Interrelationships between marijuana demand and discounting of delayed rewards: Convergence in behavioral economic methods

- This study was designed to test the reinforcer pathology model of addictive behavior.
- Demand and Delay Discounting (DD) do not exhibit a synergistic relationship with respect to marijuana.
- Demand and DD are independent risk factors for problematic marijuana use.
- Findings provide support for using area under the curve (AUC) as a singular marijuana demand metric.

BackgroundDistinct behavioral economic domains, including high perceived drug value (demand) and delay discounting (DD), have been implicated in the initiation of drug use and the progression to dependence. However, it is unclear whether frequent marijuana users conform to a “reinforcer pathology” addiction model wherein marijuana demand and DD jointly increase risk for problematic marijuana use and cannabis dependence (CD).MethodsParticipants (n = 88, 34% female, 14% cannabis dependent) completed a marijuana purchase task at baseline. A delay discounting task was completed following placebo marijuana cigarette (0% THC) administration during a separate experimental session.ResultsMarijuana demand and DD were quantified using area under the curve (AUC). In multiple regression models, demand uniquely predicted frequency of marijuana use while DD did not. In contrast, DD uniquely predicted CD symptom count while demand did not. There were no significant interactions between demand and DD in either model.ConclusionsThese findings suggest that frequent marijuana users exhibit key constituents of the reinforcer pathology model: high marijuana demand and steep discounting of delayed rewards. However, demand and DD appear to be independent rather than synergistic risk factors for elevated marijuana use and risk for progression to CD. Findings also provide support for using AUC as a singular marijuana demand metric, particularly when also examining other behavioral economic constructs that apply similar statistical approaches, such as DD, to support analytic methodological convergence.