The biased interaction game described the operation of systems rooted in boundedly rational interactions under conditions of scarcity. The game explored the influence of bias and demonstrated how hierarchy and inequality are emergent system properties when sources of bias, such as power and scarcity, affect the outcome of interactions in an environment. Bias also impacts the likelihood of the emergence of cooperation. This paper serves as a companion piece to the paper introducing the biased interaction game. It investigates the general applicability of the game and demonstrates how the consideration of bias can modify and improve upon prior systems thinking. In particular, it shows how social systems can be successfully modelled using the biased interaction game and confirms its suitability for modelling extreme examples such as hyper-capitalism and social egalitarianism. It also reveals how biased systems can demonstrate non-linear behaviour, where long periods of system stability are punctuated by short bursts of rapid hierarchical transitions, mimicking real-world observations of social mobility. The paper concludes with a simplified real-world application, modelling the merits of two competing wealth redistribution philosophies: social welfare and a universal basic income

The theory of microeconomics is revisited to gain insight into the underlying mechanics of sub-optimal systems in general. It challenges the prevailing view that efficient markets naturally arise from free competition and instead reformulates the market to reveal mechanisms whereby inefficient operation can naturally emerge. In particular, it shows how individual influence or power, and market scarcity inevitably lead to biased markets for emergent system operation that is anything but efficient. The paper concludes by incorporating these insights into a game-theoretic treatment of market interactions and proves this is simply a special case of the biased interaction game.

Efforts to fully exploit the rich potential of Bayesian Networks (BNs) have hitherto not seen a practical approach for development of domain-specific models using large-scale public statistics which have the potential to reduce the time required to develop probability tables and train the model. As a result, the duration of projects seeking to develop health BNs tend to be measured in years due to their reliance on obtaining ethics approval and collecting, normalising, and discretising collections of patient EHRs. This work addresses this challenge by investigating a new approach to developing health BNs that combines expert elicitation with knowledge from literature and national health statistics. The approach presented here is evaluated through the development of a BN for pregnancy complications and outcomes using national health statistics for all births in England and Wales during 2021. The result is a BN that when validated using vignettes against other common types of predictive models including logistic regression and nomograms produces comparable predictions. The BN using our approach and large-scale public statistics was also developed in a project with a duration measured in months rather than years. The unique contributions of this paper are a new efficient approach to BN development and a working BN capable of reasoning over a broad range of pregnancy-related conditions and outcomes.

Hybrid Bayesian networks (HBN) contain complex conditional probabilistic distributions (CPD) specified as partitioned expressions over discrete and continuous variables. The size of these CPDs grows exponentially with the number of parent nodes, and when using discrete inference methods, it results in significant execution time and space inefficiency. To reduce the CPD size, a binary factorization (BF) algorithm can be used to decompose the statistical or arithmetic functions in the CPD by factorizing the number of connected parent nodes into sets of size two. However, the BF algorithm was not designed to handle partitioned expressions. Therefore, we propose a new stacking factorization (SF) algorithm to decompose partitioned expressions. The SF algorithm creates intermediate nodes to incrementally reconstruct the conditional densities in the original partitioned expression, ensuring that no more than two continuous parent nodes are connected to each child node in the resulting HBN. It generally applies to both discrete and continuous child nodes with complex partitioned expressions. When we combine SF with a dynamic discretization (DD) inference algorithm, we achieve a significant improvement in inference efficiency. Experimental results demonstrate that the combination of SF and DD can effectively manage HBNs with complex CPDs that may challenge other algorithms, which also outperform competing inference algorithms in accuracy.

This paper advances our understanding of consumers’ risk perception, risk tolerance and utility of novel technologies (e.g., smart functionality) in home appliances and the extent to which consumers’ risk perception changes given risk communication about products from different actors in the network (e.g., government, manufacturer and media). Two experiments with a 2×2×2 design were conducted, each with a different product (microwave and vacuum cleaner) and sample of 400 British consumers. The following three factors were manipulated (between-subjects): product type (smart vs non-smart), risk communication scenario (government vs manufacturer) and media coverage scenario (small vs large). The results of the experiments indicate that consumers perceive the smart versions of home appliances as riskier, are less tolerant of the risks and find them less useful than the non-smart versions. Also, risk communication from the government, manufacturer and media increases perceived risk, decreases perceived utility and decreases risk tolerance of smart and non-smart home appliances. Also, men and women judge risk the same, and there is an inverse relationship between education and perceived risk. Overall, our results highlight that consumers’ risk perception, utility and risk tolerance of home appliances are impacted by the product, product type (smart and non-smart), the risk communication source (government, manufacturer and the media) and demographics (gender and education).

