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Elsevier

Contents

Contents

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1. Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling

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v

Chapter 1 Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling

Andrew Rixon, Magnus Moglia and Stewart Burn CSIRO Manufacturing and Infrastructure Technology Highett, Victoria, Australia 1. Introduction The pre-industrial period found water utilities developing ‘big pipe networks’, with centralised structures aimed at controlling disease outbreaks [Livingston et al., 2004]. Since then population growth, technological development, trends in urban and rural development, and human-induced climate change are have been driving future water use [Kuylenstierna et al., 1997]. This has meant that urban water utilities have continued to evolve as socio-economic and environmental conditions have changed. With large pipe networks having been established within cities, a strong supply-oriented logic of development prevailed. Water utilities were driven by the basic assumption that economic growth generates new demands for services. However, during the last decade, corporatised water utilities, guided by regulatory frameworks, have had to address environmental concerns, decaying infrastructure, water shortages and the inability to continue the supply-side expansion [Marvin et al., 1999]. What has emerged is a demand management paradigm. Under this paradigm, the water utility attempts to influence and reshape demand by mechanisms such as metering, where variable tariff schemes are used to impact on peak demand periods, and initiatives such as water recycling, and the promotion of water-saving technologies and behaviours. With such programs, the water utility can find itself embedded in a complex sociopolitical landscape. How willing are people to accept these water-demand management strategies, and how fair are tariff structures in lower socio-economic areas [Turton, 1999]? It has been suggested that policy debates could start to recognise a much wider role for water utilities 1

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within their regions, and the potential for strengthening their contribution to social cohesion [Marvin et al., 1999]. Although such demand-side strategies employed by the water utility focus directly on human behaviour, studies involving human beliefs, behavioural factors on water conservation and adoption rates of water-smart technologies are in their infancy. Although here are a few exceptions [see Clark et al., 2000; Corral-Verdugo et al., 2003; Syme et al., 2004]. The growing importance of public participation and engagement within sustainability science has led to a reformulation of tools and methodologies which can be meaningful to both the researcher and the participants [Kasemir et al., 2003]. A key strength of tools such as the agent-based modelling approach over more traditional equation-based approaches is that mathematical equations can be represented by soft sentences which make more sense to those not involved in model-building on a daily basis [Tillman et al., 2001]. The problem of how to obtain relevant data and rules to incorporate into agent-based models, as well as seeking model validation by participants has been clearly recognised within the literature [Tillman et al., 2001; D’Aquino et al., 2002; Pahl-Wostl, 2002; Barreteau, 2003]. The methodology of participatory agent-based modelling (companion modelling) has provided a framework for addressing such issues [Barreteau, 2003]. Here, public participation and engagement is a vital ingredient in a three-stage process of field study, modelling and simulation (or game playing). Such a methodology provides access to knowledge elicitation and social learning, developing a dual mechanism with implicit added value for both the participants and the modeller [Pahl-Wostl, 2002]. This chapter seeks to explore the effects of social networks and imitation on water conservation, and lay some groundwork for further exploration of water conservation behaviour using the participatory agent-based modelling approach. Two agent-based models are discussed and developed. The first is a simple model to explore the effects of social networks and tariff structures on a small population of agents, where the utility who collects the tariffs has constraints on the level of tariffs it may set. The second model, evolved from the first, seeks to explore the effect of imitation of water use behaviour within a population of agents and builds a case for the need for further studies into the potential for imitation being a key driver in water use behaviour and uptake of new technology. In both cases, social interaction forms the basis for the agent behaviour and the agents are characterised with degrees of belief for water saving.

2. Water Conservation

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2. Water Conservation 2.1. Urban Water Use In Australia, the average household residence consumes 250, 000 litres of water per year. On average, 34% of domestic water is used in the garden, 20% is used in both the toilet and shower, 12% in the washing machine, and the last 14% is used in the remaining water devices in the house [wsaa, 2001]. Studies have shown that water use per capita declined in most large urban centres during the 1990s due to increases in water pricing, consumer education, use of water-saving appliances and higher residential densities being linked to lower outdoor water use [Senate, 2002]. Australias population is estimated to grow to between 24 and 28 million by the year 2051 [abs, 2001] and such population growth leads to total water consumption levels increasing, placing pressures on resources. It has been estimated that water-efficient devices could potentially reduce consumption by 50% in both the toilet and shower, 35% for the washing machine and 20% for the dishwasher [wsaa, 2001]. The issue of technological adoption has been addressed by mainly focussing on the rate of diffusion of the innovation. Clark et al. [2000] has demonstrated an individual-based approach to the study of innovation within the water industry. In particular, it was shown that understanding the diversity of motivations and perceptions which characterise individual organisations operational experience is central to managing the implementation of new technology. 2.2. The Social Dilemma of Water Conservation Water conservation can be thought of as a social dilemma, a conflict between private and public interest. For example, in a hot summer period, the common consensus approach to conserve water may be ignored by a select few who decide to use water without regard for the consensus. If everyone used this strategy of ’defection from the norm’, then the water resource would deplete rapidly. Water metering, pricing and the effect of tariff structures on water conservation have received a lot of attention in the literature [Saleth et al., 2000; Arbues et al., 2003; Chambouleyron, 2003]. Few studies, however, have explored the effects of social cohesion on water conservation. An exception is Vugt [2001], who, treating water conservation as a natural social dilemma, identified two key approaches: the structural and the socialpsychological. The structural approach contains strategies that intervene

