Theoretical computer science

A young undergraduate in Computer Science, I become inspired by cryptography and steganography, the art of ciphering messages has always been an interest even though I didn't have the chance to indulge during my College years, I did experiment in my room for many nights and days. And this paper is my first on the subject of Cryptography, especially a cipher I invented which comes from the basic idea of several crosses, in which you can place 4 characters top left, top right, bottom left and bottom right, however, the way in which several crosses collide is the actual cipher mechanism, imagine a cross A and a cross B if i place these crosses next to each other A on the left and B on the right, you get this simple structure: however, this encryption mechanism seems rather trivial, thus making the decryption cycle rather simple, well, my idea is actually a simple idea for fast encryption, which when viewed by someone with no background knowledge in cryptography would not be able to decipher

Obligations and its theoretical approach needs some extensive work in the realm of computer science, these protocols are implemented in almost all infrastructures we experience in our day to day lives. The access control security procedures use a specimen of obligations and countless models have been developed to try and ensure stability in computer security. In this survey I will convey the various aspects of obligations in computer systems, obligations and its influence on access control, Black's dictionary and obligations in law, a link between publications on obligations and the constraints of obligations in computer science.

Political dialogue has been a topic which many analysts and politicians have focused on over
the years, however, current models leave something to be desired. Thus, an effective model has
not been widely implemented. Our model will allow computer systems to provide a means for
politicians to become more effective and better at their jobs. It would be valuable to this sector
if tools were produced to enable politicians to better understand how their political ideology
can impact the understanding of people who listen to them. Political dialogue is an area in
which argumentation theory can be embedded to convey a means for effective persuasion. Any
politically engaged person can be persuaded to vote for a political party, in fact that’s how
political campaigns work. Argumentation models will be used to simulate the outcome of
debates using logic models to calculate the effectiveness of arguments used by politicians and
people they target.
Both politicians and people have political views, and these can vary depending on their
personal preferences. During a campaign for elections, politicians will target people who fundamentally
disagree with them. People who are targeted will in turn make the case for why
they are right and they will thus counter arguments made by the politician. The politician
will never know how effective their arguments are until the end of the debate, which can be
considered to be a form of argument-based dialogue.
We address the problems of weighted arguments, argument ranking and argument impact
in argumentation dialogue and how this can be used to model a real-world scenario. We aim to
produce a model that enables strategists and political parties to understand how the arguments
made by politicians subscribed to a particular party and ideology can maximise the likelihood
that individuals will vote for them.

Understanding when and where resources should be distributed in response to dynamic spatio-temporal processes is a complex task. One example of this problem is understanding how to best deploy police resources in response to calls for service. Police agencies have finite numbers of resources that must be allocated to events occurring in a particular locality in real-time. These events can be diverse in nature, require varying levels of resources and represent different levels of importance to the responder. Moreover, they are often interdependent, both at the event level and in terms of opportunity costs when responding to one event is prioritised over another. Understanding this problem is particularly important in the 21st century where police are being asked to deal with increasingly diverse problems, often with relatively restricted resources. This complexity dictates that traditional analytical approaches often struggle to provide adequate solutions to resourcing and demand problems.

The original Sugarscape agent-based model has been adopted by the modelling community as a simple yet very insightful model. It uses event-condition-action rules to simulate a society which ages over time and consumes sugar to survive. As this model has been bench-marked on and used in multiple research areas, such as Economics, Computer Science and Biology it was only fair to adopt it in this research. The research will present an implementation of the original Sugarscape model using Reinforcement Learning. The problem addressed is, can agents make more realistic decisions by learning from their environment using reinforcement learning? The goal is to compare reactive behaviours and adaptive behaviours to see if actions performed by agents within the given environmental circumstances would be as expected if they were humans. Furthermore, agents can learn to adapt to dynamic changes in the environment and anticipate future trends, for example; learning the fastest route out of a burning building while anticipating a fire alarm. This model will be implemented in Unity which is a video game development engine. Unity has not been widely used in the agent-based modelling community, yet does provide useful facilities for the creation of models.

Sugarscape is a classic agent-based model that has been adopted by the agent-based modelling community as a simple yet very insightful model. The primary model utilised event-condition-action rules to simulate a society which ages and consumes sugar to survive. Reinforcement Learning is a machine learning technique used to enrich autonomous agents with information. Agents that adopt reinforcement learning learn to execute actions and adapt to changes over time. This research will present an implementation of the important components from the original Sugarscape model (like collecting sugar, and several new additions) as a benchmark for the reinforcement learning application. We find that when agents trained overtime using reinforcement learning algorithms inside a defined environment, complicated behaviours emerge. This research will explore the methodologies utilised, some of these are; applied reinforcement learning in agent-based models and implementation of agent-based models in 3D space using Unity (video game development soware). The analysis section of the research compiles the results from the model. The process is achieved by tracking the behaviour of individual agents. Similarly, the training process of individual agents is illustrated using Tensorboard (visualisation toolkit). To conclude, we sum up the research by describing the essential points regarding the aims and objectives of the research, and most importantly, the novel contributions the research makes to the agent-based modelling community.

