### Abstract

Aggression and other acute harms experienced in the night-time economy are topics of significant public health concern. Although policies to minimise these harms are frequently proposed, there is often little evidence available to support their effectiveness. In particular, indirect and displacement effects are rarely measured. This paper describes a proof-of-concept agent-based model ‘SimDrink’, built in NetLogo, which simulates a population of 18-25 year old heavy alcohol drinkers on a night out in Melbourne to provide a means for conducting policy experiments to inform policy decisions. The model includes demographic, setting and situational-behavioural heterogeneity and is able to capture any unintended consequences of policy changes. It consists of individuals and their friendship groups moving between private, public-commercial (e.g. nightclub) and public-niche (e.g. bar, pub) venues while tracking their alcohol consumption, spending and whether or not they experience consumption-related harms (i.e. drink too much), are involved in verbal violence, or have difficulty getting home. When compared to available literature, the model can reproduce current estimates for the prevalence of verbal violence experienced by this population on a single night out, and produce realistic values for the prevalence of consumption-related and transport-related harms. Outputs are robust to variations in underlying parameters. Further work with policy makers is required to identify several specific proposed harm reduction interventions that can be virtually implemented and compared. This will allow evidence based decisions to be made and will help to ensure any interventions have their intended effects.

Keywords:
Agent-Based Model, NetLogo, Alcohol, Night-Time Economy, Heavy Drinking, SimDrink

### Introduction

1.1
Aggression and other acute harms experienced by young adults in the night-time economy are topics of significant public health concern (Australian Institute of Health and Welfare 2013). Although policies to minimise these harms are frequently proposed, there is often little evidence available to support their effectiveness (Miller et al. 2015). This is partly due to the characteristics of Australia's drinking culture (Room 1988), which reduces the applicability of evidence from many international studies or policy evaluations. Australian evidence for the impact of policies in this area is largely based on natural experiments, where researchers have evaluated the impact of policies after they have been implemented (Kypri et al. 2011; Livingston 2008). This is critical work, but is only useful for post-hoc policy evaluations. In contrast, simulation models provide a means for assessing the likely impact of otherwise untested policies (Dray et al. 2012).

1.2
An overarching difficulty in testing and comparing night-time economy related policies is that the same policy can affect different settings in different ways. For example, although increases in on-licence alcohol prices can lead to people consuming less in these settings, this is offset to some extent by substitution of drinking in public venues for drinking in private venues (Meier et al. 2010) or people drinking at private venues before going out to save money (MacLean & Callinan 2013; Miller et al. 2013). These indirect effects are associated with a different set of harms and need to be weighed against any benefits. Policies can also address specific types of harm that are more prevalent in particular settings. For example, being stranded in the central business district (CBD) after public transport has finished is less likely for those attending private drinking settings. One consequence of setting heterogeneity and interaction is that any model testing policy changes or combinations of changes needs to consider indirect and displacement effects, and should ideally include multiple settings.

1.3
Changes in the night-time economy have different effects upon people of different income, socioeconomic background, geographic place of residence, gender and so on (Hart 2015; Meier et al. 2010). Many policy changes may have a greater effect on a subset of the population; for example changes to alcohol pricing will have more affect upon those with less money, and changes to transport options will have more effect on those who live further away from where they drink (Callinan et al. 2015; MacLean et al. 2013; MacLean & Moore 2014). Models that do not affect individuals differently are prone to error if the results are extrapolated, since they do not properly account for the dilution of effects across the entire population.

1.4
Typical models used to test alcohol policy options often inadequately capture these differences in population and setting characteristics. In particular, most modelling involves little consideration of important variables such as drinking setting and context that are known to impact consumption (Callinan et al. 2014). One way to address this issue is to use agent-based models (ABMs). ABMs use a set of autonomous 'agents' to represent a population and offer a powerful and more complex method for describing human behaviour and local interaction (Gilbert 2008). Agents follow simple behavioural rules and make decisions about how to interact with each other and their environment. Using ABMs, policies can be implemented that only effect the decisions of agents at particular times and in particular settings. Large scale patterns can then emerge from a multitude of local, stochastic interactions. Further, multiple settings and agents with different characteristics can be implemented together, providing a more realistic implementation in a larger environment.

