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Toward a Computationally Unified Behavioral-Economic Model of Addiction

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Computational Neuroscience of Drug Addiction

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI,volume 10))

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Abstract

This chapter describes an instance of computational constructionism applied to the understanding of drug addiction. Rather than devising models of increasingly smaller anatomical, physiological or chemical units of analysis, the practice exposited here was to expand the integrative scope of behavioral-economic concepts that have been used to describe addiction phenomena. We discussed (a) excessive and persistent consumption of substances, as studied in analysis of demand; and (b) concurrent-choice preference for immediate small and unhealthy reinforcers over delayed but large and healthy reinforcers, and reversals of preference between this two types of alternatives, as studied in the science of delay discounting. While all of these phenomena are characteristic of addiction, it is remarkable how seldom concepts for explaining (a) appear in scientific reports on (b). The notion of expanding the scope of an explanatory concept is introduced via consideration of the concept of unit price. This concept integrates numerous variables traditionally studied in isolation in addiction research, and provides the basis of more general and parsimonious explanations of addiction phenomena. The scope of the unit price concept is further expanded, as it plays a role in a computational formulation describing choice among, and reversals of preference between, concurrently available reinforcers, which are very complex aspects of behavior that are fundamental to addiction phenomena. Lastly, we discuss computational implications and cautions, and scientific and practical prospects that derive from the exercise of expanding the integrative scope of the unit price concept to a broader and more complex range of addiction phenomena. Future developments along these lines are expected to produce constructs with which preferences exhibited by drug-dependent individuals may be predicted more accurately, and may be therapeutically modified.

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Acknowledgements

The writing of this chapter was supported by National Institute on Drug Abuse Grants R37 DA 006526-18, R01 DA 11692-10, R01 DA022386-02, R01 DA024080-01A1, Wilbur Mills Chair Endowment, and in part by the Arkansas Biosciences Institute, a partnership of scientists from Arkansas Children’s Hospital, Arkansas State University, the University of Arkansas-Division of Agriculture, the University of Arkansas, Fayetteville, and the University of Arkansas for Medical Sciences. The Arkansas Biosciences Institute is the major research component of the Tobacco Settlement Proceeds Act of 2000.

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Authors and Affiliations

  1. Advanced Recovery Research Center, Virginia Tech Carilion Research Institute, Virginia Tech, 2 Riverside Circle, Roanoke, VA, 24016, USA

    E. Terry Mueller

  2. Center for Addiction Research, Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA

    Laurence P. Carter

  3. Advanced Recovery Research Center, Virginia Tech Carilion Research Institute and Department of Psychology, Virginia Tech, 2 Riverside Circle, Roanoke, VA, 24016, USA

    Warren K. Bickel

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  1. E. Terry Mueller
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  2. Laurence P. Carter
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  3. Warren K. Bickel
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Correspondence to Warren K. Bickel .

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Editors and Affiliations

  1. DEC-ENS, 29, rue d'Ulm, Paris, 75005, France

    Boris Gutkin

  2. CNRS UMR 5541, Lab.Neuropsychobiologie des, Univ.de Bordeaux, rue Leo-Saignat 146, Bordeaux, 33076, France

    Serge H. Ahmed

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© 2012 Springer Science+Business Media, LLC

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Mueller, E.T., Carter, L.P., Bickel, W.K. (2012). Toward a Computationally Unified Behavioral-Economic Model of Addiction. In: Gutkin, B., Ahmed, S. (eds) Computational Neuroscience of Drug Addiction. Springer Series in Computational Neuroscience, vol 10. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0751-5_10

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