Career Decision Making and Decision Theory

Gati, I. (1986) defines vocational choices as a particular case of decision making under uncertainty with the aim to reach an optimal choice among alternatives (a multi criterion decision making problem, MCDM in short). The uncertainties in the decision can be looked at in terms of the lack of clear preferences owing to lack of mental clarity by the decision maker or incomplete knowledge which can result in the uncertainty of a future outcome. Four major sources of problems in the Career Decision Making (CDM) process identified are -

  • Lack of knowledge of the preference model the career decision maker can use
  • Lack of resources (e.g. time & money ) to collect all of the required information
  • Limitations of the decision maker in processing the information
  • Lack of a framework for reaching the right decision

 

The above problems are also typical of any multi criterion decision scenario. To overcome decision support system (DSS) have been used in different applications (like finance, vendor selection etc.).  A DSS combines the heuristics in the knowledge base with the algorithmic knowledge derived from theory and based on the decision maker’s preferences can help a decision maker make a better choice.A class of DSS's called Recommendation systems are ideal when choice between multiple options has to be made (in our case careers and education courses).

 

Recommendation systems incorporate learning and intelligence capabilities and the algorithm incorporates the tenets of Operation Research (e.g. AHP, AI, genetic algorithms, Goal Programming), and Statistical Analysis. Thus multiple perspectives (the users interest etc., his/her preferences, expert opinion and a knowledge base build on earlier decisions) are synergized to generate the best fit options (Top N career recommendations).

 

Reference

  • Gati, I. (1986). Making career decisions: A sequential elimination approach. Journal of Counseling Psychology, 33(4), 408.