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Simon’s concept of decision – making


Herbert A. Simon propounded the bounded rationality model to explain why limits exist to rational decision within decision – making environment. He defined decision – making as “the alternative courses of action”. He states that, decision – making pervades the entire organization that is decisions are made at all level of organizations. Hence, he said an organization as structure of decision – makers. According to him, decision is an all – embracing activity subsuming all the administrative functions described as ‘POCCC’ by Fayol and ‘POSDCORB’ by Gullick.

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Before moving on to the discussion firstly know that- ‘What is bounded rationality?’The concept of bounded rational decision making, irrespective of an individual level of intelligence, have to work fewer than three unavoidable constraints:

  • Only limited, often unreliable information is available regarding possible alternatives and their consequences
  • Human mind has only limited capacity to evaluate and process the information that is available
  • Only a limited amount of time is available to make a decision

Therefore, in reality the individuals who propose rational choices are bound to make satisficing rather than maximizing or optimizing choices in complex situation.

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All these factors which are illustrated a diagram will influence the human or organizational decision by bounded rationality leading to ‘satisficing decisions’ as against maximizing decisions (optimizing decisions).

For example – When the principle being violated is to “draw electoral boundaries in such a way to equalize the populations within the voting districts created, “the planner’s action might be to try to ensure merely that no two population differ by more than 1 percent. This behaviour would be considered bounded rational if the costs of computing an acceptable boundary configuration were to increase with level of accuracy required, because it would require, because it would then be appropriate to tolerate small inequalities in district populations to save significant computational costs.

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