I’ve recently become a convert to Stata for data analysis tasks, and it seems to be an excellent piece of software. One thing that interests me about statistical software in general is the proliferation of different programs. Aside from Stata, there’s SAS, SPSS, R and Eviews, to name a few. Unlike operating systems or wordprocessors for example, there’s no one dominant standard, and each has its own quite loyal and sizeable user base. I guess data file sharing is less common among users of this software compared to wordprocessors, but there still are some network benefits associated with support that you can get from other users of the same software. So it’s interesting that so many varieties can persist in this market but seemingly not in other software markets.

The next-gen DVD war between HD-DVD and Blu-ray is over, and Blu-ray won. Now Engadget reports that Blu-ray player retail prices are increasing. This is not really surprising. In a market like this that is dominated by network effects, there is typically strong competition for the market in the early stages. Then a dominant standard emerges and can raise prices to make profits until the next new technology comes along.

If you only focus on the latter stages, it can look like a bad deal for consumers. However, taken as a whole (over time), competition may be no less intense than in other markets. When making an assessment of consumers’ welfare, we should consider those who bought early at a low price as well as those who buy later at a higher price. On the other hand, those who buy early also run the risk of picking the wrong standard and getting stranded. Anyway, my point is that to make a judgment about intensity of competition and consumer welfare in these markets we need to take a long-run perspective.

Synthetic biology is a hot topic right now. It is basically techniques to create synthetic designer organisms out of biological lego blocks. It is still early days, but the field has great potential, as well as big ethical issues.

From an economic point of view, there are many interesting parallels with the IT or electronics industries. A bunch of standardised parts have been created, and these can be combined in different ways to make new organisms. A lot of the issues in network economics arise as well. For example, the parts are complementary, and the value of the whole is more than the sum of the parts. The parts may also have different owners, so there could be coordination problems. There are also network effects — the parts are difficult to work with, so as more other researchers work with a specific part, experience increases and it becomes easier and thus more attractive relative to substitute parts.

Here (pdf) is a short paper about the topic. And here is an amazing talk by Craig Venter:

Web 2.0 … It’s last years buzzword for websites that involve some sort of user-generated content (blogs, flickr) or web-based software (Google Docs) or social networking (Facebook) etc. I found the “complete” web 2.0 directory at Go2Web20.net which lists many interesting sites that I’d never heard of. These sites are interesting not because of what they do, but because they haven’t been successful. In fact Go2Web20.net’s directory is more like a graveyard of Web 2.0.

For example, PeopleTrusted.com, “a democracy of trust”. This actually seems like sort of a good idea. It’s a site where people can vote for online retailers and services that they trust. As the site says, “PeopleTrusted helps bring credibility and social reputation to products, services, and companies doing business on the web.” Asymmetric information is a big problem in online markets — it’s easy to set up a website and rip people off. So in theory PeopleTrusted could solve that problem and provide a valuable service. I’ve even written this paper and this paper about exactly that idea. But PeopleTrusted seems tobe having trouble getting off the ground. It has very few members, and very few products have been voted on, so it’s not really a very useful site. I also wonder how prone it is to manipulation? I didn’t check the site carefully enough, but how do they stop people registering fake IDs and polluting their data?

Next up, Yample. I have no idea how they came up with the name ‘Yample’, but I know it’s hard to find a good domain name these days (26econ … ???). Anyway, Yample is “listings gone social”. It’s listings, ie classified ads like Craigslist, only with a “social” aspect where people can create networks of friends, tags, and vote on favourite listings etc. Unfortunately, Yample seems more antisocial than social. They have less than 100 users, most of which seem to have used the site only once.

I might be boring my readers, so just one more, Guruza (I couldn’t steal a copy of their logo easily). It’s yet another “ask an expert” type site, where you can pose a question and experts can answer them (for a fee maybe). This idea has been around for a long time and there are many other such sites, so I don’t know what’s so “Web 2.0″ about it. Anyway, Guruza seems to be suffering from a severe shortage of both questions and experts.

So why have I tortured you with these boring details about unsuccessful websites? I wanted to illustrate the fact that many of these “Web 2.0″ ideas depend on some kind of social or network aspect. All of the three sites above are not bad ideas per se, but need lots of users and usage to become useful resources. The trouble in such cases is how to get off the ground in the first place. When the value of your business depends on how many people use it, people will be reluctant to use it unless they expect others to do so. Even if the site is free to use, it still takes time and effort to register and so on.

In these kind of network markets, probably only a few businesses will survive. There’ll be a few big winners like Facebook and Digg that manage to get off the ground, achieve a critical mass of users, and can then make lots of money. How to maximise your chances of becoming such a winner? First, expectations are critical. If people have pessimistic expectations about the success of your site, they’ll stay away, and their bad expectations will be realised. If you can make people optimistic about the usage of your site, through advertising, promotions or whatever, then you’re more likely to get off the ground.

