…,ci+n, j+n)

, where the location in the cell was represented by i,j. Each cell contains information and

represents a discrete part of the area. The four land types were buildings, agriculture, forest,

and water. The number of cells that were occupied by a certain land type was pre-defined in

the input dataset of the model.

Opinions: Three groups of agents were represented in the model. In contrast to other models,

the initial opinion on a location was not based on agent’s desires but rather on distinct opinion

based on distance and land-use. Therefore, an initial opinion value between [0…1] was

determined for every cell that was occupied by a certain land characteristics. This resulted in

12 different initial spatial opinions per group. A value of 1 represents a location that was

absolutely not negotiable for the siting of wind mills. A value of 0 describes full acceptance of

the location for the siting of windmills. A description of values and parameters was described

in table 3 in section 3.5. Even though costs and environmental disturbances are important

factors in deliberative processes, they were not considered for complexity reasons of the

model.

Participation: Participation was divided in three different values based on the education level.

Actors with low education only participated with actors in near distance, actors with moderate

education participated with actors up to middle distance, and actors with high education

participated with actors up to far distance.

32

Interaction:

Fig 6: Representation of actor communication. With increasing spatial distance interaction decreases

between locations. With increasing social distance interaction decreases between agents.

Fig. 6 above describes interaction among actors based on social and spatial distance. Those

closer different actors are socially and spatially related that more intense interaction takes

place. The classification is done related to three different distance zones near, middle, and far

plus social distance based on an education level. This results to different levels of interaction

instead of continuous decay. Agents that share a location that is close to each other talk more

often to each other. The same counts for people that have a similar education. The interaction

with other agents decreases with increasing distance to their own location If distances become

bigger than a maximum accepted distance, interaction takes no longer place.

Initialization

The initial area of the final model environment contained of a world consisting of 107 rows

and 163 columns. Each area was initially occupied by one different group of agents. For the

reason of computing time one agent is in place for 10 agents with same characteristics and

opinions. Each simulation run started on the first tick and took 200 iterations in total.

33

Input

The input on spatial opinions for different land characteristics were diverse, since the three

groups of agents provided different conditions for deliberation and opinion forming. As a

consequence, consensus building in areas that were occupied by strong spatial opinion (e.g.

buildings) became more difficult than in areas with weak spatial opinion (e.g. agriculture).

Further, consensus building was dependent on the distribution of the actors within the model

area.

Sub-models based on LB approach

Opinion per location: An initial opinion on a certain location was one of the characteristics of

the OD model. Agents of a certain group k had an initial opinion O for a certain land

characteristic g. The opinion was expressed by a value between [0, 1], where 0 is lowest and 1

is strongest opinion. The individual opinion on this location at time t was expressed by:

Oi(k,g,t)

Social influence: The calculation of social and spatial influence is a process step that took

place simultaneously. Social influence in the model described the degree of opinion change or

reassurance of agent’s opinions due to the comparison and interaction with opinions of others.

Here, the intensity of influence depends on how agents are located and connected with each

other in the model environment. The main concept in this research is that the connection

between different agents is related to social and spatial distance (Fig.6). Both, the exchange of

opinions as well as the social influence between two agents decreases when they are socially

further away. In addition, spatial distances also exert influence on the communication

between actors. Those further agents are spatially away that fewer connections are made

between their locations. This results in active communication in the close neighbourhood and

rare opinion exchange with agents that are living further away. The intensity of spatial

influence on agent’s opinions decreases with increasing spatial distance between to agents.

Social influence was calculated with:

Qsoc(k,g,i,j,t) = ∑ 𝑊 (𝑙,𝑔,𝑖,𝑗,𝑡) 𝑁

𝑡=1

𝑁

, where social influence is the ratio between N w(l, g, I, j, t) as agents below a certain threshold to

enter the negotiation and N for the total number of agents having an opinion on a certain

location.

Spatial influence: Spatial influence was calculated with

Qspak,g,i,j(t+1) = ∑ ∑ 𝑂𝑘,𝑥,𝑡 𝐾

(𝑘=1) 𝑋(𝑥=1)

𝑁

34

, where the opinion of agent k for a certain land characteristic g at location i, j was influenced

by the spatial influence factor Qspak,g,i,j(t+1). This one was calculated by the sum of stored

opinions in a set of cells for X and a set of cells for K, divided by N (= |X| *|K|).

