@article{fdi:010087623,
  title = {{T}oward {AI}-designed innovation diffusion policies using agent-based simulations and reinforcement learning : the case of digital tool adoption in agriculture},
  author = {{V}inyals, {M}. and {S}abbadin, {R}. and {C}outure, {S}. and {S}adou, {L}. and {T}homopoulos, {R}. and {C}hapuis, {K}evin and {L}esquoy, {B}aptiste and {T}aillandier, {P}atrick},
  editor = {},
  language = {{ENG}},
  abstract = {{I}n this paper, we tackle innovation diffusion from the perspective of an institution which aims to encourage the adoption of a new product (i.e., an innovation) with mostly social rather than individual benefits. {D}esigning such innovation adoption policies is a very challenging task because of the difficulty to quantify and predict its effect on the behaviors of non-adopters and the exponential size of the space of possible policies. {T}o solve these issues, we propose an approach that uses agent-based modeling to simulate in a credible way the behaviors of possible adopters and (deep) reinforcement learning to efficiently explore the policy search space. {A}n application of our approach is presented for the question of the use of digital technologies in agriculture. {E}mpirical results on this case study validate our scheme and show the potential of our approach to learn effective innovation diffusion policies.},
  keywords = {innovation diffusion ; policy design ; reinforcement learning ; agent-based ; simulation ; deep reinforcement learning ; digital agriculture},
  booktitle = {},
  journal = {{F}rontiers in {A}pplied {M}athematics and {S}tatistics},
  volume = {9},
  numero = {},
  pages = {1000785 [17 ]},
  year = {2023},
  DOI = {10.3389/fams.2023.1000785},
  URL = {https://www.documentation.ird.fr/hor/fdi:010087623},
}