Markov Chains and Your Performance Rating

Russian mathematician Andrey Markov came up with concept of Markov Chains, which has found number of applications in various fields of management. Many professors talk about applying Markov chain to manpower planning/forecasting. I have reservations about using this method, as in today’s dynamic atmosphere it is not of much use. Markov’s transition matrix is useful for companies with very low clock speed and high stability.

But it can be useful in decision making if you understand your company culture and your boss quite well. Depending on your company culture and your boss’s mind-set you can prepare transition matrix which tells your probability of getting different ratings i.e. 50% chance of getting 3, 25% chances of getting 4 and so on.

This is useful for those working in organisations which have poor performance management culture and were performance appraisal is annual ritual delinked from vision of organisation, and your boss decides your ratings before even reading your self- appraisal.

You can start with initial state vector (chances of getting a particular rating when you joined company) and multiply it by transition matrix to know what are your chances of getting different ratings one year hence ex. chances of getting 4 rating next year.

Beauty of Markov Chains is you can use it to predict your ratings for infinite number of years. While the probabilities may change in initial years, in long term probabilities stabilise ex. if your chances of getting 3 ratings in year 1 are 50%, year 2 -55%, Year 3-60% and so on, after 10 years it is say 75% , then it will remain so in 15th year or 20th year or even in 50th year.

After a stage your ratings will stabilise and will not change i.e. you will keep getting same ratings year after year.

If your calculations show that 15 years hence you have 75% chances of getting 3 ratings, 20% chances of getting 2 rating and 5% chances of getting 4 ratings- what decision will you take?