ML for Management Consulting

MANAGEMENT CONSULTING WITH ML AND GAME THEORY

The Future of Management Consulting and Analytics
http://www.quantifye.co/blog/the-future-of-management-consulting-and-analytics

Making game theory work for managers
A new model, rejecting solutions optimal only for a single precisely defined future, generates answers representing the best compromise between risks and opportunities in all likely futures.
http://www.mckinsey.com/insights/strategy/making_game_theory_work_for_managers

Advances in Electrical Engineering and Computational Science

https://books.google.com/books?id=NEqlTmiEFAYC

https://books.google.com/books?id=tPkjk5s-GHgC&pg=PA171

https://www.cs.umd.edu/~basili/publications/proceedings/P120.pdf
A STATISTICAL NEURAL NETWORK FRAMEWORK FOR RISK MANAGEMENT PROCESS From the Proposal to its Preliminary Validation for Efficiency Salvatore Alessandro Sarcià, Giovanni Cantone Dip. di Informatica, Sistemi e Produzione, Università di Roma Tor Vergata, via del Politecnico 1, 00133 Roma, Italy sarcia@, cantone@uniroma2.it Victor R. Basili Dept. of Computer Science, University of Maryland, A.V. Williams Bldg. 115, College Park 20742, MD, USA basili@cs.umd.edu Keywords: Risk Management Process, Artificial Neural Networks, Experimental Software Engineering, Prior Probability, Posterior Probability, Bayes’ Theorem, Computational Software Engineering. Abstract: This paper enhances the currently available formal risk management models and related frameworks by providing an independent mechanism for checking out their results. It provides a way to compare the historical data on the risks identified by similar projects to the risk found by each framework Based on direct queries to stakeholders, existing approaches provide a mechanism for estimating the probability of achieving software project objectives before the project starts (Prior probability). However, they do not estimate the probability that objectives have actually been achieved, when risk events have occurred during project development. This involves calculating the posterior probability that a project missed its objectives, or, on the contrary, the probability that the project has succeeded. This paper provides existing frameworks with a way to calculate both prior and posterior probability. The overall risk evaluation, calculated by those two probabilities, could be compared to the evaluations that each framework has found within its own process. Therefore, the comparison is performed between what those frameworks assumed and what the historical data suggested both before and during the project. This is a control mechanism because, if those comparisons do not agree, further investigations could be carried out. A case study is presented that provides an efficient way to deal with those issues by using Artificial Neural Networks (ANN) as a statistical tool (e.g., regression and probability estimator). That is, we show that ANN can automatically derive from historical data both prior and posterior probability estimates. This paper shows the verification by simulation of the proposed approach. 

https://books.google.com/books?id=Wg7LBQAAQBAJ

Title	Risk Assessment and Decision Analysis with Bayesian Networks
Authors	Norman Fenton, Martin Neil
Edition	illustrated
Publisher	CRC Press, 2013
ISBN	1439809119, 9781439809112
Length	524 pages