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“There are idiots. Look around.”

So said economist Larry Summers in a paper challenging the idea of efficiency in financial markets, a cornerstone of American capitalism. We’ve hit a point where the same can be said of efficiency in a cornerstone of American democracy, the marketplace of ideas:

“There are bots. Look around.”

The marketplace of ideas is now struggling with the increasing incidence of algorithmic manipulation and disinformation campaigns.

Something very similar happened in finance with the advent of high-frequency trading (the world I came from as a trader at Jane Street): technology was used to distort information flows and access in much the same way it is now being used to distort and game the marketplace of ideas.

The future arrived a lot earlier for finance than for politics. There are lessons we can take from that about what’s happening right now with bots and disinformation campaign. Including, potentially, a way forward.

Efficiency, Technology and Manipulation in Finance

The technological transformation of financial markets began way back in the 1970s. The first efforts focused on streamlining market access, facilitating orders with routing and matching programs. Algorithmic trading began to take off in the 1980s, and then, in the 1990s, came the internet.

When we talk about financial market efficiency, we’re really talking about information and access. If information flows freely and people can act on it via a relatively frictionless trading platform, then the price of goods, stocks, commodities, etc. is a meaningful reflection of what’s known about the world. The internet fundamentally transformed both information flows and access. News was incorporated into the market faster than ever before. Anyone with a modem could trade. Technology eliminated the gatekeepers: human order-routers (brokers) and human matching engines (known as ‘specialists’ in finance parlance) were no longer needed. The transition from “pits to bits” led to exchange consolidation; the storied NYSE acquired an electronic upstart to remain competitive.

Facilitation turned into automation, and now computers monitor the market and decide what to trade. They route orders, globally, with no need for human involvement beyond initial configuration and occasional check-ins. News breaks everywhere, all at once and in machine-readable formats, and vast quantities of price and tick data are instantly accessible. The result is that spreads are tighter, and prices are consistent even across exchanges and geographical boundaries.

Technology transformed financial markets, increasing efficiency and making things better for everyone.

Except when it didn’t.

For decades we’ve known that algorithmic trading can result in things going spectacularly off the rails. Black Monday, in 1987, is perhaps the most famous example: programmatic trading sell orders triggered other programmatic sell orders, which triggered still more sell orders, leading to a 20% drop in the market — and that happened in the pre-Internet era. Since then, we’ve seen unanticipated feedback loops, bad code, and strange algorithmic interactions lead to steep dives or spikes in stock prices. The Knight trading fiasco is one recent example; a stale test strategy was inadvertently pushed live and it sent crazy orders into the market, resulting in thousands of rapid trades and price swings unreflective of the fundamentals of the underlying companies. Crashes – flash crashes, now – send shockwaves through the market globally, impacting all asset types across all exchanges; the entire system is thrown into chaos while people try to sort out what’s going on.

So, while automation has been a net positive for the market, that side effect — fragility — negatively impacts and erodes trust in the health of the entire system. Regular people read the news, or look at their E-trade account, and begin to feel like financial markets are dangerous or rigged, which makes them both wary and angry. Media and analysts, meanwhile, simplify the story to make a very complex issue more accessible, creating a boogeyman in doing so: high-frequency trading (HFT).

The trouble is that “high-frequency trading” is about as precise as “fake news.”

HFT is a catch-all for a collection of strategies that share several traits: extremely rapid orders, a high quantity of orders, and very short holding periods. Some HFT strategies, such as market making and arbitrage, are net beneficial because they increase liquidity and improve price discovery. But others are very harmful. The nefarious ones involve intentional, deliberate, and brazen market manipulation, carried out by bad actors gaming the system for profit.

One example is quote stuffing, which involves flooding specific instruments (like a particular stock) with thousands and thousands of orders and cancellations at rates that exceed bandwidth capabilities. The goal is to increase latency and cause confusion among other participants in the market. Another example is spoofing, placing bids and offers with the intent to cancel rather than execute, and its advanced form, layering, where this is done at several pricing tiers to create the illusion of a fuller order book (in other words, faking supply and/or demand). The goals of these strategies is to entice other market participants — including other algorithms — to respond in a way that benefits the person running the manipulation strategy. People are creative. And in the early days of HFT, slimy people could do bad things with relative ease.