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directly in the outcome structure of the dilemma. For example, installation of domestic water meters makes it possible to charge based on usage, giving a financial incentive to consume less water. The social-psychological approach consists of interventions altering the way people value and think about the resource. Such an example can be found with public education campaigns. Vugt [2001] demonstrated that a communitys social cohesion moderates the effects of tariff structures on resource use. In particular, tariff systems in which overuse is penalised are particularly effective when a communitys social cohesion is low. Greater community social cohesion is suggested as a way to help promote restraint by increasing within the community thus increasing peoples willingness to exercise restraint when needed. 2.3. Water Use: Beliefs, Attitudes and Memetics On average, a person in Australia uses 350 litres of water a day, with this usage being split across activities in the kitchen, bathroom, toilet, laundry and garden. The garden represents the highest use area, consuming approximately 34% of total daily water use. Garden watering is strongly correlated with lifestyle beliefs and conservation attitudes [Syme et al., 2004]. Water usage is also affected by the type of device and the associated behaviour with that device. For example, brushing teeth using a glass instead of leaving a tap running will save 9, 100 litres per person per year. Similarly, using a aaa-rated shower head instead of a normal shower head will save 28, 000 litres per person per year1 . Water user beliefs can be classified into two main classes [Corral-Verdugo et al., 2003]. The first class is the water utilitarians those who believe water is an unlimited resource to be used in an arbitrary way. The second class are the water ecologists (water savers) those who believe water is a limited resource to be conserved. Corral-Verdugo et al. [2003] showed that water utilitarian beliefs are related with increased water consumption. Similarly, water ecologists beliefs on water consumption believing that water is a resource to save leads to a decrease in consumption. Motives for saving water are significant predictors of water use. That is, the more motives, the greater the chance for water conservation behaviour [Corral-Verdugo et al., 2003]. Although attitudes and beliefs of residents have been shown to have a direct effect on external water use [Corral-Verdugo, et al. 2003; Syme et al., 2004], no studies on imitation of water-use behaviours have been 1 Information drawn from http://www.southeastwater.com.au/sewl/index.asp?link id=27.521 last visit 05/2006.

3. The Models

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found. Imitation is considered to be a founding block for learning in humans [Blackmore, 1999], and has been acknowledged to provide a mechanism beyond the rational actor paradigm, where individuals have perfect knowledge and try to optimise their outcomes [Jager et al., 2000]. Although a controversial theory, the meme has been described as anything passed on by imitation [Blackmore, 1999] and is considered analogous to the gene and is embedded within an evolutionary process where selection, variation and inheritance operate. For the purposes of this chapter, the adoption of both water-use behaviours and smart water devices is considered memetic. Memetics has been criticised for being merely a conceptual framework [Edmonds, 2002], however it is considered to provide a strong software engineering framework by which technology adoption and beliefs can be implemented within the software models described in this chapter. Using a memetic representation for the description of the behaviours and devices allows a decoupling of the water-use behaviour from the residents behaviour in the implementation. This decoupling at the software development level provides flexibility, allowing for scenarios such as investigating the impact of advertising campaigns on residents. Under the memetic framework, the effect of an advertising campaign is captured by introducing a specific meme into the population of residents. Whilst this chapter explores the application of memetics within the community the validity of the approach is still to be shown. 3. The Models 3.1. The Agent-Based Modelling Framework Agent-based models differ from equation-based mathematical models in that they are computational models focussing on algorithms to implement the behaviour within the model, rather then evaluating sets of system variables and equations [Parunak et al., 1998]. For a full treatment and review on building agent-based models see Rixon et al. [2005]. Whether a modeller chooses to use a agent-based modelling platform or build from scratch there are three key phases to the development of an agent-based model [Rixon et al., 2005]: Phase 1 requirements; Phase 2 model storming; Phase 3 implementation.

