Simulating impacts of water trading in an institutional perspective

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Environmental Modelling & Software 24 (2009) 191–201

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Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

Simulating impacts of water trading in an institutional perspective

Alexander Smajgl a,*, Scott Heckbert a, John Ward b, Anna Straton c

a CSIRO Sustainable Ecosystems, Townsville, Australiab CSIRO Land and Water, Adelaide, Australiac CSIRO Sustainable Ecosystems, Melbourne, Australia

a r t i c l e i n f o

Article history:Received 5 May 2007Received in revised form 7 July 2008Accepted 20 July 2008Available online 24 September 2008

Keywords:Water tradingAgent-based modellingInstitutionsOutback Australia

JEL classification:C69D02D78Q25Q56

* Corresponding author.E-mail address: [email protected] (A. Smajgl).

1364-8152/$ – see front matter � 2008 Elsevier Ltd.doi:10.1016/j.envsoft.2008.07.005

a b s t r a c t

Water availability in outback Australia is defined by the occurrence of large rainfall events, and changesoften from a situation of scarcity to temporary abundance. Informal institutions are often able totranslate such dynamics into sustainable water-use rules. Policy interventions are mainly focused onchanging access rules to avoid over-use or inefficiencies. Such formal institutional changes can lead tounexpected unsustainable outcomes; outcomes that are often captured in the ‘story’ behind informalarrangements.This paper analyses one case study on water access in outback Australia and translates field work resultsinto an agent-based model. The agent-based model is calibrated based on data from experiments con-ducted with actual farmers from the case study region. In order to project unintended outcomes ofinstitutional changes, interventions in water access is explored in an applied context. The core focus ofthe modelling exercise is the treatment of newcomers on a newly created trading scheme for wateraccess rights. Simulation results show that total water extraction is significantly lower if the burden ofwater restrictions is limited to newcomers while the regional economic performance is not statisticallydifferent from a case in which the burden is carried by all irrigators. Interview data documents thatcurrently extraction levels are regulated by informal processes as community members communicateobservations on river health to irrigators who adjust accordingly, which indicates perceived personalresponsibility. Overall, the environmental and economic performance of a water trading scheme that willreplace this informal self-regulating system depends on how water restrictions are implemented andhow valid the overall cap of 20% outtake is, which is not widely accepted throughout the community.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

One of the fundamental characteristics of a social system isa development of shared concepts (Rawls and David, 2005). Thoseshared concepts constrain behaviour of individuals through formaland informal rules and set of norms. The rules and norms devel-oped within the social system are defined in the theoreticaldiscussion as institutional arrangements (Dietz et al., 2003). In otherwords, institutions are ‘‘the shared concepts used by humans inrepetitive situations organized by rules, norms, and strategies’’(Ostrom, 1999).

A critical aspect of institutional arrangements is how propertyrights are arranged, especially in the context of constraining andenabling access to natural resources like water. Property ‘‘is a claimto a benefit or income stream, and a property right is a claim to

All rights reserved.

a benefit stream that some higher body – usually the state – willagree to protect through the assignment of duty to others who maycovet, or somehow interfere with the benefit stream’’ (Bromley,1992: 4). Property rights can therefore be defined as the individualcomponents of relationships comprising institutions (Brunckhorstand Marshall, 2007) and can assign rights to individuals (privategood) or to a community (common good).

Agrawal (2002) argues that institutions have to be analysedwithin their context, as the same rule can have different impacts indifferent contexts. This paper argues that changing an institutionchanges the context for individual decision making as constrainingor enabling parameters have changed. By changing the contexteven long-existing institutions can change their impact and lead toa different outcome. Therefore, institutional change modifiescontext and change in context has the potential to trigger unin-tended ripple effects. Institutional ripple effects describe changes inthe effectiveness of institutions that existed before a new institu-tion was introduced (Smajgl and Larson, 2006). This leads into twoimportant research domains: first, how the understanding of

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A. Smajgl et al. / Environmental Modelling & Software 24 (2009) 191–201192

institutional dynamics can be improved (Smajgl and Larson, 2006)and, second, how such dynamics and their impacts can be simu-lated. This paper is focused on the capacity to simulate institutionaldynamics and does so applying agent-based modelling to a casestudy in the Northern Territories, Australia. The agent-based modelis applied to the Katherine region and simulates the impact ofa cap-and-trade scheme for water extraction permits. Section 2describes the context and Section 3 the agent-based model. Thecalibration of agents’ behaviour is based on experiments (Section 4)with a sample of the real-world farmers that are modelled. Inter-views and simulation results are analysed from an institutionalperspective (Sections 5 and 6).

