Neuroeconomics of Addiction

Neuroeconomic Approach to Addiction
Image Unavailable
Which mechanisms influence choice under addiction?

The “Neuroeconomics of Addiction” refers to the interdisciplinary approach to addiction research that incorporates behavioural economics and neuroscience. The field draws heavily upon behavioural studies and the concept of neuroplasticity to develop and explain theories. The vast majority of neuroscience-based addiction studies are built upon the Hebbian Theory which proposes “cells that fire together, wire together”.[1] Most notably, the Bienenstock-Cooper-Munro (BCM Theory) model of synaptic plasticity builds upon Hebb’s work by proposing that increasing the postsynaptic activity of a specific neuron (ex. by repeatedly engaging in the same behaviour) will increase long-term potentiation while reducing postsynaptic activity will increase long-term depression in the same neuron.[2] The second half of the 20th century saw a sharp increase in the number of economic and psychological models designed to explain addictive behaviour. Numerous early economic addiction theories, such as Becker and Murphy’s canonical Theory of Rational Addiction included functions that rely on quantifying biomarkers (Equation 1). Due to recent advances improvements in molecular biology and neuroimaging techniques, researchers such as Taiki Takahashi and Warren Bickel have begun theoretically and experimentally merging economic and neurobiological theories to create more accurate and informative models of addiction.[16][18][19][20][22]

1. Quantifiable Behaviour

1.1 Utility Models

Figure 1: Utility Model Indifference Curve[27]

Image Unavailable

The indifference curve (IC) represents a constant level of utility. Points A and B are different quantity combinations of goods X and Y that yield the same utility.

Economists quantify the benefits of consumable goods by assigning them a numerical "utility" value, which is the amount of relative satisfaction derived from preferring and consuming one particular good over another.[3] "Expected Utility Theory" is a cornerstone of economic study and is an integral component of behavioural economic models. For example, once the rate at which a person will sacrifice some of one good in order to gain more of another is determined, the trade-off scenario can be visualized by plotting an “indifference curve”,[4] (Figure 1) which represents every possible combination of the two goods that will yield the same level of satisfaction. Known as "Indifference Theory", this approach to decision making is incredibly useful once patterns of substitution are established, as it allows economists to develop utility functions that can predict future choices made by an individual faced with a trade-off between two different goods.

1.2 Rational Behaviour

Researchers of addiction in Neuroeconomics rely on experiments using both rational humans and animals for data collection and hypothesis testing. In the context of these experiments, “rational” individuals are those who consistently make optimal or utility maximizing self-interested decisions.[5] Inevitably, rats and humans alike do not always make such decisions, indicating that any experiment that compares results to those predicted by an idealized rational model will never be completely accurate.[6]

1.3 Discount Delay Models

Figure 2: Discount Delay [28]

Image Unavailable

More impulsive individuals discount rewards more heavily over shorter periods of time.

Building upon the foundation of utility theory, economists and psychologists added temporal discounting as an additional parameter to trade-off experiments to determine how the prospect of delayed reward would impact decision-making. The concept originated in the field of psychology as "The Matching Law", which states that the environment will influence responses during decision-making.[7] Articulated and initially modeled by Richard Herrnstein in 1961,[7] the reasoning behind the law provided the basis for modern discount delay studies, in which individuals must choose between immediate or future rewards (Figure 2).[8]

Applying the principle of the matching law to trade-off experiments, psychiatrist and behavioural economist George Ainsile pioneered the field of "picoeconomics" (meaning micro-microeconomics), which investigates the competing forces and demands within an individual that are ultimately responsible for decision made by that individual.[9] Numerous picoeconomic studies have experimentally proven that a hyperbolic discounting curve (Figure 2) is representative of decision-making behaviour by humans and animals, as opposed to the traditional exponential discounting curve.[9] Ainsile maintained that subjects would continue to devalue rewards as time increased, but his improvement of the model's accuracy laid the groundwork for more thorough data analysis in contemporary discount delay studies.

