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Behavioural Analysis and Economics

Most economic and risk management models and theories have been developed on the premise that a individuals make rational decision making, i.e. to make a best case decision based purely on the facts.  The Global Financial Crisis (GFC) has once again shown this to be incorrect, i.e. human beings do not necessarily make rational decisions and it also highlighted the weaknesses of economic models that ignored the extent of how emotionality affects human behaviour and decision making.  Any type of physiological (hunger, thirst), psychological (autonomy, competence) or social (affiliation, power) need can affect the emotions of a person which therefore influence the behaviour that is produced.


Chairmont, alongside a team of psychologists and statisticians, have mastered a new behavioural analysis approach called Structural-Path Equation Modelling (SEM).  This approach allows us to understand the drivers of behaviour based on an individual event and a person’s physiological, psychological or social needs and their behavioural outcomes for that event.


Chairmont’s behavioural technique approach overcomes the rudimentary weaknesses of traditional statistical methods that focused on 1-on-1 simple relationships between “independent” and “dependent” variables.  Chairmont’s behavioural approach provides richness and value add to these relationships through applying Structural-Path Equation Modelling (SEM) to data analysis supported by detailed psychological statistical collection methods aimed at data collection.  Under SEM a third variable type is introduced that sits between the dependent and independent variables being “mediating” variables. These mediating variables may increase or decrease the impact on an independent variable.  

Our method for data collection on mediating variables is through applying a psychological based survey approach, the outcomes of which would be input to the Chairmont SEM framework. The SEM framework would then generate a series of relationships and correlations between the independent and dependent variables through understanding what patterns of behaviour lead to the latter by way of the identification and quantification of the mediating variables.

SEM can be employed for a number of business initiatives including:

  • Economics and economic modelling

  • Risk management and risk modelling

  • Product development and distribution analysis

  • Change management.

This is a new and more robust analytical approach to understanding the impacts of human behaviour.

Relevant Case Studies:

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