1.5
Using ABMs to address public health policy questions is not new; for example, these types of models have provided great insights into infectious disease transmission (Castiglione et al. 2007; Kretzschmar & Wiessing 1998; Rolls et al. 2013) and illicit drug use (Dray et al. 2008; Dray et al. 2012; Galea et al. 2009; Moore et al. 2009). In the context of alcohol use, ABMs have been useful in understanding the influence of social networks on levels of consumption, for example in estimating both how social networks can be used to predict heavy drinking behaviours (Mercken et al. 2015; Ormerod & Wiltshire 2009), and how heavy drinkers promote increased drinking through their social networks (Giabbanelli & Crutzen 2013; Gorman et al. 2006). On a population level, the Organisation for Economic Co-operation and Development (OECD) recently used similar simulation modelling techniques to estimate the economic and public health benefits of reduced alcohol consumption (Cecchini & Sassi 2015; OECD 2015), finding that even small decreases in consumption are likely to provide significant benefits. However, the existing literature is focussed on longer term (meaning more than a day) behavioural changes within individuals. There has been a shift in contemporary alcohol and other drug research towards considering the consumption event as the unit of analysis (Bøhling 2014; Callinan et al. 2014; Dilkes-Frayne 2014; Kuntsche et al. 2014); researchers are attempting to understanding individuals' decisions and their consequences within a single drinking event (a 'big night out'), and how interventions throughout the night might affect outcomes. Models with a temporal resolution designed to capture changes to social networks are less appropriate for this, since on the scale of a single drinking event it is reasonable to approximate social groups as being well established and the psychosocial characteristics of drinking as highly entrenched within each group. Instead, there is a need to model how different enabling or restricting alcohol policies—that act on the environment, rather than to the individual—may influence the night out of an already established group of heavy drinkers.

1.6
This paper describes an ABM model 'SimDrink', built using NetLogo (version 5.1.0) (Tisue & Wilensky 2004) and run with the RNetLogo package (Thiele 2014), that simulates a population of 18-25 year olds engaging in heavy sessional drinking on a night out in Melbourne. The model consists of individuals and their friendship groups moving between private, public-commercial (e.g. nightclub) and public-niche (e.g. bar, pub) venues while tracking their alcohol consumption, spending and whether or not they experience consumption-related harms (i.e. drink too much), are involved in verbal violence, or have difficulty getting home. Importantly, individuals' behaviour and decisions will be setting dependent and allowed to vary as the night progresses, influenced by their own—and also their friends'—alcohol consumption, finances and harms experienced. With this model we will be able to test and quantify the direct and indirect effects of policies such as 24 hour public transport, public venue lockouts, changes to responsible service of alcohol enforcement, public venue closing times and drink prices. Further, although the model environment is based on Melbourne's characteristics, it is highly generalizable and with minor modifications and locally valid parameters could easily be used to test policies in other locations.

### Model description

#### Model environment

2.1
The model environment consists of a circular Inner City (IC) area of radius 5km and an Outer Urban (OU) area extending radially between 5km and 25km from the centre. The IC area contains a mixture of venue types where people can consume alcohol: public venues that are classified as either niche (e.g. bars, pubs) or commercial (e.g. nightclubs); and private venues (e.g. house parties). The OU area contains only private venues since OU public venues in Melbourne are less popular among the young population being modelled, who would typically commute to the IC to attend public venues instead (MacLean & Moore 2014). All venues are distributed randomly throughout their respective regions (IC or OU). There is a taxi rank in the centre of the model that acts as a gateway for people leaving public venues after public transport stops running. Although travel time is calculated for all movements, transport issues occurring at other times or locations are not considered in this model (i.e. public transport is assumed to be adequate when it is operating, and all travel departing from private venues is assumed to be non-problematic). Finally, there is a node near the centre of the city where individuals who leave public venues unable to afford transport home wait for the first train.

#### Agent properties

2.2
At the start of the night each agent is allocated some fixed properties and some counters to track their night. Their fixed properties are gender, age (18–21 years or 22–25 years), residence (IC or OU), drinking rate, personal drinking limit, initial spending money, size of initial friendship group and planned length of night, and their counters track remaining spending money, total drinks consumed, total hours spent drinking and whether harms have been experienced (verbal, drinking too much or difficulty getting home). The distributions used to allocate fixed properties are listed in Appendix A.

2.3
Each agent forms fixed links to all of their friends (friendship groups remain linked throughout the night) and each friendship group is allocated a starting time. There is also a single temporarily link connecting agents to their current venue. Friendship groups enter the model together at their start time and once an individuals' night is over they are able to leave the model, disconnecting links to their friends and final venue.

#### Venue properties

2.4
Venues are also allocated fixed properties and counters. Their fixed properties are location (IC or OU), setting (private, public-niche or public-commercial), closing time (11pm, 12am, 1am, 3am or 5am for public venues or infinite for private venues), drink limit (the maximum number of drinks people in the venue can have before being thrown out—different values for 18–21 year olds and 22–25 year olds in public venues; infinite for private venues) and drink price, and their counters are number of drinks sold, number of verbal fights in the venue and number of patrons ejected for having total alcohol consumption over their drink limit. The distributions used to allocate fixed properties are listed in Appendix A.