See main article Decisions under Risk and Uncertainty
+** 2. Addiction Models**

2.1 Neurobiological Substrates

See main article Addiction

Over the past 4 decades, neurobiological models of addiction have focused primarily on the substrates of the mesolimbic dopaminergic pathway.[10](Figure) This is due to the popularity of the “dopamine hypothesis of reward”, which proposes that the neurotransmitter dopamine is the primary molecule responsible for motivating rewarding behaviours, such as eating, copulating, and drug consumption.[11] Although there is consensus regarding dopamine’s central role in addiction circuitry, dopamine's specific function in addiction models has been debated. The “Anhedonia Hypothesis” proposes dopamine is the “pleasure chemical” responsible for experiences of euphoria, and previous studies have shown low dopamine levels within the mesolimbic system correlate to anhedonic behaviours.[12] Another possible function of dopamine signaling may be to predict future rewards, as proposed by the “dopamine reward prediction error hypothesis”, which has been supported by data from numerous substance-dependent rodent studies.[13] A final possibility is that dopamine signaling increases incentive and desire for rewarding stimuli, such as cravings for cigarettes.[14] This theory is known as the “incentive salience” hypothesis.

Figure 3: VTA-NAcc Reward Circuit[11]
Image Unavailable
A simplified figure of the mesolimbic dopaminergic reward system. Primary reward circuitry relies on both glutaminergic (excitatory) and dopaminergic signaling from the ventral tegmental area (VTA) to the nucleus accumbens (NAcc). GABAergic (inhibitory) synapses signal in the opposite direction, from the NAcc to the VTA. The medial prefrontal cortex (mPFC), hippocampus (Hipp), and amygdala (Amy) also innervate the NAcc with glutaminergic synapses. The VTA is excited by the lateral hypothalamus (LH), lateral habenula (LHb), lateral dorsal tegmentum (LDTg). Dopamine is regarded as a neuromodulatory transmitter that impacts excitatory and inhibitory signaling in this pathway, which is activated and potentiated by engaging in addictive behaviour.

2.2 Economic Models of Addiction

The “Economic Theory of Rational Addiction” proposed by Becker and Murphy is considered to be the founding paper of the field of rational addiction.[15] The theory is based on the behaviour of a rational individual that is able to consider the short-term and long-term costs and benefits of indulging in addictive behaviour, with the intention of making decisions that will maximize their utility while considering inputs from 3 different functions.[5] Mathematically:

\begin{align} \int^T_0 U(C(t), A(t), S(t))e^{-rt}dt \end{align}

Where the consumer's horizons are between time 0 and time T. A(t) represents the consumption of the addictive good or behaviour as a function of time, C(t) represents the consumption of non-addictive goods as a function of time, and S(t) represents the strength of the addiction based on past consumption and its physiological consequences.[16] Future reward, r, is exponentially discounted. This model, like many behavioural economics models, has been criticized for idealizing behaviour and failing to be produce any substantial amount of empirical evidence[16].

Indeed, the majority of economic models consider utility, time preference, and the long-term costs and benefits of engaging in addictive behaviour.[17] Although many models acknowledge physiological changes induced by addiction can influence behaviour, most existing models do not accurately incorporate their impact due to the historical inability to reliably quantify and interpret these biological changes.

3. Neuroeconomic Models of Addiction

3.1 Emerging Models

In recent years, rapid advancements in the fields of Neuroscience and Physiology have led researchers to revisit older economic theories of addiction and lend them renewed credibility. Neuroeconomic theories of addiction concentrate on the “value” or “utility” of the addictive substance or behaviour in the eyes of the addict, and how to model the effects prolonged practice has upon decision-making.[19] Additionally, advancements in understanding physiological changes at the molecular level have allowed them to be quantified and plugged into existing economic models that were previously unclear or simply inaccurate.[18] The primary objective of many neuroeconomic studies of addiction is to identify reliable behavioural or physiological indicators using current economic models.[19]

3.1a Behavioural Markers

Figure 4: Competing Decision Systems[22]

Image Unavailable

Axes indicate the level of activity in each system. Every cell corresponds to the dominance of one system or roughly equal input from both.

