Reflections on a Virtual Experiment Addressing Human Behavior During Epidemics Liam Delaney1, Adam Kleczkowski2, Savi Maharaj2*, Susan Rasmussen3, Lynn Williams4 1
2
Economics, University of Stirling, Stirling, Scotland Computing Science and Mathematics, University of Stirling, Stirling, Scotland, *
[email protected] 3 Psychology, University of Strathclyde, Glasgow, Scotland 4 School of Social Science, University of the West of Scotland, Paisley, Scotland
Keywords: participatory simulation, virtual experiments, behavioral economics, health psychology, epidemics Abstract We report on preliminary results from a pilot study using a virtual experiment to analyse human behavior during epidemics of an infectious disease. The experiment used a two-dimensional computer game representing an epidemic scenario, linked to an agentbased simulation of an epidemic spreading through a large population. 230 participants played the game and completed questionnaires about their characteristics in relation to a psychological model of health behaviour, Protection Motivation Theory. The results show that participants responded to increasing infection load in their local neighbourhood by reducing their social contacts, as they would be expected to do in reality. However, there was no correlation between the strength of this response and a number of psychological factors that are known to be associated with health protective behavior in the real world. This suggests that participants might not have responded to the game in the same way they would respond to a real epidemic. We discuss possible explanations for this mismatch, drawing on ideas from experimental behavioral economics, psychology, computer game design, and the study of virtual worlds, and suggest ways in which our experimental methodology could be improved to produce a more realistic response.
1. INTRODUCTION When faced with an epidemic of infectious disease people often respond by changing their behavior to protect themselves, for example, by reducing their social contacts (social distancing). In previous work using agent-based simulation [21], we found that social distancing can be a highly effective method for controlling and even stopping an epidemic if applied appropriately. However, if done incorrectly, social distancing can backfire, prolonging, rather than stopping the epidemic and causing worse economic impact than if people continued to behave as usual. A key parameter in this study was risk attitude, a measure of how strongly individuals respond to a given level of infection. This study prompted curiosity about how people respond during epidemics in the real world: do they display the ideal risk attitude as indicated by our simulations, or do they behave in a way that is less optimal?
It is difficult to answer this question by looking at real world data: very little actual data exists because it is difficult to collect, and what data there is, e.g., [3], [24], [25], does not provide sufficient detail about individuals’ daily social contacts to allow close comparison with our model. For obvious reasons, it is not possible to gather data by starting epidemics in the real world. We therefore turned to virtual experiments, using a simulated epidemic scenario in which the experimental subject interacts with other simulated members of a population through which an epidemic is spreading. The experiment is presented as a computer game (the Epidemic Game). An early version of this game was presented at SCSC 2011 [22]. There is obvious motivation for using virtual experiments for scenarios such as epidemics which cannot be reproduced in the real world. However, it is not at all clear whether such experiments are scientifically credible. Is it valid to use data about people’s responses to an artificial, simulated scenario to draw conclusions about responses in the real world? To address this question, in summer 2012 we carried out a pilot study using the Epidemic Game. The game was played by 230 participants at a public science centre and a university campus in central Scotland. Participants’ risk attitudes were calculated from their responses to the game and recorded automatically as they played. Participants also answered questions about their demographic characteristics, personality, experiences, beliefs, and attitudes to disease and infection. The questions were designed to gather data relating to factors used within a psychological theory of health protective behavior, the Protection Motivation Theory [23]. The data from the pilot study has not yet been subjected to detailed statistical analysis. However, from preliminary analysis, two conclusions seem clear. First, the data shows that people playing the game did indeed respond to higher infection loads in the simulated epidemic by reducing their social contacts. This is a positive result, showing that people did not make random or perverse choices when playing the game, but responded as expected. However, the second observation is quite surprising: there is no correlation between the risk attitudes people displayed when playing the game and their health-related beliefs according to Protection Motivation Theory. Psychologists have found that these factors are predictors of how people respond to health threats in the
real world. The fact that these are uncorrelated to responses to the threats faced in the Epidemic game suggests that behavior in this game might not match behavior in the real world. Why is there this apparent mismatch, and what can be done to address it and make the Epidemic Game into a credible tool for studying health behavior? We look first for an answer in the field of behavioral economics, where it is common practice to use games (computerized or otherwise) as experimental tools. Results here suggest that the problem might be that the Epidemic Game was too formally structured to elicit natural responses, or that the incentives and rewards built into the game somehow misled the participants. Alternatively, perhaps the answer lies in the study of immersive, three-dimensional virtual worlds, some of which have been used in famous successful re-creations of classic experiments in psychology. Results here suggest that the Epidemic Game may need to be made more detailed and immersive to convince participants to respond in a realistic manner. Other possible explanations may lie in the timing of the experiments, or in the design of the game play and the information and cues given to players. Sections 2-4 describe the theoretical concepts underlying the Epidemic game experiments, namely, Protection Motivation Theory, agent-based simulation of epidemics, and participatory simulation. Section 3 shows the design of the Epidemic game. Section 4 describes the experiments and summarizes the results. In Section 5 we consider ideas from behavioural economics, psychology, virtual worlds, and computer game design, and draw some lessons from these on how the Epidemic Game could be improved. Section 6 concludes with a discussion of the way forward.