#### Time frame of model

2.5
Each time step in the model represents an hour. A complete simulation commences at $$t=0$$ corresponding to 5pm and the model runs until all agents have finished their night out. This occurs when they either go home or become stuck in the city waiting for public transport to start the morning.

#### Model assumptions and the psychosocial characteristics of drinking in Australia

2.6
The model makes several underlying assumptions about the single-occasion drinking sessions of young Australians. In particular, the model assumes:
• Public locations attended by young drinkers from both OU and IC areas are typically in the IC (MacLean & Moore 2014);
• It is common for people to move between venues (including between public and private settings) throughout the course of a single night (Dietze et al. 2014; Miller et al. 2013);
• Individuals drink at different rates in different settings (i.e. in public-niche versus public-commercial) and when intoxicated (Lindsay 2005);
• Friendship groups don't split up when changing venues, with the exception of some members going home (Miller et al. 2013—the most common reasons for young people to attend drinking environments is either to socialise with friends or for special events/celebrations);
• Due to both peer-pressure and safety concerns (in particular among OU residents), after exceeding their planned length of night people will only go home if at least one friend has also exceeded their planned length of night (Duff & Moore 2015—also based on extensive fieldwork from AH and JW); and
• Given the high cost of taxis in Melbourne, most people will be aware of the last train departure time and many people are likely to make specific efforts to catch the last train home (Duff & Moore 2015—also based on extensive fieldwork from AH and JW).

2.7
The extent to which these features are unique to Australia may limit the generalisability of this model to other international settings. For the model to be applied elsewhere, the relevance of these features (along with parameter estimates) would need to be considered.

#### Setting up a simulation

2.8
The model is initially populated according to the six steps below. Parameters can be found in Appendix A, and further details are represented schematically by the flow diagrams in Appendix B.

2.9
Each simulation is set up by: 1) generating and distributing venues throughout the model and allocating them their fixed properties; 2) generating a seed population of OU and IC residents and assigning them each a friendship group size; 3) assigning the seed population to start locations for their night; 4) creating additional agents ('friends') in the same location who are linked to the seed agents; 5) allocating fixed properties (age, sex, drinking behaviours and spending money) to all agents; and 6) making agents who do not commence their drinking at $$t=0$$ inactive at their current location (where they will not interact with anything until their starting time). Each of these steps is done according to the parameters in Appendix A.

#### Agent behaviour

2.10
Once the model is started seven main operations are performed each time step. Each of these steps is schematically represented in the flow diagrams in Appendix B, and the corresponding parameters for each decision are provided in Appendix A.
1. Offer public venues a chance to eject intoxicated patrons or close
Public venues identify patrons who have consumed more than the venue's drink limit and force them to go home. If these agents have at least one friend who has consumed more than a harms threshold, they may experience harms as they leave (see step 4). If a public venue has reached closing time, all current patrons are offered a choice of whether to go home or move on to another venue—those choosing to move to another venue do so with their remaining friends.
2. Offer agents a chance to move between venues
Agents who have been at a venue for an hour or more choose to either stay at the venue or move to another (Dietze et al. 2014; Miller et al. 2013). Those choosing to move take their entire friendship group with them (Miller et al. 2013), and their new location depends on their current setting type, their residence and the types of venues still open. The model assumes: agents only visit private locations near their residence (i.e. IC agents only go to private venues in the IC); agents don't move from OU private venues to the city once public transport has stopped; there is no gender differences in places visited; IC to IC travel is not done by taxi unless an IC resident is going home (when they choose whether to get a taxi or not); travel time between venues depends on mode of transport and is a maximum of one hour; and the cost of travel by public transport is negligible.
3. Offer agents a chance to consume drinks
Agents calculate their actual drinking rates: that is, they scale their fixed drinking rates depending on their current setting (private, public-niche, public-commercial) and whether they are intoxicated (agents decrease their drinking rate when they have consumed more than half their drinking limits). Agents then attempt to buy an hours' worth of drinks; however those who have just arrived at a venue must deduct travel and queueing time, and those who do not have enough money will buy only as many as they can afford.
4. Determine harms experienced by agents
Agents who have consumed more than their personal drinking limit are considered to have drunk too much and will go home. Agents can also experience verbal violence—this depends on their current location type and whether they have consumed more than a harms drink threshold (agents who have consumed more than 12 (men) or 6 (women) drinks are at increased risk of verbal violence—Appendix A). Agents are considered to have had difficulty getting home if they have spent two or more hours waiting for a taxi.
5. Get agents to consider going home
Agents are forced to go home if either: they have consumed more than their personal drink threshold; they are out of money; they and one or more of their friends have exceeded their planned length of night (Duff & Moore 2015); or if more than half of their initial friendship group has gone home. Agents may decide to go home if: they are in a public venue and the last train is about to leave (Duff & Moore 2015, this choice depends on their remaining money, the planned length of their night and where they live); they are in a public venue, public transport has stopped and they have only enough money for a taxi left; or if they or a friend have experienced some verbal violence.
6. Distribute some agents from the taxi rank to their new locations
Each time step agents waiting at the taxi rank have some chance of going to their new venue (either home or a private venue). This depends on the number of taxis (per 100 people) in the model and the current size of the queue. Agents who have been waiting for 2 or more hours for a taxi and have consumed more than a harms drinking threshold will loop through step 4 again.
7. Activate friendship groups
Friendship groups who have a start time corresponding to the current model time are activated and begin to interact with the rest of the model, 'starting' their night out.

