Neuroeconomical Perspectives on Addiction Treatment

Shaun Hiu Kuan Wei

THESIS STATEMENT: Neuroeconomics can improve both the severity-evaluation and treatment outcomes of drug-related addiction. The rate in which an addict performs delay discounting indicates his addiction severity. Based on the severity, an appropriate treatment method can be applied. Neuroeconomical research on treatment methods to decrease the rate of discounting contributes to the efficacy of the existing treatment process for addiction.

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Drugs have always carried an air of social stigma, even when we consume them on a nearly daily basis. From the caffeine in our coffee to nicotine in our cigarettes, drugs have found their way into our lives. The source of negative connotations comes from the dangerous brand of illicit drugs. Their notorious association with drug-abuse and addiction is responsible for giving the collective a bad name – and for good reason too. Drugs like heroin and cocaine are far more potent, addictive, and damaging to one’s health than any licit drug sold in stores. On an individual level, drug addiction impairs one’s economic and social well-being, and on a societal level, it contributes to increased crime rates and the spread of HIV and hepatitis C primarily via sharing of injecting equipment (United Nations Office on Drugs and Crime, 2012).

In 2012, the estimated number of people who have used illicit drugs amounted to startling 324 million aged 15 – 64 years old worldwide. Amongst the figure, 39 million people have been diagnosed with a drug-related addiction. Unfortunately, only one-sixth of cases a year receive treatment for their addiction. Illicit drugs not only ruin the lives of millions but take away the lives of hundreds of thousands. 183,000 cases of drug-related deaths were reported in 2012 at an average of 40 deaths per million people aged 15 – 64 years old with drug overdosing as the leading contributor (United Nations Office on Drugs and Crime, 2012). Drug overdosing is a consequence of the body’s increased tolerance and dependency towards a drug – mainly due to an addiction.

The pervasiveness of drug-related cases is constantly on the rise “with the total global number of drug users increasingly commensurate with the growth of the world population” (United Nations Office on Drugs and Crime, 2012), highlighting that current treatment methods for addiction are unable to keep up with the rate of expansion. Thus, there is a need to find new alternatives and make improvements.

In recent years, there have been several neuroeconomical studies conducted to further our understanding of drug-related addiction. Neuroeconomics is the study of how our brain processes and acts in accordance to our valuation of commodities – drugs, money, or anything we deem as a ‘reward’ for example (Monterosso et al., 2012, p. 107). Neuroeconomical research has identified a key characteristic in the behavior of addicts. This is known as delay discounting – the act of reducing the value of commodity relative to the delay prior to reception (Monterosso et al., 2012, p. 108). With this in mind, neuroeconomics can improve both the severity-evaluation and treatment outcomes of drug-related addiction. The rate in which an addict performs delay discounting indicates his addiction severity. Based on the severity, an appropriate treatment method can be applied. Neuroeconomical research on treatment methods to decrease the rate of discounting contributes to the efficacy of the existing treatment process for addiction.

Delay Discounting and its role in addiction

In accordance to several neuroeconomical studies of addiction, the findings assert that addicts unanimously perform steeper rates of delay discounting than non-users (Bickel et al., 2011; Monterosso et al., 2012; Washio et al., 2011). This leads them to choose immediate rewards over delayed rewards more frequently. Robles et al. (2011) adds that this phenomenon of delay discounting extends to most, if not all, variations of drug addictions due to “overwhelming evidence that users of tobacco, alcohol, opioids, cocaine, and methamphetamine discount by delay significantly more than matched nonusing controls” (p. 355).

The rate of discounting is a good predictor of the severity of an addiction (Mueller et al., 2011, p. 292). A neuroeconomical game called the “$1000 hypothetical game” can be employed to calculate the rate of discounting (Washio et al., 2011, p. 244). Subjects are tasked to enter their choice between a hypothetical amount of $1000 later and a lesser amount now into a computer program which will then calculate their rate of discounting. The rate is determined by the time taken to reach the indifference point – the point at which an addict would choose the delayed reward over the immediate one.