Warren Bickel has explored the difference in discounting behaviour between substance dependent and non-dependent subjects at the neurobiological level. Exploring "reinforcer pathology", which refers to the extreme preference addicts have for their addictive reinforcers in the short term. The group has postulated that a change in the activity of two distinct behavioural systems of extreme opposites is responsible for such uncontrolled behaviour (Figure 4).[22] Their studies were based upon the works of McClure et al[23], who demonstrated that participants of delay discounting tasks who selected smaller, more immediate rewards experienced heightened activation in the limbic system whereas those who waited for larger rewards registered more activity in the prefrontal cortex. Bickel's group suggested that the hyperactivity of the more primitive and emotionally-driven limbic system, combined with hypoactivity in the executive prefrontal cortex pathways is responsible for the increased demand and impulsivity of addicts in the short term.[22][23][24] According to this behavioural theory, imbalance between these two systems may indicate underlying addiction problems or a pre-disposition to addictive tendencies that would be evident in the results of a delay discount study.

Evidence supporting this theory includes the work of Hoffman et al., who investigated the brain activity of methamphetamine-dependant individuals attempting abstinence and non-dependent controls while they participated in classic discount delay tasks featuring monetary reward.[20][29] As in many studies, it was determined the methamphetamine-dependent participants had a greater average discount rate than non-dependent controls.[29] The authors found the fMRI readings of dependent participants to be nearly identical to those recorded by McClure et al[23] in 2007. Increased activation in the limbic systems of dependents compared to controls led the researchers to suggest prefrontal activation is required to override a hyperactive amygdala and decrease discounting rates.[23][29]These studies demonstrate that behavioural markers of addiction, such as impulsivity and steep discounting rates, can be attributed to underlying brain activation patterns with the use of a neuroeconomic approach.[19][23][29]

Figure 5: Normal Default Mode Network (DMN) Activation Patterns[31]

Image Unavailable

(A) MRI scan shows DMN activation during a moral dilemma task (B) The same DMN neurons activated during a stroop interference test. The white lines in both scans show areas of normal DMN activity when brain is resting and unfocused.

Behavioural marker research has also focused on how addiction strength influences the way individuals process decision making.[24][26] In 2013, an fMRI-based study of heavy drinkers was designed to investigate brain activity when making alcohol consumption choices at various prices.[26] Decisions to drink when alcohol was offered free of charge showed activation was primarily contained to the superficial prefrontal and parietal lobes, while decisions made when confronted with positive prices recorded significant activation in frontostriatal cricuits.[26] Decisions requiring cost-analysis featured nearly complete deactivation of default mode networks (DMN)(Figure 5), which are neural circuits that are typically active when individuals are focused on an abstract task.[19][26] These results suggest that the prospect of addictive commodities may alter the hierarchy of brain activity and, by extension, the way addicts approach cost-benefit analysis.[26] Essentially, addiction causes a shift in an individual's demand curve, which in turn changes the mental process of decision-making by influencing the activation of DMN.[24][26] The activity of competing systems of the brain during decision-making may explain hallmark behavioural traits of addicts, such as impulsivity and the tendency to overvalue immediate rewards.[24][26][19]

3.1b Neurobiological Markers

Taiki Takahashi is at the forefront of the neuroeconomic approach to addiction research, and has been using neurobiological models to explain variables and functions within economic models.[16][18] For instance, investigation of the mathematical description of BCM theory:

\begin{align} y = \sum_{i} w_i x_i \end{align}

Where y is the postsynaptic activity of the ith neuron, w indicates the strength of connection or synaptic weight of the ith neuron, and x is the activity of the ith neuron.[16] This equation can be derived relative to w to determine its rate of change over time:

\begin{align} \frac{dw_i}{dt} = y(y-\theta_m)x_i - \epsilon w_i \end{align}

Where $\theta_m$ becomes the threshold for synaptic plasticity. Synapses are weakened for large values of $\theta_m$ and strengthened with larger values.[16] Previous studies have demonstrated that stress hormones such as cortisol and corticosterone increase $\theta_m$ (weaken synapses), while nicotine can decrease $\theta_m$ (strengthen synapses).[16] Combining these observations with Becker and Murphy’s economic theory of rational addiction, Takahashi proposed that drug addiction will increase S(t) by decreasing the synaptic threshold ($\theta_m$) and increasing the probability of LTP.[16] This suggests that monitoring intracellular calcium level indicators such as "fura-2" [25] should allow investigators to predict the value of the S(t) function.[16] Although the accuracy of the original economic model is disputed and supporting experimental data is required, Scientists like Takahashi are formulating neuroeconomic models of addiction with quantifiable neurobiological variables.