Update social distance threshold: A weighting factor was calculated that was based on social

and spatial influence in order to adapt the social distance threshold D. The weighting factor is

first calculated with

Qk,g,i,j,t = π*Qsoc(k,g,i,j,t) + (1-π) * Qspak,g,i,j,t

The priority of social influence versus spatial influence is indicated by the parameter π. After

that d was adapted by

d’k,g,i,j (t+1) = d(k,g,i,j t) * (1-(|Ok,g,i,j,t –Qk,g,i,j,t|)τ)

, with d’k,g,i,j (t+1) as adapted d for agent k at time t+1 and τ as resilience against opinion

adaptation.

Pick random agent: After the calculation of initial opinions, social and spatial influences as

well as the update of the social distance thresholds, agents picked randomly another agent and

calculated the social distance between them.

Social distance: Defined as opinion differences on a certain location i, j between two agents k,

l and was expressed by

S(k,l,i,j,t) = o(k,i,j,t) – o(l,i,j,t)

Check social distance threshold against calculated social distance: Precondition for

interaction among agents was

S(k,l)(i,j)<dki,j

The precondition assumed that the social distance between two agents was below the social

distance threshold before they could enter negotiation. If the social distance was larger than

the given threshold, agents skipped the following step and precede directly to the last process

step and picked a next location.

35

Apply DW for location: If the precondition was fulfilled agents opinion were updated by an

DW approach that was adapted to a spatial context

O(t+1)(a,i,j) =O(t)(a,i,j)+ µa (O(t)(a, i, j)- O(t)(b,i,j))

and

O(t+1)(b,i,j) =O(t)(b,i,j)+ µb (O(t)(b, i, j)- O(t)(a,i,j))

. Agents meet each other randomly for communication, which is the base for the

communication protocol. Parameter µ was considered as agent specific but independent from

location.

36

4 Results

4.1 Case study

After formulas and concepts have been implemented to a functioning code, the model was

tested on a specific spatial planning siting problem. For a distinct area, the model should help

to identify locations that were most suitable for the siting of a wind turbine. Most suitable

were locations that were least conflicting by their average opinion and where the difference in

opinion was lowest. The northern area of the Dutch city Wijchen was chosen as location.

First, spatial circumstances were reclassified to a dataset with the four main land-use type’s

buildings, agriculture, forest, and water.

Fig. 7: Case study area in the Northern part of Wijchen/ the Netherlands

Fig. 8: The case-study area as displayed in Google Earth

37

Afterwards, statistics about residents and their education level were acquired via the Dutch

statistical agency (www.cbs.nl). In total the model was tested for three different scenarios (a,

b, and c). Each scenario was tested under three different social distance thresholds (D), which

result in a total of nine different test conditions. For each test condition highs and lows of

averaged opinions as well as opinion differences were investigated. In addition, it was tested

how individual opinions of actors were distributed after certain amount of interactions. Figure

8 above shows the case study area from aerial observation. Table 3 below gives an overview

about the different input variables of the models.

38

Tab. 3: Aspects, variables, and default values for the scenario case study on Dutch postcode 6604

(Wijchen). a,b, and c describe different default values per scenario

Overview of processes, parameters and default values

Aspect Variable Default value

Social distance threshold

non-cooperation

cooperation

strong-cooperation

0.2

0.5

0.8

Groups of 10 agents based on education level

low

moderate

high

(a,b) 30, (c) 45

(a,b) 40, (c) 30

(a,b) 37, (c) 20

Spatial distance (25x25 m res.)

near

middle

far

(a,c) < 20 (b) < 10

(a,c) 20 – 39 (b) 10 -19

(a,c) ≥ 40 (b) ≥ 20

Strength of initial opinion

opinion

low-opinion

mod-opinion

high-opinion

[0…1]

0.6 * opinion

0.8 * opinion

opinion

Opinion influenced [0...1]:

Low

Moderate

High

socially influenced

spatially influenced

resilience

socially influenced

spatially influenced

resilience

socially influenced

spatially influenced

resilience

0.7

0.3

0.5

0.5

0.5

1.5

0.3

0.7

2.5

39

4.2 Scenarios

As mentioned earlier the model has been tested on three different scenarios, namely:

Scenario a: Different participation and complete distances

Scenario b: Different participation and half distances

Scenario c: Equal participation and complete distances

Scenario a and c consider a planning situation in which all the participation of agents is

related to their education level. It is assumed that people with higher education tend to

participate more often in planning processes compared with people with moderate or low

education. In scenario c no distinction is made between participation and education level. All

agents participate to the same extent in the negotiation process.

Distances in the scenario categorize different zones in which agents are influenced in their

opinions. Complete distances in scenario a and c assume a planning situation in which agents

are influenced to a certain degree based on predefined distance zones (Table 3.). In order to

investigate the influence distance exerts on agent’s opinions scenario b halves the spatial

distances between the different distance zones.

