Introducing two key techniques

For the purposes of this course, we are going to focus on two particular techniques: graph theory and linear optimisation. The reason for focusing on these two is because many real-world problems can be solved using these techniques. Some of the most influential results in computer science, and technology-assisted decision-making, are in the field of graph theory and linear optimisation.

Graph theory

Graph theory is an exciting topic studied in mathematics. Graph theory allows us to describe a model relationships between objects in the real world. For example, consider a social network, where there are many people who are part of it and each person has a set of friends. In this example, interesting questions might be; who is the most important person in the network? How far apart are two people in the network? Do two people have a friend in common?

Graph theory is extensively used in many industries included social networks, computer networks and automobile navigation.

We will look at graph theory in Week 1, using firstly a case study to illustrate how it can be used, and then we will explore the maths behind it.

Linear optimisation

Linear optimisation is a technique used to find solutions to a specific form of problem which are commonly found in the real world. This type of problem can be found in a varied set of industries including scheduling in transportation networks and for timetables in hospitals and schools, production planning for manufacturing, and marketing, where it can be used to identify ways of spending a budget.

In the second week of the course we will focus on optimisation algorithms as they form the basis of many other techniques, including many techniques in the field of artificial intelligence. In exploring the exciting world of optimisation algorithms we will look at two specific case study examples and how decisions are extracted from problems, and the impact the decisions have on the specific scenario.

Next step

Having looked at a broad overview of what technology-assisted decision-making is and where it can be applied, let’s move on to Activity 3 to look at a case study that can benefit from technology-assisted decision-making.

Optional reading: what other techniques are used in technology-assisted decision-making?

There are lots of techniques used in technology-assisted decision-making. Some are very simple, such as a rule based systems, other are vastly more complicated such as some of the latest cutting edge artificial intelligence, machine learning and deep learning techniques.

We won’t be covering them in this course, but you may want to look them up online if you are interested in learning more:

1. Decision trees: A decision tree is a graphical representation of a decision making process that involves a series of decisions and their possible consequences. Decision trees can be used to evaluate different options and choose the best course of action.
2. Bayesian networks: A Bayesian network is a probabilistic model that represents a set of variables and their conditional dependencies. Bayesian networks can be used to make predictions and identify causal relationships between variables.
3. Artificial neural networks: An artificial neural network is a type of machine learning algorithm that is inspired by the structure of the human brain. Neural networks can be used to recognise patterns in data and make predictions based on those patterns.
4. Fuzzy logic: Fuzzy logic is a mathematical framework that allows for reasoning with uncertain or incomplete information. Fuzzy logic can be used to make decisions when there is incomplete or ambiguous data available.
5. Optimisation algorithms: Optimisation algorithms are used to find the best solution to a problem within a given set of constraints. These algorithms can be used to optimise everything from supply chain management to financial investments.
6. Natural language processing: Natural language processing is a field of computer science that focuses on the interaction between computers and human language. Natural language processing can be used to analyse large amounts of text data, such as customer reviews or social media posts, to identify trends and sentiment.