Capturing and simulating intelligent adaptive behaviours within spatially explicit individual-based models remains an ongoing challenge for researchers. While an ever-increasing abundance of realworld behavioural data are collected, few approaches exist that can quantify and formalise key individual behaviours and how they change over space and time. Consequently, commonly used agent decision-making frameworks, such as event-condition-action rules, are often required to focus only on a narrow range of behaviours. We argue that these behavioural frameworks often do not reflect real-world scenarios and fail to capture how behaviours can develop in response to stimuli. There has been an increased interest in Machine Learning methods and their potential to simulate intelligent adaptive behaviours in recent years. One method that is beginning to gain traction in this area is Reinforcement Learning (RL). This paper explores how RL can be applied to create emergent agent behaviours using a...

By 2020, over 100 countries had expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to the proven environmental and health implications of internal combustion engine vehicles (ICEVs), as evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in energy consumption; thus, simulating various levels of individual behaviour and enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that (1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behavio...

In 2020, over 3 million electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEV) were sold, a 40% increase from 2019. There are now over 10 million electric cars on the roads globally, and this number is expected to rise to 300 million by 2030. Due to the significant environmental and health implications of internal combustion engine vehicles (ICEVs), national governments are adopting cleaner vehicle technologies at an unprecedented pace. Much of the literature regarding electric vehicles is centred around charging vehicle infrastructure and levels of market penetration. Consequently, there is an opportunity to explore energy consumption characteristics and produce estimates of energy demands in urban areas. This study employs an agent-based model of vehicle activity to quantify electric energy consumption through the simulation of a heterogeneous fleet of electric vehicles within an urban street network under several experimental conditions.

This research presents a novel Discrete Event Simulation (DES) of the Lloyd's of London specialty insurance market, exploring complex market dynamics that have not been previously studied quantitatively. The proof-of-concept model allows for the simulation of various scenarios that capture important market phenomena such as the underwriting cycle, the impact of risk syndication, and the importance of appropriate exposure management. Despite minimal calibration, our model has shown that it is a valuable tool for understanding and analysing the Lloyd's of London specialty insurance market, particularly in terms of identifying areas for further investigation for regulators and participants of the market alike. The results generate the expected behaviours that, syndicates (insurers) are less likely to go insolvent if they adopt sophisticated exposure management practices, catastrophe events lead to more defined patterns of cyclicality and cause syndicates to substantially increase their premiums offered. Lastly, syndication enhances the accuracy of actuarial price estimates and narrows the divergence among syndicates. Overall, this research offers a new perspective on the Lloyd's of London market and demonstrates the potential of individual-based modelling (IBM) for understanding complex financial systems. Acknowledgements A special thanks to the Alan Turing Institute's Internship Network (TIN) in supporting the project. Accenture for their partnership with academia. Ki and Brit syndicates for bridging the relationship between academia and industry, providing the resources to undertake the research project. A special thanks to Reuben Thomas-Davis for developing and providing access to the HADES framework.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

This research presents a novel Discrete Event Simulation (DES) of the Lloyd's of London specialty insurance market, exploring complex market dynamics that have not been previously studied quantitatively. The proof-of-concept model allows for the simulation of various scenarios that capture important market phenomena such as the underwriting cycle, the impact of risk syndication, and the importance of appropriate exposure management. Despite minimal calibration, our model has shown that it is a valuable tool for understanding and analysing the Lloyd's of London specialty insurance market, particularly in terms of identifying areas for further investigation for regulators and participants of the market alike. The results generate the expected behaviours that, syndicates (insurers) are less likely to go insolvent if they adopt sophisticated exposure management practices, catastrophe events lead to more defined patterns of cyclicality and cause syndicates to substantially increase their premiums offered. Lastly, the syndication of risk via the lead and follow structure lead to less volatile and more coupled loss experiences among syndicates demonstrating that Lloyd's of London's regulatory market structure bolsters a healthier marketplace. Overall, this research offers a new perspective on the Lloyd's of London market and demonstrates the potential of individual-based modelling (IBM) for understanding complex financial systems.