40

Results scenario a

Fig. 9: Scenario a - Different participation and complete distances with average

opinion (1a, 3a, 5a) and deviations of the average opinions (2a, 4a, 6a)

The model outcomes showed visible differences. By an increased social distance threshold

(D) the amount locations with eihter a very high opinon against (red) or a very low opinion

against (green) occurred less often in all three scenarios. Tabel 4 gives an overview on the

average opinion related to different thresholds of D. An increased D led on green and red

locations to an higher value for averaged opinions. Since they act within their accepted social

distance, the increased D also led to higher equalization of opinions by an increased actor

communication. This even becomes obvious by looking at the values for opinion difference

(Tab. 5). With a low D the value for opinion difference is relatively low. With an increasing D

the value for opinion difference also increases.

41

Tab. 4: Scenario a - values of average opinion under various thresholds

Scenario Strongest averaged opinion against

(red)

Weakest averaged opinion against

(green)

(1a) D = 0.2 0.7429 0.0901

(3a) D = 0.5 0.9872 -0.1490

(5a) D = 0.8 1.0736 -0.1641

Tab. 5: Scenario a - values of opinion difference under various thresholds

Scenario Strongest opinion difference

(red)

Weakest opinion difference (green)

(2a) D = 0.2 0.0343 0

(4a) D = 0.5 0.0790 0

(6a) D = 0.8 0.1239 0

With a D of 0.2 all three scenarios show no visible influencing effect on the strength of an

opinion.That may be because of the very low threshold for the social distance. It might be

considered that the little interaction is related to the narrow narrow-minded attitude of the

actors.

With a D of 0.5 opinions are less strong influenced in scenario b and c compared with

scenario a. For scenario b this might be explained with the halving of the spatial distance.

This change in spatial distance leads to a rapid increase of the social distance and therefore

inhibited interaction between actors. In scenario c a changed amount of participating actors

leads to a redistribution of availabel opinions and a concomitant increase of opinions that are

widely seperated.

42

Results scenario b

Fig. 10: Scenario b - Different participation and half distances with average opinion

(1b, 3b, 5b) and deviations of the average opinions (2b, 4b, 6b)

With a D of 0.8 no considerable differences can be observed in the average opinions between

scenario a and c. The influence of c’s changed participation structure could be observed under

a D of 0.5. Under a D of 0.8, however, it does not not show any effect, whereas the changed

distance of b, leads to a strong rise of the averaged opinions (Tab. 6). With a D of 0.8 actors

are open-minded and approachable for opinions of other actors.

In comparison with a does the halving of the distances in b not show any significant influence

on the strength of the opinion (Tab. 7). Only under a D of 0.2 and 0.5 the halving of the

distance shows a slight increase for the value of opinion differences. In the visible outcome

these results seem to be more diverse for areas without opinion differences. While in a these

areas are confined to the upper left model area, they are more extensive in b. Most locations

43

without any opinion difference are located in areas without or with barely residential

development.

Tab. 6: Scenario b - values of average opinion under various thresholds

Scenario Strongest averaged opinion against

(red)

Weakest averaged opinion against

(green)

(1b) D = 0.2, ½ Distance 0.7229 -0.0098

(3b) D = 0.5, ½ Distance 0.8954 -0.1025

(5b) D = 0.8, ½ Distance 1.3502 -0.4086

Tab. 7: Scenario b - values of opinion difference under various thresholds

Scenario Strongest opinion difference

(red)

Weakest opinion difference

(green)

(2b) D = 0.2, ½ Distance 0.0470 0

(4b) D = 0.5, ½ Distance 0.0862 0

(6b) D = 0.8, ½ Distance 0.1282 0

44

Results scenario c

Fig. 11: Scenario c - Equal participation and complete distances with average opinion (1c,

3c, 5c) and deviations of the average opinions (2c, 4c, 6c)

The same participation of all actors in c leads to slight increased values of opinion differences

compared with the other scenarios (Tab. 9). This might be related to a shift in the distribution

of strong and weak opinions. Fewer actors with strong opinions are confronted with more

actors with weak opinions. Even though weak opinions are far less influencing, this finally

leads to a slight increase of opinion differences. The visual output of c is similar to the visual

output of a. For all three D in c a consistent distribution of opinions with some hotspots for

strong or absent opinion difference is recognizable. The values for average opinions in

scenario c do not show a serious deviation from values in scenario a and scenario b (Tab. 8).

In general it may be registered that opinion differences increase for all three scenarios with an

increasing D. This might be referred to increased communication between open-minded

actors that are more willing to interact and to adjust their opinions to those of others.

