1 - intro

Question 1

what is management science?

Answer 1

applying scientific models to improve decision making

optimisation

data-based, empirical, objective, quantitative.

aim to analyse, predict and control

Question 2

what is analytics?

Answer 2

using quantitative methods to derive meaning from data to make informed business decisions

Question 3

what is modelling?

Answer 3

translating reality into mathematical and logical formulations such that it can analyse the status quo, test alternative possible scenarios and generate insights

Question 4

what is the tradeoff when making a good model?

Answer 4

tradeoff between simplicity and accuracy. complex models need reliable, available data which is not common. even if you have it, will you be able to address the more complex model? how much improvement is expected from the complex model, and is it outweighed by the costs? diminishing returns on adding detail.

occam’s razor

Question 5

what is descriptive analysis?

Answer 5

stats, simulation, machine learning. used to give simple picture of system, identify trends and bottlenecks etc. model system in analytical way to identify relationships between components. usually followed by predictive or prescriptive steps.

Question 6

what is descriptive modelling?

Answer 6

simulation - see how the system works. can tell average queue times, utlisation rates of machines etc.

statistical analysis - descriptive stats. correlations, regression.

machine learning - e.g. finding demand functions.

Question 7

what is predictive analysis?

Answer 7

forecasting and time series analysis, regression, machine learning, game theory. predicts what’s going to happen in future. forecasting demand, which customers have high risk of default, will there be a housing market crash etc.

Question 8

what statistical methods can be used for predictive analysis?

Answer 8

time series analysis - examining and modelling patterns, trends and behs in sequential data collected over time. moving average, exponential smoothing, ARIMA, VAR. variants use exogenous variables.

regression analysis and choice modelling - e.g. linear regression, logistic regression, choice models, probit models.

Question 9

how can game theory and behavioural economics help with predictive analysis?

Answer 9

e.g. predicting russia/ukraine war, gov negotiations, competing companies in markets

beh economics - frameworks to understand and predict behaviour of individuals, e.g prospect theory.

Question 10

how can machine learning help with predictive analysis?

Answer 10

machine learning - most important nowadays. advanced techniques to find relationships between input and output variables. proper when complex relationship between input/output. examples of predicting stock prices, too many inputs for a regression. general structure input > function > output. ANN vs DNN.
decision trees and random forests - can be used for training models, random forests used when there are many decision trees with different weights. useful for things like shortlisting job applicants.

good for prediction power, flexibility and pattern recognition. however data intensive, complex, computation resource demand and interpretability.

Question 11

what are some challenges with predictive analytics?

Answer 11

data - availability, sufficiency, cost of acquisition, reliability, bias.

complexity and interpretability

computational efforts

reliance on big data

potential bias in results of analysis

Question 12

what is prescriptive analysis?

Answer 12

optimisation (linear/ mixed integer/nonlinear, Stochastic, Robust Dynamic), Heuristic approaches, Game theory, Decision analysis approaches, Simulation. all about deciding what to do to achieve certain objective.

Question 13

what is optimisation?

Answer 13

optimise objective given set of constraints. can be static or dynamic. dynamic means things like optimising pricing for seats on certain flight up to 3 months before flight dates. Deterministic models vs. models to deal with ambiguity, risk and uncertainty (e.g., robust optimisation, stochastic programming), etc. optimisation techniques stop you from having to consider every possible solution, saving time.

Question 14

what is a heuristic approach to optimisation? what are some examples?

Answer 14

These approaches aim to solve the optimization problems with lower computational efforts. They do not guarantee finding the optimal solutions, but they aim for a near-optimal solution with small optimality gap

Example of heuristic algorithm: Nearest Neighborhood for the Travelling Salesman Problem

Question 15

what is a meta-heuristic algorithm?

Answer 15

Meta-heuristic algorithms are usually flexible, and can be applied on a wide variety of problems, e.g., resource allocation, routing, finding optimal location, batch sizing, etc

Question 16

how can simulations be used for prescriptive analysis?

Answer 16

You simulate the model under different scenarios/ policies and choose the policy that gives the best overall payoff. How do you choose those scenarios? Works great for complex systems (and dynamic systems), where optimisation cannot model all important modelling elements

Question 17

how can game theory be used for prescriptive analysis?

Answer 17

What strategy should be taken to maximise your payoff, considering the other player(s) and their potential actions. E.g., how much Pepsi should invest in its advertising considering Coca-Cola’s spending on advertising. For situations with two (or more) competing/collaborative players. Often can be incorporated into optimisation models. Conventional game theory models assumes rationality of the players (which is not always the case), but there are game theoretic models to address situations of bounded rationality.

Question 18

how can agent based simulation aid with prescriptive analysis?

Answer 18

Involves modelling and simulating the actions and interactions of multiple agents (autonomous entities). Agents typically have their own set of rules, behaviours, and decision-making processes. It is somehow an intersection of game theory and simulation. Examples: trading behaviour of investors, analysing the impact of single soldier on overall mission success, studying organisational changes and adaptation. NOT COVERED.

Question 19

what are decision analysis tools?

Answer 19

Evaluating different alternatives based on one or more criteria and choosing the best. Examples: Multi Criteria Decision Making (MCDM) tools, e.g., AHP, ANP; Decision tree; etc.