2. Simulation needs

Simulation models allow testing perturbations of real-worldsystems without risking negative effects of real-world experiments.In the institutional context simulation models could provide thecapacity to test net impacts of institutional changes. This wouldrequire assumptions on how a new institution impacts othersystem components, including how existing institutions wouldadapt.

Additionally, even simplified models have the capacity toidentify patterns of interaction between two or more variables. Ifthis interaction is located in the institutional domain humanbehaviour with respect to new institutional constraints becomemost relevant. In a modelling perspective, behaviour will bedefined in behavioural response functions, which define humanbehaviour (strategy choice) as a function of explaining variables (i.e.water price changes) in the context of relevant perturbations (herethe water trading scheme) (for a similar definition see Rehmanet al., 2003).

This leads to the question about how individual behaviour linksto institutions. An improved understanding would allow enhancingthe ability of simulating institutional dynamics and institutionalripple effects. It also points out that in the context of appliedresearch behavioural characteristics of individuals that forma group have to be identified. Clearly, individual behaviour andcollective behaviour has to be distinguished (Camerer, 2003). Thispaper applied a sequence of field experiments in order to identifybehavioural changes under modified institutional arrangements.

The context for this paper is water access rights in the Katherineregion of Northern Territories, Australia. Specific to the real-worldresource problem, the water licensing authority has receivedapplications for additional groundwater extraction licences in theregion. However, concerns from various local groups have beenraised regarding whether granting the pending licences willremove groundwater from the aquifer that otherwise would bedischarged into the river system (see Straton et al., 2007). Apotential trade-off therefore exists between agricultural productionthrough irrigated land uses and the maintenance of environmentalflows in the river, and the provision of environmental (and cultural)benefits provided therein.

Current plans target the introduction of a trading scheme forwater access rights in order to use amounts of groundwater moreefficiently in agricultural production without compromising envi-ronmental needs. Scientific advice (Puhalovich, 2005) identifiesthat the Katherine River is mostly recharged by the Tindall aquifer.As ecological values (i.e. threatened species) as well as communityvalues (i.e. fishing and cultural uses) depend on these rechargerates, it has been suggested through public consultation processes(see Straton et al., 2007) that up to 20% of the recharge amount canbe withdrawn from the aquifer without compromising these non-market values. This threshold was used for approving licenses forcurrent irrigation activities. As some of the licenses are not utilisedto their full extend scope is seen for increasing economic efficiency

by creating a market for water rights, along with the rule: ‘‘Use it,trade it, or loose it’’

The simulation tool presented in this paper analyses potentialimpacts of such an institutional change. We also discuss ina broader theoretical approach how institutional change couldimpacts such agricultural systems in a long-term perspective. Thenext section describes the model followed by the field experimentbased model calibration.

3. Model description

The Tindall Aquifer Water Trading Model simulates a populationof horticultural producers (n¼ 56, and is dependant on scenariospecifications) who are involved in irrigated production land usessuch as mango production. The simulation technique used is that ofagent-based modelling (for an overview of agent-based modelsapplied to land use, see Parker et al., 2002), with the simulatedproducers referred to as ‘agents’ who perform a variety of behav-iours within the model that mimic real-world behaviours of irri-gators in the region. The methodological choice is based on theability of agent-based models to simulate individual behaviour ina bottom-up perspective (Epstein, 2007), which allows the simu-lations of emergence in social systems and the exploration of ‘‘aricher suite of policy options’’ (Batten, 2007: 655).

The real-world system can be thought of as complex adaptivesystem, which are systems defined by the fact that they evolveover time the presence of feedbacks and are sensitive to initialconditions (Holland, 1995; Gross et al., 2006). The simulationproceeds at a fortnightly time step, with events occurring duringeach time step throughout the production year. The fortnightlytime step was selected because it was the finest resolution timescale for available data such as rainfall, and casual labour contractstypically are set to a fortnightly pay period. Technically, the modelis written in the Java programming language, and uses the Repastsimulation toolkit1. For technical details see Heckbert et al. (2006).

The core element of the agent-based model is the biddingbehaviour of horticultural agents within a water market in whichagents may buy and sell water access entitlements from each other.The sequence consists of four major steps, as follows:

First, agents calculate the difference between actual waterrequirements for a given month and their licence extractionvolumes, thereby identifying their potential supply and/or demandfor that period. Desired water use is based on crop water require-ment data for agents’ crop, based on recommended minimumwatering level (ML per ha) for optimal production, as used in theindustry. The water use decision made by an agent is this croprequirement value, up to the constraints of their water licence.Agents then provide (or require) a volume of water to (from) thewater market based on the discrepancy between crop requirementsand licensed water entitlements.