Efficiency, Technology and Manipulation in Ideas

Technology brought us faster information flows and decreased barriers to access. But it also brought us increased fragility. A few bad actors in a gameable system can have a profound negative impact on participant trust, and on overall market resilience. The same thing is now happening with the marketplace of ideas in the era of social networks.

When the internet transformed media, in the late 1990s, flows of information and access changed: it was both easier to consume information and create and distribute it, though democratized publishing tools. Just as we eliminated gatekeepers in finance, we did so in media.

But unlike the somewhat esoteric and rarefied world of high finance, anybody could play in this game. Especially with the advent of social networks in the mid-2000s. Content creation began to consolidate on a handful of platforms specifically designed to facilitate sharing and engineer virality through network effects. Our social platforms became idea exchanges, and unequal ones at that: popular content, as defined by the “crowd“, rose to the top. If a crowd makes the effort, the systems are phenomenally easy to game; the crowd doesn’t have to be real people, and the content need not be true or accurate.

We’re now in a period that’s strikingly reminiscent of the early days of HFT: the intersection of automation and social networking has given us manipulative bots and an epidemic of “fake news”. Just as HFT was a simplified boogeyman for finance, “fake news” is an imprecise term used to describe a variety of disingenuous content: clickbait, propaganda, misinformation, disinformation, hoaxes, and conspiracy theories. To break this down a bit more:

Clickbait is the low-hanging fruit; it’s generally profit-driven and more about piquing interest and getting a quick click than convincing someone of something.

Misinformation is generally spread accidentally. There is a lot of overlap with hoaxes — attempts to make an audience believe that something made-up is real. These run the gamut; some are simply practical jokes, others are darker.

Conspiracy theories take a scaffolding of real facts about something, and twist it to add intrigue. They are best dealt with simply by depriving them of oxygen. The internet isn’t particularly good at depriving sensational things of oxygen, so we’ve seen a steady increase in the reach of conspiracy theories.

Disinformation campaigns are the ones that matter most, and that’s because the goal of disinformation is specifically to introduce fear, uncertainty, and doubt.

Content for a disinformation effort is disseminated strategically. The phony narrative often appears first on an “alternative” site, something outside of the mainstream press. In the quaint old days when KGB spies deployed the tactic, the goal was pickup by a major media property, because that provided legitimization and took care of distribution. Today, anonymity and the viral-sharing potential of social networks have eliminated the need for that step. Also, ironically, framing the story as something the “mainstream media” won’t touch resonates better with the media-distrusting folks who are most receptive to the content. Disinformation-campaign material is spread via mass coordinated action, supplemented by bot networks and sockpuppets (“fake people”). Once it trends, it’s nearly impossible to debunk.

Social networks enable malicious actors to operate at platform scale, because they were designed for fast information flows and virality. Bots and sockpuppets can be used to manipulate conversations, or to create the illusion of a mass groundswell of grassroots activity, with minimal effort. It’s incredibly easy to deploy bots into a hashtag to spread or to disrupt a message — quote stuffing the conversation the way a malicious HFT algorithm quote stuffs the order book of a stock. It’s easy to manipulate ratings or recommendation engines, to create networks of sockpuppets with the goal of subtly shaping opinions, preying on proximity bias and confirmation bias.

This would be a more manageable situation if the content remained on one platform. But the goal of a disinformation campaign is to ensure the greatest audience penetration, and achieving that involves spreading content across all of the popular social exchanges simultaneously. At a systems level, the social web is phenomenally easy to game because the big social platforms all have the same business model: eyes and ads. Since they directly compete with each other for dollars, they have had little incentive to cooperate on big issues. Each platform takes its own approach to troll-bashing and bot detection, with varying degrees of commitment; there’s no cross-platform policing of malicious actors happening at any kind of meaningful level.

In fact, until a very notable event in November 2016, there was no public acknowledgement by Twitter, Facebook, or Google that there even was a problem. Prior to the U.S. Presidential election, tech companies managed to move fast and break things in pursuit of user satisfaction and revenue, but then fell back on slippery-slope arguments to explain why it was too difficult to rein in propaganda campaigns, harassment, bots, etc. They chose to pretend that algorithmic manipulation was a nonissue, so that they bore no responsibility for the downstream effects. Technology platforms are simply hosts of the content; they don’t create it. But as malicious actors get more sophisticated, and it becomes increasingly difficult for regular people to determine who or what they’re communicating with, there will be a profound erosion of trust in social networks.

