Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session G31: Physics Approaches for Modeling Human Social Systems
11:30 AM–2:18 PM,
Tuesday, March 5, 2024
Room: 102C
Sponsoring
Units:
GSNP DBIO
Chair: Emma Zajdela, Northwestern University; Joseph Johnson
Abstract: G31.00004 : Dynamics of Innovation: From Particles to Ideas*
12:30 PM–12:42 PM
Presenter:
Carlo R daCunha
(Northern Arizona University)
Authors:
Carlo R daCunha
(Northern Arizona University)
Guilherme S Giardini
(Northern Arizona University)
John F Hardy
(Northern Arizona University)
Our model portrays individuals engaging with and disengaging from innovation trends through a diffusive process encapsulated by a time-dependent Markov chain.
By calculating the expected number of participants in an innovation trend, we formulate a function that captures the temporal evolution of these trends, transcending specific contextual nuances.
We validate our results using a Recurrent Neural Network (RNN). To achieve this, we introduce a novel training approach that fits ordinary differential equations (ODE) with a loss function that considers both data and its derivative. The RNN fitting process yields confidence values around 90% that substantiate the suitability of our proposed model for describing innovation trends.
Our model also exhibits predictive capabilities, enabling the anticipation of future tendencies with limited observations, particularly beneficial in contexts necessitating trend management. Additionally, our observations reveal that deviations from the model's analytical solution can be attributed to unforeseeable external
factors influencing innovation dynamics.
*We acknowledge funds from Northern Arizona University to conduct this work.
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