It is recognised that many studies reporting high efficacy for Covid-19 vaccines suffer from various selection biases. Systematic review identified thirty-nine studies that suffered from one particular and serious form of bias called miscategorisation bias, whereby study participants who have been vaccinated are categorised as unvaccinated up to and until some arbitrarily defined time after vaccination occurred. Simulation demonstrates that this miscategorisation bias artificially boosts vaccine efficacy and infection rates even when a vaccine has zero or negative efficacy. Furthermore, simulation demonstrates that repeated boosters, given every few months, are needed to maintain this misleading impression of efficacy. Given this, any claims of Covid-19 vaccine efficacy based on these studies are likely to be a statistical illusion.### Competing Interest StatementThe authors have declared no competing interest.### Funding StatementNo funding was received in relation to this work### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesI confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesAll relevant data is contained within the manuscript

Risk analysis methods for medical devices, including fault tree analysis, have limitations such as handling uncertainty and providing reasonable risk estimates with limited or no testing data. To address these limitations, this paper proposes a novel systematic method for medical device risk management using hybrid Bayesian networks (BNs). We apply the method to a Defibrillator device to demonstrate the process involved for risk management during production and post-production using 4 different scenarios: (1) where there are available testing data; (2) where there are limited or no testing data; (3) where it is a completely new device with no testing data; (4) where we are reassessing the risk of a previous model on the market based on reported hazards and injuries. In each scenario, the BN model, for the available data, provides the full probability of failure per demand distribution for each category of injury severity (fatal, critical, major, minor, negligible) and the probabilities associated with various risk acceptability criteria. The model results are validated using publicly available data for the LIFEPAK 1000 Defibrillator (PN: 320371500XX), which was recalled by Physio-Control in 2017. The results show that the device would fail the acceptability criteria for probability of fatal injury.

Efforts to fully exploit the rich potential of Bayesian Networks (BNs) have hitherto not seen a practical approach for development of domain-specific models using large-scale public statistics which have the potential to reduce the time required to develop probability tables and train the model. As a result, the duration of projects seeking to develop health BNs tend to be measured in years due to their reliance on obtaining ethics approval and collecting, normalising, and discretising collections of patient EHRs. This work addresses this challenge by investigating a new approach to developing health BNs that combines expert elicitation with knowledge from literature and national health statistics. The approach presented here is evaluated through the development of a BN for pregnancy complications and outcomes using national health statistics for all births in England and Wales during 2021. The result is a BN that when validated using vignettes against other common types of predictive models including multivariable logistic regression and nomograms produces comparable predictions. The BN using our approach and large-scale public statistics was also developed in a project with a duration measured in months rather than years. The unique contributions of this paper are a new efficient approach to BN development and a working BN capable of reasoning over a broad range of pregnancy-related conditions and outcomes.

Higher Degree by Research (HDR) students are an important part of Australian university research culture. They contribute significantly to the generation of new knowledge, research outputs, industry engagement and the continual development of higher education. This article is the first to systematically review existing research to synthesise the key areas of HDR student experience within the Australian context. A systematic review of the literature was conducted following PRISMA protocols, and 7 themes were identified across the 68 papers included in the review. Themes reflected supervisory relationships, challenges for international students, engagement with research communities, balancing life contexts, administrative challenges, thesis by publication, and industry-based research. The overall findings suggest a need for universities to be more proactive in supporting the unique needs of HDR students in a changing educational context.

