Convergence properties of the El Farol bar problem with social learning

The El Farol Bar problem proposed by Arthur in [1] is a study of economic system. Though Arthur's main objective was to highlight how humans are more apt in making inductive reasoning for complex decision making process rather than deductive reasoning; the model has been widely used in analysis of economic systems, particularly when congestion issues arise. The original model is described as follows. A population of agents have to decide every week whether to go to the El Farol bar or not. If many agents attend the bar, for example more that 60%, it will be overcrowded and that results unpleasant experience for the attendees. The decision made by each agent is purely individual and based on a random subset of predictors. Arthur's simulation results showed that the system kept fluctuating near the 60% threshold and the agents divided themselves into a 60/40 ratio of bar attendance. In this research, we are interested in interaction-based decision making processes, which are lacking in Arthur's model. Several attempts have been made in the literature to introduce such interaction processes or communication mechanisms to the original model. Those extended models often involve a xed network/neighborhood structure over the agents and the system dynamics were mainly studied with computer simulations [4, 6, 7]. Our contribution is a novel mechanism of information exchange and decision making among the agents, resulting an extended model for the El Farol 1 bar problem. The idea is similar to social communication. Each agent randomly communicates with two other agents within the population to obtain information about the last bar attendance. Based on this information the agent makes a stochastic decision to go to the bar. The aim of the study is to experimentally and rigorously analyse how such a system behaves, in particular how the bar attendance varies. The first part of this thesis is dedicated to simulation results. We first investigate the system settings for which an equilibrium corresponding to the threshold of the bar can be reached. The behaviours of the system related to the initial state of below and above the threshold are discussed. From the perspective of individual attendance, we also address the formation of structures within the population. With the proposed model, the population of agents eventually divides into two groups of attendees: regulars and casuals. In the second part, we show that the dynamics of the proposed system can be analyzed by mean of rigorous mathematics, and the expected time for the system to reach the equilibrium can be proved. For this purpose, we use Drift Analysis as the main tool. Note that Drift Analysis is widely used in Evolutionary Computation to compute expected runtime of Randomised Search Heuristics (see Lehre [8]). Due to the nature of the system that the bar attendance can wamble around the threshold, the importance of analyzing the reduction in the variance is further detailed. The proof of the runtime is shown in Chapter 4 followed by further discussion in Chapter 5. In summary, this study investigate a novel model of the El Farol Bar problem from a social coordination perspective. We show that with the right settings, the system eventually converges to the equilibrium associated with the threshold of the bar. A rigorous analysis of the system dynamics is initiated using advanced probability tools

A smart phone based multi-floor indoor positioning system for occupancy detection

At present there is a lot of research being done simulating building environment with artificial agents and predicting energy usage and other building performance related factors that helps to promote understanding of more sustainable buildings. To understand these energy demands it is important to understand how the building spaces are being used by individuals i.e. the occupancy pattern of individuals. There are lots of other sensors and methodology being used to understand building occupancy such as PIR sensors, logging information of Wi-Fi APs or ambient sensors such as light or CO2 composition. Indoor positioning can also play an important role in understanding building occupancy pattern. Due to the growing interest and progress being made in this field it is only a matter of time before we start to see extensive application of indoor positioning in our daily lives. This research proposes an indoor positioning system that makes use of the smart phone and its built-in integrated sensors; Wi-Fi, Bluetooth, accelerometer and gyroscope. Since smart phones are easy to carry helps participants carry on with their usual daily work without any distraction but at the same time provide a reliable pedestrian positioning solution for detecting occupancy. The positioning system uses the traditional Wi-Fi and Bluetooth fingerprinting together with pedestrian dead reckoning to develop a cheap but effective multi floor positioning solution. The paper discusses the novel application of indoor positioning technology to solve a real world problem of understanding building occupancy. It discusses the positioning methodology adopted when trying to use existing positioning algorithm and fusing multiple sensor data. It also describes the novel approach taken to identify step like motion in absence of a foot mounted inertial system. Finally the paper discusses results from limited scale trials showing trajectory of motion throughout the Nottingham Geospatial Building covering multiple floors

Demonstrating the potential of indoor positioning for monitoring building occupancy through ecologically valid trials

Assessing building performance related to energy consumption in post-design-occupancy stage requires knowledge of building occupancy pattern. These occupancy data can potentially be collected from trials and used to improve the prediction capability of building performance models. Due to the limitations of passive sensors in detecting an individual’s occupancy throughout the building, indoor positioning can provide a viable alternative. Previous work on using indoor positioning techniques for detecting building occupancy mainly focused on passive monitoring through Wi-Fi or BLE proximity sensing by estimating the number of occupants at any given time. This paper extends our previous research and demonstrates the merit of occupancy monitoring through active tracking at an individual level using a smartphone-based multi-floor indoor positioning system. The paper discusses the design of a novel occupancy detection trial setup, mimicking real-world office occupancy and discusses the outcome of the ecologically valid trials using the developed positioning system. In total 50 occupancy trials were carried out by around 22 participants comprising of a variety of routes within the building. The trial results are presented to demonstrate the level of accuracy achievable against a specific set of the performance metric necessary for building occupancy detection and modelling