Second, potential buyers and sellers calculate their bidregarding quantity and price for water based on their marginalvalue for water, as well as a mark-up which serves to deviate theprice value from the marginal value. This mark-up is calibratedfrom experimental data as described in the following section.Determining agents’ marginal value is accomplished throughcalculating the value of the water demanded divided by the volumedemanded. The value of water is determined by forecasting theprofit that could be generated under current water use versus profitderived with water use adjusted to accommodate demand. Thiscalculation takes into account the past history of outcomes for cropwatering (through groundwater extraction and rainfall) throughoutthe production year (reset at the start of the growing season), and

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A. Smajgl et al. / Environmental Modelling & Software 24 (2009) 191–201 193

includes forecasted prices for total output produced at harvest time(forecasted using recommended irrigation rates).

Once an agent has calculated their price for water, they entera market for trading water entitlements placing offers to sell or bidsto buy entitlements from other agents. As this model simulatesa double call market structure, potential buyers randomly accessone offer to sell, and compare the price on offer with their marginalvalue. If the marginal value is higher than the selling price, they buythe water. The randomness of access approximates the system asplanned by the relevant real-world government departments,where water rights holders can place bids in an online system, andothers can immediately buy the entitlements via that medium. Thismeans a first-come first-serve situation has to be mimicked asdescribed here.

The buyer may purchase the volume of water up to theirdemanded volume. If the buyer purchases the seller’s entire supplyand has not fulfilled their demanded volume, the buyer proceeds tothe next seller’s offer and repeats the process. Once the buyer haspurchased their demanded volume or no offers to sell havea sufficiently low price, the next buyer agent repeats the sameprocess until all demand is satisfied, all offers to sell are purchased,or no more transactions take place due to discrepancies in buyingand selling price. Once water is bought/sold, the licensed waterentitlements for the buyer and seller for that particular month areupdated. Hence the trading is for the right to access water amongthe computational agents, and does not represent a physicalexchange of water. The buyer incurs a cost according to the seller’soffer price and the volume demanded, and the seller receives andassociated revenue. The market process is now complete for thegiven month, agents realise their actual water use levels, and themodel may proceed to calculate the outcome of the agents’decisions.

The third major model step represents the realisation ofproduction outcomes once water use decisions and marketoutcomes are known. Water is applied to crops based on thelicensed volume allocated to agents, including trades on themarket. The crop production function is represented by a logisticgrowth function, where the growth rate is dependant on water use(thorough rain and groundwater application) and crop require-ments, such that:

O0i ¼ Ai

�Ot�1 þ

�rW�

1� Ot�1

Omax

�Ot�1

��

Where Ot is the cumulative output [trays/tree] production in periodt, Ai is the area (ha) under production, Omax is the carrying capacity,or full production limit, and rW is the growth rate [0–1], dependanton water use, such that:

rWit ¼

rMaxW � rW¼0

Wt

!ðRt þ NitÞ

!þ rW¼0

Where, rW¼0is the productivity associated with the minimum levelof recommended water use Wt, and rMaxW is the productivityassociated with maximum output. Hence, r is a linear function fromlowest to highest productivity depending on moisture from irri-gation and rainfall.

The timing of watering is crucial because of changing crop waterrequirements throughout the year. Harvesting occurs at theappropriate time of year, and labour is consumed in the harvest,with producers receiving revenues based on volume of producetaken to market. At this point, agents’ profit is realised throughthe total amount of revenues earned through selling produce on themarket as well as revenues derived in the water market. Associatedcosts include a variable and semi-fixed costs of production cali-brated from secondary literature, as well as costs incurred on the

water market. The realised profit levels can then be considered bythe agents in their decisions on potential land use changes.

The fourth step is agents’ adaptive behaviour. Agents considera number of possible changes to their farm, including:

� increase of off-farm income where one family member wouldremove their labour contribution to farm activities and gainpaid employment elsewhere,� altered water use decisions,� decreased production levels and sell excess water on the water

market to gain revenues therein and� increased production levels and use the water market to

purchase necessary water volumes to support crop growth.

