# A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation
## 1 Introduction  
In the postgenomic era, technological advances in data collection, such as DNA sequencing to build an individual genetic profiles, have generated a vast amount of data. Visual and data analytics are critical functions that build new insights, enable knowledge discovery, and support diagnoses or personalised treatment planning for genetically driven diseases like cancer [1, 2]. However, the complexity of the human genome with thousands of relevant genes exceeds the ability of humans, e.g., clinicians, who make clinical decisions based on limited available facts [3]. The complexity of the human genome makes a patient unique, and it opens new opportunities for data analysts in identifying and understanding the unique factors underlying a patient's disease. Computational and statistical analytic methods alone may not solve complex data problems effectively, and their results could be rejected by medical domain experts due to lack of trust and low interpretability [4, 5]. Interactive visualisation can explain and verify and allow users to explore and see computed and original data in interpretable ways informing pattern recognition, insight, and relationship discovery to amplify cognition and enable visual analytics [6]. The ability to handle complex data sets of visualisations has gained popularity in big data analytics and has been used as a precursor to data science algorithms for data exploration [7, 8]. Specifically, visualisation can facilitate early-stagedata discovery in cases where the data problems are ill-defined or unsuitable for algorithmic analysis. A classic example is that the correlation coefficients of different data sets have the same value, but visual analytics would have detected their vast differences [9]. In recent years, immersive technologies have also been utilised to further the visual analytics capabilities of multisensory immersive analytics. Immersive analytics utilises new displays and interaction technologies to provide multisensory interfaces where users can immerse themselves inside the data. Visualisation in the immersive environment, also called immersive visualisation, allows the user to gain greater insight into complex data by applying multisensory input, applying physicalisation of data, and using the natural interface [10]. Some of the examples of work in the application of immersive visualisation for genomic data are MinOmis [11], StarMap [12], BioVR [13], SinglecellVR [14], and recently VROOM [15]. Unfortunately, existing immersive analytics systems do not integrate intelligent decision-making aids that can guide users in the analysis and exploration processes. In such analytical environments, there could be many ways that analysts can look at the data based on different scenarios, hypotheses, and parameters; hence, a theoretical-based guiding system would be essential. Game theory, also known as the \"theory of social situation\", studies strategies for negotiation and coordination among self-interested agents to maximise their returns in the forms of utility value [16; 17; 18; 19; 20]. Game theory, closely related to Decision Theory, has its root in [PERSON] and [PERSON]'s mathematical framework [16; 21]. Game theory assumes that in most multiagent systems, all the involved agents' choices determine the overall results. To achieve that, agents will act strategically by anticipating what other agents will do and must assume that their actions will yield a result that favours them. Game theory led to development of notions such as Nash Equilibrium, Pareto optimal, and Social Optimality to analyse such systems [17; 21; 22; 23]. Game theory has been widely applied in economics, social science, psychology, and medical decision-making [19; 21; 24]. Unfortunately, game theory has not been applied to assist decision-making in immersive environments, especially for supporting medical professionals with analytics.