Markets can’t function without trust.

So we’re at a point in which our marketplace of ideas bears striking resemblance to the financial markets in the early days of HFT: deliberate manipulation, unanticipated feedback loops, and malicious algorithms are poisoning the ecosystem, introducing fragility and destroying confidence. But unlike in finance, it’s no one’s job to be looking at this. It’s no one’s job to regulate this.

So now what?

Regulation is a dirty word, especially in Silicon Valley where the cult of disruption venerates business models that are often little more than regulatory arbitrage plays. The aversion to regulation isn’t entirely unwarranted; governments are often ill-suited to regulate new technologies, sometimes because they don’t understand them, and other times because they’re being hastily reactive in response to public outcry.

In the case of finance, most people accept — even welcome! — some regulation to protect the little guy’s interests against the big and powerful, or the malicious and crooked. This wasn’t always the case. The primary regulator of U.S. financial markets is the the Securities and Exchange Commission (SEC). It was established in 1934, following the Great Depression: “Before the Great Crash of 1929, there was little support for federal regulation of the securities markets. Proposals that the federal government require financial disclosure and prevent the fraudulent sale of stock were never seriously pursued.” People saw the need for someone to be responsible for ensuring fair, orderly markets after the stock market crashed and wiped out the livelihoods of millions of people.

While the marketplace of ideas hasn’t crashed just yet, we’re seeing substantial fragility and loss of trust. We’re at the point where tech industry leaders should be collaboratively considering ways to prevent it. They haven’t been particularly motivated thus far. Much of the tech industry leadership has an idealistic libertarian alignment, and “the free speech wing of the free speech party” has not gone out of its way to prevent Russian bots from exercising their right to free speech. This attitude is going to be difficult to maintain in light of changing public sentiment. Europe is already talking about regulation, tech employees are staging internal mutinies, and one of Twitter’s founders is acknowledging a “broken” internet. Things are going to change.

A middle way that may be more palatable is that of the SRO (self-regulatory organization). SROs are industry-funded, industry-established voluntary-participation frameworks that exist in dozens of industries, including medicine (the American Medical Association), legal (BAR associations), and real estate (Realtor associations). One of the main goals of an SRO is to keep the industry well-functioning, in order to avoid systemic problems that could lead to crises and wipe out trust. There are numerous industry SROs for consumer protection.

When it became obvious that malicious HFT strategies were threatening the integrity and resilience of the financial markets, the financial industry’s largest SRO, FINRA, worked alongside the SEC to address the problem. FINRA began to require specific licensing and other requirements of HFT firms. Some exchanges banned specific HFT strategies outright from their platforms. Governments around the world, regulatory bodies, SROs, and exchanges all took steps to protect the integrity of the system, fix the loopholes, and maintain the trust of market participants.

By contrast, the tech industry attempts largely uninspired, isolated tweaks despite the fact that the exchange-platforms have both a business case and a moral case for doing more. Becoming hosts of unchecked disinformation campaigns negatively impacts the three things businesses care most about: top line revenue, downstream profit, and mitigating risk. It will ultimately destroy the value of their networks. Social network companies often cite Metcalfe’s Law (the value of a network is proportional to the square of the number of the nodes on the network) in IPO filings and shareholder reports. Here’s a new corollary: they have to be real users.

But the moral case should carry equal weight. The conversations that impact our democracy and shape our lives increasingly happen on this small collection of platforms. The downstream cost of serving users disinformation, conspiracies, and radicalized propaganda became clear in the elections of 2016 and 2017. We’re heading down the path of an arms race in algorithmic manipulation, in which every company, political party, activist group, and candidate is going to feel compelled to leverage these strategies. We’re at an inflection point, and only Big Tech has the power to reset the playing field. And in the meantime, the marketplace of ideas is growing increasingly inefficient as unchecked manipulation influences our most important conversations.

There are bots. Look around.