In order to capture realistic changes in land-use decisionsresulting from the introduction of a water rights trading schemetwo elements had to be simulated: (1) the ability of agents toobserve other agents strategy choice and (2) to add those obser-vations to own considerations of physical, financial and knowledgeconstraints. The process therefore examines others’ strategies forthe above examples within the agent’s own on-farm conditions, toexplore the space of outcomes if different strategies wereemployed. Agents may adopt desirable strategies found therein.

The core cycle of the four steps described above regarding wateruse, bidding behaviour within the water market, crop outcomes,and adaptive behaviour is embedded in a model of the broadersystem. First, the imposition of water use restrictions is the firstdriver for the need to trade water access rights. The calculation ofwater restrictions is based on a simplified hydrological model of theTindall aquifer which measures the aquifer’s volume and dischargeinto the Katherine River based on rainfall patterns. Governmentand community organisations have identified a minimum accept-able flow rate within the river to be at least 80% of the naturalenvironmental water flow. To maintain this, extraction of ground-water from the Tindall aquifer, which discharges into the riversystems, is restricted accordingly.

This defines a minimum level of aquifer volume, past whichgroundwater extraction levels are ‘capped’, thereby maintainingthis minimum acceptable volume, and hence the flow of water intothe Katherine River. It is assumed that 80% of aquifer volumecorresponds to 80% of environmental flow, although in realityaquifer dynamics would challenge this assumption. The minimumvolume is ensured through ‘capping’ the licensed extraction levelsfrom irrigators by the ratio of difference between total waterlicences and maximum extractable water, depending on how waterrestrictions are distributed within the community of irrigators.

The simulation model has a number of scenario specificationsthat aim to compare institutional arrangements regarding thedesign of the water trading scheme. Discussed in this paper is thedetermination of ‘newcomers’ into the community of licensedwater users, the imposition of a cap to maintain environmentalflows, and the distribution of water use restrictions associated withthe cap whereby newcomers must bear the burden of capped watervolumes, or if all water users equally are affected by the cap.

4. Experiment-based model calibration

The model parameterisation includes two critical values:

(1) price of first bid,(2) bid adaptation process based on perceived price signals.

As the market for trading water rights does not currently exist,these two values cannot be based on historic data. Instead, they canbe parameterised based either on expert opinion or on historic datafrom other locations. It is also possible to use both of these options


at the same time and weigh data from other locations by expertopinion. A further technique is employed in this project, utilisingfield experiments with actual producers in the region to elicitbidding behaviour in a hypothetical market. The case studydescribed in this paper is based on field experiments. The format ofthe field experiments is outlined in Ward et al. (2006), whereexperiment subjects are asked to select volumes of water to trade.Results used for calibration of model parameters come fromexperimental treatments controlling for the effect of communica-tion, provision of environmental information, and provision ofaggregate water extraction levels.

The field experiments were conducted in three sessions (Wardet al., 2006), in which 11 horticulturalists from the Tindall aquiferregion around Katherine took part. All 56 producers in the regionwere invited to experience a situation of trading water entitle-ments, of which 11 agreed to participate (see Straton et al., 2007).These producers played the part of individuals within the hypo-thetical water market and decided in monthly time steps if theirgiven water allocation was used in irrigated production, sold on themarket or if additional water rights were purchased. As incentive,all participants had equal opportunity to earn money during these2-day experiments, with earnings varying from AU$20 to AU$500.Results allowed comparison of how bidding behaviour changedbetween a closed and an open call market and how information onexternal effects changed individual strategies. The main purposewas to collate data for the two critical parameters of the model, firstbid and bidding adaptation.

As the resulting database was limited in size, econometrictechniques such as cluster analysis were not employed to generatea grouping for the first bid value. Instead, it was assumed thatthe participants of the field experiment were representative for theregion, and hence the range of values realised in the experimentswould be related with the range of values observed in a real marketsetting for the region. Therefore, the bidding strategies observed bythe 11 farmers in the experiment are used to calibrate the biddingbehaviour of the population of 56 farmers simulated in the model.This was done according to the range of marginal values observedin the experiments, and normalised over the range calculated foragents in the model. The strategy associated with each range fromexperimental data was applied to the simulated agents in equiva-lent ranges of calculated marginal values. While calibrating themodel parameters for the first bids of each agent is simply based onthe experimental data on the difference between marginal valueand first bid (equalling the mark-up), the adaptation behaviourrequires the identification of explicit rules. Calibrating biddingadaptation is based on rules that define a grouping of agents. Theserules are based on changes in bids in relation market success andmarket clearance price. The following section describes the biddingstrategies in further detail.

