Showing posts with label efficient markets. Show all posts
Showing posts with label efficient markets. Show all posts

Sunday, July 29, 2012

The disintermediation of the firm: The feature belongs to individuals

My experience with online outsourcing

I joined the Stern Business School, back in 2004. In my first couple of year, my research approach was pretty much a continuation of my PhD years: I was doing a lot of coding and experimentation myself. However, at some point I got tired to writing boring code: Crawlers, front-end websites, and other "non-research" pieces of code were not only uninteresting but were also a huge drain of time.

So, I started experimenting with hiring coders. First locally at NYU. Unfortunately, non-research student coders turned out to be a bad choice. They were not experienced enough to write good code, and were doing this task purely for monetary reasons, not for learning. I got nothing useful out of this. Just expensive pieces of crap code.

In summer of 2005, I started experimenting with online outsourcing. I tried eLance, Guru, and Rent-A-Coder. I tentatively started posting there programming projects that were not interesting conceptually (e.g., "crawl this website and store the data in a CSV file", "grab the CSV data from that website and put them in a database", "create a demo website that does that", etc)

Quickly, I realized that this was a win-win situation: The code was completed quickly, the quality of the websites was much better than what I could prepare myself, and I was free to focus on my research. Once I started getting PhD students, outsourcing non-research coding requirements became a key part of my research approach: PhD time was too valuable to waste on writing crawlers and dealing with HTML parsing peculiarities.

Seven years of outsourcing: Looking back

Seven years have passed since my first outsourcing experiments. I thought it is now a good time to look back and evaluate.

Across all outsourcing sites (excluding Mechanical Turk), I realized that I had posted and hired contractors for a total of 235 projects. Honestly, I was amazed by the number but amortized this is just one project per 10 days, which is reasonably close to my expectations.

Given the reasonably large number of projects, I thought that I may be able to do some basic quantitative analysis to figure out what patterns lead to my own, personal satisfaction with the result. I started coding the results, adding variables that were both personal (how much did I care about the project? how detailed were the specs? how much did I want to spend?) and contractor-specific (past history, country of origin, communication while bidding, etc).

Quickly, even before finished coding, a pattern emerged: All the "exceeded expectations" projects were done by individual contractors or small teams of 2-3 people. All the "disappointments" were with contractors that were employees of a bigger contracting firm.

In retrospect, it is a matter of incentives: The employees do not have the incentive to produce to the maximum of their labor power. In contrast, individuals with their own company, produce much closer to their maximum capacity; the contractor-owners are also are connected to the product of their work, and they are better workers overall.

I would not attribute causality to my observation but rather self-selection: Individuals that are knowledgeable understand that the bigger firm does not have much to offer. In the past, the bigger firm was fulfilling the role of being visible and, therefore, bringing projects; the firm also offers a stable salary but for talented individuals this quickly becomes a stagnating salary.

With the presence of online marketplaces, the need to have a big firm to get jobs started decreasing. Therefore, the talented contractors do not really a bigger firm to bring the work.

The capable individuals disintermediate the firm.

The emergence of the individual

Although the phenomenon is still in its infancy, I expect to see the rise of individuals and the emergence of small teams to be an important trend in the next few years. The bigger firms will feel the increase pressure from agile teams of individuals that can operate faster and get things done quicker. Furthermore, talented individuals, knowing that they can find good job prospects online, they will start putting higher pressure on their employers: Either there is a more equitable share of the surplus, or the value-producing individuals will move into their own ventures.

Marx would have been proud: The value-generating labor is now getting into the position of reaping the value of the generated work. Ironically, this "emancipation" is happening through the introduction of capitalist free markets that connect the planet, and not through a communist revolution.

Sunday, February 19, 2012

The Need for Standardization in Crowdsourcing

[This is the blog version of a brief position paper that John Horton and I wrote last year on the advantages of standardization for crowdsourcing. Edited for brevity. Random pictures added for fun.]

Crowdsourcing has shown itself to be well-suited for the accomplishment of various tasks. Yet many crowdsourceable tasks still require extensive structuring and managerial effort to make crowdsourcing feasible. This overhead could be substantially reduced via standardization. In the same way that task standardization enabled the mass production of physical goods, standardization of basic “building block” tasks would make crowdsourcing more scalable. Standardization would make it easier to set prices, spread best practices, build meaningful reputation systems and track quality.