Further investigating neurobiological markers that influence economic behaviour, Takahashi et al investigated the effect of stress hormones on temporal discounting.[30] Over the course of six months, the salivary stress hormone levels (cortisol and cortisone) of smokers and non-smokers were measured as participants completed discount delay tasks involving monetary payoffs. Interestingly, high stress hormone levels corresponded to lower discounting rates in non-smoking men and higher discounting rates in non-smoking women.[30] There was no correlation between stress hormones and discounting rates in smokers of either gender, indicating that nicotine addiction influences how stress hormones interact with decision-making systems in the brain.[28][30]

3.2 Challenges

As previously mentioned, there is much debate within the scientific community regarding the viability and accuracy of rational utility-based behavioural models.[6][11][19][22] Although reasonable neuroeconomic models of addiction have been designed, Warren Bickel established a set of observations that must be explained by any robust model: [22]

  • What makes certain commodities addictive?
  • Why is substance abuse difficult for addicts to accept as a problem?
  • Why do most addictions follow a predictable course?
  • Why is addiction associated with impulsivity and other behavioural changes?
  • How to facilitate the creation of effective therapy?

In addition to addressing all of the above, neuroeconomic models of addiction need to substantiate the viability and accuracy of the rational behavioural economic models upon which they are based.[6][19]

3.3 Applications and Future Research

The primary focus of neuroeconomics research into addiction is to create new economic models based on reliable data gathered on neurobiological substrates at the molecular level, such as intracellular calcium levels and neuroactivation patterns.[16][22][26] Further research into the expression patterns of AMPA and NMDA receptor expression in reward circuits as potential biomarkers of addiction is needed [18], and further investigation into the function of regions such as the insular cortex and their role in decision making could produce viable behavioural markers of addiction. [26] The push to find new biomarkers of addiction was initially facilitated by technological improvements in fMRI and electrophysiological recording techniques, but even more precise tools will be needed to build more robust models.[19] As progressively better models are designed, more accurate biological and behavioural markers for addiction could lead to improved screening and prediction of addiction problems, significantly improved personalized treatments and interventions, and the prospect of preventative treatments.[20]

As this relatively new but controversial field matures, further advancements will eventually allow us to understand and trace addictive behaviour from molecular to social levels. A viable, integrated neuroeconomic model of addiction has the potential to streamline the search for biomarkers indicating susceptibility for addiction, thereby promoting proactive approaches over the current reactive models. Most interesting of all, a functional neuroeconomic model of addiction would be capable of determining the true costs and benefits of substance addiction by assessing how it induces changes at the molecular level and how those changes influence decision-making.

