This is a math-heavy, long, but extremely rewarding paper modelling collective behaviour from a dynamic evolutionary perspective, demonstrating (I think very effectively) that collective ignorance and polarization are likely to be amplified by attempts to regulate against undesirable outcomes such as the spread of disinformation, racial discrimination, and so on. The authors provide suggestions about ways to alter the environment to counteract negative feedback loops (actually, also positive feedback loops). As they put it:

"More generally, proactively providing educational, social, and economic opportunities to counteract negative feedback loops, encouraging more accurate beliefs among current and future generations through early exposure, and shaping the environment to favor collective intelligence are likely to be more successful policies than attempting to outlaw undesirable behaviors. As long as the environmental factors giving rise to these behaviors are still in force, the banned behaviors will re-emerge in one form or another."

This is a really thought-provoking paper that has a lot of relevance to anyone attempting to build or manage a social computing system.

Abstract:

Despite its success in financial markets and other domains, collective intelligence seems to fall short in many critical contexts, including infrequent but repeated financial crises, political polarization and deadlock, and various forms of bias and discrimination. We propose an evolutionary framework that provides fundamental insights into the role of heterogeneity and feedback loops in contributing to failures of collective intelligence. The framework is based on a binary choice model of behavior that affects fitness; hence, behavior is shaped by evolutionary dynamics and stochastic changes in environmental conditions. We derive collective intelligence as an emergent property of evolution in this framework, and also specify conditions under which it fails. We find that political polarization emerges in stochastic environments with reproductive risks that are correlated across individuals. Bias and discrimination emerge when individuals incorrectly attribute random adverse events to observable features that may have nothing to do with those events. In addition, path dependence and negative feedback in evolution may lead to even stronger biases and levels of discrimination, which are locally evolutionarily stable strategies. These results suggest potential policy interventions to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through targeted shifts in the environment.