Why standardizing?

Crowdsourcing has emerged over the last few years as a promising solution for a variety of problems. What most problems have in common is one or more sub-problems that cannot be fully automated, and require human labor. This labor demand is being met by workers recruited from online labor markets such as Amazon Mechanical Turk, Microtask, oDesk and Elance or from casual participants recruited by intermediaries like CrowdFlower and CloudCrowd.

In labor markets, buyers and sellers have great flexibility in the tasks they propose and the making and accepting of offers. The flexibility of online labor markets is similar to the flexibility of traditional labor markets. In both markets, buyers and sellers are free to trade almost any kind of labor at almost any terms. However, an important distinction between online and offline is that once a worker is hired off an offline, traditional market, they are not allocated to tasks via a spot market. Workers within firms are employees who have been screened, trained for their jobs and are have incentives for good performance—at a minimum, poor performance can cause them to lose their jobs. Furthermore, for many jobs—particularly those focusing on the production of physical goods—good performance is very well defined, in that workers must adhere to a standard set of instructions.

This standardization of tasks is the essential feature of modern production. The question is how to apply this idea in crowdsourcing.

Crowdsourcing, the dream: The assembly line for knowledge work

With task standardization, innovators like Henry Ford could ensure that hired workers—after suitable training—could complete those tasks easily, predictably and in a way that training was easy to replicate for new workers. To return to paid crowdsourcing, most of the high demand crowdsourcing tasks are relatively low-skilled and require workers to closely and consistently adhere to instructions for a particular, standardized task.

Crowdsourcing, the reality: The bazaar of knowledge work

As it currently stands, existing crowdsourcing platforms bear little resemblance to Henry Ford’s car plants. In crowdsourcing markets, the factory would be more like an open bazaar where workers could come and go as they pleased, receiving or making offers on tasks that different in their difficulty and skill requirements (“install engines!”, “add windshields!”, “design a new chassis!”) for different rates of pay—and with different pricing structures (fixed payment, hourly wages, incentives etc.). Some buyers would be offering work on buses, some on cars, some on lawnmowers. Reputations would be weak and easily subverted. Among both buyers and sellers, one can find scammers; some buyers are simply recruiting accomplices for nefarious activities.

The upside of such a disorganized market is that workers and buyers have lots of flexibility. There are good reasons for not wanting to just recreate the on-line equivalent of single-firm factory. However, we do not think it is an “either-or” proposition. In this paper, we discuss ways that we can have more structure on a marketplace platform, without undermining its key advantages. In particular, we believe that greater task standardization, a cultivated garden approach to work-pools and a market-making type work allocation mechanism to help arrive at prices could help us build scalable human-powered systems that meet real-world needs.

Current status

Despite the excitement and apparent industry maturation, there has been relatively little innovation—at least at the micro-work level—in the technology of how workers are allocated tasks, how reputation is managed and how tasks are presented etc. As innovative as MTurk is, it is basically unchanged since its launch. The criticism of MTurk—the difficulty of pricing work, the difficulty in predicting completion times and gaining quality, the inadequacy of the way that workers can search for tasks—are recurrent and still unanswered. Would-be users of crowdsourcing often fumble, with even technically savvy users getting mixed results. Best practices feel more like folk wisdom than an emerging consensus. Even more troubling, there is some evidence that at least some markets are becoming inundated with spammers.

uTest: An example of verticalized crowdsourcing
One part of the crowdsourcing ecosystem that appears to be thriving is the “curated garden” approach used by companies like uTest (testing software), MicroTask (quality assurance for data entry), CloudCrowd (proofreading and translation), and LiveOps (call centers). These firms recruit and train workers for their standardized tasks and they set prices of both sides of the market. Because the task is relatively narrow, it is easier to build meaningful, informative feedback and verify ex ante that workers can do the task, rather than try to screen bad work out ex post. While this kind of control is not free, practitioners gain the scalability and cost savings of crowdsourcing without the confusion of the open market. The downside of these walled gardens is that access as both a buyer and seller is limited. One of the great virtues of more market like platforms is that they are democratic and easy to experiment on. The natural question is whether it is possible to create labor pools that look more like curated gardens—with well defined, standardized tasks—and yet are still relatively open, both to new buyers and sellers?