in performing it, with the actual performance of the precautionary behaviors. Participants in our study were asked to state the extent to which they agreed or disagreed with several statements designed to measure their characteristics according to PMT. Table 1 shows examples of these statements. Perceived severity of illness: • If I were to develop an infectious disease (e.g. flu) I would suffer a lot of unpleasant symptoms. • Developing an infectious disease would be unlikely to cause me to die prematurely. Perceived vulnerability: • My chances of developing an infectious disease (e.g. flu) in the future are likely. • I am unlikely to develop an infectious disease (e.g. flu) in the future. Response efficacy: • If I were to engage in social distancing (e.g. by avoiding public transport and social events) I would lessen my chance of developing an infectious disease. Self efficacy: • I am discouraged from engaging in social distancing during times of infectious disease, because I feel it would be difficult to do so. • I feel confident in my ability to engage in social distancing during times of infectious disease.
2. PROTECTION MOTIVATION THEORY
Table 1: Example statements for measuring PMT beliefs.
Protection Motivation Theory (PMT) [23] is a psychological framework for understanding what motivates people to change their behavior in order to protect their health. According to PMT, people’s motivation to protect themselves from health threats is determined by four main factors: perceived severity of the threat, perceived vulnerability to the threat, perceived efficacy of the recommended protective behavior (response-efficacy), and belief in one’s ability to carry out this behavior (self-efficacy). Support for various aspects of PMT as predictors of behavior during epidemics comes from studies assessing how people responded to recent outbreaks of SARS, Avian Flu and Swine Flu [4], [9], [10], [16], [17], [18], [19], [20], [24], [25], [27]. For example, studies on responses to SARS and to Avian Flu in Hong Kong indicate that higher perceived vulnerability, and higher perceived severity of the disease were associated with adopting recommended behaviors such as washing hands, mask wearing, being vaccinated and social distancing. Studies also report associations between perceived efficacy of protective behavior, and self-efficacy
3. SIMULATION MODEL The Epidemic game uses an underlying agent-based simulation of an epidemic spreading on a spatial network, described more fully in [13] and [22]. The disease model is based on the classic Susceptible-Infected-Recovered (SIR) model [1] of the spread of infectious diseases. The spatial network is a two-dimensional square lattice of 50x50 cells, each of which is occupied by an individual. Initially, a few individuals are infected and the remainder are susceptible, meaning that they may become infected by contact with an infected individual. Infected individuals eventually recover, after which they are immune from further infection. These transitions are governed by two parameters, p, the probability that a single contact with an infected individual will cause a susceptible to become infected, and q, the probability at a given time-step that an infected individual will recover. Social distancing is modeled by making use of the spatial structure of the network. Each individual has its own contact radius, which is reduced or increased during the
course of the epidemic as the individual practices social distancing. Contact takes place between individuals who are within each other’s contact radius (as measured using Euclidean distance). An individual’s decision whether to practise social distancing is based upon the current infection load (ratio of infected to non-infected individuals) within that individual’s awareness radius. The awareness radius (also measured as Euclidean distance) is the same for all individuals and remains fixed through the duration of the simulation. Figure 1 summarizes the processes that take place during a single time-step of the simulation. Susceptible individuals observe the infection load within their awareness radius and modify their contact radius accordingly. They then make contact with all their neighbours within their (new) contact radius. This may result in the susceptible becoming infected in the next time step. Currently infected individuals may become recovered in the next time step.
a lukewarm response which does not stop the epidemic but prolongs its spread while causing significant economic cost from the loss of social contacts. One of the purposes of the Epidemic Game is to allow us to compare these results with the values of α adopted when real people participate in the simulation.