-OBJECTIVE: To discuss the treatment of intracranial fusiform and giant internal carotid artery (ICA) aneurysms via revascularization based on our institutional experience.-METHODS: An institutional review board-approved retrospective analysis was performed of patients with -nruptured fusiform and giant intracranial ICA aneurysms treated from November 1991 to May 2020. All patients were evaluated for extracranial-intracranial (EC-IC) bypass and ICA occlusion.-RESULTS: Thirty-eight patients were identified. Initially, patients failing preoperative balloon test occlusion were treated with superficial temporal artery (STA)-middle ce-rebral artery (MCA) bypass and concurrent proximal ICA ligation. We then treated them with STA-MCA bypass, followed by staged balloon test occlusion, and, if they passed, endovascular ICA coil occlusion. We treat all surgical medically uncomplicated patients with double-barrel STA-MCA bypass and concurrent proximal ICA ligation. The mean length of follow-up was 99 months. Symptom stability or improvement was noted in 85% of patients. Bypass graft patency was 92.1%, and all surviving patients had patent bypasses at their last angiogram. Aneurysm occlusion was complete in 90.9% of patients completing proximal ICA ligation. Three patients experi-enced ischemic complications and 4 patients experienced hemorrhagic complications.-CONCLUSIONS: Not all fusiform intracranial ICA aneurysms require intervention, except when life-threatening rupture risk is high or symptomatic management is necessary to preserve function and quality of life. EC-IC bypass can augment the safety of proximal ICA occlu-sion. The rate of complete aneurysm occlusion with this treatment is 90.9%, and long-term bypass graft-related complications are rare. Perioperative stroke is a major risk, and continued evolution of treatment is required.

Background Recent data have found an overall survival benefit from prostate-directed radiotherapy in patients with low-volume metastatic prostate cancer. Prostate SBRT is an attractive treatment in this setting and may be optimised with MR-guided adaptive treatment. Here, we share our institutional experience delivering stereotactic MR-guided adaptive prostate SBRT (SMART) for patients with low-volume metastatic disease. Methods We reviewed patients with low-volume metastatic disease who received prostate SMART from October 2019 to December 2021 on a 0.35T MR-Linac. The cohort included 14 patients. Genitourinary (GU) and gastrointestinal (GI) toxicities were assessed using CTCAE v 5.0. Progression was defined as a change in systemic or hormonal therapy regimen as a result of PSA rise or disease progression. Results The median follow-up time was 29 months. Seven patients had hormone sensitive prostate cancer and 7 had castrate resistant prostate cancer (CRPC). 13 patients received 36.25 Gy in 5 fractions and one patient received 33 Gy in 5 fractions. At the time of last follow-up, 11 patients had not experienced progression and three patients, all with CRPC, had experienced progression. No patients developed local progression in the prostate after SMART. One patient experienced acute grade 2 urinary toxicity (7%) and no patients experienced acute grade 2 GI toxicity (0%). No grade 3 + acute toxicities were observed. Conclusions Prostate SMART was found to be well tolerated and all patients had local control of disease within the prostate at the time of last follow-up. Prostate SMART may represent a low-risk and well-tolerated approach for delivering prostate-directed radiotherapy for patients with limited metastatic disease.

Stereotactic magnetic resonance (MR)-guided adaptive radiotherapy (SMART) for renal cell carcinoma may result in more precise treatment delivery through the capabilities for improved image quality, daily adaptive planning, and accounting for respiratory motion during treatment with real-time MR tracking. In this study, we aimed to characterize the safety and feasibility of SMART for localized kidney cancer. Twenty patients with localized kidney cancer (ten treated in a prospective phase 1 trial and ten in the supplemental cohort) were treated to 40 Gy in five fractions on a 0.35 T MR-guided linear accelerator with daily adaptive planning and a cine MR-guided inspiratory breath hold technique. The median follow-up time was 17 mo (interquartile range: 13-20 months). A single patient developed local failure at 30 mo. No grade ≥3 adverse events were reported. The mean decrease in estimated glomerular filtration rate was -1.8 ml/min/1.73 m2 (95% confidence interval or CI [-6.6 to 3.1 ml/min/1.73 m2]), and the mean decrease in tumor diameter was -0.20 cm (95% CI [-0.6 to 0.2 cm]) at the last follow-up. Anterior location and overlap of the 25 or 28 Gy isodose line with gastrointestinal organs at risk were predictive of the benefit from online adaptive planning. Kidney SMART is feasible and, at the early time point evaluated in this study, was well tolerated with minimal decline in renal function. More studies are warranted to further evaluate the safety and efficacy of this technique. PATIENT SUMMARY: For patients with localized renal cell carcinoma who are not surgical candidates, stereotactic magnetic resonance--guided adaptive radiotherapy is a feasible and safe noninvasive treatment option that results in minimal impact on kidney function.

