Some recently published articles by:
Dr Warren R Hughes B Com M Com MBA DBA
THE WORLD OUTLOOK - 10th August 2022
WORLD ECONOMIC OUTLOOK – August 2022
WORLD ECONOMIC OUTLOOK – Late July 2022
WORLD ECONOMIC OUTLOOK – July 2022
STAGFLATION ALERT – May 2022
WORLD THOUGHTS – Late-April 2022
WORLD ECONOMIC OUTLOOK – Mid-April 2022
A NEW APPROACH TO PROBABILITY ASSESSMENT – March 2022
BITCOIN PRICE MOVEMENTS USING PROBABILITIES – March 2022
THE UKRAINE QUANDARY III – April 2022
THE UKRAINE QUANDARY II – April 2022
WORLD ECONOMIC OUTLOOK – April 2022
RANDOM THOUGHTS ON PROBABILITY ASSESSMENT– April 2022
THE UKRAINE QUANDARY – March 2022
WORLD ECONOMIC OUTLOOK – March 2022
WORLD ECONOMIC OUTLOOK – February 2022
THINKING PROBABILISTICALLY ADDENDUM - February 2022
THINKING PROBABILISTICALLY REVISITED - January 2022
WORLD ECONOMIC OUTLOOK – January 2022
THINKING PROBABILISTICALLY VI - December 2021
WORLD ECONOMIC OUTLOOK – December 2021
WORLD ECONOMIC OUTLOOK – Mid-November 2021
WORLD ECONOMIC OUTLOOK – November 2021
WORLD ECONOMIC OUTLOOK – October 2021
THINKING PROBABILISTICALLY IV - September 2021
WORLD ECONOMIC OUTLOOK – September 2021
THINKING PROBABILISTICALLY III
THINKING PROBABILISTICALLY II
WORLD ECONOMIC OUTLOOK – August 2021
WORLD ECONOMIC OUTLOOK – late July 2021
THINKING PROBABILISTICALLY – July 2021
WORLD ECONOMIC OUTLOOK – Late June 2021
WORLD ECONOMIC OUTLOOK – June 2021
WORLD ECONOMIC OUTLOOK – May 2021
Tesla Share Price - May 2021
RANDOM THOUGHTS ON PROBABILITY ASSESSMENT– May 2021
Tesla Share Price - April 2021
WORLD ECONOMIC OUTLOOK – March 2021
WORLD ECONOMIC OUTLOOK – February 2021
WORLD ECONOMIC OUTLOOK – January 2021
WORLD ECONOMIC OUTLOOK – Christmas 2020
WORLD ECONOMIC OUTLOOK – December 2020
WORLD ECONOMIC OUTLOOK – November 2020
STRUCTURING PROBABILITY ASSESSMENTS - May 2020
Probability Calculations Using the Geometric Mean & Comparisons with Eigenvalues - August 2019
ASSESSING PROBABILITIES OF UNIQUE EVENTS: A NEW APPROACH
Chinese Business Review, January 2019, Vol. 18, No. 1, 43 - 46.
2019 FIELDAYS™ IN HAMILTON:
ECONOMIC IMPACTS FOR THE WAIKATO REGION AND NZ
Report prepared by Dr Warren R Hughes November 2019.
ASSESSING PROBABILITIES OF UNIQUE EVENTS IN DECISION MAKING
Chinese Business Review, January 2018, Vol. 17, No. 1, 33 - 37
LEHMAN FAILURE CALCULATIONS - November 2019
Assessing Probabilities for Events Pertaining to Buy/Sell and Similar Decisions
Chinese Business Review, May 2017, Vol. 16, No. 5
Decision Trees to Derive Fair Prices in Unique Situations Under Uncertainty.
Proceedings of the 2013 Hawaii International Conference on Business. Pages 454 – 461. May 2013.
ABSTRACT: Decision trees can be used to detail a transparent derivation of a “fair” price
for an asset, security etc. (Fair Price) where various outcomes are possible leading
to differing final prices on completion of the transaction. A final branch of the tree or
endpoint will show the dollar value realized should that path be the one followed. This
paper outlines a new procedure for determining the probability of that endpoint being
realized where the situation under analysis is unique. That is, subjective probabilities
need to be calculated based only on the judgment of the decision-maker (DM) since
historical frequencies are not pertinent to the unique situation. Procedures outlined here
make probability assessment easier for business professionals by minimizing the
mathematical input required. Once all probabilities are determined, a Fair Price or expected
value can be calculated for the asset, security, action etc. under analysis. The probability deriving
procedure can also be utilized in a Bayesian Revision process to revise the Fair Price following news
or information relevant to the unique situation. The procedure is illustrated using an example
showing the derivation of a Fair Price for a share in a company subject to an unsolicited
KEYWORDS: Decision trees, probability assessment, fair prices, Bayesian revision.
Assessing Probabilities of Unique Events: Calibrating Qualitative Likelihood Judgments into a Probability Distribution.
Chinese Business Review, Volume 9 Number 9 (Serial number 87), Sept. 2010, pages 54 - 60.
ABSTRACT: Multifaceted events in an organizational environment usually need to be assigned probabilities as a prerequisite to analytical decision-making. If the decision situation is unique, a lack of relevant historical frequency data may preclude use of traditional probability models such as the normal, binomial etc. In this case, an individual decision maker (DM) or an informed group of persons can input into a procedure as outlined here to determine a probability distribution that leads to expected values of alternative actions or fair values of securities. The individual or group member must decide qualitatively on the extent to which one event is "more likely" than another where both events are ranked adjacent (i.e. closest to each other) in terms of likelihood. Even though the individual or group members may lack experience in orthodox probability assessment, these pairwise "more likely" judgments are not overly demanding for persons familiar with the possible outcomes in the situation under analysis.
KEYWORDS: Decision analysis; probability assessment; Bayesian revision; strategic planning.
A Statistical Framework for Strategic Decision Making with AHP: Probability Assessment and Bayesian Revision.
OMEGA, Volume 37 Issue 2, April 2009, pages 463 - 470.
ABSTRACT: A probability assessment framework is outlined for an organizational decision involving a conditioning event (CE). The decision may, for example, involve a new-product launch (strategic decision) dependent on the outcome of market research (CE). The framework illustrates how Bayesian revision could be employed as related "news" arrives intermittently to revise current probabilities prior to decision implementation. A unique contribution of this paper is its utilization of the analytic hierarchy process to ascertain a set of consistent and coherent probabilities for the event/sample spaces at all stages of the decision process.
KEYWORDS: Strategic decision making, probability assessment, analytic hierarchy process, Bayesian revision.