modify contact radius in response to local infection load
Susceptible
Infected
Recovered
Figure 2: Social distancing response to infection load for different risk attitudes (α). 4. THE EPIDEMIC GAME
become infected after contact with an infected person, probability p
recover, probability q
Figure 1: Transitions of an individual in the model The extent to which a susceptible individual reduces its contact radius in response to a given infection load is governed by another key parameter, the risk attitude, α. Figure 2 shows how α relates to the social distancing response for different values of α. Lower values of α represent a more cautious, or risk-averse response, i.e., there is a greater reduction in the contact radius in response to a given infection load. In earlier work [21] using this model to study social distancing we determined that for social distancing to be effective, α should either be very low (so that the epidemic is quickly suppressed) or, if such a strong social distancing response is not feasible, then α should be relatively high (so that the epidemic runs its course, perhaps affecting most of the population, but social contacts are maintained so there is no economic cost from the loss of social contact). The worst outcome occurs if α is between these extremes, resulting in
The design of the Epidemic Game is based on the idea of participatory simulation [29], where users interact with an agent-based simulation by controlling the behavior of one or more agents. Underlying the game is a simulation of an epidemic spreading through a large population. The player controls the actions of a single susceptible individual in this population. A round in the game represents a single day in the epidemic (or a time step in the underlying simulation). On each day, the player is informed about the infection load within his/her local neighbourhood (corresponding to the awareness radius of the individual controlled by the player). The player must then choose how many contacts to make that day. The player’s choice is used to calculate his/her effective risk attitude α for that day. The player’s chosen α is then adopted as the global risk attitude to be used by all the simulated susceptible individuals in modifying their contact radius during the current time step. In other words, if the player has been very cautious, then the simulated susceptibles will mirror this caution in their own response; similarly, if the player behaves recklessly, the simulated susceptibles will follow suit. The reason for introducing this mirroring is to magnify the effect of the player’s choices so that they can have a real influence on the course of the epidemic, making the game more engaging for the player.
The game is played via an interactive user interface written in Java which communicates with a back-end agentbased model created with NetLogo [30]. The player is greeted with a welcome screen and then shown instructions (Figure 3). The instructions state that the player must try to achieve two goals: to earn money by making as many social contacts as possible, and to remain uninfected. Because making more social contacts raises the risk of infection, these two goals create a tension which the player must resolve.
Figure 4: A day in the game
Figure 3: Epidemic game instruction screen On each “day” in the game, the player is shown a representation of the current infection load in his/her local neighbourhood (Figure 4). Red figures represent infected neighbours and green figures represent uninfected neighbours (which may be either susceptible or recovered in the underlying simulation model). The player uses the mouse to modify his/her contact radius, represented as a light blue circle on the screen, and then clicks a button to proceed. The figures are animated and move too quickly for the player to control which figures are within the circle when the button is pressed. This means that the player can control how many neighbours to contact, but cannot discriminate between infected and uninfected neighbours. There are a number of possible outcomes after the choice is submitted (Figure 5). If the player remains uninfected and the epidemic is ongoing, then he/she is credited with earnings corresponding to the number of contacts made and play continues to the next day. If the player is infected, the game ends, and the player is told how long he/she survived and how much was earned during the epidemic.
Figure 5: Three responses to clicking the submit button: game continues another day; game ends when player is ingame ends and because epidemicisisover over.(i.e., there are Iffected; the player is well the epidemic If the player is well and the epidemic is over (i.e., there are no infected individuals remaining in the total population) the game ends, the player is congratulated on helping to stop the epidemic, and the earnings during the epidemic are reported. In addition, the player is credited with additional earnings for contacts made “after” the epidemic; the purpose of this is to prevent the player being unfairly penalized for ending the epidemic early and thereby having fewer opportunities to make contacts during the epidemic.