Process flow diagrams like caremaps are common in clinical practice guidelines and treatment texts. However, their context is often limited to a single diagnostic or treatment event. While a method has been proposed for creating a health and disease lifecycle called the health condition timeline (HCT), that method is yet to be demonstrated for an entire health condition. This paper investigates development of an HCT for gestational diabetes mellitus (GDM), and whether the HCT and caremaps it incorporates can be used to support patient care to develop decision support tools. We show that this approach can be used to expedite development of clinical decision-support and clinician- and patient-facing applications. Caremaps, HCT and the decision support tools created with them could improve patient awareness for their condition and reduce the impact of their disease on themselves and the limited resources of our healthcare systems.

Abstract BACKGROUND Neuro-oncology (NOC) patients experience various symptoms from disease and treatment which are deleterious to function and quality of life (QOL). Evidence demonstrates the use of electronic patient-reported outcomes (ePROs) improves health-related QOL, mental functioning, patient-provider communication, and overall survival. Despite this knowledge, uptake across cancer services has been slow. In this pilot study, we sought to understand the utility of an electronic symptom monitoring platform (ESMP) in this population. METHODS We implemented an ESMP at a NOC practice and conducted a preliminary analysis of ePROs over 7-month period. Patients were prompted to report their symptoms at least 3 times/week through multiple-choice questions targeting 19 distinct symptoms and their severity (tiered 1-4). The care team was automatically notified through the electronic medical record predefined symptom severities. RESULTS During a 7-month period, 28 neuro-oncology patients were enrolled and monitored for a total of 660.7 patient-weeks or an average of 23.6 weeks per patient. In total, 314 questionnaires were completed; each patient completed 0.6 per week on average. We found questionnaire compliance to be the highest initially, declining in subsequent weeks. Symptoms were reported in 42.8% of questionnaires; the most frequent of which were peripheral neuropathy (16.6%), fatigue (11.5%) and headache (10.5%). Alerts were sent to the care team for 103 (32.8%) questionnaires, 38 resulted in additional interventions for the patient. CONCLUSION Our results support the hypothesis that technology-enabled integration of ePROs enables monitoring followed by automated alerts and customized responses to NOC patients. We note that peak usage was at the beginning of active treatment which occurred concurrently with onboarding. Opportunities and challenges involve the continuous promotion of patient engagement and optimization of workflow to encourage discussions and utilization. Future work also includes determining if this platform can lead to improved clinician efficiency and patient outcomes in this patient population.

Background: Strength and mobility are essential for activities of daily living. With aging, weaker handgrip strength, mobility, and asymmetry predict poorer cognition. We therefore sought to quantify the relationship between handgrip metrics and volumes quantified on brain magnetic resonance imaging (MRI). Objective: To model the relationships between handgrip strength, mobility, and MRI volumetry. Methods: We selected 38 participants with Alzheimer's disease dementia: biomarker evidence of amyloidosis and impaired cognition. Handgrip strength on dominant and non-dominant hands was measured with a hand dynamometer. Handgrip asymmetry was calculated. Two-minute walk test (2MWT) mobility evaluation was combined with handgrip strength to identify non-frail versus frail persons. Brain MRI volumes were quantified with Neuroreader. Multiple regression adjusting for age, sex, education, handedness, body mass index, and head size modeled handgrip strength, asymmetry and 2MWT with brain volumes. We modeled non-frail versus frail status relationships with brain structures by analysis of covariance. Results: Higher non-dominant handgrip strengthwas associated with larger volumes in the hippocampus (p = 0.02). Dominant handgrip strength was related to higher frontal lobe volumes (p = 0.02). Higher 2MWT scores were associated with larger hippocampal (p = 0.04), frontal (p = 0.01), temporal (p = 0.03), parietal (p = 0.009), and occipital lobe (p = 0.005) volumes. Frailty was associated with reduced frontal, temporal, and parietal lobe volumes. Conclusion: Greater handgrip strength and mobility were related to larger hippocampal and lobar brain volumes. Interventions focused on improving handgrip strength and mobility may seek to include quantified brain volumes on MR imaging as endpoints.