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In Phase 1 Designs for multi-agent systems generally require mechanisms for [Gilbert and Terna, 1999] 1. receiving input from the environment; 2. storing a history of previous inputs and actions (audit trails); 3. carrying out actions and distributing outputs (scenarios). In Phase 2 it is common to work through higher level issues that are not well addressed with code. This might be an inclusive modelling session with stakeholder(s) to understand their requirements, or it could be a quick design session with other developers to determine how to build something. The technique of class responsibility collaborator or crc cards has been shown to be an effective tool for facilitating group model storming processes [Biddle et al., 2002]. Finally, when building agent-based simulations from scratch, there are two methods for dealing with time and events those being the discrete event simulation; and time stepped simulation. With a time stepped simulation, there is an internal clock which ticks the model over to where the next group of events takes place. A discrete event simulation, however, does not use the tick within the model; instead it utilises a stack of events which are queued and then scheduled and released once conditions are met [Tyszer, 1999]. Table 1.1 details the key elements of the two agent-based models described in this paper based on the taxonomy provided by Hare et al. [2004]. By using an object-oriented approach for software development of agentbased models, it becomes possible to create a flexible framework of classes (and objects) that can be extended and evolved as the modelling process proceeds and the questions under investigation become clarified. 3.2. Model Validation: Understanding Robustness and Prediction In this paper, two models are described which aim at exploring some aggregated properties relating to water use behaviour that emerge from the interactions between individuals in a society. This is done for two key reasons. The first reason is to provide understanding and the second goal is to provide prediction. The focus however is put on developing an understanding because prediction is not a good indicator of the validity of a complex system model, in particular for a micro-simulation model. This is because of:

3. The Models

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Table 1.1: Key characteristics of the explored agent-based models. Criterion

Simple Model

Memes Models

Coupling of social and environmental models

Spatially non-explicit Collection of agents with friend networks not geo-referenced

Micro-level decision making

Individual calculation of how much water to use based on demographics Friend networks seek to influence agents water use

Spatially explicit Collection of georeferenced households each containing residents with friend networks Types/frequencies of water use activities based on beliefs of individuals Types/frequencies of water use activities based on beliefs of individuals Group based tasks Households/families and friends become more similar in beliefs Feedback based on belief distribution through the population

Social Interaction

Micro-level decision making

Adaptation

Individual calculation of how much water to use based on demographics Feedback based on cost stress provided by the water utility, friend network and demographic profile Water stressed residents seek out water savers

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1. Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling • Non-linear features: the object of study is a non-linear system where only one ‘true’ model exists, but there is often an infinite number of models that all provide perfectly accurate prediction [Richardson, 2003]. • Emergent features: even a perfect understanding of the micro-behaviour of all components (i.e. individuals) is insufficient to predict group behaviour [Gilbert et al., 1999]

In addition to the non-linear and emergent features, the studied system also has second order emergence which is typical of human social systems. Second-order emergence refers to when institutions result from behaviour which takes into account emergent features, for example governments, churches and business organisations [Gilbert et al., 1999]. There is hence second order emergence within our studied system because institutions and organisations adjust, at least on the long time scale, reflexively according to the expected emergent behaviour of the collection of individuals. This provides a self-referential property (i.e. feedback) which again makes prediction very difficult. Therefore, while a solid understanding of micro-behaviour, and a reasonable predictive capacity are both indicators of a reasonable model, absolute validation of a model is virtually impossible, and a common path towards ensuring leigitimacy of social simulation models is validation through stakeholders [Tillman et al., 2001; D’Aquino et al., 2002; Pahl-Wostl, 2002; Barreteau, 2003], which is typically attempted within Companion Modelling and Participatory Agent-Based Modelling [Barreteau et al., 2003]. In line with the Companion Modelling approach, D’Aquino et al. [2001] distinguish between three types of validation processes: • Confrontation to the present reality: in essence a reality check. • Reconstruction by the model of a real past dynamics: can the model be used to reproduce and understand past dynamics (assuming data is available)? • Model acceptance by concerned people: can the model be understood and does it make sense? This requires transparency in terms of being able to understand the components of the model In the Participatory Agent-Based Modelling framework, a pragmatic constructivism is applied in the sense that validation of the model is not strictly necessary, but legitimacy is critical because models are used in a framework to support discussion and social learning. Within this process,