Convergence properties of the El Farol bar problem with social learning

The El Farol Bar problem proposed by Arthur in [1] is a study of economic system. Though Arthur's main objective was to highlight how humans are more apt in making inductive reasoning for complex decision making process rather than deductive reasoning; the model has been widely used in analysis of economic systems, particularly when congestion issues arise. The original model is described as follows. A population of agents have to decide every week whether to go to the El Farol bar or not. If many agents attend the bar, for example more that 60%, it will be overcrowded and that results unpleasant experience for the attendees. The decision made by each agent is purely individual and based on a random subset of predictors. Arthur's simulation results showed that the system kept fluctuating near the 60% threshold and the agents divided themselves into a 60/40 ratio of bar attendance. In this research, we are interested in interaction-based decision making processes, which are lacking in Arthur's model. Several attempts have been made in the literature to introduce such interaction processes or communication mechanisms to the original model. Those extended models often involve a xed network/neighborhood structure over the agents and the system dynamics were mainly studied with computer simulations [4, 6, 7]. Our contribution is a novel mechanism of information exchange and decision making among the agents, resulting an extended model for the El Farol 1 bar problem. The idea is similar to social communication. Each agent randomly communicates with two other agents within the population to obtain information about the last bar attendance. Based on this information the agent makes a stochastic decision to go to the bar. The aim of the study is to experimentally and rigorously analyse how such a system behaves, in particular how the bar attendance varies. The first part of this thesis is dedicated to simulation results. We first investigate the system settings for which an equilibrium corresponding to the threshold of the bar can be reached. The behaviours of the system related to the initial state of below and above the threshold are discussed. From the perspective of individual attendance, we also address the formation of structures within the population. With the proposed model, the population of agents eventually divides into two groups of attendees: regulars and casuals. In the second part, we show that the dynamics of the proposed system can be analyzed by mean of rigorous mathematics, and the expected time for the system to reach the equilibrium can be proved. For this purpose, we use Drift Analysis as the main tool. Note that Drift Analysis is widely used in Evolutionary Computation to compute expected runtime of Randomised Search Heuristics (see Lehre [8]). Due to the nature of the system that the bar attendance can wamble around the threshold, the importance of analyzing the reduction in the variance is further detailed. The proof of the runtime is shown in Chapter 4 followed by further discussion in Chapter 5. In summary, this study investigate a novel model of the El Farol Bar problem from a social coordination perspective. We show that with the right settings, the system eventually converges to the equilibrium associated with the threshold of the bar. A rigorous analysis of the system dynamics is initiated using advanced probability tools

Comparing different approaches of agent-based occupancy modelling for predicting realistic electricity consumption in office buildings

Having a good grasp on modelling the dynamics of occupants for estimating electricity consumption in office buildings is a vital asset for realistic predictions. Nowadays, agent-based models are widely used for this purpose. Previous approaches to modelling dynamics of occupants in multi-floor office buildings simplified the models by teleporting agents between zones during transitions without considering the routes used to reach their final destination such as going through corridors, stairways and hallways, thus, underestimating the potential energy usage during those transition period. This paper proposes a more realistic approach by incorporating detailed routes of agent movement when transiting from one zone to another. To demonstrate the case, detailed routes and route choice preferences are used as inputs within the model for the agents to make independent decisions when transiting from one place to another within the simulated office building. The route choice preferences are computed from data gained from an earlier extensive real world occupancy detection trial conducted within the model office building using state of the art indoor positioning system. The simulation experiments compare the previous approach against the proposed approach and based on the evaluation it is found that there is approximately 19% underestimation of electricity consumption per day when detailed routes are not considered. The research demonstrates, the proposed approach is applicable to any office buildings and will produce predictions which will be much more realistic and closer to the real world electricity consumption level

Comparing different approaches of agent-based occupancy modelling for predicting realistic electricity consumption in office buildings

Having a good grasp on modelling the dynamics of occupants for estimating electricity consumption in office buildings is a vital asset for realistic predictions. Nowadays, agent-based models are widely used for this purpose. Previous approaches to modelling dynamics of occupants in multi-floor office buildings simplified the models by teleporting agents between zones during transitions without considering the routes used to reach their final destination such as going through corridors, stairways and hallways, thus, underestimating the potential energy usage during those transition period. This paper proposes a more realistic approach by incorporating detailed routes of agent movement when transiting from one zone to another. To demonstrate the case, detailed routes and route choice preferences are used as inputs within the model for the agents to make independent decisions when transiting from one place to another within the simulated office building. The route choice preferences are computed from data gained from an earlier extensive real world occupancy detection trial conducted within the model office building using state of the art indoor positioning system. The simulation experiments compare the previous approach against the proposed approach and based on the evaluation it is found that there is approximately 19% underestimation of electricity consumption per day when detailed routes are not considered. The research demonstrates, the proposed approach is applicable to any office buildings and will produce predictions which will be much more realistic and closer to the real world electricity consumption level

A New Adopted YOLOv9 Model for Detecting Mould Regions Inside of Buildings

Molds on wall and ceiling surfaces in damp indoor environments especially in houses with poor insulation and ventilation are common in the UK. Since it releases toxic chemicals as it grows, it is a serious health hazard for occupants who live in such houses. For example, eye irritation, sneezing, nose bleeds, respiratory infections and skin irritations. Furthermore, there are chances of developing serious medical conditions like lung infections and respiratory diseases which may even lead to death. The main challenge here is that due to their irregular patterns, camouflaged with the background, it is not so easy to detect with our naked eyes in the early stage and often confused as stains. Therefore, inspired by the accomplishments of the Yolo architecture for object detection, the Yolov9 model is explored for mould detection by considering mould region as an object in this work. The overall result shows a promising 76% average classification rate. Since the mould does not have a shape, specific pattern or colour, adapting the Yolov9 for accurate mould detection is challenging. To the best of our knowledge, this is the first of its kind compared to existing methods. Since it is the first work, we constructed a dataset to perform experiments and evaluate the proposed method. To demonstrate the proposed method's effectiveness, the results were also compared with the results of the Yolov8 and Yolov10 models