BACKGROUND:Encephaloduroarteriosynangiosis (EDAS) is a form of indirect revascularization for cerebral arterial steno-occlusive disorders. EDAS has gained growing interest as a technique applicable to pediatric and adult populations for several types of ischemic cerebral steno-occlusive conditions.OBJECTIVE:To present a team-oriented, multidisciplinary update of the EDAS technique for application in challenging adult cases of cerebrovascular stenosis/occlusion, successfully implemented in more than 200 cases.METHODS:We describe and demonstrate step-by-step a multidisciplinary-modified EDAS technique, adapted to maintain uninterrupted intensive medical management of patients' stroke risk factors and anesthesia protocols to maintain strict hemodynamic control.RESULTS:A total of 216 EDAS surgeries were performed in 164 adult patients, including 65 surgeries for patients with intracranial atherosclerotic disease and 151 operations in 99 patients with moyamoya disease. Five patients with intracranial atherosclerotic disease had recurrent strokes (3%), and there was one perioperative death. The mean clinical follow-up was 32.9 mo with a standard deviation of 31.1. There was one deviation from the surgical protocol. There were deviations from the anesthesia protocol in 3 patients (0.01%), which were promptly corrected and did not have any clinical impact on the patients' condition.CONCLUSION:The EDAS protocol described here implements a team-oriented, multidisciplinary adaptation of the EDAS technique. This adaptation resides mainly in 3 points: (1) uninterrupted administration of intensive medical management, (2) strict hemodynamic control during anesthesia, and (3) meticulous standardized surgical technique.

The partition function. is a normalization constant for normalizing all the distributions in probabilistic inference.. is closely related to the log probability of evidence (log p(e)) for Bayesian networks (BNs), which plays an important role in many applications, such as parameter learning, classification, and clustering. However, evaluating. and log p(e) for BNs containing both discrete and continuous variables (known as hybrid Bayesian networks [HBNs]) is generally difficult for analytical and simulationbased solutions. This paper describes the work addressing the problem of estimating the partition function for HBNs when exact methods become inefficient or numerically unstable. We use dynamic discretization to provide a discretized version of the model and then perform regionbased free energy optimization to obtain the log p(e) for the HBN efficiently, which is called the DDJT-region partition function (RPR) algorithm. It is novel since the region-based methods have previously only been applied to discrete BNs. We show that the log p(e) obtained by DDJT-RPR is exact for discretized models and with space complexity equal to that in marginal inference, thus generally applicable to a wide range of applications. To demonstrate these properties, we combine the DDJT-RPR algorithm with an improved expectation-maximization algorithm to learn Gaussian mixture models (GMMs), for applications requiring data only and with prior knowledge, which performed more robustly than conventional GMM learning algorithms.

Personalized multi-modal interventions for Alzheimer dementia hold promise to slow progression of symptoms yet related quantitative neuroimaging biomarkers have not been investigated[1,2]. We selected 16 participants (mean age 68.6±5.9 [range 57-77] years, 50% female) from the Pacific Brain Health Center at Providence St. John’s Health Center, with biomarker evidence of Alzheimer dementia amyloidosis. All participants received data-supported clinical recommendations (DSCR), [3–7] which included a modified SHIELD program[8] recommending a low carbohydrate diet. DSCRs were personalized based on clinical evaluations and laboratory values and closely overseen by a dementia specialist (DM, CW). T1-weighted MR images were acquired at baseline and with a 1-year average follow up. Total gray and white matter, hippocampal, lateral ventricle, temporal, parietal, occipital and frontal lobe volumes were quantified using Neuroreader[9]. Global cognition was tested using the Montreal Cognitive Assessment (MoCA). Paired t-tests were done for these metrics. Changes in hippocampal volumes (t=+1.6, p=0.11), temporal (t=+0.03, p=0.97) and frontal lobes (t=-0.63, p=0.53) were not significant. There was a marginally significant decline in the parietal lobes (t=+2.1, p=.048) and statistically significant increased lateral ventricles (t=-4.9, p<0.01). White matter volume declined significantly (t=-2.8, p=0.01) while gray matter did not change significantly (t=+1.2, p=0.21). Table 1 shows annualized percent changes for these regions. These changes were attenuated compared to literature values for the following regions: gray matter at -2%/year [10], hippocampus at -3.5%/year [11], temporal lobes at -3.23%/year, parietal lobes at -3.62%/year and frontal lobes at -2.88%/year [12]. Total gray matter and frontal lobe volumes showed increased average annualized changes at +1.97%/year and +0.87%/year respectively. Over the same time period as the brain-volume trajectory measurements, there was an average decline in MoCA (-1.5 from 22.06±4.1 to 20.56±6.1, p=0.06) that trended towards but was not statistically significant. Regional volume loss, while present, was moderated compared to literature derived rates. Increased ventricular volumes appear driven by white matter volume loss. These preliminary data suggest that efforts to track and report on clinical outcomes in settings providing close medical management and multimodal lifestyle recommendations may be a worthwhile step towards validating research findings.