3. The Models

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the model and methodology is used for exploratory purposes and to generate theories in order to ask new questions on the real systems [Barreteau et al., 2004]. A model is then legitimate, rather than valid, when it provides participants with new theories about the real system and is an acceptable representation of the real system However, it should also be remembered that agent-based simulations can sometimes provide a false sense of realism, which makes stakeholder validation difficult. Other key methods to improve the validity and to increase understanding is to apply • Simplicity: make the model as basic or simple as possible so that assumptions or potential errors can easily be evaluated. • Sensitivity analysis: by varying the models parameter values, it is possible to explore the solution space of qualitatively different results, and to evaluate robustness, or in other words, evaluate how sensitive the model is to individual parameter values in terms of what is needed to achieve a qualitatively or quantitatively different result. Further work remains on the models explored in this paper around the areas of sensitivity analysis and stakeholder validation. 3.3. A Simple Model for Exploring Tariff Structures The first simple agent-based model uses social networks to embed resident agents within a water-use environment where a water utility agent controls the tariff structure. The tariff structures explored are fixed and variable. The fixed tariff structure refers to a flat daily cost for water (independent of actual usage) determined by the water utility. The variable tariff structure sees a charge dependent on the amount of water used by the resident. All resident agents belong to a social network. The social network is either randomly generated or reconstructed from a ‘real’ data set of friendship links2 . Resident agents unable to get the amount of water that they require, due to the price placed on water usage by the water utility and their income levels, become water stressed. This water stress leads residents to seek out friends who are using more water and place peer pressure on them to reduce water use. Figure 1.1 details the properties and behaviours of the resident and utility classes. 2 Data sourced from http://www.sfu.ca/∼insna/INSNA/data inf.html last visit 05/2006.

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Figure 1.1: UML diagram of agent classes within the simple model.

The integer variables of the resident agent, such as IncomeLevel and BlockSizeLevel, are chosen to represent high, medium and low scaling. Extrapolating from Syme et al. [2004] water use becomes a function of the resident agents IncomeLevel, BlockSizeLevel, Occupancy, WaterTechnology Level, LifestyleLevel, GardenRecreationLevel and GardenState. This water usage figure is then multiplied by a compensating factor based on whether the resident is a water saver or water utilitarian. The utility agents ’willingness to pay’ algorithm is constrained by the acceptable degree of water stress within the resident population, and whether or not the water levels have reached critical levels. The algorithm for increasing or decreasing the tariff is described in Fig. 1.2.

3.4. Model Calibration The simple model is initialised first with either a real social network loaded from file or by creating a random friend network as discussed earlier. Acceptable water stress is determined as a percentage proportion of the chosen agent population level. Resident agents demographic variables are assigned random values, leading to a distribution of income levels, block sizes, occupancies etc. Since the water utility agent maximises its profits there is no need to calibrate the initial level of cost for each tariff.

3. The Models

11 Current water levels

Increase tarif

Yes

Critical No

PSfrag replacements

Population water stresses

Increase tarif

No

Critical Yes Decrease tarif

Figure 1.2: Utility agents ‘willingness to pay’ algorithm.

3.5. Results Figures 1.3 and 1.4 show 100 iterations with 21 resident agents and random social networks (3 friends per resident). In these simulations, the utility agent is allowing a 40% water stress within the population and the tariff is fixed; residents pay per day independent of volume used. The periodic nature of Fig. 1.3 reflects the decision-making behaviour of the residents who can no longer afford water based on the cost per day imposed by the utility. The residents who cant afford the daily tariff for water have no option but to cease water use. This creates water stress amongst the resident agents and leads to the water utility temporarily reducing its daily tariff, resulting in the periodic equilibrium of the fixed tariff price shown in Fig. 1.4. Figures 1.5 and 1.6 demonstrate the effects on resident water usage of a utility operating under the variable pricing tariff with the same initial conditions as the fixed tariff simulation. Figure 1.6 shows the fluctuation in cost per litre as the utility agent attempts to maximise its profit. Unlike the fixed tariff regime, the variable tariff regime produces more complex dynamics regarding total water use (see Fig. 1.5). To further understand the dynamics of this simulation model, 1000 simulations each of 200 time steps were run. Tables 1.2 and 1.3 describe the key results obtained from fixed and variable tariff regimes.

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1. Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling 3000

2500

litres

2000

1500

1000

500

0

0

20

40

iteration

60

80

100

Figure 1.3: Fixed tariff total water use (in litres) over a population of 21 agents for 100 iterations (daily).

10 9 8 7

$/day

6 5 4 3 2 1 0

0

20

40

Iteration

60

80

100

Figure 1.4: Fixed tariff cost per day with a population of 21 agents and 100 iterations (daily).

3. The Models

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4000

Litre

3000

2000

1000

0

0

20

40

Iteration

60

80

100

Figure 1.5: Variable tariff total water use for a population of 21 agents and 100 iterations (daily).

