Redish et al. document tremendous progress in the scientific understanding of the “what” and “how” of drug addiction. Strangely, as our understanding of addiction advances, its implications for rational drug policy seem to recede – at least relative to the now discredited “rational addiction” account of microeconomists Gary Becker and his colleagues (e.g., Becker et al. Reference Becker, Murphy and Grossman2004).
There are various ways in which the science of addiction might produce a more reasoned, less ideological approach to drug abuse policy (see MacCoun Reference MacCoun, Vuchinich and Heather2003). The influence could be technological, in the form of more powerful treatments, diagnostic tools, or even safer psychoactive drugs. The influence could be behavioral, in the form of advice about the design of optimal prevention, deterrence, and harm reduction tactics. Or the influence could be rhetorical and conceptual, changing the very way we think about the problem.
Advances have been steady but incremental on the technological front, and I leave it to other commentators to address that topic. On the rhetorical front, one couldn't ask for a more potent reframing than the mid-1990s claim that addiction is a chronic relapsing disorder (O'Brien & McLellan Reference O'Brien and McLellan1996). This view arguably helped facilitate the growth of the drug court movement, but it still wasn't enough to curtail the steep growth in the federal drug prisoner population in the decade that followed. The public's flexible image of the addict as enslaved and yet still blameworthy allows ready assimilation of most new facts about addiction (see MacCoun & Reuter Reference MacCoun and Reuter2001). At least in the United States, citizens seem less interested in how someone becomes addicted than they are in the question “could the actor have done otherwise?” But addiction scientists have been more reticent at addressing the middle ground between philosophy and technology – behavioral questions about how to do prevention, deterrence, and harm reduction.
Redish et al. are admirably circumspect about offering prescriptions for policy makers. Still, it is worth highlighting some ways in which an improved neuroscience complicates, rather than simplifying, drug policy analysis. As our understanding of addiction has grown in sophistication, it has also grown in complexity. Where earlier models implicated a small handful of core mechanisms (e.g., tolerance, withdrawal, and craving), Redish and colleagues implicate eight distinct vulnerabilities. If one wanted to design an addiction machine robust enough to withstand random environmental shocks or hostile tinkering by saboteurs, eight overlapping mechanisms would seem to offer more than enough redundancy.
If, as some contend (e.g., Becker et al. Reference Becker, Murphy and Grossman2004), drug addicts behave like rational microeconomic consumers, then we could deduce fairly clear policy predictions about the perils of prohibition and the promise of sin taxes and optimal regulatory enforcement. And in one way, the rational choice analysis fares better than some might expect; estimates of the elasticity of demand suggest that even heavy drug users are price sensitive (see Manski et al. Reference Manski, Pepper and Petrie2001). But ultimately the rational addiction model fails on both empirical and theoretical grounds (Auld & Grootendorst Reference Auld and Grootendorst2004; Gruber & Koszegi Reference Gruber and Koszegi2001; Skog & Melberg Reference Skog and Melberg2006).
Behavioral economic models of addiction (see Vuchinich & Heather Reference Vuchinich and Heather2003) are more realistic and better supported, though perhaps still too simplistic. At first glance, a mechanism like hyperbolic discounting is appealing because it offers a fairly modest modification to the economic choice equation. But because hyperbolic discounting produces preference reversals (unlike the standard exponential account), it makes less determinate predictions. Still, the behavioral economic approach teaches us that heavy users may respond to fairly modest carrots (Bickel et al. Reference Bickel, DeGrandpre and Higgins1995) and sticks (Kleiman Reference Kleiman, Heymann and Brownsberger2001) if we deploy them promptly and saliently.
Redish et al. offer an even richer framework for thinking about the “what” and “how” of addiction. But I would urge addiction scientists to take up the “whom” and “when” questions as well. Drug policy has a broad set of tools, including the legal status of a drug; criminal sanctions against users and dealers; interdiction and source country controls; drug prevention, education, and rhetoric from the bully pulpit; drug treatment; taxes, advertising controls, and other regulatory mechanisms; drug testing; and bans on employment, welfare, and other benefits. If we want to deploy those tools more effectively, efficiently, and humanely, we need to better understand how these eight decision vulnerabilities play out in real-world behavior. For example, which people are at greatest risk of each vulnerability? Can we identify them early, and if so, how should we help them? When is the most effective time to intervene, and should timing trump concerns about paternalism or stigma? How does each vulnerability influence the choice among drugs (substitution and complementarity), as well the likelihood of any “gateway” progression across drugs? Do differences in vulnerabilities across drugs imply that there should be differences in our policies across drugs? And are there better ways to design secondary prevention and deterrence strategies to overcome impairments in the decision process?
