The Matthew effect: success helps gain access to resources which in turn results in further success and resources. Credit is given to those who are already famous (or rich), also called preferential attachment (attraction of largest hubs). Example in peer review, ranking by ‘likes’ or popularity, star system, meritocracy, winner take all markets. Same phenomenon happens in reverse with scapegoating, bullying, swiftboating campaigns. This is normative and may stifle difference or disruptive innovation.
> Feedback driven by reputation, ‘winner’ attraction


The Network effect: When network effect is present, the value of a product or service is dependent on the number of others using it. Each new entrant adds value to the whole network. Over time, positive network effects can create a bandwagon effect as the network becomes more valuable and more people join, in a positive feedback loop. That’s why most of us are still on facebook… Newer networks have more difficulty to attract.
> Feedback driven by critical mass attraction

The bandwagon effect or herd behavior: "the probability of any individual adopting a behavior increases with the proportion who have already done so". People chose the most visible, obvious, ‘attractive’ options. Examples in rankings, recommended purchases, likes, SEO, best practices. In management, and investment optimization, ‘winning models’ are over-applied, reducing the diversity of behaviors, and in the end of competitive advantage.
> Feedback driven by Imitation

Self-fulfilling prophecy: a prediction that directly or indirectly causes itself to become true, by the very terms of the prophecy itself, due to positive feedback between belief and behavior that brings the behavior to reality. Examples: Bear or bull markets. Assuming people cannot be trusted makes them more dishonest, assuming people are driven by self-interest makes them worry more of their self interest. Behavioral engineering plays on this.
> Assumption feedback
 
These effects all overlap and feed into each other, amplified by form, scope (distribution), intensity (pressure) and frequency of communication that influence propagation.

Consequences of reinforcing feedback loops

Pareto principle: (also known as the 80–20 rule, the law of the vital few, and the principle of factor sparsity) for many events, roughly 80% of the effects come from 20% of the causes/occurrences.  Efforts tend to concentrate on the 20%.

Power law: a functional relationship between two quantities, where one quantity varies as a power or exponentiation of another. "In systems where many people are free to choose between many options, a small subset of the whole will get a disproportionate amount of traffic (or attention, or income), even if no members of the system actively work towards such an outcome. The very act of choosing, spread widely enough and freely enough, creates a power law distribution.” (Clay Shirky)

Cumulative advantage: once a social agent gains a small advantage over other agents, that advantage will compound over time into an increasingly larger advantage, attracting an increasing amount of resources.

Examples of phenomena that can be seen when an equilibrium frame is not applied. From Brian Arthur Complexity Economics: a different framework for economic thought pp 9-11 Abstracted here.

Self-reinforcing asset-price changes (aka bubbles and crashes)
As wins or losses are based on forecast capacity, investors typically generate (or discover) their own forecasting methods, try out promising ones, drop those that don’t work, and periodically generate new ones to replace them. The stock price forms from their bids and offers, and thus ultimately from agents’ forecasts. Our market becomes an ecology of forecasting methods that either succeed or are winnowed out, an ecology that perpetually changes as this happens. And we see several phenomena, chief among them, spontaneous bubbles and crashes. If the price of a stock rises for a while from a first type of forecast, investors will buy in thus validating it, which may cause a further rise. Eventually this drives the price high enough to trigger a second type of forecast. Investors holding these sell, the price drops, which switches off the upward forecasts, causing other investors to sell too, and a crash ensues. The scale and duration of such disruptions vary, they happen randomly in time, so they cannot be predicted. What can be predicted is that such phenomena will occur, and will have certain probability distributions of size and scale. (how does this compare/combine with ‘self fulfilling prophecy’?)

Clustered volatility.
This is the appearance of random periods of low activity followed by periods of high activity. In our artificial market these show up as periods of low and high price volatility. Low volatility reigns when agents’ forecasts are working reasonably well mutually; then there is little incentive to change them or the results they produce. High volatility happens when some agent or group of agents “discover” better predictors. This perturbs the overall pattern, so that other investors have to change their predictors to readapt, causing further perturbation and further re-adaptation. The result is a period of intense readjustment or volatility. Such random periods of low volatility alternating with high volatility show up in actual financial market data, where they are called GARCH behavior.

Sudden percolation.
When a transmissible change happens somewhere in a network, if the network is sparsely connected the change will sooner or later peter out for lack of onward connections. If the network is densely connected, the change will propagate and continue to propagate. In a network of banks, an individual bank might discover it holds distressed assets. It then comes under pressure to increase its liquidity and calls on its counterparty banks. These in turn come under pressure to increase their liquidity and call on their counterparties, and so the distress cascades across the network. Such events can cause serious damage. They peter out in a low-connection network, but propagate—or percolate—for long periods as the degree of connection passes some point and gets large.

Also interesting to crack are paradoxes:

The Jevons Paradox

In economics, the Jevons paradox, sometimes Jevons effect) is the proposition that as technology progresses, the increase in efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource.

This however is not a 'systematic' unescapable consequence of increases in energy efficiency. The PL should help express visually in which conditions (example freed outflow of the resource on 'offer' that seeks an outlet, i.e analogy with open tap). With a circular economy and output tap closed, or the availability of an alternative clean renewable resource, the jevons effect does not kick in.