A lot of ink has been spilled of late about what causes mass protests, and what connects episodes of protest across different countries. Economic inequality, unemployment, corruption, middle-class rage, youth, social media…the lists go on.

Availability bias and selection bias are real problems in many of these on-the-fly analyses. Often, we see mass protests erupt in one country, search our minds for relevant examples, and then deduce the causes of those events from the things we notice they have in common. The set of potentially relevant examples is constrained by our memory, which is usually pretty limited. At the same time, the supposed relevance of those other examples is often affected by their emotional salience, which, in turn, is influenced by the amount and kind of news coverage they received. When we try to impose some structure on this thought process, we usually start with, and often don’t get past, geographic and temporal proximity. Equally important, we rarely stop to think about similar cases in which protests didn’t occur, a crucial step in causal inference.

I think we can get a better handle on why episodes of mass protest happen where and when they do—and how episodes in different countries are connected to each other—by taking a broader view from the start. We live in a global system. This means that large-scale political actions result from interactions between structural features and processes at the local and larger levels.

Local structural features don’t determine whether or not societies will experience specific types of political action, but they do shape a society’s propensity for them. Borrowing a phrase from John Miller and Scott Page (p. 96), these features “mediate agent interactions by constraining the flow of information and action.” In the case of mass protest, evidence indicates that number of relatively simple and obvious things have powerful effects on the likelihood that large numbers of people will take to the streets to make political demands. Among those things are population size, urbanization, and what political sociologists call the “political opportunity structure,” which is mostly but not entirely about the scope of civil liberties and state repression.

Notably absent from that short list are structural sources of grievance, like income inequality or corruption. I’ve left inequality off the list because I’ve seen and discussed evidence that its effects are weak (here). It’s harder to test the claim that corruption causes mass protest because we don’t have great cross-national time-series data on corruption, but my hunch is that it’s not especially powerful, either. Instead and like Fabio Rojas, I think participants and observers often select these issues as framing devices for protests that are already underway, but that doesn’t mean those issues actually caused the action in the first place.

Whatever the true set is, though, these structural features change slowly, and many societies exhibit many of them, yet mass protest is rare. So, to explain why protests happen, we need some more dynamic elements in our model. Based on empirical evidence, I see a few as especially important: economic downturns; austerity; inflation, especially for basics like food and fuel; and elections.

As we’ve seen in the past few years, these processes often have common underlying causes and are interconnected. Shifts in global oil markets can cause fuel prices to rise; rising fuel prices often drive food prices up; inflation can trigger recessions; recessions can compel governments to adopt austerity measures; frustrations over austerity can catalyze no-confidence votes that trigger early elections; elections can spur spending that produces inflation; and, crucially, swings in one market or one country’s economy can reverberate across many others. These interrelationships help to explain why mass protest seems to occur in waves, or episodes that cluster in time and space.

The systemic character of those common “triggers” isn’t the only thing that can connect mass protests across space and time, however. Contagion plays a role, too, and it comes in various forms. As Marc Beissinger observes, apparently successful protests can inspire imitators, a process he calls emulation. Protest may also spread through the deliberate efforts of interested parties, including but not limited to foreign governments and transnational activist organizations like Avaaz and the Open Society Foundations. (Just because dictators are often paranoid about “foreign agents” doesn’t mean someone isn’t really out to get them.)

All of the dynamic processes discussed so far are sources of positive feedback. They amplify earlier changes, pushing the system toward greater instability. Of course, would-be protesters aren’t the only ones imitating and teaching. As Beissinger observes and Donatella della Porta and Sidney Tarrow also argue (see here), state officials, soldiers, police, and other counter-revolutionary forces also watch and talk to each other and adapt their tactics in response to the forms and patterns they see elsewhere. While economic reverberations and contagion produce positive feedback, adaptation by groups invested in the status quo produces negative feedback. What the former amplifies, the latter dampens. Coupled with structural sources of friction and inertia, these negative feedback loops help explain why mass protest doesn’t spiral into local and then global chaos.

New communications infrastructures and technologies aren’t triggering the protests we’re seeing, but they’re probably making them easier. Other things being equal, easier and broader communication creates larger networks with less friction. Governments and police also use and adapt to these technologies, however, so they aren’t always and automatically facilitators of collective action and sources of positive feedback for activism already underway.

Mass protest surely involves many causal mechanisms, at least some of which reside at the level of the (potential) participant. Importantly, though, those mechanisms need not be the same for everyone. One of the most important feature of human societies is their heterogeneity, and that heterogeneity means that there can be many different paths to choosing to participate or to stay home.

That said, one of the individual-level mechanisms that probably plays a role in many mass protests is loss aversion. As summarized in Tversky and Kahneman’s prospect theory, when making decisions in the face of risk, humans tend to think in terms of changes from the status quo instead of final states, and in their thinking about those changes, losses are weighted more heavily than gains. The resulting loss aversion helps explain why economic losses caused by recessions, austerity, and inflation can spur people to protest in ways that opportunities for economic gain rarely do. The threat of those losses has a psychological effect that is stronger than the prospect of similar gains and is not strictly determined by the likelihood of the ensuing protests’ success.

Risk-seeking behavior may be another important causal mechanism. While many individuals prefer to avoid risky situations, others seek them out (see here for relevant evidence). This is one place where the aforementioned heterogeneity plays an important role. Risk-embracing individuals are more likely to become the “early risers” whose daring actions change the information others have about their predicament, and thus their willingness to act out.

As Deborah Minkoff shows, the function early risers serve isn’t just informational. At a level in between individuals and the global system, early risers can also catalyze changes in the density of relevant organizations. These changes in organizational density, in turn, can help carve out a resource niche and create an infrastructure that allows protests to persist and expand over time. As the niche becomes increasingly crowded, however, barriers to entry rise, and the resulting feedback can tip from positive to negative. Along with the aforementioned Red Queen’s Race between protesters and police, this organizational ecology helps explain why mass protests eventually peter out, even in cases where relevant organizations survive and perhaps even continue to grow.

Finally, and I hope obviously, there is also a healthy dose of randomness in this system. We can recognize that mass protests unfold and interconnect in ways that exhibit patterns without believing that the system as a whole is deterministic.