Standardizing basic work units

Currently, the labor markets operate in a completely uncoordinated manner. Every employer generates its own work request, prices the request independently, and evaluates the answers separately from everyone else. Although this approach have some intuitive appeal in terms of worker and employer flexibility, it is a fundamentally inefficient approach.
  • Every employer has to implement from scratch the “best practices” for each type of work. For example, there are multiple UI’s for labeling images, or for transcribing audio. The longterm employers learn from their mistakes and fix the design problems, while newcomers have to learn the lessons of bad design the hard way.
  • Every employer needs to price its work unit without knowing the conditions of the market and this price cannot fluctuate without removing and reposting the tasks.
  • Workers need to learn the intricacies of the interface for each separate employer.
  • Workers need to adapt to the different quality requirements of each employer.
The efficiency of the market can increase tremendously if there is at least some basic standardization of the common types of (micro-)work that is being posted on online labor markets.

So, what are these common types of (micro-)work that we can standardize? Amazon Mechanical Turk lists a set of basic templates, which give a good idea of what tasks are good candidates to standardize first. The analysis of the Mechanical Turk marketplace also indicates a set of tasks that are very frequent on Mechanical Turk and are also good candidates to standardize.

Simple Machines, the standardized units for mechanics. Can we create corresponding simple machines for labor?

We can draw in parallel with engineering: In mechanics, we have a set of “simple machines,” such as screws, levers, wheel and axle, and so on. These simple machines are typically standardized and serve as components for larger, significantly more complicated creations. Analogously, in crowdsourcing, we can define a set of such simple tasks, standardize them, and then build, if necessary, more complicated tasks on top. What are the advantages of standardizing the simple tasks, if we only need them as components?
  • Reusability: First of all, as mentioned above, there is no need for requesters to think on how to create the user interfaces and best practices for such simple tasks. These standardized tasks can be, of course, revised over time to reflect our knowledge on how to best accomplish them.
  • Trading commodities: Second, and potentially more important, these simple tasks can be traded in the market in the same way that stocks and commodities are currently traded in financial markets. In stock markets, the buyer does not need to know who is the seller, or whether the order was fulfilled by a single seller or multiple ones: it is the task of the market maker to match and fulfill buy and sell orders. In the same way, we can have a queue of standardized tasks that need to be completed, and workers can complete them at any time, without having to think about the reputation of the requester or to refamiliarize themselves with the task. This should lead to much more efficient task execution.
  • True market pricing: A third advantage of standardized work units is that pricing becomes significantly simpler. Instead of “testing the market” to see what price points leads to an optimal setting, we can instead have a very “liquid” market with a large number of offered tasks and a large number of workers that work on these tasks. This can lead to a stock-market-like pricing. The tasks get completed by the workers, in priority order according to the offered price for the work unit: the highest paying units get completed first. So, if requesters want to prioritize their own tasks, they can simply price them higher than the current market price. This corresponds to an increase in demand, which moves up the market price. On the other hand, if no requesters post tasks then, once the tasks with the highest prices get completed, then we automatically move to the tasks that have lower price associated with them. This corresponds to the case where the supply of work is higher than the demand, and market prices for the work unit move down.
In cases where there is not enough “liquidity” in the market (i.e., when the workers are not willing to work for the posted prices), then we can employ automated market makers, such as the ones currently used by prediction markets. The process would then operate like this: The workers identify the price for which they are willing to work. Then, the automated market maker takes into consideration the “ask” (the worker quote) and the “bid” (the price of the task), and can perform the trade by “bridging” the difference. Essentially, such automated market makers provide a subsidy in order for the transactions to happen. We should note that a market owner can typically benefit even in scenarios, where they need to subsidize the market through an automated market maker: the fee from a transaction that happens can cover the necessary subsidy which is consumed by the automated market maker.

Can we trade and price standardized crowdsourced tasks as we trade and price securities?

Having basic, standardized work units with highly liquid, high-volume markets can serve as a catalyst for companies to adopt crowdsourcing. Standardization can strengthen the network effects, can provide the basis for better reputation systems, can facilitate pricing, and can lead to the easier development of more complicated tasks that comprise of an arbitrary combination of small work units.


Once we have some basic work units in place, we can start generating tasks that consist of multiple such units, to generate tasks that cannot be achieved with just using basic units. Again we can draw the analogs from mechanical engineering: the “simple machines” (screws, levers, wheel and axle, and so on) can then be assembled together to generate machines of arbitrary complexity. Similarly, in crowdsourcing we can use these standardized set of “simple work units” that can be later assembled to generate tasks of arbitrary complexity.

