  In an effort to understand why individuals choose to participate in
personally-expensive pro-environmental behaviors, environmental and behavioral
economists have examined a moral-motivation model in which the decision to
adopt a pro-environmental behavior depends on the society-wide market share of
that behavior. An increasing body of practical research on adoption of
pro-environmental behavior emphasizes the importance of encouragement from
local social contacts and messaging about locally-embraced norms: we respond by
extending the moral-motivation model to a social networks setting. We obtain a
new decision rule: an individual adopts a pro-environmental behavior if he or
she observes a certain threshold of adoption within their local social
neighborhood. This gives rise to a concurrent update process which describes
adoption of a pro-environmental behavior spreading through a network. The
original moral-motivation model corresponds to the special case of our network
version in a complete graph.
  By improving convergence results, we formulate modest-size Integer Programs
that accurately (but not efficiently) find minimum-size sets of nodes that
convert the entire network, or alternately that maximize long-term adoption in
the network given a limited number of nodes which may be temporarily converted.
Issues of stability in determining long-term adoption are key. We give hardness
of approximation results for these optimization problems. We demonstrate that
there exist classes of networks which qualitatively have severely different
behavior than the non-networked version, and provide preliminary computational
results in in modestly-sized highly-clustered small-world networks related to
the famous small-world networks of Watts and Strogatz.
