Simon. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99–118. https://doi.org/10.2307/1884852
Summary
Classical economic theory supposes the existence of “homo economicus” -a man who has total knowledge of his environment and options, a stable set of preferences, and is able to calculate amongst those preferences. In real life, humans rarely meet this criteria. Because of the cognitive limits of a human, “actual human rationality-striving can at best be an extremely crude and simplified approximation.” Later, Simon referred to this as bounded rationality, the inability of human beings to be perfect decision makers.
For rational behavior to exists, there needs to be (1) a set of alternatives, (2) a perceived set of alternatives, (3) a possible set of future outcomes, (4) a “pay-off” function representing the utility achieved by each outcome, (5) a set of outcomes that can result from a certain alternative, and (6) a range of probabilities that, given an alternative, a specific outcome will occur. The author states that, “there is a complete lack of evidence that, in actual human choice situations of any complexity, these computations can be, or are in fact, performed.” Instead, the author elaborates on some observed behavioral processes.
A ”simple” pay-off function attaches one of two values (1 or 0, correlating with satisfactory of unsatisfactory) to an alternative. Three values (the same as before, with the addition of a neutral) are sometimes used. Search (information gathering) might stop at arrival upon an alternative with a satisfactory outcome as opposed to the best (or optimized) outcome. Simon, in other work, refers to this as satisficing. In a satisficing function, there are significant order effects. Many times, decisions involve multiple, incomparable categories of outcomes. In this case, partial ordering of pay-offs allows for a vector function (multiple inputs and outputs) as opposed to a scalar function (one single output). This model can also be used for group decision making, where each dimension of a vector represents the preferences of an individual group member on a single issue. Aspiration level refers to a point above which outcomes are satisfactory. If, during the process of information gathering, it is easy to find satisfactory outcomes, the aspiration level will rise. If it is difficult, the aspiration level will fall. If an organism is persistent in its search, its aspiration level will become increasingly tailored to the situation at hand. Additionally, both the past and future can be taken into account. Previous aspiration levels and previous levels of attainment can be taken into consideration for determining the current aspiration level. Attainment of a certain outcome might prompt reappraisal of its desirability. Future aspiration levels can also be accounted for (e.g., choosing X at time 1 puts one in a better spot to achieve Y at time 2).
Application
Like Schein (1996), this also reminds me of the importance of basing theories on actual observations. While humans don’t always act in perfect rationality (i.e., always selecting an optimized choice), we do strive for rationality, in our own bounded way.
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