Quality Assurance

Assume that we have a basic work unit for a task such as comment moderation, that guarantees an accuracy of 80% or higher (e.g., by screening and testing continuously the workers that can complete these tasks). If we want to have a work unit that has higher quality guarantees, we can generate a composite unit that uses multiple, redundant work units and relies on, say, majority vote to generate a work unit with higher quality guarantees.

Pricing Workflows

There is already work available on how to create and control the quality of workflows in crowdsourced environments. We also have a set of design patterns for workflows in general. If we have a crowdsourced workflow that consists of standardized work units, we can also accurately price the overall workflow.

Pricing complex, workflow-based tasks becomes significantly easier when the basic execution units in the workflow are standardized and priced by the market.

We do not even have to reinvent the wheel: there is a significant amount of work on pricing combinatorial contracts in prediction markets. (An example of a combinatorial contract: “Obama will win the 2012 election and will win Ohio” or “Obama will win the 2012 election given that he will win Ohio”.) A workflow can be expressed as a combinatorial expression of the underlying simple work units. Since we know the price of standard units, we can easily leverage work from prediction markets to price tasks of almost arbitrary complexity. The successful deployment of Predictalot by Yahoo! during the 2010 soccer World Cup, with the extensive real-time pricing of complicated combinatorial contracts, gives us the confidence that such a pricing mechanism is also possible for online labor markets.

Timing and Optimizing Workflows

There is already significant amount of work in distributed computing on optimizing execution of task workflows in Mapreduce-like environments. This research should be directly applicable in an environment where the basic computation is performed not by computers but by humans. Also, since the work units will be completed through easy-to-model waiting queues, we can easily leverage the work from queuing theory to estimate how long a task will remain within the system: by identifying the critical parts of execution we can also identify potential bottlenecks and increase the offered prices for only the work units that critically affect the completion time of the overall task.

Role of platforms

One helpful way to think about the role and incentives of online labor platforms is to consider that they are analogous to a commerce-promoting government in a traditional labor market. Most platforms levy an ad valorem charge and thus they have an incentive to increase the size of the total wage bill. While there are many steps these markets can take, their efforts fall into two categories:
  1. remedying externalities, and
  2. setting enforceable standards and rules, i.e., their “weights and measures” function.
Remedying Externalities

An externality is created whenever the costs and benefits from some activity are not solely internalized by the person choosing to engage in that activity. A negative example is pollution—the factory owner gets the goods, others get the smoke—while a positive example is yard beautification (the gardener works and buys the plants, others get to enjoy the scenery). Because the parties making the decision do not fully internalize the costs and benefits, activities producing negative externalities are (inefficiently) over-provided, and activities producing positive externalities are (inefficiently) under-provided. In such cases, “government” intervention can improve efficiency.

Road traffic is an example of a product with negative externalities.
Negative examples are easy to find in on-line labor markets— fraud is one example. Not only is fraud unjust, it also makes everyone else more distrustful, lowering the volume and value of trade. Removing bad actors helps ameliorate the market-killing problem of information asymmetry, as uncertainty about the quality of some good or service is often just the probability that the other trading partner is a fraud.

A positive example is honest feedback after a trade. Giving feedback is costly to both buyers and sellers: It takes time and giving negative feedback invites retaliation or scares off future trading partners. In the negative case, the platform needs to fight fraud—not simply fraud directed at itself but fraud directed at others on the platform, which has a negative second-order effect on the platform creator. In the positive case, the firm can make offering feedback more attractive, by offering rewards, making in mandatory, making it easier, changing rules to prevent retaliation etc.

There are lots of options in both the positive and negative case— the important point is that platform creators recognize externalities and act to encourage positive externalities and eliminate the negative ones. Individual participants do not have the incentives (or even the ability) to fix the negative externalities for all other market participants. For example, no employer has the incentive to publish his own evaluation of the workers that work for his, as this is a signal earned after a significant cost for the employer. This is a case where the market owner can provide the appropriate incentives and designs for the necessary transparency.

Setting Enforceable Standards

Task standardization will probably require buy-in from on online labor markets and intermediaries. Setting cross-platform standards is likely to be a contentious process, as the introduction of standards gives different incentives to different firms, depending upon their business model and market share. However, at least within a particular platform and ignoring their competitors, there is powerful incentive to create standards as they raise the value of paid crowdsourcing and promote efficiency. For example, the market for SMS’s took off in the US only when the big carriers agreed on a common interoperable standard for sending and receiving SMS’s across carrier’s networks.