Tables 1.2 and 1.3 demonstrate the increase in the utilitys profit resulting from the variable tariff regime. Table 1.3 clearly illustrates the effect of increasing the number of friends in the friend network per resident agent on the mean water use. Interestingly, the real social network data appears to fit between the random four and eight friend networks. 3.6. The Water Memes Model In the water memes model, residents are considered to have a selection of water memes, some which are water saving memes and some which are not. These memes have a direct mapping between the type of device, the frequency of use and the amount of water used. In this model, ten types of water meme will be used (see Tab. 1.4). A view of a residents set of memes is: M 1M 2M 3M 4M 5M 6M 7M 8M 9M 10, where M 1 could represent the garden meme, M 2 a shower meme, and so on. Each meme, M i, has a cost associated with imitation. This cost is implemented as a required belief in water saving. For example, a resident who comes into contact with a rainwater tank meme would have to have a strong belief in water saving to copy and implement this meme. The

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0.055 0.05 0.045 0.04

$/litre

0.035 0.03 0.025 0.02 0.015 0.01 0.005

0

10

20

30

40

50 Iteration

60

70

80

90

100

Figure 1.6: Variable tariff cost per litre with a population of 21 agents and 100 iterations (daily).

Table 1.2: Effects of fixed tariff and social network on mean water use and mean utility profit.

Mean water use Mean utility profit

No social network 124.13 1466.49

Random 4 friends 124.42 1483.13


Real social network 124.69 1490.65

3. The Models

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memes that are inactive within a resident agent are determined not to be enabled. Resident agents are born with a degree of belief in water saving. This water saving belief is dynamic and is calculated by the percentage of water saving memes in their current meme set. A resident agents water worry is an inverse function of the current dam levels capacity the closer to full capacity, the less worried the agent. Water worry is scaled by the resident agents degree of belief in water saving. The stronger the belief in water saving, the more the resident will care about the state of the water reserves. Table 1.3: Effect of variable tariff and social networks on mean water use and mean utility profit.

Mean water use Mean utility profit

No social network 163.12 2153.76



Real social network 117.10 1942.66

Water memes are copied based on social interaction. Residents within a household tend to become more like each other, sharing a common set of beliefs. Resident agents who are water worried look to their friendship networks to seek out water savers. In the case of no water worry, resident agents generally seek to become more like their friends, independent of whether their friends are water savers or water utilitarians. The key algorithms for the propagation of memes belong to the resident agent and are the SeekSimilarBeliefs and SeekWaterSavers functions. Resident agents who employ the SeekSimilarBeliefs algorithm iterate through their friend networks seeking a friend who shares a similar degree of belief in water saving. Once found, the resident agent employs the BecomeMoreLike(Friend) function, which randomly picks a meme out of the friends set of memes and copies it into their meme set. Similarly, resident agents who employ the SeekWaterSavers algorithm iterate through their friend networks to find a friend who has greater water saving beliefs then themselves, once found the resident agent then employs the BecomeMoreLike(Friend) function ensuring the transfer of a water saving meme. A simple water balance equation is used for the garden [Cook et al., 2003]. This water balance incorporates rainfall, evapotranspiration and block size data to determine the level of water use required for a green garden.

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Table 1.4: Names and descriptions of the ten water memes used in the water memes model. Name

Description

Garden

Five types of garden watering technology available, each with differing water use. These are bucket, hose, hose sprinkler, fixed sprinkler and drip system AAA-rated shower head is 45% more efficient than normal head Dual-flush toilet saves water over the single flush Brushing the teeth using a glass saves over having a tap running Preparing food in the kitchen sink with the plug in is more efficient than with a running tap Ensuring that the washing machine is full each wash The knowledge of how much water is saved by stopping leaking taps The knowledge of how much water is saved by diagnosing a leaky toilet Whether dishes are washed in the kitchen sink or in a dishwasher Installing a rainwater tank can reduce external water demands

Shower Toilet Brush teeth Prepare food

Wash clothes Leaking tap Leaky toilet Dishwasher Rainwater tank

Figure 1.7 illustrates the core classes with attributes and behaviours for the water memes model. Finally, this agent-based simulation uses fixed increment time steps with conditional events. The core algorithm ticks the simulation through its daily routine. Here, each household (containing residents) is made to do the ‘activities’ which consist of using each of the core water usage areas: toilet, bathroom, kitchen, laundry and garden. Using an object-oriented approach enables events to be used which can fall outside the ticked simulation. For example, the garden object fires a “Needs water” event whenever its stored water level is below what is required to keep it green. This event initiates the gardener of the household to go and water the garden.

3. The Models

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Figure 1.7: UML diagram containing classes within water memes model.

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3.7. Model Calibration The model first loads real climate data from a file which includes rainfall and evapotranspiration data. All households were artificially created for the simulation, this includes selecting the number of residents per household and the type of residence (apartment or house) which were both randomised. Each resident agents created has a random friend networks assigned. Resident agents are randomly assigned income levels, lifestyle levels and their water saving beliefs. Each resident agent is assigned a set of 10 random memes which form the basis for the water use behaviours. Finally the rainwater tank objects are initialised with a maximum volume of 1500 litres and the garden objects belonging to each household object are initialised with crop factors, garden areas, soil storage level, maximum soil storage levels, and green garden storage levels realistic for urban environments.