To mathematically represent the bidding strategies, each agentwas programmed with a ‘price mark-up’ variable, which deviatesthe price an agent uses when making bids on the market away fromtheir calculated marginal value. This mark-up represents a varietyof effects on price which are not otherwise captured in the marginalvalue calculation as described above. Simply basing the price onmarginal values would be appropriate for traditional neo-classicalassumptions regarding agent rationality, but is limited in its abilityto capture some of the more interesting processes that may affecta real producer’s behaviour towards pricing water (see for exampleWard et al., 2006).

Bidding strategies may contain ‘rules’ for buying and selling (orone but not the other), and describe how an experiment participant(or a simulated agent) varies their dollar values put to the marketaway from their ‘rational’ marginal value amount. The 11 biddingstrategies observed in the economic experiments are as follows,and are summarised visually in Fig. 1:

� Strategy 1 corresponds to agents within the range of the lowestobserved marginal value. Hence, it was in their best interest toonly make offers to sell water allocations to other bidders withpotentially higher willingness to pay values. It was observedthat the selling offers using this strategy were set at a constantrate of above the perceived marginal value.� Strategy 2 and strategy 3 again apply to only selling offers,

and have an increasing value based on the success of markettransactions. If a transaction occurs, the agent will proceed toraise the bid to the next highest level in an attempt to gainmore revenue in the following potential transaction. Ifa transaction does not occur, they maintain their bid for 6months. If still no transaction has occurred at this time, thebid value goes back down to the next lowest bid, repeatingthis process over time.� Strategy 4 is the first of a number of ‘converging’ strategies

observed. This strategy again only applies to selling bids. Theagent begins with a large deviation from their marginal value(in an attempt to gain the most revenues from selling theirallocation), and slowly proceeds to ‘test’ the market with anoverall trend downwards, converging on the marginal value. Inthe following year, the difference between their marginal valueand last period’s bidding price is again subject to this pattern,such that a convergence continues to occur toward themarginal value over time.� Strategy 5 is similar to strategy 1, in that there is a constant

price value in relation to the agent’s marginal value, and againonly applies to selling bids. The difference is that the agent willattempt to sell water at a high price during the periods wherewater demand is likely to be highest, and failing a successfultransaction, will revert to a lower value.� Strategy 6 is the first strategy with both a selling and buying

component. It is similar to strategy 1, in that the offers to sell orbids to buy are set at a constant value throughout the year,depending on whether the agent is buying (price lower thanmarginal value) or selling (price higher that marginal value).� Strategy 7 is similar to strategy 1, but applies only to buying

bids. There is a constant mark-up value, and agents willmaintain the bids to buy at this level below their marginalvalue.� Strategy 8 is a ‘double convergence’ strategy, in that the agent

will make both offers to sell or to buy. Each strategy convergeseventually towards their marginal value, in the same fashiondescribed for strategy 4, above.� Strategy 9 is a buying only convergence strategy, similar to the

prior convergence strategies described, except that it wasshown in the experimental data that this agent overshot their‘rational’ bidding value. Such behaviour articulates that thisindividual perceived a higher value than the economicmarginal value communicated on the screen during theexperiment. The strategy overshoots the marginal value line,but re-converges from the other side.� Strategy 10 is a buying only strategy with 2 components, the

first is a convergence as described above, where bids to buyconverge toward the marginal value, however, a very strongoutlier was found in the experimental data, much lower thanthe converging trend seen for other data points. Hence, thisstrategy behaves like other converging strategies, with anaddition of a stochastic ‘shock’ during one random month ofthe year, where the agent will offer to buy for a markedlylower price, as if testing to see how low they can go to buywater.� Strategy 11 is a ‘double stochastic shock’, in that a trend for

buying and selling water, with non-rational outliers was seen.Hence, the agent will behave in a converging fashion for bothbuying and selling, but both have a number of stochastic

Bidding Strategy Type 1

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Fig. 1. Eleven strategies for making bids to buy or offers to sell, as observed from field experiments, where the price deviates from the perceived marginal value for water. Solid linesrepresent a bid recorded within the field experiments, and dotted lines represent the maintenance of this bid value over time.


shocks. On the buying side, they will buy once a year at wellabove their marginal value. On the selling side, they will sellseveral times during the year at ‘rock bottom’ prices.