5. PRELIMINARY EXPERIMENTAL RESULTS In summer 2012 the Epidemic Game was played by 230 participants, who were recruited from two locations. The majority of participants were recruited during their visit to the Glasgow Science Centre, a large science museum open to the general public and popular with families. In addition, participants were recruited from the population of the campus of the University of the West of Scotland. Subjects ranged in age from 18 to 89, with a mean age of 32.4 years. There were 109 males, and 121 females. All participants played the game at least three times (with the first game designated as a “practice run”) and completed a questionnaire which assessed the PMT variables, personality and social support. The full analysis of the data from these experiments is still in progress. However, from a preliminary analysis, there are two striking results. The first is that the majority of participants do indeed respond to increased infection load by reducing their contact radius. Figure 6 shows one participant’s responses during four games. The black solid circles represent the practice run, when the participant was playing the game for the first time and, presumably, experimenting with different responses to discover how the game worked. The single cross is from a very short game, where the player unfortunately became infected on the first day. The open triangles and circles, however, are from longer games where the player was exposed to a variety of infection loads and responded accordingly. The player’s behavior in these games closely matches a theoretical response with α = 0.42, shown by the red line on the graph. Not all participants’ responses matched the theory so neatly, and there were a few participants whose responses were bizarre, suggested that they did not understand the game or chose not to engage with it in the intended way. But most participants did appear to respond in accordance with some theoretical α, though the actual value of α varied greatly among participants. This first result is reassuring, as it indicates that most people were able to understand what the game was about and responded to it in a logical fashion. The second preliminary result, although unexpected, is even more interesting. Data from the questionnaires was used to measure for each participant the four factors of Protection Motivation Theory, namely, perceived severity of illness, perceived vulnerability, response-efficacy, and selfefficacy. Figure 7 shows scatter-plots of the participants’ mean risk attitude during all games, plotted against these four measures. There is a striking lack of correlation: the factors of PMT, which are known to be predictors of how people respond to health threats in the real world, appear to bear no relation to their response to threats in the Epidemic Game. This is an unexpected result which raises questions about the usefulness of the virtual experiment methodology.
Given the practical importance of learning ways to control epidemics and the unfeasibility of doing experiments with epidemics in the real world, it is worth investigating further to understand why this has happened and explore possibilities for improving the methodology. We discuss this in more detail in the next section.
Figure 6: A participant’s actual responses to different infection loads during four games, represented by four different symbols. Black solid circles represent the days of the first, “practice”, game. The line shows the theoretical response with α = 0.42.
Figure 7: A preliminary analysis of participants’ risk attitude (vertical axis) plotted against PMT factors.
6. DISCUSSION In this section we consider what can be learned from other areas of study in which games and simulations have been used with apparent success to carry out experiments on human behavior. Our hope is to find ways to improve the Epidemic Game and make it into a more useful tool. Insights from behavioral economics Behavioral economics is a modern departure from traditional neoclassical economics in which ideas from psychology and cognitive science are incorporated into the study of human economic decision-making. The use of experimental methods is fundamental and many experiments are presented as games, which may be played face-to-face in a laboratory setting, or on a computer. A key part of behavioral economics is prospect theory [12] [2], a model of risk attitudes and risk evaluation in experimental settings. Prospect theory includes the idea of loss aversion: people are much more strongly affected by losses, even small losses, than to gains of the same magnitude. In the Epidemic Game players did not experience any losses: the consequence of becoming infected was that the game ended and the player missed out on the potential bonus for earnings “after” the epidemic. Loss aversion could be incorporated into the game by giving players an initial number of points, some of which would be lost if the player became ill. This could be explained as the cost of medical treatment. There is strong evidence that loss aversion is an important component in economic decision making in the real world, so adding it into the Epidemic Game might help to evoke a more realistic response. Another possible explanation for the Epidemic Game results is that people may be modeling their initial decisions as an information-seeking tradeoff [11]. When first faced with the game, people may be uncertain of the rules and the best strategies to use, and therefore try out various options in order to learn how the game works. The design of the Epidemic Game permits the subject to play the game as many times as desired to perform this exploration. Aware that he/she has been asked to play a structured game with defined rules and rewards, and lacking a full understanding of these, the player experiments by trying out different options in order to get information about the rules and work out the best strategy. This kind of behavior is very far removed from a person’s real world experience of dealing with epidemics (where there are no second chances) and much closer to how someone might behave when solving a crossword puzzle or learning to play chess. If the problem is indeed that the highly structured design of the Epidemic Game leads subjects to treat it as a puzzle to be solved rather than an experience to be lived, it might be possible to fix this by redesigning the game to make it
