I recently presented a talk on tobacco harm reduction at the City Health 2012 conference in London. I believe that a video of the actual presentation and ensuing discussion will appear on their website eventually (and I will update this post to link to it). In the meantime I recorded a voiceover version of the slideshow:
[I will suggest/request that anyone who wants to link to the video please link to this post instead. I would like to encourage comments and discussion here, and will probably not monitor the comments on the youtube page itself. Also, there is more background that might be useful.]
The heart of the presentation is a social dynamics model of how THR (e.g., switching from smoking to e-cigarettes) occurs in a community thanks to the education and communication of social norms that come from social interaction. It starts out with a general overview of THR since many in the audience were not familiar with that. If you are not interested in the overview, you might want to skip to about 9:30 and just see the presentation of the new model. (On the other hand, I have been told that it is one of the better existing presentations about the core concepts and justifications for THR. Not as good as what I presented at the Beirut IHRA conference, unfortunately, but I do not have a recording of that. So you might want to view that part even if you already are familiar.)
The presentation speaks for itself so I will not try to summarize it here. But to provide a bit more background on the modeling (and if this is confusing, just watch the video — it is less technical than what follows, but still explains what you need to know): There is an interest in predicting THR behavior, in part for obvious reasons, and in part because of a make-work exercise that the US FDA is imposing on anyone trying to promote THR. As with any modeling of population dynamics, there are various methods available.
The simplest is to just project a trend by extending past numbers. This is largely useless for anything that involves conscious choices by people, and utterly useless when there are emerging technologies involved. Despite this, these are the models that are used when people make simplistic predictions about how many smokers there will be 20 years in the future, which others then report as fact. Such projections about tobacco/nicotine use are perhaps slightly better than trying to project a trend about how many people will be using 11-inch tablet computers 20 years from now, but not much better.
Next simplest is Markov modeling, which basically divides people into different bins (smoker, e-cigarette user, non-user, etc.) and assumes that knowing how many people are in each bin is all you need to know to know about them to determine what happens in the next period (i.e., the next day or year). This allows for much more robust modeling of some interacting influences, but under the hood, it is still based on projections of population level trends (e.g., what portion of current smokers will adopt THR as a function of how many have already done so). Allowing for subpopulation-based trends is an improvement over just projecting graphs into the future, as it were, but at its core it is still just a version of that, with all its limitations.
Agent-based models are based on the recognition that the behavior of a population, when considering a decision-based process like THR, is really the aggregation of a lot of individual decisions. Thus, such models are based on individual actors rather than just population percentages, and the population statistics are emergent properties of the actions of individuals. The individual decisions are based on economic motives (i.e., considerations of costs and benefits) which are affected by various global factors as well as social interactions. Individuals can be realistically modeled as having different preferences and other characteristics rather than being all the same. The agent-based models also allows for social interactions at an individual level — i.e., people can affect their neighbors and those they encounter, and the results of this may not be the same as treating everyone as if they just have the “average” experience ever period.
The model that we have created is about the simplest model possible that still captures the social dynamics, individual variability, and economic decision making that affects a population’s adoption of THR. It allows for THR adoption to be a social contagion, with someone’s chance of adopting it being a function of how much of it they encounter, as well as global forces. People learn (and their level of learning persists through time) and decide (based on individual motives). This contrasts with a simple projection or subpopulation-based model, where the future is basically determined by the choice of a single function — e.g., “P% of the population smokes and that is trending down at a rate of R, so next year the number of smokers will be….” or “if X people have adopted THR in period t, then D% of the rest will adopt it in period t+1, for a total of X+D”. As shown in the video, this produces population outcomes that are not just the obvious immediate result of the choice of those functions.
Update: I discuss some of the implications of this model in the context of anti-THR claims at the antiTHRlies blog.
Update (13 Nov 12): The “live” version of this (the presentation I actually gave in London) has been posted by the conference. As is usually the case, it is a bit rougher than the studio version, but for those who are are completists (are there any Phillips completists? I doubt it — I am not even one :-), there it is. I think there are also some bootlegs, but I don’t have them. Unlike most live versions, this one is a bit shorter (the studio version includes a bit more information).