Steven Novella in NeuroLogica:
What do economics, biological evolution, and democracy have in common? They are all complex adaptive systems. This realization reflects one of the core strengths of a diverse intellectual background – there are meaningful commonalities underlying different systems and areas of knowledge. In fact, science and academia themselves are complex adaptive systems that benefit from diversity of knowledge and perspective. All such systems benefit from diversity, and suffer when that diversity is narrowed, possibly even fatally.
A recent collection of studies focuses on American democracy as a complex adaptive system, and explores the mathematical underpinnings of how democracies behave and change over time in response to specific variables. Some of the insights are not surprising, but the research adds mathematical rigor to these phenomena. For example, you will likely not be surprised to learn that social media echochambers (what they call “epistemic bubbles”) lead to increased polarization of political views. But how, exactly, does this happen? What various researchers found is that when we obtain our political news from a network of like-minded people several things happen. First, the group tends to narrow over time in terms of political diversity. This happens because those who are considered “not pure enough” are ejected from the network, or leave because they feel less welcome. Further, people within the network tend to get access to less and less political news total, and the news they are exposed to is increasingly polarized. This doesn’t happen when such networks do not routinely share political news to begin with.
The core problem, therefore, seems to be the diversity of sources of information. Similar networks of people, in fact, can have a moderating effect on individual members, if the group maintains a diversity of sources of information reflecting a diversity of political opinions. Further, a healthy moderating effect is supported by individual members exploring outside the group for sources of information.
These patterns follow similar mathematical trends to other very different phenomena in other complex adaptive systems. For example, such trends tend to be non-linear, meaning the more extreme they get the more the trend accelerates. Further, there seems to be tipping points of no return. Once such information networks are radicalized beyond a certain point there may be no way back. Their models indicate that Republicans are likely already beyond this tipping point, while Democrats are rapidly approaching it.
What, then, can be done?