Strategies were programmed based on data revealed during thefield experiments. The difference between the true marginal valueand the price actually chosen by human experiment subjects wasmeasured and loaded into the model as a set of reference data.From this, each strategy draws the difference between the price andmarginal value to calculate the mark-up value. Pseudo code rep-resenting the strategies is presented below;

if(strategy¼¼ 1){alpha¼ revealedPriceStrat1/margValueStrat1;// constant value

}if(strategy¼¼ 2){alpha¼ revealedPriceStrat2base/margValueStrat2;// base

markupif (madeTrade¼¼ true){alpha¼ revealedPriceStrat2nextAlpha/margValueStrat2;//

increase alpha markuptradeCountdown¼ 12;}tradeCountdown–;if (tradeCountdown< 1){alpha¼ revealedPriceStrat2lastAlpha/margValueStrat2;//

decrease alpha markup


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Fig. 1. (continued).


}}/*strategy 3 through 11*/

5. Scenario definitions and simulation results

Simulations representing a population of agents involved inirrigation farming were run according to the model structure laidout in Section 3 above. The simulation runs were altered accordingto various scenarios to determine the effect of different policyinstruments (and their structure and implementation). Modelscenarios are described below and define different options for the

introduction of a trading scheme for water entitlements. Reportedhere are outcomes of a policy directed to the implementation ofa market for water entitlements, with conditions for who withinthat market is subject to possible restrictions to their license whenthis policy instrument is introduced.

Scenario 1 represents a baseline ‘business-as-usual’ situation,where new applications are not granted (n¼ 18), and a watermarket is not implemented. Scenarios 2 and 3 represent the situ-ation where the outstanding applications for groundwater extrac-tion are granted (n¼ 56), and are subject to the ability of a regulatorto implement a cap on water extraction (through altering licensedvolumes), as discussed earlier.

The difference between these two scenarios is that in scenario 2,all licensed water users (the original users and the new applicants)

Fig. 2. Trading volumes of water access rights, presented as Repast-generated graphical output.


must adjust their overall entitlement in the situation where theregulator implements a water restriction. The restriction is borne inthis scenario by every water user, by the same percentage of theirtotal water use. Scenario 3, however, alters this responsibility tobear the effect of water restrictions, and makes the new applicantswho have recently had their licences approved bear the totalresponsibility of any water restrictions that the regulator sets. Inthis sense, the original irrigators who were licensed before theimplementation of the water market have their entitlements‘grandfathered’ into the new system, and may produce as they hadbefore the creation of the water market and the implementation ofrestrictions. The newcomers, however, must not only bear the

Fig. 3. Rainfall and total groundwater extraction by simulated agents

restrictions that may be required as a result of their increased waterextraction, but also cover the amounts that original userscontribute to water shortages through adjusting their licences bya higher percentage than would have occurred under scenario 2.

Of particular interest, the effect of each of these scenarios ismeasured on a variety of indicators. Reported here are the effectson:

� otal amount of groundwater extraction for irrigation (Ml),� otal profit derived from irritation production ($),� he distribution of profit within the community of irrigators, as

measured by the Shannon diversity index (S), where

for the base case scenario of 18 irrigators and no water market.

Fig. 4. Total profit derived by simulated agents for the base case scenario of 18 irrigators and no water market.


S ¼ piP0B@1� log

0B@ piP

1CA1CA

ipi

ipi

where S is the Shannon diversity index at time t, p is profit forindividual i¼ 1.n.

The Repast simulation toolkit was used to simulate the modeldescribed in Section 3. Each simulation was run for 528 time steps,each representing a fortnightly period, extending over 22 years ofhistorical rainfall data for the region. Results are generated suchthat only the scenarios under question are altered for the simula-tion, and are done so at model initialisation. Fig. 2 shows the tradedvolume of water access rights in one of the simulation runs. Theseries of Figs. 3–7 depict the mean outcome for 30 simulation runs,per scenario. The variation observed in output data comes from twosources: variation from stochastic elements, and variation fromchanges in scenario specifications. In each of the following figures(where relevant) a mean value across multiple simulation runs isdisplayed with confidence intervals on either side. For each meanand confidence interval, sets of simulation runs were performedholding scenario specifications constant. From this, the confidenceintervals, as shown, reveal the statistical range of variation attrib-uted to stochastic elements. The variation explained by changes in

Fig. 5. Total groundwater extraction by simulated agents for two scenarios, both employingseen to realise significantly lower groundwater extraction levels.

scenario specifications is determined through comparison of sets ofsimulations, in other words, by comparing the difference in themean and confidence intervals over the temporal extent of thesimulation. Multiple runs are performed, comparing changes toscenarios to ‘baseline’ conditions; the difference reveals variationfrom changes in scenario specifications.

