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Information cascades and the acceptance of new technologies

In this day and age, energy is a commodity.  It has also became clear that the traditional sources of energy are being depleted which implies a growing need for alternative sources of energy.  However, there are many technical challenges as well as adverse network effects.  This blog will look into the effects of information cascades on the adoption of alternative fuel-based transportation.

In economics, a group of people’s choices can be attributed to various parameters.  These parameters allow researchers and economists to create monetary utility functions that can predict the probability of people purchasing a product or a commodity.  This can apply for various markets and situations though the particular example I like to show is on the topic of sustainable energy and the effects of a network in adopting a new type of transportation that has less carbon emissions.  One of the most common utility functions used in sustainable energy is a logit model.  Let’s assume that the utility function for a conventional fuel-based (CF) vehicles is given by: VCFp PCF+ βoc OCCF+ βperf perfCF where the β are the marginal utilities of the given quantity, p is the purchasing price, OC is the operating cost and perf is the performance of the vehicle.  Similarly, we can assume that the utility of function for an alternative fuel-based (AF) vehicle is given by: VAF=βAFp PAF+ βoc OCAF+ βperf perfAF Because we are using a logit model, the choice probability function for a conventional fuel-based (CF) vehicle is given by PCF=VCF/(VAF + VCF) while the choice probability function for an alternative fuel-based vehicle is given by PAF=VAF/(VAF+VCF) .  From here it is clear that there are many parameters that need to be taken into account and optimized to make the choice possibility of a person to purchase an alternative-fuel based car as close to 1 as possible.

Even if all the technical factors of an alternative-fuel based vehicle is the same as a conventional-fuel based vehicle, there is still one challenge that needs to be hurdled.  This challenge is to change an individual’s perception of alternative fuels to be a positive one, in other wards make the individual accepting of the new source of fuel.  Mathematically, βAF is the parameter that models this behavior as a person who dislikes the technology would have a (-) value of βAF which decreases the choice probability for an alternative fuel-based car.  In terms of networks, it is clear that this perception challenge is quite difficult to handle as one bad initial perception in the technology can cascade down and affect everyone else’s perception of the technology.  In terms of the above model, it is clear that we cannot model perception as a constant but rather as a function of all the perceptions in a given network.  This model has yet to be developed but it has become evident that information cascades can destroy the chances of a better future should the wrong information be perceived.  While not the only factor, perception is one of the major factors in determining the successfulness of new and better technologies and is extremely hard to control due to network effects.


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