Delivering a useable method for mapping groups in attitude networks by developing a robust prototype, development community, and exploitation model to maximize the social value of the breakthrough

Our ERC project developed a mathematical method to construct bipartite graphs directly from surveys of attitudes and opinions, providing a network representation of opinion-based groups.

This approach reveals group structure, locates individuals in the space, allows the application of network methods to analyze their relations, and can detect ideological polarization, even without extremism.

Like the invention of a new instrument for observing the physical world (e.g. a telescope), the method gives a new view on social attitudes and opinions, with huge value for detecting partisan dynamics in a wide range of disciplines interested in modelling attitudes and human behaviour.

Understanding these processes is essential for tackling urgent social issues which require cross-partisan synchronization of core attitudes/opinions (e.g. climate change; vaccine hesitancy; cohesive democracy). Applying these methods currently requires high-level mathematics and coding skills. To realize the social value of the method we must get it into the hands of end-users in useable form.

We will (1) prototype a robust software architecture; (2) prototype training resources; (3) implement end-user testing and evaluation of the prototype software and training; (4) build a community of early adopters and developers to test, validate and evaluate the method across a range of multidisciplinary applications; (5) explore synergistic commercialisation opportunities; and (6) determine the optimal IP strategy for maximizing the social value of the innovation.

Our intention is to maximize the value of the advance already achieved in our ERC project by completing the groundwork to make the innovation maximally accessible to the interdisciplinary scientific community and to identify synergistic commercialization opportunities.