Once a rating has been established, an appropriate treatment method which suits the level of addiction can then be administered. Certain treatment methods are most effective at varying levels of severity such as Contingency Management (CM) for those who discount steeply (Washio et al., 2011, p. 244). CM is a treatment program which is aimed to promote reinforcement from “naturalistic reinforcers such as improved health or family relations by remaining abstinent” (Washio et al., 2011, p. 246). By identifying those who discount steeply, we can nominate them for CM. However, because neuroeconomics is such a novel field, research has yet to identify other treatment methods to suit the varying levels of severity.

Although currently the Addiction Severity Index (ASI) is currently used as the primary method of evaluating the severity of an addiction worldwide – comprising of questions that allow the clinician to assess the problems in an area in life. Each life area, along with its relevant questions, is called an index. Further examination of the ASI by Makela (2004) and Melberg (2004) revealed several limitations and weaknesses. Firstly, it is largely influenced by the subject’s memory, understanding of the questions, and the sincerity with which he reports (Makela, 2004, p. 406). Secondly, both authors agree that the ASI does not take into account variations in an individual’s problems in each index (Makela, 2004, p. 406). Because the information obtained from patients are very subjective, evaluating a score based on such information can result in “systematic misinterpretations” because everyone has different thresholds for what they might consider a “serious” problem in an area of their life (Melberg, 2004, p. 124).

Thirdly, the ASI is inaccurate in recognizing individuals who do not have an addiction due to the magnitude of the number of domains assessed (Makela, 2004, p. 407).

Lastly, its scoring system is flawed. According to Melberg (2004), “it is easier to get a high score on some indices than on others” ( p. 121). In the ASI, there is a stark differences in the number of questions pertaining to each index: there are 3 questions under the employment and financial support while there are 16 questions in drug and alcohol use. Moreover, certain questions are structured in a manner such that it is easier to attain a higher “problem score” (Melberg, 2004, p. 121).

The limitations of the ASI’s reliability, validity, and accuracy can be addressed by using delay discounting in gauging the severity of an addiction respectively. The neuroeconomical game will not take gather any form of self-reported information nor background information of the subject. Personal and subjective factors such as employment and financial support will be obsolete because the only information that is essential in calculating the rate of discounting, and ergo the severity of an addiction, is their indifference point. Having an indifference point is unanimous in all drug-related addictions, therefore providing a degree of consistency without the influence of subjective traits. The issue of misunderstanding of the questions will be greatly reduced as there will only be one self-explanatory question used. The question will draw from the one used in the study conducted by Washio et al. (2011): “Imagine that you have a choice between waiting (length of time) and then receiving $1000 and receiving a smaller amount of money right away. Please choose between the two options” (p. 244). To avoid the possibility of malingering, patients will be led to believe that the monetary reward is in fact real – producing authentic responses. The neuroeconomic game will be able to accurately assess those who do not suffer from an addiction by observing a low rate of discounting.

– provide sense of anonymity

The rate of discounting has an important role in the efficacy of treatment as an indicator of treatment outcomes, with lower rates being predictive of positive treatment outcomes (Washio et al., 2011, p. 244). Robles et al. (2011) hypothesize that the rate may also be a contributor to one’s probability towards recovering from an addiction (p. 360). Positive treatment outcomes include greater likelihood of abstinence upon cessation, reduced number of days before first lapse, and lowered chances of relapse. In relation to that, Working Memory Training (WMT) is effective in greatly decreasing the rate of discounting (Bickel et al., 2011, p. 413). Working memory games require subjects to retain and use information during an activity. A simple scenario would be having subjects read an instruction manual for building a model robot, then have them begin constructing it as they recall the steps. Working memory stimulates the executive processing of the brain which aids in overriding the “impulsive” processing – a trait dominant in addiction. If employed before the treatment process, it would improve the likelihood of increased abstinence rates and lower rates of relapse.