45

The results of the three different scenarios illustrate that the calculation of suitable locations

for the siting of a wind turbine requires both, spatial distances as well as the collocation of the

participating actors as an important factor. These factors need to be considered in a simulation

model for a spatial planning siting problem.

Tab. 8: Scenario c - values of average opinion under various thresholds

Scenario Strongest averaged opinion against

(red)

Weakest averaged opinion against

(green)

(1c) D = 0.2 0.7629 0.0802

(3c) D = 0.5 0.8854 -0.0264

(5c) D = 0.8 1.0746 -0.1705

Tab. 9: Scenario c - values of average opinion under various thresholds

Scenario Strongest opinion difference

(red)

Weakest opinion difference (green)

(2c) D = 0.2 0.0509 0

(4c) D = 0.5 0.1043 0

(6c) D = 0.8 0.1436 0

46

4.3 Opinion distribution

47

Fig. 12: Distribution of all opinions after 20 interactions (ticks)

Tab. 10: Statistical outcome for all scenarios after 20 rounds of interaction

Scenario Minimum Maximimum Mean Standard deviation

(1a) D = 0.2, SP 0.1507 0.8500 0.4164 0.1214

(3a) D = 0.5, SP 0.0649 0.7500 0.4283 0.1282

(5a) D = 0.8, SP 0.0619 0.8106 0.3938 0.1253

(1b) D = 0.2, HD 0.0686 0.6469 0.3674 0.1024

(3b) D = 0.5, HD 0.0907 0.700 0.3844 0.1114

(5b) D = 0.8, HD 0.0044 0.7874 0.3791 0.1291

(1c) D = 0.2, EP 0.1000 0.9000 0.4154 0.1329

(3c) D = 0.5, EP 0.1000 0.8250 0.4015 0.1259

(5c) D = 0.8, EP 0.0001 0.9000 0.4114 0.1499

To analyse how the opinions of different actors were compounded, a histogram was made for

each scenario that represents all opinions after 20 model ticks. Observing the results makes

obvious that in each scenario most opinions are concentrated around one or two peaks. The

results show that certain parameter settings lead to bi-modal distribution and not normal

distributions. This behaviour means that the values for traditional mean and standard

deviation in table 10 are not the best measure. The opinions that shows the highest frequency

in each scenario is located around 0.3.

In scenario a the distribution of opinions gets wider by an increasing D. This also leads to a

decreasing opinion frequency of individual peaks, which results in a assimilation on a normal

distribution of the opinions. The highest frequency of individuell opinions lays for all D

between 0.3 and 0.4.

In scenario b there are barely visible differences between a D of 0.2 and 0.5. Both have two

peaks around 0.3 and 0.5. The peak around 0.3 shows a frequency of 500, which is five times

higher compared with the second highest value. With a D of 0.8 the frequency of this peak is

around 350. Opinions are wider distributed and converge at one the peak around 0.3.

48

In scenario c the curve shape is similar to a. At a D of 0.2 and 0.5 it is recognizable that

opinions are not or barely adjusted and that the historam provides gaps in the opinion

distribution. With a D of 0.8 the distribution becomes wider and the distribution approaches

the shape of a normal distribution with the highest frequency around 0.3.

On the basis of scenario 5a under a D of 0.8 it was analysed how the opinion distribution

changed during the first ticks. Therefore changes were observed for the first five, the tenth,

and the twentieth tick. The results show obviously that strong variations of maximum and

minimum values especially occur during the first ticks. Opinions are mostly influenced in

their frequency during the first two ticks. Within the first two ticks, the frequency of the

dominant opinion strongly decreases and more opinions occur within the range between

lowest and highest opinion. Table 11 and figure 13 below show the statistical and visual

outcome of the results.

Tab. 11: Statistical outcome after different rounds of interaction for scenario 5a

Ticks Minimum Maximum Mean Standard deviation

1 0.2000 0.7000 0.3725 0.1183

2 0.0875 0.7250 0.3932 0.1205

3 0.1328 0.7696 0.3958 0.1340

4 0.1394 0.6844 0.3979 0.1197

5 0.1767 0.7694 0.4063 0.1263

10 0.1104 0.7144 0.3828 0.1250

20 0.0717 0.7018 0.3928 0.1267

49

Fig. 13: Opinion distribution after different amount of interaction for scenario 5a

50

5 Discussion and conclusions

The subject of this thesis was to use opinion dynamics in combination with a spatial context

in order to simulate opinion formation processes for spatial decision making in spatial

planning. The previous chapters dealt with issues on the acquisition of knowledge on opinion

forming processes. The discussion exposed the results of the model against the background of

the finding in the literature review. In addition, it is discussed in how far the used methods

were suitable in receiving the results. Further, strong points and limitations of the model are

addressed. A summary of all findings in the background of the research question is written as

last step in the conclusion.