The mean value across the 30 simulation runs is depicted by theprominent line, with the associated confidence intervals (a¼ 0.05)depicted by the lighter lines on either side of the mean value. Themultiple simulation runs and reported confidence intervals areneeded to compare sets of simulations runs against each otherbecause stochastic variables are present. In this case, we comparemean results from 30 simulations of the baseline conditions againstthe mean results of 30 simulations under a different scenario. In thefollowing figures we interpret trajectories whose confidenceintervals to be statistically different from one another.

Fig. 3 depicts outcomes for extraction under the base casescenario by the original 18 irrigators licensed. The assumption ofhistoric rainfall data leads to two periods of wet years (after year13) that reduce volumes of groundwater extraction.

Fig. 4 depicts total profit under the baseline scenario for theagent population over the 22-year simulation run. The downwardtrend is explained partially by forecasted prices (set to decrease to2013 and level out for important commodities such as mangos),

a water market. Scenario 3 where ‘newcomer’ agents solely bear water restrictions is

Fig. 6. Total profit for simulated agents for the two water restriction scenarios, both employing a water market. Outcomes between the two scenarios are not significantly differentfrom one another.

2 The average between scenarios 2 and 3 is taken here for this rudimentarycalculation.


and is affected mainly by crop production and the availability oflabour during harvest time.

The following figures depict outcomes comparing scenarios 2and 3, with water restrictions borne equally in scenario 2, andwater restrictions borne solely by newcomers in scenario 3.

Fig. 5 depicts total groundwater extraction volumes from theTindall aquifer for all agents. There is a significant differencebetween the two scenarios, showing that in scenario 3, wherenewcomers solely bear the burden of water restrictions, extractionlevels are lower for the population as a whole. It should be notedthat the total restricted volume is not lower in this case, but ratherthat it is not distributed evenly across the agent population. Theoverall lower extraction volumes observed can be attributed to thefact that the extra financial stress placed on new applicants boththrough decreased output under water restrictions, and alsoa higher price being required to be paid on the water market duringtimes of water restrictions. Both effects contribute to lower overallpurchases on the water market, and hence lower extraction.

Fig. 6 depicts the outcomes for total profit for the population ofagents for scenarios 2 and 3. The trajectories of profit between thetwo scenarios are not significantly different at any point during thesimulation. The difference in water restriction rules is not seen tosignificantly alter the total profit derived by the population asa whole. This could be because the effect on agents affected bywater restrictions is hidden within the larger population, or thatlosses by newcomers are balanced out by gains made by those whodo not face restrictions.

Fig. 7 describes the Shannon diversity index for profit, asa measure of dispersion of profit within the agent population. Alarger S value corresponds with a more even distribution acrossagents. Comparing scenarios 2 and 3, we see approximately fourperiods during simulation runs where the distribution of profitwithin the agent population is significantly different between thetwo scenarios, corresponding roughly to times 9, 13, 17, and 19 to 21.

6. Discussion

The focus of this paper was the development of an agent-basedmodel in order to improve our capacity to simulate institutionaldynamics using as a case study the introduction of a market forwater access rights in the Katherine region, Northern Territories ofAustralia.

Experimental data of our field work showed differencesbetween bidding behaviour in an open market, a closed market anda non-market situation. Depending on the marginal value of water

each farmer selected different mark-ups and just some irrigators’bids converged over time towards their marginal value. The focus ofthese experiments was the effect of information on environmentalimpact the experiments were not controlled regarding identifyingthe institutional footprint of a water market in the Katherine Riverregion.

The simulation results identified that under different rules forwhich part of the irrigation community carries the burden of waterrestrictions, the levels of total extraction vary significantly. Thismatters because the overall extraction cap of 20% of available waterto protect environmental flows is not widely considered sufficientand higher usage levels imply higher environmental risks. At thesame time, the difference in total profit generated from the use ofnatural resources is not statistically significant between the twoscenarios. These results suggest that implementing a marketwhereby the burden of water restrictions is solely borne by newapplicants yields a lower environmental risk at equal economicefficiency. Compared with the baseline scenario, the total profitover the 22-year period2 when the new applicants are licensed anda market introduced is approximately three times larger than thetotal derived in the baseline scenario. This should not be taken asa precise economic estimate of the value of this future possiblescenario, as this was not the primary focus of this research project,and is highly dependant on model assumptions. However, the levelof potential value increase is notable from the initial 18 licences to56 under the set-and-cap scenarios employing a water market.Furthermore, this situation allows the maintenance of theminimum environmental flows through the policy instrument ofcapping water licences. The success of such an instrument is ofcourse dependant on irrigators complying with their licensedvolumes, and raises the issue of monitoring and enforcement.