Standardizing units of measure facilitate transactions and gives us flexibility to create more complex units on top. Can we achieve the same standardization for labor?

In traditional markets, market-wide agreement about basic units of measure facilitate trade. In commodity markets, agreements about quality standards serve a similar role, in that buyers know what they are getting and sellers know what they are supposed to provide. (For example, electricity producers are required to produce electricity adhering to some minimum standards before being able to connect to the grid and sell to other parties.) It should be clear that having public standards make quality assurance easier for the platform: enforcing standards on standardized units of work can be done much easier than enforcing quality standards in a wide variety of adhoc tasks. With such standards, it easier to imagine platform owners more willingly taking the role of testing for and enforcing quality standards for the participants that provide labor.

If we define weights and measures more broadly to include verification of claims, the platform role becomes even wider. They can verify credentials, test scores, work and payment histories, reputation scores and every other piece of information that individuals cannot credibly report themselves. Individuals are also not able to credibly report the quality of their work, but at least with an objective standard, validating those claims is possible. (For example, one of the main innovations made by oDesk was that they logged a worker’s time spent on a task, enabling truthful hourly billing.)


As our knowledge increases and platforms and practices mature, more work will be outsourced to remote workers. On the whole, we think this is a positive development, particularly because paid crowdsourcing gives people in poor countries access to buyers in rich countries, enabling a kind of virtual migration. 

At the same time, access to an on demand, inexpensive labor force, more often than than not, enables the creation of products and services that were not possible before: Once you solve a problem that was deemed too-costly-to-solve before, people start looking for the next thing to fix. This in turn generates more positions, more demand, and so on. It is a virtuous cycle, not the Armageddon.

Tuesday, March 22, 2011

Crowdsourcing goes professional: The rise of the verticals

Over the last few months, I see a trend. Instead of letting end-users interact directly with the crowd (e.g., on Mechanical Turk), we see a rise of the number of solutions that target a very specific vertical.
Add services like Trada for crowd-optimizing paid advertising campaigns, uTest for crowd-testing software applications, etc. and you will see that for most crowd applications there is now a professionally developed crowd-app.

Why do we see these efforts? This is the time that most people realize that crowdsourcing is not that simple. Using Mechanical Turk directly is a very costly enterprise and cannot be done effectively by amateurs: The interface needs to be professionally designed, quality control needs to be done intelligently, and the crowd needs to be managed in the same way that any employee is managed. Most companies do not have time or the resources to invest in such solutions. So, we see the rise of such verticals that address the most common tasks that were accomplished on Mechanical Turk.

(Interestingly enough, if I remember correctly, the rise of vertical solutions was also a phase during web search. In the period in which AltaVista started being spammed and full of irrelevant results, we saw the rise of topic-specific search engines that were trying to eliminate the problems of polysemy by letting you search only for web pages within a given topic.)

For me, this is the signal that crowdsourcing will stop being the fad of the day. Amateurish solutions will be shunned, and most people will find it cheaper to just use the services of the verticals above. Saying "oh, I paid just $[add offensively low dollar amount] to do [add trivial task] on Mechanical Turk" will stop being a novelty and people will just point to a company that does the same thing professionally and in a large scale.

This also means that the crowdsourcing space will become increasingly "boring." All the low-hanging fruits will be gone. Only people that are willing to invest time and effort in the long term will get into the space. 

And it will be the time that we will get to separate the wheat from the chaff.

Saturday, January 12, 2008

Definining Probability in Prediction Markets

The New Hampshire Democratic primary was one of the few(?) events in which prediction markets did not give an "accurate" forecast for the winner. In a typical "accurate" prediction, the candidate that has the contract with the highest price ends up winning the election.

This result, combined with an increasing interest/hype about the predictive accuracy of prediction markets, generated a huge backslash. Many opponents of prediction markets pointed out the "failure" and started questioning the overall concept and the ability of prediction markets to aggregate information.

