There are many different ways in which people incorporate rates into their everyday lives.  People are constantly bombarded with rate problems when making decisions about which banks to go to, taking loans and paying off car/mortgage payments, investments in stock markets, etc.  In each of these situations, a person must make a decision about interest rates and rates of return. 

One obvious example is choosing employment based on wage rates.  Imagine a student with a choice of two summer job offers: one will pay $10/hour, and another that offers $15/hour.  In the simplest case (assuming number of hours per week and desirability of the employment are constant), the student will easily choose the job with the higher wage rate.  The problem becomes slightly more complex if the number of hours worked each week or the desirability of the jobs are different.  However, these variables (amount and quality) are accounted for by Herrnstein’s Matching Law (as discussed in the Theory section).

            George and Hopkins (1989) discussed an interesting aspect of wage rates for servers in three family restaurants.  The servers were paid an hourly wage of $1.90/hour.  Unsatisfied with this wage rate, the servers asked for a raise.  However, the restaurants were already losing money and could not increase the wage rate without an increase in productivity.  On the advice of behavioural consultants, the restaurants owners made the wage rate contingent upon productivity (that is, the servers were paid a percentage of their gross sales).  In this case, the productivity-contingent wage rate increased both  productivity and rate of reinforcement (average earnings of the servers).

             Another common way to study rate estimation in humans is to study gambling behaviour.  In gambling situations, a person must estimate complex rates of return.  The rates of return are complex since most gambling situations can result in a variety of outcomes, each with a different amount of reinforcement.  In these situations, people can make a variety of errors in their estimation of rates.  These errors fall into two major classes:

Reliance on Heuristics and Biases – people often depend on heuristics and biases (or shortcuts) that lead them to misrepresent statistics and the actual nature of chance.

Irrational Thinking – people often misrepresent the amount of chance vs. skill involved in a given situation which leads them to believe that they can control the rate of reinforcement.

In this section, I will discuss and give examples of each of these types of errors in rate estimation.

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