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Addiction as a dynamical rationality disorder

  • Research Article
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Frontiers of Electrical and Electronic Engineering in China

Abstract

Addiction is frequently modeled as a behavioral disorder resulting from the internal battle between two subsystems: one model describes slow planning versus fast habitual action; another, hot versus cold modes. In another model, one subsystem pushes the individual toward substance abuse, while the other tries to pull him away. These models all describe one side winning over the other at each point of confrontation, represented as a simple binary switch: on or off, win or lose. We propose however, an alternative model, in which opposing systems work in parallel, tipping toward one subsystem or the other, in greater or lesser degree, based on a continuous rationality factor. Our approach results in a dynamical system that qualitatively emulates seeking behavior, cessation, and relapse—enabling the accurate description of a process that can lead to recovery. As an adjunct to the model, we are in the process of creating an associated, interactive website that will enable addicts to journal their thoughts, emotions and actions on a daily basis. The site is not only a potentially rich source of data for our model, but will in its primary function aid addicts to individually identify parameters affecting their decisions and behavior.

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Authors and Affiliations

  1. Department of Computer Science, UMass Amherst, Amherst, MA, 01003, USA

    Hava T. Siegelmann

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Correspondence to Hava T. Siegelmann.

Additional information

Hava T. SIEGELMANN is an associate professor of Computer Science and Neuroscience at the University of Massachusetts at Amherst, and the director of the BINDS (Biologically Inspired Neural and Dynamical Systems) laboratory. Dr. Siegelmann’s research focuses on mathematical modeling of biological systems with particular interest in memory, epigenetics, cellular development and disease evolution. Her research into biologically inspired memory and artificial intelligence has led to machine systems, which are more autonomous: capable of learning, tracking, clustering, associating, and inferring and are more robust and capable of operating in real-world environments. She introduced the highly utile Support Vector Clustering algorithm with Vladimir Vapnik and colleagues. Siegelmann’s seminal Turing machine equivalence of recurrent neural networks theorem and the super-Turing theory, which greatly impacted current thinking on computation, have found new utility in her work on machine memory reconsolidation and intelligent cellular function. Her work is often interdisciplinary, and combines methods from the fields of Complexity Science, Networks Theory, Dynamical Systems, Artificial Intelligence and Machine Learning. Dynamical Health is Siegelmann’s recent thesis stating that an unbalanced dynamic is the cause of most systemic disorders, that returning the system to balance is extremely beneficial to healing and further, that it is too limiting to focus only on primary causes, when any treatment that returns balance is sufficient for healing. Modeling these systems mathematically provides a means of exploring many possible solutions, which can then be translated to actual treatment. Her recent system biology studies include genetic networks, circadian system, memory reconsolidation, miRNA, cancer, and now addiction. She remains active in supporting young researchers and encouraging minorities and women to enter and advance in the sciences.

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Siegelmann, H.T. Addiction as a dynamical rationality disorder. Front. Electr. Electron. Eng. China 6, 151–158 (2011). https://doi.org/10.1007/s11460-011-0134-2

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  • DOI: https://doi.org/10.1007/s11460-011-0134-2

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