5.1 Discussion

It was assumed that spatial planning is a highly complex process in which actor

communication and opinion formation are essential in achieving legitimacy for certain

planning decisions and that this processes could be simulated within a computer based model

environment. Findings in the literature review described that existing simulation models for

opinion dynamics failed so far in considering space as an important component in the

simulation of opinion formation processes (Ligtenberg & Bregt, 2014) and that in multi-actor

spatial planning the role of opinions were yet narrowly included. In the model simulation

spatial distances and the composition of participating groups of actors wield considerable

influence on the opinion of actors. This influence could particularly be observed during the

interaction under open-minded conditions. Especially the change of spatial distances had a

major effect on the strength of the opinions for or against certain locations as siting location

for a wind turbine. This result makes obvious that space may not be ignored from models

whose attempt is the simulation of opinion dynamics for the purpose of spatial decision

making. Ignoring people’s spatial interest and preferences from decision making processes in

spatial planning would lead to conflicts in actor communication and distorted result. Related

to the results of the model simulations the findings in literature assumed similarity of opinions

as precondition (Kaur et al., 2013) for interaction seems reasonable.

The elaborated approach simulated opinion dynamics under consideration of spatial aspects.

Based on the simulation results this seemed a reasonable approach to cope with the growing

amount of actors with divergent interests as well as the time stage in which participation

starts. Those were identified by Geertman (2006) as two of three reasons for growing

complexity in spatial planning. In literature findings was described that a simulation model

should be kept to simple and non-complex (Hegselmann & Krause, 2002). This, however,

restricted the model to major limitations in the integration of overmuch fields of policy. The

assumption that actors are influenced by both, social and spatial condition was approved by

findings in the literature review (Kaur et al., 2013; Ligtenberg & Bregt, 2014). Opinion

formation can indeed be considered as an evolutionary process in which opinions are

influenced by others. It is agreed that described difficulty by Ligtenberg and Bregt (2014)

about neglected spatial influence of opinions in OD-models is justified, since the results of

51

this study show obvious that spatial aspects have a major impact on the distribution of

opinions.

The simulation of preferences and beliefs however, was restricted to the assigned values by

the model implementer. The current model results are rather descriptive. In addition, the

model results are based on a subjective perception. This means that the results of the model

are based on the individual meaning that was attributed to the characteristics of each group of

actors. Social perception in particular provides scope for different perceptions and personal

valuations. Findings in literature (Habermas & Habermas, 1989; Jager, Janssen, De Vries, De

Greef, & Vlek, 2000) reinforced the assumption that opinions and communication are

subjectively perceived and dependent on our interpretation. However, due to this reasons

validation of the model remains problematic since it could not be overcome that the model

lacked connection to a real world scenario. Therefore, the model might not be considered as

useful to predict a possible future. Rather, it can be used to simulate, experiment, and learn

from the outcomes under certain conditions, since different assigned properties in the

beginning of the simulation lead to different results.

The simulation with the implemented model shows certain strength and weaknesses.

Weaknesses are discussed together with limitations later in this section. The model can easily

be extended by additional group of actors and influencing factors. However, it should be

considered that this might increase the complexity of the model. But, the implemented model

provides the opportunity to explore the behaviour under different conditions quite easily, what

makes it a strong tool for the investigation of opinion formation under various conditions.

This is meaningful as it comes to explore opinion distribution and the communication of

preferences under multi-actor processes. This is an important condition in current spatial

planning as it was described by Van der Valk (2002). Agent-based models were described as

opportunity to reproduce the social interaction of human networks in their spatial

environment. The implementation of an opinion dynamics model including both a social and a

spatial component might be seen as quite new. The results are a model addressing both social

and spatial preferences in decision making processes. Since individual opinions of agent’s are

dynamic the degree of influence on the opinion also depends on two main issues, the opinions

of the other linked agents as well as the time (tick) during the model run in which they are

linked together. The number of interactions is important since each tick influences the agents

in their opinion to a certain degree from the beginning of the model run.

Even though the actors in the model of this research were assigned to one out of three distinct

groups, actors have incomplete knowledge and might be seen as autonomous. In the

beginning of the simulation actors are part of a social network. However, they are not aware

about the opinions and preferences of others and adapt their knowledge in the course of the

model simulation. The approach and the consideration of others opinions as well as the

acquisition of new calculated opinions for further negotiation endowed actors with adaptive

learning capacity. This adaptive capability was described by Billari (2006) as one of the four

main building blocks for the developing of an ABM. The others (object of simulation, agent

population, and the interaction paradigm among agents) have been described and

52

implemented within the study. This confirms that a model in an ABM approach is useful to

simulate mutual influence on the opinions between multiple agents.