Second, if possible, leverage off an existing small but well-connected community. Facebook was initially restricted to college students. This was a perfect way to get off the ground, since students already have strong social networks in the real world, and Facebook provided the technology for them to take those networks online. If Facebook had just set up a website and made it open to all, I think it would have been harder to get off the ground.

Third, piggy-back on an existing network if you can. Can you make your service ‘compatible’ with an existing network so that it’s easy for people to switch? This is another thing that Facebook have done right — they make it very easy for you to import your existing networks of contacts from email services like hotmail and gmail. This greatly reduces the pain for users to join Facebook’s network, as they can re-use data that they’ve already plugged in to another network.

In all, starting a network-based business, like a Web 2.0 site that’s based on social networking or user generated content isn’t easy, and I’m not optimistic about the chances of success of many sites in this category.

The creator of the Financial Next website asked me to take a look at his site. It’s a website that facilitates ‘prediction markets‘ with play money (ie not real money). This is not a new idea, for example the Iowa Electronic Markets have apparently been around since 1988. The basic idea is to create a virtual online market for a well-defined future event, for example ‘Hillary Clinton wins the 2008 US presidential election’. For a yes/no event like that, if you own a share in that idea, you get some payoff, say $100, if it turns out to be true, and nothing if it’s not true. How likely you think the event is will determine what you should pay for the share. If you think Hillary has an 80% chance of winning, then you should be willing to pay up to $80 for a share in that idea.

Aggregated over many people, the market price in such an idea converges on the overall ‘consensus’ opinion of all the traders in the market. Basically, markets are tools for extracting information. In this case we’re extracting people’s beliefs about the probability of some unknown future event. We could do the same thing by just taking a survey of people’s opinions. However, there’s some reasons why markets might generate more accurate results than surveys. First, if money is at stake, people might take the question more seriously and try to come up with an informed opinion about the issue in question. Second, it makes clear the distinction between “Do you want Hillary to win?” and “Do you think Hillary will win?”. There are probably other reasons why markets might outperform surveys, but those are the two that come to mind for me.

On the downside, it’s probably more expensive to run prediction markets than conducting surveys, and people have to be willing to spend more effort participating in them. You can answer a survey question in one minute, but a prediction market probably requires more intensive effort over a period of time. Thus it might be easier to gather the opinions of more people with surveys, compared to what could be achieved with prediction markets. I’m not sure whether surveys or prediction markets would win in a cost-benefit analysis of the tradeoff between cost of running the process and accuracy of the results.

In any case, as an economist I like the idea. The Financial Next site implements this pretty well and makes it easy for users to participate in markets, and even create their own markets. Since it’s just for play money, there’s no real harm done if people set up strange markets on the site. On the other hand, I wonder how seriously people take it when only play money is at stake. As the site says, the main motivation for participating in the site is fun and beating other people. So it’s a kind of game.

I think this is another interesting idea — using games to motivate people to participate in economic experiments. These days experimental economics is popular, and economists try to set up artificial situations with subjects in a lab to test various economic theories. One criticism of these is that the subjects are not really motivated enough and won’t behave as they would in a real situation. I think well-designed games (here I mean not the game theory type of game but the ‘fun’ type of game) may be able to help overcome this problem. If people enjoy playing a game, and have a stake in it, they may take it seriously, and if the game it set up well maybe we can test economic theories. In other words, give people a fun game as a carrot, but manipulate the environment in some way to run some subtle experiments. One online game, EVE, recently hired a real economist, and it seems like he’s having some of these same thoughts.

In terms of the Financial Next site, its main problem at the moment seems to be a lack of traders. Since it’s only for play money, people will only be motivated to join if the site provides them with some other kind of benefits aside from making money. The competitive/play motivation is fine, but that works best when there’s a large population of players. If I’m the best out of 10 players that’s not so impressive, but best out of 100,000 is something to be proud of. So Financial Next is another example of a network that will become more valuable to its members as it gets bigger. I’m not sure exactly how they can attract more users and reach some sort of ‘critical mass’, but I’d suggest emphasising the competitive aspect more and trying to make trading more ‘fun’ somehow.

The other problem with a lack of traders is that the market becomes quite ‘thin’. This means that its predictions won’t be so good, because they don’t aggregate the opinions of very many people. In addition, traders’ behaviour probably becomes somewhat strategic. In a small market with few traders, each of them has quite a lot of influence over the market price. This can affect players’ strategies, and may lead them not to trade in such a way that best reveals their estimates of the probability of the event that we’re interested in. In contrast, in a thicker market with many traders, each will assume that they can’t really affect the price, and will be more likely to trade according to their true estimate of the probability.