### Calibration

3.1
A complete list of parameter values and their sources is provided in Appendix A. Public transport and venue setting properties have been determined using publicly available information for Melbourne (Public Transport Victoria 2015; Victorian Commission for Gambling and Liquor Regulation 2015), and where possible agent behaviour has been parametrized using the Young Adults Alcohol Study (YAAS, Dietze et al. 2014). Any remaining parameter estimates have been taken from available literature; where no studies were available to explicitly inform parameters, plausible estimates were made by the authors based on their extensive experience conducting social research on alcohol and other drug use in the night-time economy, including ethnographic research on young people's drinking events in OU and IC areas of Melbourne. These parameters were tested in a sensitivity analysis and as part of a Latin Hypercube uncertainty analysis.

3.2
YAAS is a study of 802 young (18–25 year olds) people from Melbourne that asks specific questions about the most recent occasion when they consumed more than 7 (women) or 10 (men) standard drinks (in Australia, 10g of alcohol). This includes the number and types of venues attended; number of drinks consumed; total time and money spent in each venue; and whether or not verbal violence was experienced during the course of the night. Due to oversampling from particular areas, participants could be classified as residing in either the Local Government Areas (Department of Transport 2015) of Yarra ($$n=127$$, proxy for IC), Hume ($$n=275$$, proxy for OU) or the Rest of Melbourne ($$n=400$$). YAAS participants from Yarra or Hume have been used to determine model parameters, while participants from the Rest of Melbourne have had their nights compared to the outputs of the model to determine its accuracy. This procedure avoids using the dataset for both parameter determination and model calibration.

3.3
Due to low reports of verbal violence among Yarra and Hume participants in the YAAS ($$N=28$$ reported verbal violence on their most recent big night out), all YAAS data were used to determine the verbal violence harm parameters. Hence it is no longer valid to compare model outputs for these harms to those reported by YAAS participants from the Rest of Melbourne. However, a follow-up wave has since been conducted ($$N=531$$ (66%) of the original sample were retained), and model outputs for verbal harms have been compared against those reported in the follow-up data.

3.4
Among YAAS participants, verbal violence was more likely to be reported by older males, and on nights when more venues were visited, more drinks were consumed, more hours were spent out and more money was spent (Table 1). However, the low number of reports of verbal violence means that these differences were not statistically significant and adjusted odds ratios provided no further insight.
 Table 1: Gender and age categories of individuals from the Young Adults Alcohol Study (Dietze et al. 2014) who experienced verbal violence on their most recent occasion consuming more than 7 (women)/10 (men) standard drinks; and characteristics of their nights.

3.5
Once parameters were determined (see Appendix A for further details), the model was run 100 times to account for stochastic variation and the output distribution properties (e.g. mean, median, interquartile range) of the results were compared to available data.

### Model robustness

4.1
Many of the parameters in the model relate to the likelihood of individuals making particular decisions under specific circumstances; for example p_PTrush_OU_plan_$(Appendix A)—the probability that an individual will choose to catch the last train home if they have less than$50 left, had only planned to stay out for up to one hour longer and live in an OU area. These types of features have been included based on qualitative studies suggesting that they play a role in young people's drinking events, with quantitative data either unavailable or unfeasible to obtain for many of the related parameters. Nevertheless, by including such features—even using authors' estimates for their values—we believe the model has been improved, in particular as the model outputs can now be probed for sensitivities when they vary.

#### Individual parameter variations

4.2
To test model robustness to these unreferenced parameters, each was independently set to a lower bound and upper bound and 100 further simulations were undertaken.

4.3

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