The model results about the distribution of agent’s opinions, demonstrate that consensus

building is among others highly related to the openness of the agents. That more open-minded

an agent is that faster consensus is reached by a high amount of deliberation. That agrees with

the description by Kaur et al. (2013), that consensus is reached by continuous influence from

each other. The exerted influence, however, that could be observed during the first two

interactions was quite high. Since actors were randomly linked to each other, this seems not to

be realistic and applicable for the real world. The influence is rather based on the outcome of

mathematical calculation and less on the logic of arguments. Generally, the distribution and

the influence of opinions in the model does not follow most of the conditions by Schmidt

(2010) which he assumed as important for decision making and consulting according to the

theory of communicative action by Habermas (1991). A shift from random influence to

continuous influence that is based on logical arguments would require changes in the model

structure. A parameter that links agents to each other based on spatial distance and opinion

difference might be introduced. Here, agents that are spatially and socially more close to each

other would be linked together more frequently. The extent to which agents are influenced in

these communicative processes is based on four questions that should be considered in an

argumentative pattern. According to Kemmis and McTaggart (2005) these four questions ask:

What is comprehensible to agents?

What is true in the light of agents own knowledge?

What do agents themselves regard as sincerely and truthfully stated?

What agents themselves regard as morally right and appropriate in terms of their

individual and mutual judgment about what it is right, proper, and prudent to do under

the circumstances in which they find themselves?

These questions offer a good starting point for further research on argumentative patterns that

describe continuous influence based on logical arguments.

In this research agent-based modelling seems a feasible approach for the simulation of

opinion dynamic processes under consideration of spatial and social aspects. The downscaling

from real world scenario to a model environment seemed a feasible method in order to choose

for decent values and variables. The description of the whole model concept according to the

ODD-protocol (Grimm et al., 2010) was perceived as good choice, since it provides a detailed

overview about model intentions by clear and simple structure. Using the ODD-protocol was

beneficial for a systematic and simple approach. This was consistent with assumed conditions

by Kaur et al. (2013) for the simulation of opinion dynamics. The experience from this

approach is that it was beneficial to keep track about all important issues that play a role for

the model implementation quite easily. Further, it provides a high amount of transparency.

This enables the reader to reconstruct steps and decisions that have been taken during the

process without any big effort. The approach of Ligtenberg and Bregt that was chosen for

simulation could be implemented in the NetLogo environment and showed distinct results that

53

could be interpreted. NetLogo as simulation software is restricted to a short but powerful

language. During the implementation of table structures and loops in NetLogo were identified

as inconvenient and difficult to implement if there is only little experience with this software.

In addition NetLogo suffers from a lacking support in relevant internet forum, since its user

group is relatively small.

In the scope of the time limit and the described extent of the research the model works

flawless and showed expected results. However, the model contains weak points by being

unmindful of certain important factors that were not included due to time limitations and

complexity reasons. Three main weaknesses were identified. Firstly, it should be considered

that under real conditions the actors and their opinions and participation is much more

diverse. Here, the final model was limited to three groups of actors. Per scenario all actors

were tested with the same social distance threshold. It is assumed that in a real world scenario

this interaction is far more complex. Secondly, the model only included distances and land-

use as influencing factors on actors’ individual initial opinion. In a real world scenario other

important factors like for instance costs, soil conditions or cultural values are expected to have

a high impact in influencing the opinions of actors. Lastly, due to the quite low resolution of

25 x 25 m the model contained of approximately 17.400 cells, which required a huge amount

of computational power by a large amount of actors. By assigning groups of actors the

computational power could be reduced. However, this led to the fact that far less cells were

occupied by individual actors. This led to irregular distribution of the actors within the model

environment what caused a bias of the results.

The model might find a practically application in the future in the simulation of opinion

formation and communication between residents that are affected by certain spatial planning

processes. However, even if the model showed capacity to simulate the complexity of opinion

dynamics under consideration of social and spatial influence the model is still far off from

being realistic. Therefore, too many important factors that should be considered for opinion

forming processes are not implemented in the model. Agents in the model are restricted to

only several pre-defined opportunities for action which are established within pre-defined

code. This means, that “real-communication” will never take place. Within the model the

influence of agent’s on another depends on the strength of the pre-defined values in the source

code. This does not agree with Habermas and Habermas (1989) who assumes the strength of

an argument as a main factor for opinion formation. In addition, the people’s perceptions and

opinions in the real world are much more diverse as it could be simulated within a model.

