Measuring Stakeholder Agreement and Stability in a Decentralised Organisation

A decentralised organisation (DO) is a multi-stakeholder institution where decision making is assigned to various levels of the organisation. Decentralised stakeholders play an important role in the governance of a decentralised organisation. The ability to measure DO stability will help monitor the health of the organisation and acts as an early warning system for disagreement and group exit, leading to its destabilisation/collapse. For example, blockchain hard forks. We propose the organisational tension quadrilateral to study agreement between stakeholders and build a tool based on voting data (information as vote choices) to measure its stability. The stakeholders are permitted to vote their choice into an electronic ballot box. Here, each vote choice represents a measure of agreement. When voting ends, this information is aggregated and used to build a metric for DO stability. To the best of our knowledge, there are no similar tools available to measure DO stability

An agent-based framework for problem solving in symbiotic simulation systems

Symbiotic simulation can be used for problem solving processes in the context of applications concerned with real-world physical systems. These kind of systems are often highly complex and exhibit non-linear behaviour which necessitates the use of simulation techniques to evaluate possible solutions to problems concerned with these systems. Problem solving can be described as an optimisation process concerned with the minimisation/maximisation of one or more objectives (expressed in terms of performance indicators). Optimisation requires the use of knowledge regarding the problem in order to effectively and efficiently solve it. Although black-box optimisation (i.e., without use of any knowledge regarding the problem) can be performed, it is easily outperformed by algorithms that incorporate domain knowledge. The more knowledge about a certain problem is incorporated into an algorithm, the more specialised the algorithm becomes. While specialisation generally improves the performance of an algorithm in solving a particular problem, it does so only at cost of decreasing re-usability of the algorithm for other problems. This is due to the implications of the no-free-lunch theorems. An autonomous problem solver agent which is meant to replace a human problem solver and automatically perform what-if analyses, needs to be able to solve different problems during its life span. This requires a flexible approach that does not statically hard-code information about a problem into the problem solving algorithm. Instead, it is necessary to dynamically incorporate problem-specific knowledge. In this dissertation we address this issue and establish a framework for constructing problem solver agents, based on symbiotic simulation. The approach in this dissertation is three-fold. First, we establish a theory on symbiotic simulation which also takes consideration of related work. As a result we propose a taxonomy on symbiotic simulations and introduce various classes of symbiotic simulation systems. Based on this taxonomy we can clearly define problem solving in symbiotic simulation and specify the what-if analysis process. Second, we argue for the use of evolutionary computing and propose our method for separating problem specific knowledge from the implementation of an evolutionary algorithm using an appropriate language.DOCTOR OF PHILOSOPHY (SCE

System Analysis of Mesoscale and Microscale Urban Climate Simulation Workflows

The Cooling Singapore project studies the urban climate of Singapore and, in particular, evaluates measures to mitigate urban heat. For this purpose, Cooling Singapore utilises a variety of tools for modelling, simulation, data processing and analysis. Some of these tools are off-the-shelf software (e.g., MATLAB, ANSYS Fluent), while others are third-party open-source software modified to meet the needs of the project (e.g., WRF), or software that has been developed in-house specifically for the purpose of the project. The resulting ecosystem of tools is thus highly diverse, requiring a diverse set of skills and operating environments in order to perform integrated studies across multiple domains. At the moment, such integrated studies are carried out in a collaborative manner involving the contributions from various researchers. The ability to carry out integrated what-if scenario analysis is important to investigate the potential impact of mitigation measures on the urban climate. This ability is not only important for researchers but also for practitioners, such as urban planning authorities for example. Manual workflow execution, involving the contributions of several researchers, represents a significant constraint. Not only is this process time-consuming, it is also prone to human error. A better approach would be to clearly specify the individual steps needed to carry out a particular what-if scenario analysis and automate (as far as possible) the entire process. This would not only reduce the amount of manual work (thus freeing researchers’ time to focus on other things) but also vastly improve the reproducibility of results and reduce the likelihood of human error. The first steps towards automated workflow execution are to analyse the workflows to better understand the various steps that are needed in order to carry out integrated studies and what-if scenario analyses. This document represents a systematic analysis of the primary workflows and their principal components in the context of the Cooling Singapore project. In particular, this document provides an overview of all principal components, as well as their input and output data. The information provided in this document is not meant to be a detailed documentation for each of the various model components. Instead, it is a system-level analysis that focuses on the flow of data from one model to another. It should also be noted that the information provided here is valid as of the time of writing. However, as the project evolves, so will the workflows and the components of the system

Spatial and Temporal Analysis of Mismatch between Planned Road Infrastructure and Traffic Demand in Large Cities

large cities evolve in time, the traffic demand and the road network adapt to the mutually presented changes by each other. As a result of this process, previously planned roads and intersections that were designed according to some optimality criteria at the time, turn out to be sub-optimal when traffic conditions change. This paper presents a method that can be used in order to identify intersections whose capacity is no longer in correspondence with the demand of vehicles on them and the choices agent make at those locations. Using real data from a survey describing the travel patterns of people in the city of Singapore we are able to model the routing choices of commuters and simulate the traffic demands on the road network. After calculating the turning probabilities on every intersection we are able to compare the traffic demand for every turn with the planned physical roads' capabilities. Furthermore we define a measure, which quantifies the deviation of the whole road network from the ideal demand-calculated values. We use these measures to evaluate the temporal and spatial profile of the mismatch between the roads and the demand for them. The measure is designed such that it is universal in nature and invariant to the absolute values of the traffic flows in the city. It can, therefore, be used to compare the proper utilization of road networks among different cities

SEMSim: A Distributed Architecture for Multi-scale Traffic Simulation

With the fast urbanization of our modern society, trans-portation systems in cities are facing increasing problems such as congestion, collisions, and high levels of emissions. Researchers have been searching for solutions by investigatin

Symbiotic simulation for future electro-mobility transportation systems

Electro-mobility is widely regarded as the future of transportation systems. The shift from fossil fuel-based engines to electro-mobility will pose new challenges to the operations of future transportation systems. Our vision of a smart transportation system for the future entails a collaborative communication and simulation infrastructure that can help to mitigate common traffic-related problems as well as problems that are specific to electric vehicles. At the core of this smart transportation system would be a symbiotic simulation system which incorporates information provided by the various traffic participants into city-scale traffic simulations. We describe the symbiotic simulation system and highlight the research challenges that need to be addressed in order to realize such a system. This includes a server-based city-scale simulation which would forecast general traffic patterns and conditions in the near-future. The outcome of these simulations can be used by server-based smart routing services and/or in-car navigation systems

Information Dynamics in Transportation Systems with Traffic Lights Control

Due to recent advanced communication possibilities between traffic infrastructure, vehicles and drivers, the optimization of traffic lights control can be approached in novel ways. At the same time, this may introduce new unexpected dynamics in transportation systems. The authors' research aims to determine how drivers and traffic lights systems interact and influence each other when they are informed one about another's behaviour. In order to study this, the authors developed an agent based model to simulate transportation systems with static and dynamic traffic lights and drivers using information about the traffic lights behaviour. Experiments reveal that the system's performance improves when a bigger share of drivers receive information for both static and dynamic traffic lights systems. This performance improvement is due to drivers managing to avoid stopping at red light rather them adapting their speed to different distances to the traffic lights systems. Additionally, it is demonstrated that the duration of the fixed phases also influences the performance when drivers use speed recommendations. Moreover, the results show that dynamic traffic lights can produce positive effects for roads with high speed limits and high traffic intensity, while in the rest of the cases static control is better. The findings can be used for building more efficient traffic lights systems. Document type: Articl