121 Background: Outcomes of stereotactic body radiation therapy (SBRT) with respect to androgen receptor signaling inhibitors (ARSI) have not been characterized for metastatic prostate cancer. Our purpose is to characterize prostate specific antigen (PSA) response and progression free survival (PFS) following SBRT among men who have received ARSI in castration sensitive and resistant settings. Methods: A single institution retrospective analysis was performed for men treated with SBRT and ARSI and categorized into 4 groups: 1) oligometastatic castration-sensitive prostate cancer (omCSPC), 2) ARSI-sensitive (ARSI-s) oligometastatic castration-resistant prostate cancer (omCRPC), 3) ARSI-resistant (ARSI-r) omCRPC, and 4) polymetastatic CRPC (pmCRPC). We calculated the PSA reduction greater than 50% (PSA50) and median PFS (PSA or radiographic progression) as determined by routine care. We also used Cox regression analysis to determine factors influencing PFS for ARSI-r disease. Results: 73 men with 126 lesions were treated with SBRT and followed for a median of 14.4 months. The percentages of men who achieved a PSA50 for omCSPC, ARSI-s omCRPC, ARSI-r omCRPC and pmCRPC were 100%, 90%, 62.9%, and 16.7%, respectively. Respective median PFS values were: not reached, 17.3, 9.0, and 1.6 months. For the 35 men with ARSI-r omCRPC, incomplete ablation (defined as the presence of untreated lesions after SBRT or prior palliative external beam radiation therapy (EBRT)) (HR 3.51 [1.36, 9.06]; p = 0.01) was associated with worse PFS on multivariable analysis. For the subgroup of 22 men with ARSI-r omCRPC without prior palliative EBRT or untreated metastases, the median PFS was 13.1 months. Conclusions: SBRT may augment the efficacy of ARSI, particularly among men with ARSI-r omCRPC, provided that all lesions received ablative radiation doses. Future prospective study of SBRT for men receiving ARSI is warranted.

Driving is an intuitive task that requires skills, constant alertness and vigilance for unexpected events. The driving task also requires long concentration spans focusing on the entire task for prolonged periods, and sophisticated negotiation skills with other road users, including wild animals. These requirements are particularly important when approaching intersections, overtaking, giving way, merging, turning and while adhering to the vast body of road rules. Modern motor vehicles now include an array of smart assistive and autonomous driving systems capable of subsuming some, most, or in limited cases, all of the driving task. The UK Department of Transport’s response to the Safe Use of Automated Lane Keeping System consultation proposes that these systems are tested for compliance with relevant traffic rules. Building these smart automotive systems requires software developers with highly technical software engineering skills, and now a lawyer’s in-depth knowledge of traffic legislation as well. These skills are required to ensure the systems are able to safely perform their tasks while being observant of the law. This paper presents an approach for deconstructing the complicated legalese of traffic law and representing its requirements and flow. The approach (de)constructs road rules in legal terminology and specifies them in structured English logic that is expressed as Boolean logic for automation and Lawmaps for visualisation. We demonstrate an example using these tools leading to the construction and validation of a Bayesian Network model. We strongly believe these tools to be approachable by programmers and the general public, and capable of use in developing Artificial Intelligence to underpin motor vehicle smart systems, and in validation to ensure these systems are considerate of the law when making decisions.