Within the model individual opinions are rather individual opinions of similar groups of

actors instead of individual opinions of individuals. This simplification of model agents is one

major reason why the model outcomes are not realistic for the real world. Another one is that

actions of agents are according to the code purely rational. This means that unexpected or

spontaneous behaviour is not simulated and has no influencing effect on the actors opinions

even though this also belongs to a realistic social mechanism as described by Hedström and

Ylikoski (2010).

The named limitations of the model might be broader adapted by using further research to

make the concept more suitable. The influence on opinions could be further elaborated by

54

considering additional factors like for instance costs, soil conditions, or cultural value. On the

one hand, this would make the model results more realistic. On the other hand, this would

lead to much larger complexity of the model. Here, the decision is a balancing act between the

usefulness of the model on the one and simplicity of the model on the other side. Another

improvement might be received by ensuring that all buildings are occupied by residents. Due

to reasons of computational power, the model currently assigns an only a small amount of

actors to one cell of land-use type buildings. Since there are several small farm buildings

within the area, some cells with land-use type building are not occupied by agents. By not

considering these cells the distribution of opinions may become distorted. A third approach to

make the model results more realistic could be achieved by survey about real opinions and

preferences of real world actors. Even though this might make the model far more complex

and assumes more computational power that is needed, might this approach supply results that

are more close to realistic conditions. The scope of this research was limited to the

investigation of opinion forming processes by only using three different actors. Actors were

assigned to certain stereotypes that were implemented in the case study. Due to the lacking

connection to the real world the results were difficult to validate. This agrees with a general

described difficulty in validating socio-spatial systems (Windrum et al., 2007). Improving the

model with the previously described approach, however, might be supportive for the

validation of the results for a real case of spatial decision making. Nevertheless, the results are

an interesting attempt to simulate siting decisions in spatial planning processes by actor

communication that considers social and spatial aspects as influencing factors.

5.2 Conclusion

The main objective of this research was to gain knowledge on opinion formation processes

and to use this knowledge to simulate opinion dynamics for a planning siting problem in an

agent-based model approach. In order to reach this objective four research questions were

formulated that were answered during the approach of the research.

The first research question was about the current role of opinions in multi-actor spatial

planning. Here, the literature review made obvious that the meaning of opinions is yet barely

included. Main problem is the explanation of opinion diffusion in a spatial context. The

influence that is exerted by cultural norms, interaction and mass media is the driver for

individual decision making and is based on social interaction. However, current computer

based models are limited in simulating complex societal processes like opinion formation.

This makes it difficult to include opinions as a meaningful factor into current spatial planning

models. OD-models might be seen as meaningful option to simulate the exchange of and

influence by opinions. However, an important factor of spatial planning models and that is

currently missing in OD models is a missing spatial component.

The second research question asked how opinion formation and dynamics could be

formalized into a logical set of equations. It is of high importance to follow a clear and

distinct structure for the formalization of real world scenarios into equations that can be used

as input values for simulation. Small increases in the quantity of model factors have already a

55

large increasing impact on model complexity. It might be interesting to explore the model

under more complex conditions. The fragmentation of agents in more diverse groups might be

one interesting approach. In addition, the consideration of more diverse land-use types, cost

aspects, or soil conditions might be interesting aspects to make the model conditions more

complex. However, this would extend the scope of time of this research. Therefore, it is

important to thoroughly consider about the factors and variables that have to be included into

the model. The model results, however, show obviously that spatial planning is far more

complex and opinion formation cannot easily be simulated by equations that are only based

on some few factors. The notation of the model approach according to the ODD-protocol is

perceived as beneficial, since it enables reader and implementer to understand certain decision

and provides good starting points for criticism.

The third research question asked how these set of equations could be simulated in an agent-

based model. Agent-based modelling provides different group of agents with the capacity to

learn from and adapt to the opinions of others. Here, it is important that actors collate the

differences between their own opinions and those of others. The extent, however, to which

agent-based models can include important influencing factors for opinion formation is

limited, since each additional added factor raises the complexity of the model and requires

more computational power. This entails that the simulation of opinion dynamics with ABMs

is less useful to represent the whole complexity of social interaction and opinion forming

processes. Rather, it enables the user to explore and understand potential influence on

opinions some few factors exert on each other. This understanding might be useful to put

forward new theory on opinion forming processes.

Research question four asked about the added value of this approach to the current

understanding of land use/planning dynamics. The approach is still far away from being

realistic. This makes validation of the results still difficult. Valid validation could only be

reached in relation to findings in literature and the functioning of the implemented model.