One of the critical questions is how representative the 11producers (20% of the population) are. It can be interpreted that theprocess (invitation of all producers) led to a random selection ofproducers. But it can also be stated that the process led to a selectionthat is not representative. As mentioned earlier, model validationand evaluation is difficult for a model that aims simulating aninstitutional situation that does not exist (Jakeman et al., 2006) inorder to inform the decision making process if such an institutionalchange could be recommended. Empirical data does not exist to testif, for instance, any given sample delivers data on representative

Fig. 7. Shannon Diversity Index of profit for simulated agents for the two water restriction scenarios, both employing a water market. Differences are significantly different from onescenario to the other during time periods 9, 13, 17, and 19 to 21.


behaviour. A strategy for the underlying model would be to repeatthe experiments with the remaining 80% of the population. Otherfuture options for model evaluation are the discussion of modelassumptions and simulation results in workshops.

However, these modelling results are limited to one environ-mental and one economic indicator and require in the appliedpolicy setting a broader institutional discussion. From a theoreticalperspective and from related experimental results (Reeson, 2008)evidence emerges that institutional changes such as the introduc-tion of a water market can have significant institutional rippleeffects. As a result of changes in formal rules, informal rules adapt.Reeson (forthcoming) shows that changing conditions for usinga natural resource leads to long-term behavioural consequences.Reversing the institutional change does not lead to the populationreverting to behaviours that were initially chosen. Therefore,people show an ‘institutional footprint’. People learned that the setof institutions can be different and following from such experiencesthey modify their decision making. For instance, people that showaltruistic behaviour under a common property regime seem toreduce their individually perceived responsibility after havingexperienced a competitive situation. In other words, individuallyperceived responsibility is transferred from own behaviour to ‘themarket’, ‘the market regulators’, or ‘newcomers’.

For the case of the Tindall aquifer, interviews have shown thatexcessive groundwater extraction has historically caused reducedfishing possibilities, a concern which has been communicated by thecommunity. If an individual gets their intrinsic motivation frombelonging to the community, such communication of informalpressure is linked to behavioural responses, in that persons that wantto be part of the community are likely to signal with their behaviourthe intention to reduce their extraction levels. The downside of a cap-and-trade system can be that it replaces perceived responsibilitywithin the community, which is likely to impact extraction rates inthe long-term if a water market was just a temporary institution.

In other words, informal institutions in this case have in the pastbeen partly responsible for regulation of environmental flows.Adding a market as a formal institution to perform this duty couldpotentially be successful, but the situation exists where communitymembers then transfer their sense of community responsibilityonto the functioning of the market. Were the market to undergochanges or cease to exist sometime in the future, the situation couldarise where both the informal and formal rules for regulating theenvironmental flows are not present. Unfortunately, the experi-mental design this paper has developed for obtaining data for the

bidding behaviour did not include these potential behaviouralchanges as the political focus was set on the primary effects ofa trading scheme and its consequences regarding efficiency.

The main reason for policy makers to introduce a market forwater access rights is higher efficiency of potential groundwateruse. Current use levels are perceived as being inefficient. Modelresults show that extraction levels increase, which means that thepolicy goal of more development potential for horticulture in theKatherine River region could be confirmed while still maintainingminimum environmental flows. The rule ‘Use it, trade it or loose it’plays a major role in the behavioural change. Total profits generatedin the Tindall aquifer region increase notably, with an overalldifference between the scenarios 2 and 3 that is not significantlysignificant between the two burden sharing options. However, thedistribution of profit within the community between thesescenarios is significant for a number of periods over the 22-yearsimulation. Furthermore, the environmental indicator of ground-water extraction show that the systems as a whole operates closerat the agreed limit of 20% outtake, which maintains environmentalrisks to the recommended maximum level. As this level is notwidely accepted throughout the community, it seems crucial toverify the threshold of acceptable groundwater extraction; espe-cially in the context of such a significant institutional change.

Acknowledgements

The authors wish to thank Silva Larson and Ryan McAllister fortheir thoughtful reviews and two anonymous reviewers for theirconstructive comments. This research was funded by the CSIROEmerging Science initiative Social and Economic Integration, theDesert Knowledge CRC, and the Tropical Savannas CRC.

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