3.8. Results No water worry. The following results were obtained for a simulation run with 30 households over 730 days (2 years). Real rainfall and evapotranspiration data were used. The residents had no water worries (the dam levels were always close to capacity), and each resident had 3 friends in their friend network. The simulation begins in January which is summer time in the southern hemisphere. Figure 1.8 clearly shows seasonal fluctuation in water use, with the highest usage occurring during the summer months (with gardens requiring most watering), and then dropping off as the winter months arrive (when gardens require least watering). Moreover, the mean total water use per household is around 800 litres per day, remarkably close to actual reported figures of 750 litres per household per day. Runoff water from garden watering or water lost through leaks is defined as water waste. Figure 1.9 shows an increase in mean water waste at the very beginning of the simulation. In this scenario, with no water worry, the water utilitarians outnumber the water savers (see Fig. 1.10). Water worry. The next results were obtained using a simulation scenario identical to the previous set. However, in this case the residents face a depleting water resource (dam levels are below 50% capacity). As with Fig. 1.8, Fig. 1.11 shows the seasonal fluctuations in water use, however in the case of Fig. 1.11, it is clear that the peaks and troughs of the seasonal fluctuations are decreasing.

3. The Models

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1000 900 800 700

Litre

600 500 400 300 200 100 0

Winter Spring

Summer Autumn

0

100

200

300

Summer Autumn

400 Time

500

Winter Spring

600

700

Figure 1.8: Mean total water use (in litres) per household for 730 days of simulations.

Interestingly, Fig. 1.12 shows an initial increase in mean water waste, followed by a reduction in water waste, which corresponds to an increase in the number of water savers in the simulation. Finally, in contrast to the previous simulation where the water utilitarian was the predominant belief, Fig. 1.13 demonstrates the flip to the water saver being the major belief within the population. Effects of friend networks on water saving beliefs. To explore the apparent alternate states of water saving beliefs detailed in Figs. 1.10 and 1.13 (the water saver belief is dominant in water worried scenarios, and the water utilitarian belief is predominant in non-water-worried scenarios), a Monte Carlo simulation was carried out with 250 scenario runs. Each scenario had 30 households with 730 days of simulations being run. Random friendship networks of 0, 3, 6 and 9 friends per resident were simulated. Figures 1.14 and 1.15 demonstrate a general declining trend for water saver beliefs as the number of friends per resident increases, independent of water worry. In particular, Figs. 1.14 and 1.15 demonstrate that the waterutilitarian belief is a dominant attractor within the simulations. That is, in general, resident agents within the simulation scenario will tend to become

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1. Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling 60

50

Litre

40

30

20

10

0

0

100

200

300

400 Time

500

600

700

Figure 1.9: Simulation of mean water waste (in litres) per household for 730 days

more oriented towards non-water-saving beliefs as the number of friends in their friend network increases. Utility campaigning and water worry. Introducing the ability for a water utility to campaign to its population of residents when the water resources reach critical levels is simple with the memetic approach. In this case, the utility agent simply monitors the current water levels and, when they reach critical levels (in this case, capacity below 50%), the utility sends out a random water-saving meme with the bill (analogous to sending specific water-saving information kits in the mail). Residents who are water worried are able to copy and implement this meme. Figure 1.16 demonstrates the marked decline in mean water waste. Curiously, the wintertime water usage is seen to increase during this scenario. Using this memetic approach, the water usage is dependent on which water memes are dominant within the population of residents. As discussed earlier, memes are embedded within an evolutionary process of variation, inheritance and selection. Figure 1.17 illustrates the initial and final distributions for the various memes (described in Tab. 1.3) in this simulation. Memes which are water

4. Discussion

21

80 70 60

%

50 40 30 20 10 0 Water Savers

Water Utilitarians

Figure 1.10: Distribution of water savers and water utilitarians for no utility campaigning and no water worry.

saving are listed as, for example, clothes1, dishes1, garden1. The frequency of use low, medium or high is then represented as f1, f2 and f3 respectively. In the case of the garden label: 1 = water by bucket, 2 = water by hose, 3 = water by hose sprinkler, 4 = water by fixed sprinkler and 5 = water by drip system. Interestingly, several memes have become extinct within this scenario. Specifically, washing clothes with many loads (clothes2f2), medium and high hose sprinkler usage (garden3f2,3), and low usage drip systems (garden5f1). Profile of the water saver and water utilitarian. Table 1.5 profiles the set of water memes of the strongest water utilitarian belief resident and the strongest water saver belief resident for the previous simulation where residents are facing depleting water resources and the utility is actively campaigning water saving ideas. Each of these memes were initially assigned randomly to the residents. 4. Discussion This chapter describes an approach to implementing a simple agent-based model to explore the effects of social networks and tariff structures on