However, the studies carried about by Robles et al. (2011) create three counter-arguments that contend with the relationship between delay discounting and addiction (pp. 359-360).

Firstly, contrary to previous studies, they exert that the rate of delay discounting and addiction severity has no relationship whatsoever (Robles et al., 2011, p. 359). However, it has been established that this is not the case. Numerous studies conducted by Bickel et al. (2011), Monterosso et al. (2012), Mueller et al. (2011), Robles et al. (2011), and Washio et al. (2011) prove otherwise, citing that the rate of discounting does indeed have a correlation with severity of an addiction and treatment outcome.

Secondly, they also assert that the rate of discounting is not affected by increased drug use or abstinence, implying that there is no treatment to alter the rate (Robles et al., 2011, p. 360). WMT has been shown to disprove this assertion as studies have indicated that by stimulating an addict’s executive processing with working memory games, rates of discounting have shown to decrease (Bickel et al., 2011, p. 413).

Thirdly, the neuroeconomic game is based on a hypothetical monetary reinforcer, making it an unrealistic assessment of how addicts might respond to “real” reinforcers. However, studies have shown that real and hypothetical reinforcers “have produced generally comparable results” (Washio et al., 2011, p. 247).


The pervasiveness of drug-related addiction is deeply unsettling with more needing to be done to combat this issue. Neuroeconomics has opened new doors in the study of addiction with its key focus on delay discounting. The use of delay discounting in severity evaluation would allow one to appropriate a suitable treatment method that would be most efficacious depending on the rate of discounting. Unfortunately, given the scarcity of available research in this novel field, only contingency management has been shown to be effective for severe cases. Perhaps future investigations would uncover more treatment methods that would suit the varying levels of severity. Treatment outcome has been shown to positively correlate with low rates of discounting. Addicts who undergo working memory training can be expected to produce favorable results upon cessation. Perhaps in time and with more resources allocated into the study of neuroeconomics and addiction, the future might look brighter in the millions.


Bickel, W. K., Jarmolowicz, D. P., Mueller, E. T., & Gatchalian, K. M. (2011). The behavioral economics and neuroeconomics of reinforce pathologies: Implications for etiology and treatment of addiction. Current Psychiatry Reports, 13(5), 406-415. doi: 10.1007/s11920-011-0215-1

Makela, K. (2004). Studies of the reliability and validity of the addiction severity index. Addiction, 99(4), 398-410. doi: 10.1111/j.1360-0443.2003.00665.x

Melberg, H. O. (2004). Three problems with the ASI composite scores. Journal of Substance Use, 9(3-4), 120-126. Retrieved from

Monterosso, J., Pirayb, P., & Luoa, S. (2012). Neuroeconomics and the study of addiction. Biological Psychiatry, 72(2), 107-112. doi: 10.1016/j.biopsych.2012.03.012

Mueller, E. T., Carter, L. P., & Bickel, W. K. (2011). Behavioral economics of addiction II: Analysis of inter-temporal choice. In Gutkin, B., & Ahmed, S. H. (1st ed.), Computational Neuroscience of Drug Addiction (pp. 289-292). NY: Springer-Verlag New York Inc. Retrieved from

Robles, E., Huang, B. E., Simpson, P. M., & McMillan, D. E. (2011). Delay discounting, impulsiveness, and addiction severity in opioid-dependent patients. Journal of Substance Abuse Treatment, 41(4), 354-362. doi: 10.1016/j.jsat.2011.05.003

United Nations Office on Drugs and Crime (2012). World Drug Report 2012. Retrieved from

Washio, Y., Higgins, S. T., Heil, S. H., McKerchar, T. L., Badger, G. J., Skelly, J. M., & Dantona, R. L. (2011). Delay discounting is associated with treatment response among cocaine-dependent outpatients.Experimental And Clinical Psychopharmacology,19(3), 243-248. doi:10.1037/a0023617

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