1. Hebb, D.O. (1949). The Organization of Behaviour. New York. Wiley & Sons.
2. Bienenstock EL, Cooper LN, Munro PW (1982). Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex. J Neurosci 2(1), 32-48.
3. Rubin, PH. Capra, CM. The evolutionary psychology of economics. In Roberts, S. C. (2011). Roberts, S. Craig, ed. Applied Evolutionary Psychology. Oxford University.
4. Besanko David, Braeutigam Ronald. Microeconomics 4th Edition. Hoboken: Wiley, (2010).
5. Becker G, Murphy K (1988). A theory of rational addiction. J Polti Econ (96), 675-700.
6. Sen, AK (1977). Rational Fools: A Critique of The Behavioural Foundations of Economic Theory. Philosophy and Public Affairs. 6(4), 317-344.
7. Poling A, Edwards T, Weeden M, Foster M (2011). The Matching Law. Psychological Record. 61(2), p.313-322.
8. Frederick S, Loewenstein G, O’Donoghue T (2002). Time Discounting and Time Preference: A Critical Review. Journal of Economic Literature, 40(2), 351-401.
9. Ainslie, G. (1992). Picoeconomics. Cambridge: Cambridge University Press.
10. Baik JH (2013). Dopamine signaling in reward-related behaviours. Front Neural Circuits. Oct 2013.
11. Russo S, Nestler E. The brain reward circuitry in mood disorders. Nature Reviews Neuroscience. (14), 609-625.
12. Pizzagalli D (2014). Depression, Stress, and Anhedonia: Toward a Synthesis and Integrated Model. Annual Review of Clinical Psychology.
13. Herman A, DeVito E, Jensen K, Sofuoglo M (2014). Pharmacogenetics of Nicotine Addiction: Role of Dopamine. Pharmacogenomics. 15(2), 221-234.
14. Freeman TP, Da RK, Kamboj SK, Curran HV (2014). Dopamine, urges to smoke, and the relative salience of drug versus non-drug reward. Soc Cogn Effect Neurosci.
15. Melberg H, Rogeberg O (2010). Rational Addiction Theory: A Survey of Opinions. Journal of Drug Policy Analysis. 3(1).
16. Takahashi, Taiki (2010). A neuroeconomic theory of bidirectional synaptic plasticity and addiction. Medical Hypotheses. (75),356-358.
17. Ferguson BS (2006). Economic modeling of the rational consumption of addictive substances. Subst Use Misuse. 41(4).
18. Takahashi, Taiki (2004). Cortisol levels and time-discounting of monetary gain in humans. Neuroreport. 15(13), 2145-2147.
19. Monterosso J, Piray P, Luo S (2012). Neuroeconomics and the study of addiction. Biol Psychiatry. 72(2), 107-112.
20. Bickel WK. Koffarnus MN, Moody L, Wilson AG (2014). The behavioural- and neuro-economic process of temporal discounting: a candidate behavioural marker of addiction. Neuropharmacology.
21. Harvey-Lewis C, Perdrizet J, Franklin KB (2014). Delay Discounting of Oral Morphine and Sweetened Juice Rewards in Dependent and Non-Dependent Rats. Psychopharmacology (Berl).
22. Bickel WK. Jarmolowicz DP. Mueller ET. Gatchalian KM (2011). The behavioural economics and neuroeconomics of reinforcer pathologies: implications for etiology and treatment of addiction. Curr Psychiatry rep. 13(5), 406-415.
23. McClure SM, Ericson KM, Laibson DI, et al (2007). Time discounting for primary rewards. J Neurosci. (27), 5796-804.
24. Croxson PL, Walton ME, O'Reilly JX, et al (2009). Effort-based cost-benefit valuation and the human brain. J. Neurosci. (29) 4531-41.
25. Takahashi T, Kimoto T, Tanabe N, Hattori T, Yasumatsu N, Kawato S (2002). Corticosterone acutely prolonged N-methyl-D-aspartate receptor-mediated Ca2+ elevation in cultured rat hippocampal neurons. J Neurochem. (83), 1441–51.
26. MacKillop et al (2014). The Neuroeconomics of Alcohol Demand: An Initial Investigation of the Neural Correlates of Alcohol Cost-Benefit Decision Making in Heavy Drinking Men. Neuropsychopharmacology. DOI: 10.1038/npp.2014.47.
27. Transtutors. 2014. Retrieved March 26, 2014, from
28. McClure J, Podos J, Richardson HN (2014). Isolating the delay component of impulsive choice in adolescent rats. Front. Integr. Neurosci. DOI: 10.3389/fnint.2014.00003
29. Hoffman WF, Schwartz DL, Huckans MS, McFarland BH, Meiri G, Stevens AA, Mitchell SH (2008). Cortical activation during delay discounting in abstinent methamphetamine dependent individuals. Psychopharmacology. (Berl) (201), 183-193.
30. Takahashi T, Shinada M, Inukai K, Tanida S, Takahashi C, Mifune N, Takagishi H, Horita Y, Hashimoto H, Yokota K, Kameda T, Yamagishi T (2010). Stress hormones predict hyperbolic time-discount rates six months later in adults. Neuro Endocrinol Lett. 31(5), 616-21.
31. PNAS. 2008. Retrieved March 30, 2014, from

Add a New Comment
Unless otherwise stated, the content of this page is licensed under Creative Commons Attribution-ShareAlike 3.0 License