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1. Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling 1000 900 800 700

Litre

600 500 400 300 200

0

0

100

Summer Autumn

Winter Spring

Summer Autumn

100

200

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400 Time

500

Winter Spring

600

700

Figure 1.11: Mean total water use (in litres) in a daily simulation for 730 days with residents worried about their water reserves.

water use behaviour. Social networks were demonstrated to have no effect on simulated water usage under the fixed tariff structure, as residents are unable to place pressure on their friends to cease their water use, only reduce it. Compared to the fixed tariff regime, the variable tariff regime provides the most equitable and fair allocation of water to residents, with residents always being able to afford daily water usage under the variable tariff regime. This is due to the fact that under the fixed tariff regime, there are residents who are unable to afford the daily cost of water and so completely cease their usage for that day (see Fig. 1.3). Most interesting in this simple agent-based model, however, is that under the variable tariff regime, social networks result in a significant reduction in simulated water use. This result suggests that within small communities where social cohesion is strong, there is an ability for nontariff-based strategies to successfully impact on water use. Further, the result provides a mechanism for decentralisation of water management, with residents empowered to seek sustainable usage of their precious resource. The first criticism of the simple model, however, is that the mean water use predicted per day is inaccurate. Estimations show that actual usage is on average around 350 litres per person per day, or 730 litres per house-

4. Discussion

23

70 60 50

Litre

40 30 20 10 0

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300

400 Time

500

600

700

Figure 1.12: Simulation showing effect of targetting campaign on water waste running for 730 days.

hold per day, this simple model shows mean water use of approximately 120 litres per day (see Tabs. 1.2 and 1.3). Secondly, although the simple model provided a framework for exploring the effects of social networks on water usage, the reality of this functionality is limited to, at best, small communities where hardships in the supply of water are common. Thirdly, the simple model provides no flexibility in how beliefs and actions are actually manifested. As discussed earlier, there is strong evidence for beliefs affecting water use and conservation behaviour. Finally, although water utilities today are interested in assessing customers willingness to pay, their roles also now require a water conservation component using advertising campaigns and more complex social involvement. The simple agent-based model contained no such behaviour for the utility agent. The agent-based water memes model was formulated to extend on these limitations of the simple model. In particular, it sought to explore the effects and transfer of water-saving beliefs on water conservation using a memetic framework. The water memes model demonstrated realistic seasonally fluctuating water use behaviour, with households using approximately 800 litres per day (see Fig. 1.8). Furthermore, with water worry, residents are found to reduce their water usage significantly, explained pri-

24

1. Exploring Water Conservation Behaviour through Participatory Agent-Based Modelling 80 70 60

%

50 40 30 20 10 0 Water Savers

Water Utilitarians

Figure 1.13: Distribution of water savers and water utilitarians under water worry.

marily by the uptake of water-smart garden technologies such as using a bucket or medium/high usage drip systems to water the garden. It was found that with increasing numbers of friends in residents friendship networks, there was a greater trend towards water utilitarian beliefs irrespective of water worry (see Figs. 1.14 and 1.15). Underlying this trend towards water utilitarian beliefs is the implicit cost associated with copying water saver memes. For example, if a resident finds a water saver resident who uses a bucket to water the garden, then this resident would have to believe strongly in water saving to change from hose watering, to this method of watering the garden. Hence, even with water worry, where residents are concerned about the capacity of the dams and seek out water savers to copy, the extent of their friendship networks can have a large impact on the uptake of water saving behaviours. In this case, the water saving memes are lost from the population based on cliquey friendship networks. This selection on the basis of water memes was demonstrated in Figs 1.16 and 1.17, where the water utility can seek to influence residents behaviour when the capacity of the dam becomes critical. In this case, the water utility engages in a water saving campaign, where water saving ideas (water

4. Discussion

25

80 70 60

0 Friends

50 40

3 Friends

30 6 Friends 20

9 Friends

10 0

Figure 1.14: Proportion of water savers as the number of friends per resident increases in a non-water worried scenario.

saver memes) are distributed to residents with the water bills. Ironically, even though the summertime water usage is seen to decline marginally with the uptake of rainwater tanks and water saving garden technologies, the winter water usage is seen to increase. Those water memes lost from the system were unsuitable for the current water-using environment. For example, the low frequency usage drip system did not satisfy residents gardens water requirements. Further study is required into the actual imitation behaviours of people to water use and technology uptake. In particular, further observation and empirical studies of the effects of friends on imitation behaviour is needed. The basis of the water memes model assumes that friends and family have a large effect on the degree of imitating behaviour. Such studies will allow for the rates and degree of infection to be determined more accurately. Studies to determine the actual distribution of memes within communities and the impact this has on spread and infection rates will aid in calibration/validation issues relating to the water memes model. Finally, there is a tendency to use agent-based models in a predictive exploratory capacity. Such uses are fraught with problems such as calibration, validation and sensitivity which all serve as inputs into the variability


26

100 3 Friends

90

Number of water savers [%]

80

6 Friends

70

9 Friends

0 Friends

60 50 40 30 20 10 0

Figure 1.15: Proportion of water savers as the number of friends per resident increases in a water worried scenario.