Since model results were lacking in relationship to the real world, results were rather

described than validated. This coincides with a general problem for the validation of complex

adaptive systems. However, the approach demonstrates obviously the dynamics of actor’s

opinions. Actors are able to change their opinions by learning capacity and self-adaptation in

the course of time. It adds value to the current understanding of planning dynamics by the

consideration of space as an important factor. In the model results it has been shown, that this

is in spatial planning indeed necessary for the simulation of opinions dynamics.

In the course of this research some weaknesses were found that might be researched in future

studies. Firstly, it might be investigated if the inclusion of other important factors of common

spatial planning would make the whole model more realistic. In addition, the group of actors

should be extended to a larger amount of people with different opinions and preferences. This

also requires further abilities for communication. Further, it could be investigated if actors

could negotiate my means of the logic of an argument. However, for all further research, it is

important to keep in view the scope of complexity.

Finally, it has to be said, that complexity and dynamics are opinion properties which make it

already difficult to understand issues of opinion formation and opinion change. Even harder it

56

becomes if those processes should be simulated and validated for spatial planning process.

The scope of this research was only a small step in understanding opinion dynamics in a

spatial context. The complexity of opinions also includes opinion change over time by

changing environmental factors. It would be a giant step forward towards realistic conditions

and model validity, if future models could include these changes and the logic of arguments

within their simulation.

57

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7 Appendix

Rules of the OD model of Ligtenberg and Bregt (2014):

Opinion O Opinion of an agent is in domain [0,1],

with 0 as maximum negative opinion

Total num. agents N

Agents k, l

Land-use g

Time t t+1 is certain moment in time

Location i, j

Soc. dist. threshold d

Deffuant-Weisbuch

model

x (t+1) = x + µ(x’ –x)

x’(t+1) = x’+µ(x- x’)

Agents only adjust the opinion if

|x-x’| < d

Opinion dynamics in a

social-spatial system

Ok,(t+1) = f(Oi(k,g,t), Qs(k,g,t), Qg(k,g,t))

Oi is individual opinions on certain land-

use;

Qs is the effect of social influence;

Qg is the effect of spatial influence on the

opinion

Social influence

Qsoc(k,g,i,j,t) = ∑ W (l,g,i,j,t) N

t=1

N

Qsoc(k,g,i,j,t) is social influence factor of k,g

at i,j for t;

w(l, g, I, j, t) is Dirac-delta function

Dirac-delta function

w(l, g, I, j, t) { 1 if S(k, l, i, j, t) < d(k, g, i, j, t)

0 otherwise

d(k,i,j,g) is threshold value for agent k to

enter negotiation

Qspak,g,i,j(t+1) is spatial influence factor of

k,g at i,j ;

X= set of cells of the neighbourhood;

63

Spatial influence Qspak,g,i,j(t+1) =

∑ ∑ Ok,x,t K (k=1)

X(x=1)

N

N= |X|*|K|;

Oa,k,l,t are opinions of agent a stored in cell

x

Update d

Qk,g,i,j,t = π*Qsoc(k,g,i,j,t) + (1-π) * Qspak,g,i,j,t

d’k,g,i,j (t+1) = d(k,g,i,j t) * (1-(|Ok,g,i,j,t –Qk,g,i,j,t|)τ)

π is parameter indicating the priority of

social influence vs. spatial influence;

d’k,g,i,j (t+1) is adapted d for k at t +1;

τ is parameter to define resilience of an

agent against adaptation of its opinion

Social distance

S(k,l,i,j,t) = o(k,i,j,t) – o(l,i,j,t)

S(k,l,i,j,t) is social distance;

o(k,i,j,t) is opinion of k about i,j;

o(l,i,j,t) is opinion of l about i,j

Precondition for

opinion update

S(k,l)(i,j)<dki,j

Social distance of agents needs to be

below a social distance threshold

DW adapted for spatial

context

O(t+1)(a,i,j) =O(t)(a,i,j)+ µa (O(t)(a, i, j)- O(t)(b,i,j))

and

O(t+1)(b,i,j) =O(t)(b,i,j)+ µb (O(t)(b, i, j)- O(t)(a,i,j))

µ is agent specific location independent

parameter

Representation of the

spatial environment by

ordered collection of

cells

C=(ci,j,ci+1,j,ci+1,j+1,….,ci+n, j+n)

i,j are indexes that determine the location

of the cell in a lattice;

Each cell represents a discrete part of the

area and contains information about the

state of the environment, here:

64

p(i,g) as agents preference and the opinion

at time t