Reducing socio-ecological conflict using social influence modelling.

Polarisation of opinions across communities can lead to social conflict, reputational damage and the disruption of operations and markets. Social influence models have been widely used to better understand processes driving conflict from a theoretical perspective. Using aquaculture as a case study, we demonstrate how such models can be extended to accurately hindcast the transition from population consensus to high conflict, including observed catastrophic tipping points. We then use the model to quantitatively evaluate strategies aimed at reducing aquaculture conflict. We found that persuasive advocacy was ineffective and often counterproductive, whereas meaningful engagement, collaborative learning and improving scientific literacy targeted broadly across the population was effective in moderating opinions and reducing conflict. When such messaging was targeted too narrowly or too infrequently, it tended to be negated by ongoing exchange of misinformation within the population. Both the modelling approach and lessons on effective communication strategies are relevant to a broad range of environmental conflicts.

Stochastic events can explain sustained clustering and polarisation of opinions in social networks.

Understanding the processes underlying development and persistence of polarised opinions has been one of the key challenges in social networks for more than two decades. While plausible mechanisms have been suggested, they assume quite specialised interactions between individuals or groups that may only be relevant in particular contexts. We propose that a more broadly relevant explanation might be associated with the influence of external events. An agent-based bounded-confidence model has been used to demonstrate persistent polarisation of opinions within populations exposed to stochastic events (of positive and negative influence) even when all interactions between individuals are noisy and assimilative. Events can have a large impact on the distribution of opinions because their influence acts synchronistically across a large proportion of the population, whereas an individual can only interact with small numbers of other individuals at any particular time.