60

1000

55

900

50

800

45

700 600

35

Litre

Litre

40

30

400

25

300

20

200

15

100

10 5

500

0

100

200

300

400 Time

500

600

700

0

0

100

200

300

400 Time

500

600

700

Figure 1.16: Decrease in mean water waste (left panel) and increase in wintertime mean total water use (right panel) in a daily simulation with water-worried residents and a utility randomly campaigning with watersaving ideas.

4. Discussion

27

80

Number of Memes in Population

70 60 50 GARDEN5F1

40 GARDEN3F3

30

CLOTHES2F2 GARDEN3F2

20 10 0

Water Mames

Figure 1.17: Change in distribution of water memes throughout the population of residents from the initial situation (dark gray) to the final one (light gray).

of the output of an agent-based model. As observed in Richardson [2002], a strong focus on the model rather than the modelling process dominates the field of bottom-up computer simulation. It is the authors view that the use of a participatory modelling process is a powerful inclusion into the method of agent-based modelling helping to take the focus away from the model itself and to address issues of validation. Future residential demand models are expected to analyse alternative demand programs, such as the promotion of low-consumption technologies [Arbues et al., 2003]. This chapter has demonstrated the utility of an agent-based approach for incorporating aspects such as social networks and imitation into the study of water conservation behaviour. In particular, the water memes approach has been shown to be a productive framework for the exploration of different scenarios for the adoption of different water use

28


Table 1.5: Water memes profile for the strongest water utilitarian belief and water saver belief. Meme Key Name Frequency

80% Water Utilitarian Born 5% Water Saver Shower Normal Shower Medium

90% Water Saver Born 47% Water Saver Shower AAA Shower Low

Key Name

Toilet Dual Flush

Toilet Dual Flush

Key Name

PrepareFood Running Tap

PrepareFood Kitchen Sink

Key Enable

CheckLeakyToilet False

CheckLeakyToilet True

Key Name Frequency

DishWasher Kitchen Sink High

DishWasher Kitchen Sink High

Key Enable

RainwaterTank False

RainwaterTank True

Key Name Enable

BrushTeeth Tap Low

BrushTeeth Glass Medium

Key Enable

CheckLeakingTap False

CheckLeakingTap False

Key Name Frequency

Garden Fixed Sprinkler Low

Garden Bucket High

Key Name Frequency

WashClothes Many Loads Medium

WashClothes Ensure Full Load High

5. Acknowledgements

29

technologies, producing realistic water usage based on residential water use behaviours and beliefs. Furthermore, it is concluded that utilising a memetic framework in the software development of an agent-based model, where the specifics of the behaviours are encapsulated within the meme, provides an extremely flexible object-oriented implementation. With further research into the role and importance of imitation in the uptake of water use beliefs, behaviours and devices, it would be possible to use this approach to study and forecast the potential impact of campaigns targeted for specific water user profiles. Embedding such a study within the participatory agent-based methodology can start a dialogue with participants and stakeholders, engaging and increasing their awareness surrounding the issues of water use and water conservation as well as providing a valuable validation process for the modeller. 5. Acknowledgements All authors would like to thank Andrea Castelletti. Without his generosity of spirit, patience and willingness to share his time and expertise with latex and matlab this book chapter would be only a possibility. Thank you so much Andrea. We are in your debt. 6. Bibliography ABS (2001). Australia’s environment: Issues and trends. Australian Bureau of Statistics Catalogue. Arbues, F., M.A. Garcia-Valinas and R. Matrinez-Espineira (2003). Estimation of residential water demand: A state-of-the-art review. Journal of Socio-Economics 32, 81–102. Barreteau, O. (2003). Our companion modelling approach. Journal of Artificial Societies and Social Simulation. Barreteau, O., F. Bousquet, C. Millier and J. Weber (2004). Suitability of multi-agent simulations to study irrigated system viability: application to case studies in the senegal river valley. J. Appl. Stat. 80, 255– 275. Biddle, R. and Noble J. (2002). Reflections on crc cards and oo design. In: Proceedings 40th International Conference on Technology of ObjectOriented Languages and Systems (TOOLS Pacific 2002). Vol. 10. Sydney, AUS.

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