Some recently published articles by:

Dr  Warren R Hughes  B Com M Com MBA DBA

STRUCTURING PROBABILITY ASSESSMENTS - August 2020 

NEW ZEALAND ELECTION 2020 - 20th September 2020

The list below shows the possibilities for the government following the election. The listing has been ordered from least to most likely:

WORLD ECONOMIC OUTLOOK – SEPTEMBER 2020

WORLD ECONOMIC OUTLOOK – August 2020

WORLD ECONOMIC OUTLOOK – July 2020

WORLD ECONOMIC OUTLOOK – SEPTEMBER 2019

RANDOM THOUGHTS ON PROBABILITY ASSESSMENT

July 2020

The following ideas use the methodology outlined in the paper Assessing Probabilities of Unique Events: Calibrating Qualitative Likelihood Judgements into a Probability Distribution.

ASSESSING PROBABILITIES OF UNIQUE EVENTS: A NEW APPROACH

Chinese Business Review, January 2019, Vol. 18, No. 1, 43 - 46.

Probability Calculations Using the Geometric Mean & Comparisons with Eigenvalues - August 2019

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

takeover offer.

 

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.

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Detailed Economic Modelling 

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.

Full paper and latest update available below:

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