Interestingly enough, such failed predictions are absolutely necessary if we want to take the concept of prediction markets seriously. If the frontrunner in a prediction market was always the winner, then the markets would have been a seriously flawed mechanism. In such a case, an obvious trading strategy would be to buy the frontrunner's contract and then simply wait for the market to expire to get a guaranteed, huge profit. If for example Obama was trading at 66 cents and Clinton at 33 cents (indicating that Obama is twice as likely to be the winner), and the markets were "always accurate" then it would make sense to buy Obama's contract the day before the election and get $1 back the next day. If this was happening every time, then this would not be an efficient market. This would be a flawed, inefficient market.

In fact, I would like to argue that the late streak of successes of the markets to always pick the winner of the elections lately has been an anomaly, indicating the favorite bias that exists in these markets. The markets were more accurate than they should, according to the trading prices. If the market never fails then the prices do not reflect reality, and the favorite is actually underpriced.

The other point that has been raised in many discussions (mainly from a mainstream audience) is how we can even define probability for an one-time event like the Democratic nomination for the 2008 presidential election. What it means that Clinton has 60% probability of being the nominee and Obama has 40% probability? The common answer is that "if we repeat the event for many times, 60% of the cases Clinton will be the nominee and 40% of the cases, it will be Obama". Even though this is an acceptable answer for someone used to work with probabilities, it makes very little sense for the "average Joe" who wants to understand how these markets work. The notion of repeating the nomination process multiple times is an absurd concept.

The discussion brings in mind the ferocious battles between Frequentists and Bayesians for the definition of probability. Bayesians could not accept that we can use a Frequentist approach for defining probabilities for events. "How can we define the probability of success for an one-time event?" The Frequentist would approach the prediction market problem by defining a space of events and would say:
After examining prediction markets for many state-level primaries, we observed that 60% of the cases the frontrunners who had a contract priced at 0.60 one day before the election, were actually the winners of the election. In 30% of the cases, the candidates who had a contract priced at 0.30 one day before the election, was actually the winners of the election, and so on.
A Bayesian would criticize such an approach, especially when the sample size of measurement is small, and would point to the need to have an initial belief function, that should be updated as information signals come from the market. Interestingly enough, the two approaches tend to be equivalent in the presence of infinite samples, which is however rarely the case.

I just could not help but notice that the fight between the proponents and enemies of the prediction markets was reminiscent of the battles between Bayesians and Frequentists :-)

Sunday, December 9, 2007

Political Prediction Markets: Some Thoughts

Apparently, my last postings on the predictability of the political prediction markets generated some interest. The analysis is more difficult in this scenario, but for the next few days we see stabilizing signals with a trend to go upwards" and we were proven wrong: the price declined from 43 on Dec 2nd, to 39.5 on Dec 9th, an 8% decline. I realized what was wrong in my reasoning. What was stabilizing was the sentiment index, not the price. And a stabilized sentiment around 50% tends to be a pretty bad adviser on how the market will move.

Bo's comment made me think about parallels in "prediction market trading" and "stock market trading". As Bo pointed out, in existing stock markets, there is a significant amount of algorithmic trading. This algorithmic trading makes the stock market significantly more efficient than, say, in the early 1980's where the programmatic trading was at its infancy. In fact, I have heard many stories from old-timers, saying that in the early days it was extremely easy to find inefficiencies in the markets and get healthy profits. As algorithmic trading proliferated, it became increasingly harder to spot inefficiencies in the market.

Something similar can happen today with prediction markets. If we have a prediction market platform that allows automatic/algorithmic trading, then we can improve tremendously the efficiency of today's prediction markets. Furthermore, such a tool (if done with play money) can be used as a great educational tool, similar to the now inactive Penn-Lehman Automated Trading (PLAT) Project. Allowing also for some data integration from the existing prediction markets (BetFair, Intrade, etc.) we could have a pretty realistic tool that can be used for many educational purposes that, at the same time, can generate useful and efficient prediction markets.

Now, I need to find someone willing to fund the idea. Ah, there are a couple of NSF call for proposals still open :-)

Wednesday, June 13, 2007

Are Prediction Markets Efficient?

I was reading Fred Wilson's post about the information efficiency of the venture capital market.

While reading the posting, I started wondering whether prediction markets are efficient, according to the definition of Eugene Fama. In other words, how long does it take for a prediction market to incorporate all the available information about an event? Liquidity seems to be an issue for the existing prediction markets, preventing them from reaching equilibrium quickly. But if we had enough liquidity, how long would it take for humans to "agree" on prices that incorporate all the available information about an event?