much less structured with open-ended options for players to explore. The behavioral economics literature also contains many interesting results and guidelines about the measurement of risk attitudes and risk aversion, much of which is relevant to the Epidemic Game. For example, it has been found that risk attitudes may be domain-specific [28], meaning that the same individual may display different risk attitudes in different contexts. It is important to take this into account, as it could mean that risk taking behavior when playing a computer game is not representative of behavior when faced with health threats in the real world. Other relevant results include gender differences in risk attitudes [7] and the phenomenon of ambiguity aversion, where people prefer to take options with known probabilities [5] rather than options whose probabilities are uncertain. Insights from virtual environments research
Figure 8: Screenshots from a prototype of a more realistic Epidemic Game. The Epidemic Game presented players with a simple, two-dimensional, cartoon-like representation of an epidemic, lacking in realistic detail and immersive content. Virtual worlds, of which the best known is Second Life, are highly immersive three-dimensional simulated scenarios containing high levels of realistic detail. It has been claimed that virtual worlds evoke in the user a sense of “presence” within the simulated world [6], [15], leading to realistic social behaviour that mimics that in the real world. Virtual worlds have also been used successfully to re-create classic experiments in psychology, such as the famous Milgram Obedience experiments [26]. This suggests that it might be possible to improve the Epidemic Game by making it more detailed, realistic and immersive, perhaps by reimplementing it within a three-dimensional virtual environ-
ment. Figure 8 shows screenshots from a prototype version of this [14] which is currently under development. There is a fair amount of evidence that virtual worlds prompt users to behave according to social norms similar to those that govern real world behaviour. Examples include proxemics (social norms about personal space) in Second Life [8], and eye gaze and eye contact in massivelymultiplayer online role-playing games (MMORPGs) [31]. However, it is undeniable that not all behaviors in virtual worlds translate to the real world: the inhabitants of Second Life can fly and teleport, and in MMORPGs players fight and kill, and death is not permanent. In moving the Epidemic Game into a virtual environment, where the player is expected to become immersed and to suspend disbelief in the scenario presented, great care will need to be taken to ensure that this illusion cannot be accidentally dispelled by some mishap or unanticipated action by the player. Another important aspect to consider when considering the use of virtual worlds for scientific experimentation is the experimental control / mundane realism trade-off [3]. The more detailed the virtual world, the more difficult it becomes to design controlled experiments, as the number of experimental factors grows with every new detail that is added. However, it may be that realistic detail is necessary in order to make participants forget that they are in an experiment and behave in a natural way. A balance will need to be struck between the need for realistic, immersive environments and the requirement to keep experiments as simple as possible and reduce the number of confounding factors. Game design issues An alternative to re-implementing the Epidemic Game in a virtual world is to improve the interface and playability of the existing game. The current interface was designed to faithfully represent the underlying agent-based simulation: neighbours are shown as anonymous stick figures, and awareness and contact radii are depicted literally as circles. The resulting interface is rather abstract and perhaps unengaging for the player. There are several ways in which the interface could be made more engaging, for example by presenting information differently, or by giving more varied and entertaining feedback. Increasing players’ engagement with the game may encourage them to treat it less as an abstract exercise and more as a real, role-playing scenario. Timing issues A final observation about the experiments is that they were carried out during the summer months, when seasonal influenza is at its lowest ebb in Scotland. Participants were therefore less likely to have had recent experience of an
influenza outbreak, and this may have reduced their ability to identify with the scenario presented in the game and influenced their responses to the questionnaire. This suggests that the experiment should be repeated during the winter, when seasonal influenza is at its peak, so that the results can be compared. 7. SUMMARY AND FUTURE WORK This Epidemic Game experiment was a pilot study in the use of virtual experiments and participatory simulation to study human behaviour during epidemics. The results showed that although participants responded to the threats faced in the game, the risk attitudes they displayed were uncorrelated with their psychological characteristics according to Protection Motivation Theory, and therefore might not have been representative of their responses to health threats in the real world. Insights from behavioral economics suggest a number of factors influencing decision-making under risk that should be considered in order to improve the Epidemic Game. These include incorporating loss aversion, making the game less structured so as to prompt a natural response rather than strategic information-seeking behavior, and considering whether domain-specific risk attitudes and ambiguity aversion have a role to play. Insights from the study of virtual worlds suggest that reimplementing the Epidemic Game within a realistically detailed three-dimensional virtual environment might prompt a greater sense of immersion and presence in the players, and cause them to respond more naturally. However there are tradeoffs to consider between the desire for more realism and the need to be able to carry out controlled experiments. Other possibilities to consider are whether the current two-dimensional game interface could be improved so as to make it more engaging for the players, and whether different results might be obtained if the experiments were to be repeated in the winter, when seasonal influenza is at its peak. Our immediate next step is to complete the comprehensive statistical analysis of the data from the already completed experiments. In future work we will explore a number of lines of investigation, including redoing the experiments at a different time of year, improving the game play in the current two-dimensional Epidemic Game, and developing a more realistic three-dimensional version. Our long term goal is to create a framework that can be used for experimental study of human behaviour within simulations of scenarios, of which epidemics are just one example, which cannot practicably be studied experimentally in the real world.
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