Imaging the brain at multiple scales: how to integrate multi...
Can We Put Computational Modelling to Use In the Social Sciences?
In a recent guest lecture, Peter Sloot talked about multi-level modelling of the HIV epidemic. Sloot, a computational scientist at the University of Amsterdam, is part of a team that develops the ViroLab, a decision-support system for the treatment of HIV. His approach to the disease is to model it in its full complexity - "from molecule to man" (Sloot et al., 2009). The output? A personalized recommendation of treatment for each individual patient.
I was fascinated (if dumbfounded by the vocabulary from medicine and computer sciences) by the idea of modelling the behaviour of a phenomenon as complex as HIV on its many levels - biological as well as sociological. The model Sloot presented is not restricted to the functioning of the immune system of AIDS patients or the mating behaviour of those infected with HIV, but it rather takes their interdependence into account. But as evident as the use of multi-level modelling is for biological processes, could it also be applied in the social sciences?
Consider an example from my current work on a paper evaluating the economic impact of mobile telephony in developing countries. Mathematical models from the consumer search literature predict that cheaper access to information on prices should make markets more efficient, and indeed quantitative studies find that the introduction of mobile phone networks reduces price dispersion (e.g. Aker, 2010; Jensen, 2007). But follow-up research reveals that the resulting net welfare gain to society is unevenly distributed: poor farmers, for instance, do not seem to utilize mobile phones for arbitrage (Aker & Fafchamps, 2010).
Qualitative studies have revealed some of the factors which inhibit people's efficient use of mobile phones. For instance, many small farmers rely on wholesalers for credit to buy seeds. Using the mobile phone to find the highest bidder for their yield might provide them with higher income - but destroying the trusting relationship to their regular wholesaler would rob them of their traditional source of credit (Molony, 2008). Yet the underlying reasons for usage and non-usage are certainly manifold - and might sometimes well be irrational.
In a broader context, this relates to the emerging field of behavioral economics. These researchers say good bye to the rational, self-interested "homo oeconomicus", and take psychological processes into account. For example, in a recent piece for a Boston Review forum on behavioral economics in development projects, Rachel Glennerster and Michael Kremer show that small fees reduce people's investment in non-acute health care or education more than would be "rational". Or, as the authors put it: "human beings don’t always make the best decisions".
My personal interest lies with the diffusion and usage of information and communication technologies (ICTs) and their interaction with society. Could computational multi-level modelling help us understand the impacts of mobile telephony on developing economies? I imagine that it could facilitate the linking of findings from different disciplines and their integration into a holistic model of technological change.
From the psychology of the individual agent dealing with a new technology (should I use it? and how?), to the social context in which a technology is introduced (how do mobile phones alter the relationships between individuals?), to the changes in market behavior observed by economists (how are price levels and dispersion influenced?), to the macroeconomic impacts on growth and employment - surely, at the moment, such a broad model seems utopian. Indeed, obtaining sufficient data to feed it might never be possible. Nevertheless, social scientists should not miss the advantages of computational multi-level modelling for their field.
Aker, J. C. (2010). Information from Markets Near and Far: Mobile Phones and Agricultural Markets in Niger. American Economic Journal: Applied Economics, 2(3), 46-59.
Aker, J. C., & Fafchamps, M. (2010). How Does Mobile Phone Coverage Affect Farm-Gate Prices? Evidence from West Africa. Working Paper.
Jensen, R. (2007). The Digital Provide: Information (Technology), Market Performance, and Welfare in the South Indian Fisheries Sector. Quarterly Journal of Economics, 122(3), 879-924.
Molony, T. (2008). Running out of credit: the limitations of mobile telephony in a Tanzanian agricultural marketing system. Journal of Modern African Studies, 46(4), 637-658.
Sloot, P. M. A., Coveney, P. V., Ertaylan, G., Müller, V., Boucher, C. A., & Bubak, M. (2009). HIV decision support: from molecule to man. Philosophical Transactions of the Royal Society, 367, 2691-2703.