Front. Electr. Electron. Eng. China 2011, 6(1): 151–158 DOI 10.1007/s11460-011-0134-2 Hava T. SIEGELMANN Addiction as a dynamical rationality disorder c  Higher Education Press and Springer-Verlag Berlin Heidelberg 2011 Abstract Addiction is frequently modeled as a behav- ioral disorder resulting from the internal battle between two subsystems: one model describes slow planning ver- sus 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 oppos- ing systems work in parallel, tipping toward one subsys- tem 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 cre- ating 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. Keywords addiction, emotional-cognitive rationality, dynamical systems, dynamical disease, dynamical reha- bilitation, relapse, higher power 1 Introduction Drug and alcohol abuse affects over 24 million individ- uals in the U.S [1] and its reach is increasing globally. The complexity of addiction is immense, encompassing diverse, but associated spheres: genetic predisposition [2], altering brain structures that change size over the duration of abuse [3], social and economic status [4] and Received November 1, 2010; accepted December 17, 2010 Hava T. SIEGELMANN Department of Computer Science, UMass Amherst, Amherst, MA 01003, USA E-mail: Hava@cs.umass.edu depression [5], to name only a few. The complexity of the disease is daunting; the eco- nomic toll—vast: from governmental expenditures, reha- bilitation programs, personal economic loss, legal costs, etc.; the devastating effect addiction has on the lives of the addicts and their families is equally costly both eco- nomically and emotionally. Since 2003, I have researched this complex phenomenon in an effort to use my com- puter science, mathematic and dynamical systems skills to find ways to assist addicts in their efforts to overcome drug use. A number of different addiction models exist. At one end of the spectrum, addiction is portrayed as a disorder that becomes monotonically worse, leaving the addict no way out, due to the positive feedback created by addic- tive substances [6]. At the other end of the spectrum, addiction is viewed as a sort of life phase people grow into and out of: youngsters, who during high school and college, try different drugs, then at a later point in their lives overcome substance abuse with no need of external help [7]; our view lies somewhere in between. Animal modeling in rats reveals parameters causing rodents to reinstate drug use, including stress, drug cues, and priming [8], as well as genetic factors [9], and liv- ing conditions [10]. Valid analogies can be drawn from the rat studies, but obviously, some issues pertaining to higher cognition and rationality are undoubtedly miss- ing. Studies describe addiction as a cyclical condition [11], characterized by periods of abstinence followed by relapse. It is relapse that makes addiction so difficult to treat. Both clinical and anecdotal evidence show clearly that short-term treatment does not help in avoiding re- lapse. It is this observation that made us resort to dy- namical system theory to look for recovery and relapse prediction. Recent seminal studies [12] followed addicts, providing them with mobile devices and communicating with them a few times a day. Addicts were asked to input any signif- icant, drug-use related events. This sort of approach has the potential to be able to predict tendencies that fore- warn of upcoming relapse, which can be extraordinarily beneficial in clinical recovery. Unfortunately, statistical